# Agriculture and Digital Technology

Véronique Bellon Maurel, Ludovic Brossard, Frédérick Garcia, Nathalie Mitton, Alexandre Termier

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Véronique Bellon Maurel, Ludovic Brossard, Frédérick Garcia, Nathalie Mitton, Alexandre Termier. Agriculture and Digital Technology: Getting the most out of digital technology to contribute to the transition to sustainable agriculture and food systems. pp.1-185, 2022, 10.17180/wmkb-ty56-en. hal-03604970

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# Agriculture and Digital Technology

Getting the most out of digital technology to contribute to the transition to sustainable agriculture and food systems

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# Agriculture and Digital Technology

## Foreword

This white paper is an initiative of the executive boards of INRAE and Inria, who brought us together, tasked us and gave us carte blanche in coordinating and writing this work on digital technology in agriculture from a research perspective.

Our reflections have been carried out in the context of the principal observed and foreseeable dynamics of agriculture in countries around the world that aim to support the development of more sustainable agricultural and food systems.

In constructing the analysis and proposals presented in this publication, based on an inventory of the challenges, opportunities and risks associated with digital technology in agriculture, we have not aimed to simply focus on the consensus of opinion, but to express the diversity of the groups involved, in particular in terms of prioritising the challenges or scope of the dangers for agriculture and food systems of the future. This white paper is a collective work that aims to address the question of digital technology in agriculture as a balanced whole and its chapters are sequenced and interconnected in a way that allows all aspects to be covered.

Each section should therefore be considered in perspective of the others, and using them in isolation without referring to the whole could present an unbalanced view.

Veronique Bellon-Maurel, INRAE, Ludovic Brossard, INRAE, Frédérick Garcia, INRAE, Nathalie Mitton Inria, Alexandre Termier, Université de Rennes 1 / Inria.

Design: Françoise Perret

Art direction: Sophie Barbier

Publication February 2022

ISSN in progress – ISBN 978-2-7261-1310-3.
# Table of contents

<table><tr><td><strong>Executive summary</strong></td><td><strong>5</strong></td></tr><tr><td>The challenges facing agriculture require a reconsideration of food production and supply methods</td><td>6</td></tr><tr><td>Foundations and the state of the art in technologies and methodologies</td><td>8</td></tr><tr><td>Digital technology offers opportunities for agroecology and sustainable food</td><td>10</td></tr><tr><td>Risks identified to be avoided</td><td>12</td></tr><tr><td>Challenges in the development of digital technology for the agriculture of tomorrow</td><td>14</td></tr><tr><td>General conclusion</td><td>16</td></tr></table>

<table><tr><td><strong>1. Introduction</strong></td><td><strong>17</strong></td></tr></table>

<table><tr><td><strong>2. What are the challenges facing agriculture?</strong></td><td><strong>21</strong></td></tr><tr><td>2.1 World food security under strain</td><td>22</td></tr><tr><td>2.2 There is an urgent need to reduce the negative environmental impacts of agriculture</td><td>23</td></tr><tr><td>2.3 Agricultural dynamics have favoured intensification and specialisation</td><td>24</td></tr><tr><td>2.4 What are the agricultural models of the future?</td><td>25</td></tr><tr><td>2.5 The territorial nature of agriculture</td><td>27</td></tr><tr><td>Conclusion</td><td>28</td></tr></table>

<table><tr><td><strong>3. Foundations and state of the art</strong></td><td><strong>30</strong></td></tr><tr><td>3.1 Data</td><td>31</td></tr><tr><td>  Data capture (what, why, where and how)</td><td>31</td></tr><tr><td>  Data collection and transmission (What data to send, when and how)</td><td>33</td></tr><tr><td>  Data storage and exchange, traceability</td><td>36</td></tr><tr><td>3.2 Modelling, simulation and optimisation</td><td>38</td></tr><tr><td>  What to model, for what purposes and with which tools</td><td>39</td></tr><tr><td>  Representation frameworks</td><td>41</td></tr><tr><td>  Modelling and simulation</td><td>45</td></tr><tr><td>  Modelling and optimisation</td><td>46</td></tr></table>
<table><tr><td>3.3 Multi-scale learning and knowledge extraction</td><td>48</td></tr><tr><td>Massive data in agriculture</td><td>48</td></tr><tr><td>Data pre-processing</td><td>49</td></tr><tr><td>Supervised approaches</td><td>52</td></tr><tr><td>Unsupervised approaches</td><td>53</td></tr><tr><td>Reinforcement learning</td><td>55</td></tr><tr><td>Data warehouses and OLAP analysis</td><td>56</td></tr></table>

<table><tr><td>3.4 Knowledge management and engineering for decision support in agriculture</td><td>58</td></tr><tr><td>Knowledge-based systems in agriculture</td><td>59</td></tr><tr><td>Knowledge restitution, visualisation and human-machine interaction in agriculture</td><td>66</td></tr><tr><td>Decision Support Systems (DSS)</td><td>69</td></tr></table>

<table><tr><td>3.5 Automation, control and robotics</td><td>71</td></tr><tr><td>Structured environments: allies of robots</td><td>72</td></tr><tr><td>From adaptation to reconfiguration</td><td>74</td></tr><tr><td>Conclusion</td><td>75</td></tr></table>

4. Digital technology and agroecology: opportunities to explore, challenges to overcome 76

<table><tr><td>4.1 Improving production: creating knowledge to support the transition towards agroecology.</td><td>78</td></tr><tr><td>Representing complex systems within agroecology</td><td>79</td></tr><tr><td>Large scale data collection for new agroecosystems</td><td>79</td></tr><tr><td>Data-based modelling: a step towards new knowledge</td><td>80</td></tr></table>

<table><tr><td>4.2 Improving production: using digital technology to assist farmers with the running of their farms</td><td>80</td></tr><tr><td>Adapting the principles of precision agriculture to agroecology: observing and taking decisions.</td><td>81</td></tr><tr><td>Multi-objective decision-making in agroecology</td><td>83</td></tr><tr><td>Co-designing innovative agricultural equipment and agroecosystems</td><td>83</td></tr></table>

<table><tr><td>4.3 Improving integration within the agricultural regional or economic ecosystem</td><td>85</td></tr><tr><td>Agricultural services reshaped by digital technology</td><td>85</td></tr><tr><td>Reshaping value chains with greater market connectivity</td><td>86</td></tr><tr><td>Managing resources at a regional level</td><td>89</td></tr></table>
<table><tr><td>4.4 Supporting the transition: sharing data, information and knowledge</td><td>91</td></tr><tr><td>Digital technology: an asset for sharing knowledge</td><td>91</td></tr><tr><td>A participatory approach and open innovation</td><td>92</td></tr><tr><td>Farmers as data producers</td><td>93</td></tr><tr><td>4.5 Specific challenges facing the Global South</td><td>94</td></tr><tr><td>Conclusion</td><td>97</td></tr></table>

## 5. Risks 98

<table><tr><td>5.1 Compromising the ecological transition in agriculture</td><td>100</td></tr><tr><td>The agroecological transition and technological lock-in</td><td>100</td></tr><tr><td>Taking humans further away from nature</td><td>101</td></tr><tr><td>Contributing to digital's growing environmental imprint</td><td>102</td></tr><tr><td>5.2 Widening inequality and power imbalance</td><td>103</td></tr><tr><td>Risks of exclusion</td><td>103</td></tr><tr><td>A loss of autonomy for farmers</td><td>104</td></tr><tr><td>Upstream and downstream control</td><td>104</td></tr><tr><td>Accessing information and training - what role can advice play?</td><td>105</td></tr></table>

<table><tr><td>5.3 Loss of sovereignty</td><td>106</td></tr><tr><td>A loss of autonomy over food supplies</td><td>106</td></tr><tr><td>Seizure of agricultural data</td><td>107</td></tr><tr><td>A loss of control over production equipment</td><td>108</td></tr><tr><td>A challenge for cybersecurity</td><td>108</td></tr></table>

<table><tr><td>5.4 Accentuating vulnerabilities and negative yields</td><td>109</td></tr><tr><td>The vulnerabilities of the agrifood system</td><td>109</td></tr><tr><td>Increasing complexity, diminishing returns and associated risks</td><td>109</td></tr><tr><td>Conclusion</td><td>111</td></tr></table>

## 6. Challenges for the future 112

<table><tr><td>6.1 Providing digital tools for collective management at a regional level</td><td>113</td></tr><tr><td>Monitoring and measurement at a regional level</td><td>113</td></tr><tr><td>Visualisation</td><td>115</td></tr><tr><td>Digital devices for participation, mediation and governance</td><td>116</td></tr></table>

<table><tr><td>6.2 Helping farmers to manage their technical journey</td><td>118</td></tr><tr><td>Acquisition and diagnostic systems</td><td>118</td></tr><tr><td>The challenges posed by robotisation and the digital transformation of agricultural labour</td><td>120</td></tr></table>
<table><tr><td>Modelling to incorporate systemic effects and build practical, usable decision-support tools</td><td>122</td></tr><tr><td>6.3 Transforming relationships between stakeholders within sectors</td><td>125</td></tr><tr><td>Service: advice and insurance</td><td>125</td></tr><tr><td>Traceability, full supply chain transparency, data life-cycle</td><td>126</td></tr><tr><td>Platformisation and reconfiguration of channels</td><td>128</td></tr><tr><td>6.4 Creating and sharing data and knowledge</td><td>129</td></tr><tr><td>Conclusion</td><td>131</td></tr><tr><td>Conclusion</td><td>135</td></tr><tr><td>Contributions and acknowledgements</td><td>144</td></tr><tr><td>Bibliographic references</td><td>146</td></tr><tr><td>Glossary</td><td>184</td></tr></table>
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© Julien Canavezes

Executive summary

All over the world, food systems are undergoing profound changes caused by external pressures (climate change, organisation of value chains, etc.) and intrinsic factors (innovation, reduction in the number of farmers, etc.). Food security is the number one concern at the global level and is today accompanied by a strong demand for production methods to become more sustainable and for the protection of a living rural structure based on attractive family farming. This is why, as the FAO reminds us,<fnref n="1" /> agroecology is a vital issue in a growing number of countries, including France. In parallel to these changes, agriculture, like all economic sectors, is seeing an upsurge in digital technology. Since the mid-2010s, the concept of “digital agriculture” has emerged. It defines both a form of agriculture and a food system that uses digital science and technology such as data science and technologies for acquisition (satellites, sensors, connected objects, smartphones, etc.), transfer and storage (3G/4G/5G coverage, low-speed terrestrial or satellite networks, clouds) and on-board or remote processing (supercomputers

<footnote n="1">1. FAO (2018b), Initiative de passage à l'échelle supérieure de l'agroécologie, available at http://www.fao.org/3/i9049FR/i9049fr.pdf</footnote>
accessible via very high-speed communication networks, artificial intelligence) at all levels of agricultural production and its ecosystem: farms, support services, territories, value chains.<fnref n="2" />

Digital technology is often seen by governments and experts as an opportunity to contribute to the development of agriculture for the benefit of farmers, consumers and society in general. But what does this mean? What digital tools should be developed?

This white paper aims to shed light on these issues and present research perspectives to better understand, master, prepare, equip and support the deployment of digital technology in agriculture and the food chain, while taking into account the way in which it will transform sectors and their ecosystems, with the aim of using it to support the agroecological transition (AET) and the territorialisation of food and rebalanced supply chains. It is structured in six chapters. After the introduction, Chapter 2 presents the challenges of transforming agriculture and food systems. An overview of the state of the art then presents existing digital technologies (Chapter 3). The possibilities offered by digital technology for the agroecological transition and better inclusion in society are then inventoried (Chapter 4). Identification of the risks linked with the uncontrolled development of digital agriculture is just as necessary to avoid or minimise the pitfalls (Chapter 5). Chapter 6 presents the technical issues and challenges identified that could mobilise our two institutes, INRAE and Inria, but also the French research ecosystem, in particular to develop responsible digital technology for agriculture.

# The challenges facing agriculture require a reconsideration of food production and supply methods

Today, a series of global changes are placing the agri-food system under strain. On the one hand, the growing population (9.5 billion people in 2050 according to the UN's median scenario) with a changing diet (as in China for example), must be fed while adapting to a context of increasing devastation: climate change, collapse of biodiversity, reduction of resources (soil, fresh water, phosphorus). On the other hand, agriculture must accelerate changes to implement livestock production systems that are more respectful of animal welfare and reduce its impact on the environment (reduction of the use of inputs such as antimicrobials, fertilisers,

<footnote n="2">2. Bellon-Maurel V., Huyghe C. (2016) *L'innovation technologique en agriculture. Géoéconomie*, 80 - May/June 2016, pp. 159-180</footnote>
pesticides, reasoned use of natural resources such as water, reduction of soil compaction and greenhouse gas emissions, better use of biological regulations) and contribute to CO₂ storage<fnref n="3" /> and the preservation of biodiversity. In the last 70 years, agricultural dynamics have favoured intensification and specialisation. Farm sectors are based on competitive pricing, a phenomenon that is exacerbated by globalisation. Essentially, they are subject to unbalanced power relations between actors with diverse and even divergent interests. In addition, farming is carried out in territories that have, in many cases, become specialised, leading to imbalances. This leads to great complexity (in terms of specialisation and interdependence of these elements) that amplifies instabilities, multiplies the risks of failure and is ultimately a major hindrance to change. It is therefore crucial to very quickly implement strategies to improve production techniques and ways of organising the agrifood system to increase their resilience.

According to the FAO, production can evolve towards two models:<fnref n="4" /> either sustainable intensification (improvement of process efficiency and integration into long supply chains), agroecology,<fnref n="5" /> which is based on natural production processes and uses local and sovereign food systems. This second model is now supported by the French EGAlim law<fnref n="6" /> and many local authorities and citizens. Farm structure is also a point of attention: it is important to provide the conditions for decent work for farmers and protect family farming, which is in the majority in the world.

In this context, digital technology could contribute to the virtuous transition toward agroecology in territorialised food systems and the protection of family farming by providing information to better understand these complex systems and individual or collective decision-making support as well as supporting concrete action, exchange, the reconfiguration of value chains, the development of strategies and policies, etc. It is precisely this path of placing digital technology at the service of the transition to agroecology and the renewal of food systems that we have chosen to explore in this white paper.

<footnote n="3">3. https://www.4p1000.org/fr/linitiative-4-pour-1000-en-quelques-mots</footnote>

<footnote n="4">4. HLPE (2016). Sustainable agricultural development for food security and nutrition: what roles for livestock? A report by the High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security. Rome, Italy. http://www.fao.org/3/a-i5795e.pdf</footnote>

<footnote n="5">5. Agroecology is a set of practices that aim to improve agricultural systems by «mimicking» natural processes, thus creating beneficial biological interactions between the components of the agroecosystem.</footnote>

<footnote n="6">6. Law No. 2018-938 of 30 October 2018 for balanced trade relations in the agricultural and food sector and healthy, sustainable food accessible to all (1), https://www.legifrance.gouv.fr/loda/id/JORFTEXT000037547946/</footnote>
# Foundations and the state of the art in technologies and methodologies

Before reflecting on digital technology designed to assist this transformative ambition, the first step is to see what can be offered by current research advances. Digital agriculture is based on three levers for action which, when mobilised together, lead to innovations: (1) the abundance of data due to the development of sensors (from nanosensors to satellites) and facilitated communication and storage, (2) computing capacities, which make it possible to implement artificial intelligence and new modelling methods and (3) connectivity and information exchange interfaces. In addition to these three levers, there is a fourth one which already existed but is being renewed by measurement and computing capacities: automation and robotisation. In this paper, we will focus in particular on the following technology and methodologies.

## DATA<fnref n="7" />

Sensors, which provide data acquired on the ground, present hardware and software challenges. It is important to define the nature and scope of what needs to be measured and which measurement technology(ies) should be prioritised and how to implement it (or them) to obtain useful information at the lowest cost. Free satellite images (such as from Sentinel-2), connected objects and collaborative applications on mobile phones are all massive data sources. By processing this data, we can quantify the desired property either directly or indirectly by merging data from multiple sources. This latter approach is a strategy to improve the accuracy of the assessed value but raises multiple aggregation issues due to the nature of the sources and their granularity and precision. Data access is also an issue: the use of FAIR data facilitates its reuse while blockchains allow data to be shared in an unfalsifiable way between actors with divergent interests (as in value chains).

<footnote n="7">7. It is important to bear in mind that digital agriculture originated in precision farming and livestock production, which explains why, in the current consensus, digital agriculture uses data collected on the ground, initially in the context of precision agriculture, but not genomic data. A convergence between the two communities will certainly develop in the coming years, in particular through phenotyping.</footnote>
## MODELLING

This is the key element in representing agroecosystems, which are inherently complex, in order to simulate, optimise and manage them. Different levels are involved, from plants and animals to the human population, the territory or the value chain, with an additional challenge associated with the coupling of levels and models representing subsystems. Modelling is a relatively old approach in agronomy but it is today being renewed by digital technology. On the one hand, the profusion of data leads us to supplement traditional mechanistic approaches based on human analysis with empirical models from data processing (linear or non-linear multivariate methods, artificial intelligence). On the other hand, modelled systems are becoming more complex and now include humans. Decision modelling therefore involves additional concepts (bounded rationality) and mobilises specific frameworks to take account of the human agent: discrete event systems, agent-based systems, constraint-based models, etc. Simulation is used to represent agrosystems or even socioecosystems with behaviours that are difficult to analyse; it provides a description of the possible states and can be used for many purposes such as individual or collective decision support (guidance models) and training. Optimisation goes further in decision support (prescription), because it searches for solutions to a given problem according to one or several criteria. It is used in automatic control (managing greenhouses or agrivoltaic devices), robotics, individual decision making (food formulation, crop planning, etc.) and collective decision making (land or water management, economic decisions, etc.). It relies on a range of methods to integrate more and more complexity (deterministic methods, metaheuristic stochastic methods, etc.). It raises numerous research questions linked to uncertainty, temporality, the complexity of the processes to be modelled, etc.

## KNOWLEDGE EXTRACTION

In addition to these analytical modelling methods, new families of methods are emerging with models that are directly inferred from the data, when there is sufficient data to cover the parameter space. This is particularly the case for data from remote sensing or time series (such as those collected via connected objects). After a necessary pre-processing phase to improve the reliability of the data ("cleaning", comparison with expert data), it is processed using different formalisms to extract intelligible information. Supervised approaches consist in predicting values (that are impossible to measure directly) or classifying data into different categories using linear or non-linear methods (e.g. neural networks), which implies having sets of measured and reference data (the desired value). If reference data is not available, non-supervised approaches are used (clustering, pattern mining). Specific methods are implemented to improve the performance of learning systems (reinforcement learning) or data aggregation (OLAP processing).
New knowledge is extracted or generated by the models and is formalised and organised to make it available to different users through decision support systems adapted to each activity, whether cropping or livestock production, and at all levels. New ontologies are needed to create ergonomic interfaces that will allow this knowledge to be shared, linked and used, in particular by targeting the new opportunities offered by the Semantic Web.

## ROBOTICS

Automated and/or robotic systems are another aspect of digital technology that are becoming increasingly accurate and reliable. Robotics initially developed in the livestock sector (fixed milking robots, cleaning robots in closed environments), but it faces additional challenges in crop production (changing and uncontrolled outdoor environment). Current issues include the use of GPS, the question of precise, secure and low-cost localisation, the safety of mobile robots (avoiding collisions, being able to negotiate obstacles) and modes of cooperation between humans and robots, animals and robots and even ground robots and drones.

# Digital technology offers opportunities for agroecology and sustainable food

Steered in the right direction, digital technology has the potential to open up many opportunities to respond to the challenges of the agroecological transition (AET), improve integration into agriculture's vertical (upstream-downstream) and horizontal (territorial) ecosystems and increase farmers' capacity for action.

## IMPROVING PRODUCTION

Devices to assist the farmer at the sensory (sensors), cognitive (DSS for "decision support systems") and physical (machines) levels could improve production methods. Today, the concept of precision agriculture or precision livestock production is generally associated with intensive farming, but it is nonetheless valid in agroecology, in particular for monitoring plant and animal health using automated observations from sensors and models and for implementing more complex cultivation processes (mixed cropping, selective harvesting, etc.) on a large scale. This requires sensors and models capable of analysing the signal received to give either a description of the state, a prediction of the future state or a prescription. Beyond the tactical decision behind the intervention, some models could help in strategic decision making for production organisation, a phase that is particularly delicate in transition processes (AET, climate change) and multi-objective decision making. As physical assistants to reduce arduousness, robots could provide solutions to the very specific requirements of new cropping
systems (mixed cropping, agroforestry) and livestock systems (milking robots for pastoral farming). However, improving production also requires building knowledge beforehand about these new, diverse and complex systems. The construction of essential knowledge for AET could benefit from three interconnected digital levers: (i) representation of these complex systems, (ii) massive (and potentially participative) data collection on new methods in cropping and livestock production (on-farm phenotyping) and (iii) inference of new models from the data.

## IMPROVING INTEGRATION INTO THE ECOSYSTEM

Digital technology could help renew the agricultural ecosystem including agricultural services (insurance, advice), the organisation of value chains and the management of agricultural territories. Value chains are being transformed by disintermediation - encouraged by the Internet - and the possibility for product transparency, which is increasingly desired among consumers today. Blockchains, which are often evoked as a way of guaranteeing this transparency, still pose multiple technical and governance issues. Territorial management is another aspect of the agricultural ecosystem that is impacted by digital technology. The territorial level concerns both agroecology (landscape ecology, closing cycles via the circular economy) and agriculture, which plays a central role in territories and is the subject of tensions linked to the use of resources (land, water) and the role it plays in ecological services. Digital technology could offer tools for better identifying material flows and facilitating mediation and collective decision-making (support models, etc.).

## IMPROVING SHARING AND LEARNING

The connectivity offered by digital science and technology facilitates individual and collective sharing and learning, both sources of innovation in agroecology. Knowledge (including traditional knowledge) is capitalised and exchanged between peers, either directly (social networks), or through participatory collective processes in which digital technology plays an increasing role (Digital Farmers Field School®). Participatory approaches with an innovative aim (open innovation, living labs) could be enhanced by technologies that facilitate the capitalisation, representation, expression and processing of data and trace the contribution of each individual. Lastly, the farmer could become a data supplier to private or public actors (research through on-farm experimentation, territorial documentation, etc.), which could change their status for better integration and recognition.

<footnote n="8">8. Witteveen, L., Lie, R., Goris, M., Jane Ingram.V. (2017) *Design and development of a Digital Farmer Field School. Experiences with a digital learning environment for cocoa production and certification in Sierra Leone.* Telematics and Informatics 34(8), DOI: 10.1016/j.tele.2017.07.013</footnote>
## OPPORTUNITIES IN THE GLOBAL SOUTH

Most international organisations and backers see digital technology as a major source of transformation in the Global South, particularly in Africa. Digital technology could help diversify the service economy, accelerate the structural transformation of agriculture and make it more attractive to young people, improve value chains at local (building territorial food systems) and long-distance (guaranteeing product traceability) levels and help build the information capital of territories. However, technical issues persist -related to the weakness of the network coverage, the lack of infrastructures, the weakness of information systems, and the taking into account of the diversity of languages or even illiteracy in the Human-Machine interfaces. Digital technology also raises political, economic and social issues.

## Risks identified to be avoided

The development of digital technology in society and the changes it brings are naturally accompanied by inherent risks. The agricultural sector is no exception and many questions are being raised about whether digital agriculture can meet expectations, what difficulties may be encountered and what vulnerabilities it could accentuate.

The first risk identified is that of failing to respond to the demand for a more ecological form of agriculture. While the development of digital technology in agriculture offers solutions for reducing inputs, this benefit could be accompanied by a technological lock-in that would hinder the implementation of alternative practices that are more radical and systemic, or of the set up of alternative organisational methods that could offer greater environmental and socioeconomic benefits. Furthermore, the widespread introduction of digital interfaces between farmers and animals or plants, in a context of the increasing technologisation of agricultural production, could also weaken connections to nature (particularly the human-animal connection), but society today is clearly looking for a type of agriculture that is more strongly connected to the living world around us. Lastly, digital technology has a definite ecological footprint, which is still poorly understood in agriculture, and the increase in equipment and operations for data capture, transfer, storage and calculation could worsen the environmental impact.

Another group of risks to be considered concerns the social consequences of reinforcing, through digital technology, a trajectory of industrialisation and the concentration of production in ever larger units and farms that are geared towards productivity. This movement would risk excluding minority forms of agriculture, mainly farms operating at a smaller economic scale. The development of robotics
could contribute to precarious employment in agriculture, especially for poor migrant worker populations. Difficulties in access to digital technology could also be a factor of exclusion in agriculture, whether at the individual (lack of skills) or territorial level (underdeveloped digital infrastructure). Digitalisation could affect farmers' decision-making autonomy (use of DSS) or even the meaning of their profession, with the fear of becoming mere "data workers". Another question concerning digitalisation is the change in power relationships between farming and its upstream and downstream sectors. Upstream, digital technologies could affect the understanding and use of production tools due to their increasing complexity, making maintenance and training increasingly difficult and potentially leading to dependence on certain specific inputs<fnref n="9" /> Downstream, new data technologies could change the role of certain actors (such as companies in the digital sector) in value chains, with consequences in terms of sharing and governance and the risk of forms of subsidiary creation driven by downstream actors. Lastly, it is important to consider the concerns raised by digital technology for agricultural advising and its actors, methods, content and legitimacy.

The third group of risks identified concerns digital and food sovereignty. Growing digitalisation in the food chain could later also include agriculture with the emergence of monopolistic players and tools. In addition, digital sovereignty involves data control and there is a risk of agricultural data being seized by suppliers of digital technology or services (agri-equipment, AgTech companies, digital giants, etc.) The sharing of agricultural data, which is a priority for innovation, must therefore be structured and the governance of this data clarified. Lastly, cybersecurity risks must also be taken into account, such as potential attacks via connected objects (sensors, robots, etc.), the availability of geolocation systems and the challenge of preventing piracy (theft, damage, destruction) of agricultural data. Our food systems, which have remained relatively unimpacted until now, are of vital importance, which could make them potential targets in the future.

Lastly, the digitalisation of the agrifood system may increase dependence on resources among the different actors in the system and create new dependences with those who produce and own these technologies. This may accentuate the vulnerabilities of the system in the face of the many upheavals that will inevitably strongly impact on the functioning of our societies in the coming decades. More generally, it should be noted that agriculture and its upstream and downstream sectors form a complex sociotechnical system of which the overall energy cost is rising; it is important that this does not outweigh the anticipated benefits of digitalisation. The development of digital technology can also amplify the dynamic

<footnote n="9">⁹ An interesting parallel is that of the "printer/ink" lock-in: printers are sold very cheaply but require ink of the same brand to be used, which itself is sold at a very high price.</footnote>
of growing complexity, and must not be allowed to spark a headlong technological
rush that would lock us in a spiral of uncontrolled complexity.

Challenges in the development of digital
technology for the agriculture of tomorrow

Seizing the opportunities offered by digital technology for agroecology and rebalanced value chains, while identifying and anticipating the risks, also poses scientific, technical, economic, organisational and political challenges. We will focus in detail on the scientific and technical challenges and the associated human challenges in order to respond to four main types of issues for the sustainable food system: (i) improving collective management and incorporating the territorial level, (ii) improving farm management, (iii) rebalancing the value chain between upstream and downstream and (iv) creating and sharing data and knowledge.

COLLECTIVE MANAGEMENT

This generates new needs: (i) collecting data at the territorial level (while managing the balance between specificity, scope of measurement, resolution and heterogeneity of multisource data), (ii) visualising this data and the post-processing results for non-specialist audiences (by elucidating complex concepts like uncertainty, incompleteness), (iii) enriching territorial engineering methods to facilitate participation and open innovation (need for support models, gamification, analysis tools for participatory sessions), collective decision making (digital tools for deliberation, negotiation and voting processes) and mediation (creation of digital "boundary objects" such as support models to encourage dialogue between stakeholders).

INDIVIDUAL FARM MANAGEMENT

The aim is to increase the farmer's perceptive, cognitive and physical capacities.
For perception, there is a need for highly accurate and secure geolocation, early
warning systems for plant or animal problems, frugal, inexpensive and non-invasive
sensors, distributed data processing to limit transfer, fusion of heterogeneous
data to construct relevant indicators and uncertainty reduction strategies.
With regard to decision making, research challenges concern several aspects of
model construction: representing complex and extensive socio-agroecosystems
(multiscale and multi-temporal modelling, integration of interactions, digital
twin concept), incorporating expert knowledge into models (knowledge of the
environment, strategic choices), building user-centred DSS (personalising inferred
information, creating evolving models, ensuring the correct functioning of the
recommendation made, adapting the user interface to the specific aspects of
agricultural work), managing uncertainty and its spread. In robotics, there are
still challenges to be met in scene perception and interpretation in a dynamic
environment (i.e. the analysis of images and other "perceptions" by the robot),
mobile manipulation and coordination with a moving carrier, human-machine
interaction and shared autonomy, soundness of operation and adaptation to new
production systems and, besides these technical aspects, questions in the field
of humanities about the link between robotics and the transformation of work.

THE AGRICULTURAL ECOSYSTEM AND STAKEHOLDER RELATIONS

Upstream, services could be improved through digital technology by developing more personalised “digital” advice, which would require incorporating the farm’s characteristics and potential as well as the farmer’s preferences and underlying models. Insurance policies could be improved by creating new and fairer indicators based on automatically collected data (remote sensing, connected objects). Downstream, reconnecting farmers with consumers could be assisted through simple digital solutions that increase transparency (information on the products) and by improving platformisation. Blockchains could be a solution in response to the demand for transparency, bringing with them even more technical challenges (link between the flow of information and physical flows, coupling between private and public blockchains, environmental impact of public blockchains, integration of data for reuse) and institutional challenges (how should blockchains be governed?). Platformisation and disintermediation pose other challenges: production planning between multiple farmers (to supply catering or urban needs); coordinating actors within “food hubs” grouping different products; developing logistics solutions for peri-urban production. In the Global South, these issues are crucial in avoiding post-harvest losses, aggravated by specific weather conditions.

TOOLS FOR SHARING DATA AND KNOWLEDGE

Data and knowledge will be core to any digital system designed to promote agroecology, if the scientific, technological, regulatory, organisational and institutional challenges associated with their sharing can be met. Participatory data (from professionals or not) will be available in greater volume, raising questions about data quality, estimation and calculation of the value produced and recognition of the role of actors in the resultant innovation. What forms of governance should be used for this data in the context of reuse? How can virtuous and secure data circulation that avoids power appropriation by a single actor be supported? As for knowledge – which needs to be co-constructed (participatory, serious games, etc.) and formalised – it raises the question of digital commons and the collection and connection of expertise via ontologies that will require pooling and alignment.
## IN CONCLUSION, ALL RESEARCH IN DIGITAL AGRICULTURE WILL BE UNDERPINNED BY CROSS-CUTTING CHALLENGES

Research aimed at building responsible digital technology for sustainable agriculture must absolutely incorporate (i) a systemic vision for agriculture and digital technology (taking account of the impacts of technology), (ii) the search for frugality (to reduce environmental impact and economic cost), (iii) the search for resilience rather than economic optimisation in food systems and protection of farmers' autonomy and (iv) cybersecurity (attacks via the IoT, data hijacking, geolocation jamming, etc.), a topic that is all the more essential as it affects food sovereignty.

## General conclusion

The transition of food systems and agriculture towards more sustainable methods (agroecology, territorialized food systems, rebalanced long chains etc.) in a context of climate change is one of the biggest challenges for the years to come. Digital technology is being deployed rapidly in agriculture and, while it can provide solutions, it can also contribute to damaging fragile balances. It can accompany and accelerate virtuous transitions but it is essential to anticipate and avoid the pitfalls of misuse. The most pertinent research challenges concern: data acquisition at different levels and associated governance issues; devices to assist farmers at the cognitive (decision support), sensory (acquisition and transmission of information) and physical levels (tools, robots and cobots);<fnref n="10" /> the modelling of these complex systems and management of the associated uncertainty; digital tools for encouraging participatory processes (essential in AET); traceability and use of data among consumers and, lastly, the key issue of cybersecurity due to the fact that agricultural production concerns food sovereignty. The adopted approach will play a fundamental role in building responsible digital technology for farmers: the priority research position could use the responsible research and innovation (RRI) framework, which is starting to emerge in digital agriculture and requires a degree of inter- and cross-disciplinary work. Lastly, it is important to remember that several agricultural models exist and the future will bring even more diversity, leading to the development of digital offers adapted to each model and its needs.

<footnote n="10">10. Robotic elements that work collaboratively with humans</footnote>
1

Introduction

<figure><img src="image_4.png" /></figure>
All over the world, food systems, i.e. "the ways in which humans organise themselves, in space and time, to obtain and consume their food" (Malassis, 1994) are undergoing profound changes caused by external pressures (climate change, value chain organisation, etc.) and intrinsic factors (innovation, reduction in the number of farmers, etc.). Consumption patterns are changing under the pressure of five types of factors: (i) demographic and lifestyle factors, (ii) economic factors, (iii) cultural and value factors, (iv) technological factors and (v) regulatory factors (Blezat consulting et al., 2017). At a worldwide level, "the primary concern about the future of food and agriculture is knowing whether these systems will be able to feed everyone sustainably and efficiently by 2050 and beyond, while meeting the additional demand for agricultural products due to non-food uses" (FAO, 2018a). But while food security is the primary concern at the global level, agroecology,<fnref n="11" /> and particularly its large-scale deployment – including small farms – are also highlighted by the FAO (FAO, 2018b). Preserving family farming and lively rural structures is instrumental to the attractiveness of agricultural professions, another issue found worldwide. In France, agriculture is turning to the agroecological transition to increase its resilience (adapting to climate change), reduce its environmental impact (fewer pesticides, antibiotics, fertilisers, etc.), respect animal welfare and ensure a decent income for farmers.

In parallel to these major changes in food production and consumption patterns, another phenomenon is emerging in food systems, as in all sectors of the economy: the deployment of digital technology offering "versatile technology that is transforming processes and life in all areas across the planet" (Scholz et al., 2018). This is known as digital agriculture.

"Digital agriculture" refers to a form of agriculture and, beyond that, a food system "that uses information and communication technology (ICT): technologies for data acquisition (satellites, sensors, connected objects, smartphones, etc.), data transfer and storage (3G/4G coverage, low-speed terrestrial or satellite networks, clouds) and on-board or remote processing (supercomputers accessible via very high-speed communication) [...] at every level of agricultural production and its ecosystem, whether on the farm (optimisation of cropping operations, herd management, etc.), in support services (new agricultural advisory services based on automatically collected data), or more widely at territorial level (water

<footnote n="11">11. We use here the definition of (Caquet et al. 2020): "Agroecology is at once a scientific field, a practice and a social movement. [...] Agroecology is above all a new paradigm that aims to utilise biological processes to respond to the demands for both agricultural production and other agroecosystem services: protecting resources, contributing to climate change mitigation, preserving habitats and cultural heritage."</footnote>
management) or in the value chain (enhancing inputs such as seeds,<fnref n="12" /> improving harmony between production and the market, etc.)” (Bellon-Maurel and Huyghe, 2016). It is important to specify that there is now a global consensus on the definition of digital agriculture, which is essentially based on the multiplication of data collected “in the field”, the role of artificial intelligence, as well as connectivity and automation. Today, this consensus does not include genomics, where data is collected in the laboratory. We therefore deliberately do not address this field in this paper. Connections will be drawn between these two topics in the conclusion (in particular in terms of the advantages of collecting very precise data to characterise the environment).

Digital technology was first used in agriculture about 50 years ago (satellites, then computing capacities, GPS, etc.) and has become more widespread with new data acquisition systems (Sentinel satellites, connected objects and the Internet of Things (IoT), high-speed phenotyping, supply-chain traceability) and the explosion of processing capacities. The term “digital agriculture” was coined in the early 2000s, while the concept of precision agriculture has existed in the literature since the mid-1990s. The number of articles (journals and conferences) on digital agriculture (with “Digital Agriculture” in the title, keywords and abstract) stagnated at fewer than ten per year until 2017, when it began to rise exponentially with 59 publications in 2019 (compared with 1000 publications on Precision Agriculture), including 6 in a special issue of NJAS (Wageningen Journal of Life Sciences), and 94 publications in 2020. The concept is therefore still in its infancy, even in research. In the political sphere it began to draw attention between 2010 and 2015, with expert reports and political positions in favour of the development of digital technology in agriculture, whether in the USA (The Hale Group & LSC, 2014), France (the “Agriculture innovation 2025” report in 2015),<fnref n="13" /> Europe (the “A smart and sustainable digital future for European agriculture and rural areas” declaration in 2019),<fnref n="14" /> or elsewhere in the world (Dinash et al., 2017; FAO, 2019; OECD, 2019). In France, digital technology, connected objects, precision agriculture and the use of data have been identified as key technologies in the field of agriculture and agrifood (DGE, 2019).

<footnote n="12">12. Seed enhancement is a vast field that encompasses genomics (knowledge of genetic potential), phenotyping (measurement of the expression of this potential) and the relationships between the two. Digital agriculture takes an approach to genetic improvement based on phenotyping due to the enhanced possibilities of characterising plants or animals, brought about by new sensors; it does not include genomic research, which is carried out upstream.</footnote>

<footnote n="13">13. https://agriculture.gouv.fr/sites/minagri/files/rapport-agriculture-innovation2025.pdf</footnote>

<footnote n="14">14. Available at https://ec.europa.eu/digital-single-market/en/news/eu-member-states-join-forces-digitalisation-european-agriculture-and-rural-areas</footnote>
The development of digital technology in the agrifood chain is proving to be inevitable and is often seen by governments and experts as an opportunity to contribute to changes in agriculture for the benefit of farmers, consumers and society in general. What is at the heart of the matter? How can research institutes like INRAE and Inria accompany this change?

This white paper aims to address these questions. They have already been covered in numerous publications in recent years, but they are still few white papers (the most extensive examples are *Isaac and Pouyat, 2015* and *Scandurra et al., 2020*, among others). The originality of this white paper will be to study to what extent digital technology can contribute to agroecology and how research can be oriented in this direction. It will also attempt to answer the following question: what research questions must be addressed in order to understand, master, prepare and support the deployment of digital technology in agriculture and the food chain while taking account of the way in which it will transform sectors and their ecosystems, with the aim of placing it at the service of agriculture, farmers and the common good? We have chosen to concentrate on research needs and not to cover the field of innovation, which is very dynamic with numerous start-ups and therefore very fluid, meaning that the paper would quickly become outdated. In particular, we will study the role that digital technology can play in the development of agroecology and sustainable food systems. With this paper, we aim to provide an overview of the state of play and propose paths which, we hope, will lead us collectively towards responsible research and innovation in digital agriculture (*Owen et al., 2012*).

The white paper is structured as follows: first of all, it presents the challenges of the transformation of agricultural and food chains, then provides an overview of the state of the art and existing technology. Lastly, it explores the opportunities offered by digital technology in broad terms and the risks linked with an uncontrolled development of digital agriculture. Finally, core reflections highlight the technical issues and challenges that arise in developing the digital agriculture of the future, and in particular those that could mobilise the research teams of our two institutes.
<figure><img src="image_5.png" /></figure>

# What are the challenges facing agriculture?

Authors – Benoît Dedieu, Emmanuel Prados.

Acknowledgements (contribution, proofreading, editing) – Véronique Bellon-Maurel, Ludovic Brossard, Frédérick Garcia, Nathalie Mitton, Alexandre Termier.

<figure><img src="image_6.png" /></figure>
Agriculture is already facing multiple challenges. These concern food security, the environmental impact of agriculture and the organisation of the sector. Can digital technology help build a desirable future to respond to these issues?

## 2.1 World food security under strain

The world's population is growing at an annual rate of 1.1% and is expected to reach around 9.5 billion people in 2050, according to the UN's median scenario. This population growth generates a considerable increase in global food demand, which is also accelerated by rapid development and the changing diet in China (Bai et al., 2020). The world's agrifood system is increasingly subject to constraints, especially since it relies on a number of non-renewable resources that are becoming scarcer or more and more damaged (fresh water, phosphorus, oil, cultivable soil, etc.). This system will soon feel the full force of the impact of climate change, both directly (extreme weather events, drought, etc.) and indirectly (melting glaciers, proliferation and spread of harmful species of organisms and diseases, rising sea levels) (IPCC, 2014; UNESCO, 2019). It is also under threat from the current collapse of biodiversity in seeds, pollinators, crop auxiliaries, etc., which endangers many ecosystem services necessary for its proper functioning (FAO, 2019a). Conflicts over the use of products, land and water will also increase with, for example, the use of biomass for energy and the implementation of afforestation/reforestation programmes to capture CO₂ (also known as "negative emissions" techniques, which now underpin all IPCC scenarios limiting the temperature increase to 2°C). Moreover, for a number of cereals deemed critical<fnref n="15" /> for food security, agricultural yields seem to have reached their limits in developed countries.<fnref n="16" /> Lastly, the current agrifood system is not very resilient. It depends, for example, on globalised resources that are unevenly distributed around the world (phosphorus, oil, etc.) and a whole range of potentially fragile exogenous systems such as just-in-time transport and logistics systems, global markets and finance (speculation, price volatility, etc.) and flows of seasonal migrant workers. The Covid-19 health crisis has highlighted some of these vulnerabilities.<fnref n="17" />

The growing tension between supply and demand leads to a risk of worldwide food shortage in the medium term with multiple geopolitical consequences (Brown, 2012). The latest statistics from the FAO show that hunger is on the rise

<footnote n="15">15. i.e. which form the basis of diets.</footnote>

<footnote n="16">16. For example, see analyses of the French Academy of Agriculture on the evolution of the average annual yield of wheat in France from 1815 to 2018 and on that of maize from 1960 to 2017.</footnote>

<footnote n="17">17. See for example the Cirad press release of 29/04/2020 (https://www.cirad.fr/actualites/toutes-les-actualites/communiques-de-presse/2020/pandemie-coronavirus-menace-agriculture-alimentation) and the analysis by the association les Greniers d'Abondance of 16/04/2020 (https://resiliencealimentaire.org/covid-19-qui-veille-au-grain-pour-demain-lanalyse).</footnote>
again (FAO, 2017). However, certain levers and opportunities could potentially have a positive impact such as changing eating habits in developed countries, drastic reduction of losses and waste (FAO, 2019b), capacities for recycling and use of by-products/co-products and the improvement of production techniques and organisations in the agrifood system to increase its resilience and adaptive capacities. While these levers are probably within our reach, it is crucial to move forward on these issues very quickly.

Alongside these food security issues, the WHO has observed that 13% of adults worldwide are obese. This aspect of malnutrition is another critical issue for the agrifood system alongside the development of related chronic diseases (cancer, diabetes, cardiovascular events) through the production of ultra-processed foods that often have a very high sugar content. The same is true of the use of antibiotics as growth promoters in certain livestock farming models. These food-related health issues are the focus of growing attention (see, in France, the *États généraux de l'alimentation* and the Égalim Law<fnref n="18" /> in 2017-2018) and prompt a demand for healthy and sustainable food that is accessible to all and preferably local, agroecological and minimally processed.

## 2.2 There is an urgent need to reduce the negative environmental impacts of agriculture

The agricultural production system based on intensive farming has strongly contributed to the current collapse of biodiversity (Sánchez-Bayo and Wyckhuys, 2019)<fnref n="19" /> and the reduction of soil fertility and water quality (Caquet et al., 2020). There is an urgent need to drastically reduce the use of phytosanitary products and mineral fertilisers and reconcile agriculture and the environment generally. In addition, we must rethink our interactions with "natural" ecosystems (wildlife, forest and pastoral biodiversity reserves), bearing in mind that "natural" does not mean excluding all human intervention and activity. It is just as essential to improve livestock welfare: animals should be considered as subjects that are both sentient and conscious, working in cooperation with the farmer, and no longer as mere objects with biological functions. Farm systems that respect the animal and ensure a good life and an acceptable death must be designed (Porcher, 2011).

<footnote n="18">18. See issue 1566 (2017) of the Alim'agri magazine by the Ministry for Agriculture and Food, which summarises the various projects underway https://agriculture.gouv.fr/alimagri-les-etats-generaux-de-lalimentation. The Égalim Law (agriculture and food), which follows on from this work, was enacted in 2018.</footnote>

<footnote n="19">19. In thirty years, almost 80% of insects have disappeared in Europe.</footnote>
The circulation of zoonoses (Covid-19 and other recent zoonoses such as H1N1) remind us of the porosity with regard to certain diseases between the animal – both wild and domestic – and human worlds. According to the OIE (International Organisation for Animal Health), more than 60% of human infectious diseases are zoonotic. This figure increases if we consider emerging infectious diseases (70%)<fnref n="20" /> interactions between animal, human and ecosystem health are leading to the concept of "One Health" (Gibbs, 2014; Zinsstag et al., 2015).

Lastly, agriculture is the third largest source of greenhouse gas (GHG) emissions in France (19% of the national total in 2018) (CITEPA, 2018). Agricultural and forestry machinery only account for 12% of these emissions, while livestock farming accounts for 48% (mainly *via* methane emissions) and crops for 40% (mainly *via* nitrous oxide emissions during soil fertilisation). Between 1990 and 2018, agricultural emissions decreased by 8% (national GHG emissions increased by 6% in the same period). Transformations and efforts must therefore increase if the country is to meet France's low-carbon strategy objectives<fnref n="21" /> and its commitments in the framework of the Paris Agreements. In this context, crops, forests, grasslands and pastoral land and corridors could be of great help as they play an important role in carbon storage.<fnref n="22" /> For example, the "4 pour 1000" (4 for 1000) project launched by INRAE aims to increase carbon storage in all the world's agricultural soils by 0.4% every year (i.e. the equivalent of the world's annual CO₂ emissions linked to human activities)<fnref n="23" /> by developing alternative cropping practices such as intermediate crops, intra-plot agroforestry and temporary grasslands in crop rotation.

## 2.3 Agricultural dynamics have favoured intensification and specialisation

Farming is part of sector chains in which agricultural production and processing models are designed and locked, but also transformed and invented. At all levels of the chain, the agrifood model imposes costly requirements for competitiveness and sanitary standards linked with processes and product logistics. These chains

<footnote n="20">20. See https://www.oie.int/fr/pour-les-medias/une-seule-sante/</footnote>

<footnote n="21">21. The 2018-2019 SNBC strategy aims to reduce emissions from the agricultural sector by 18% compared with 2015 by 2030 and by 46% by 2050. It aims for a global reduction of greenhouse gas emissions by 40% by 2030 (compared with 1990) and carbon neutrality, i.e. zero net emissions, by 2050.</footnote>

<footnote n="22">22. For example, according to the ONF, forests store the equivalent of 15% of national GHG emissions.</footnote>

<footnote n="23">23. The "4% sur les sols pour la sécurité alimentaire et le climat" initiative (4% on soils for food security and the climate) launched by France on the occasion of the Paris Climate Conference (COP-21), proposes to increase the carbon stock in the world's soils by 0.4% per year. This figure is the result of a simple initial calculation, considering that total annual CO₂ emissions from human activity currently account for the equivalent of 0.4% of the carbon stock (C) of the planet's soils (approximately 2,400 gigatonnes of C). An annual storage of 4 per 1000 (4% or 0.4%) in the full depth of the soil would therefore compensate for these emissions.</footnote>
are part of sociotechnical regimes<fnref n="24" /> focused on competitiveness through pricing (as low as possible). This has been made possible by greater productivity of labour through the substitution of the latter by capital (in the form of increasingly efficient machines and automated, climate-controlled buildings) and workforce competition, exacerbated by globalisation. This system is underpinned by the intensive use of increasingly sophisticated technology, which many users gradually rely on (agricultural machinery, biotechnology, pesticides, etc.). Among other things, this has led to a steady fall in agricultural employment in all OECD countries, including France<fnref n="25" />, and to the specialisation of a growing number of farms in a single type of production. Through the CAP (Common Agricultural Policy), purchasing organisations and oligopolistic multinationals, the organisation of this system, from the individual farm to the consumer, is driven by tensions and unbalanced power relations between actors with different and sometimes divergent interests, contributing to lock-ins. Moreover, agricultural activity takes place in territories that have, in many cases, become specialised, leading for example to the spatial dissociation of livestock and crop production and the geographical concentration of sectors. All this forms a highly complex system characterised by the specialisation and interdependence of each element at the different levels, which demands very high levels of resources. This amplifies instabilities and multiplies the risk of failure. It is also an obstacle to change.

### 2.4 What are the agricultural models of the future?

The debate on which agricultural models would best respond to the present challenges is wide open, particularly since the rise in organic farming in France (which now accounts for 8% of the agricultural area) and the promotion of agroecology. These debates are not limited to the national framework but have taken on an international dimension. In 2016, the HLPE (High Level Panel of Experts) of the FAO proposed to address the future of agriculture (including livestock farming) according to two standard models: sustainable intensification and agroecology (HLPE, 2016). The first matches current trends to improve process efficiency and integration into long-chain systems. It is based on cutting-edge scientific knowledge and technological advances made possible by precision agriculture

<footnote n="24">24. According to Geels (2002) and the "multi-level perspective" model: a regime is a network formed of different economic and social actors around a product, service or, in our case, an agricultural model. The stakeholders are private and cooperative actors from the chains, but the system can be strengthened by teaching, research, the farm advisory system, national or European policies, etc., whether deliberately or not.</footnote>

<footnote n="25">25. In some areas of the world, particularly Africa, demographic dynamics and the limited opportunities offered by the industrial and service sectors mean that the creation of jobs in rural and peri-urban areas must be considered as part of the challenges facing agriculture. This also applies to some hinterland areas in France.</footnote>
and livestock production and genomics. The second standard model promotes agriculture based on natural processes and integration into local and sovereign food systems. It prioritises all forms of diversity (biodiversity, farming diversity and integration of cropping and livestock production), peer learning and the search for coherent systems that promote autonomy with regard to inputs and cost savings. Organic farming is one such approach (see insert on the Métabio metaprogram by INRAE). This model is increasingly supported by associations and local authorities who are developing territorial food projects and are strongly committed to promoting short supply chains. More detailed modelling has also been proposed. In particular, *Therond et al.* (2017) identify eight agricultural models positioned along two axes: dependence on inputs versus the implementation of ecosystem services; the territorial anchoring of food products versus long supply chains.

<figure><img src="image_7.png" /></figure>

For example...

The "**INRAE METABIO**" metaprogram launched in 2019 proposing the "change in scale of organic farming" looks to explore the hypothesis in which the majority of the national supply of products comes from organic farming, in a context of strong demand and agroecological transition. It studies the challenges, levers and consequences of the change in scale of organic farming throughout the agrifood system. The aim is to explore scientifically supported proposals to anticipate consequences and inform the deployment of organic agrifood systems.

Another debate on models revolves around the structural characteristics of farms that could provide a response to the challenges outlined above. Here, there is a marked difference between family farming and more capitalistic models, such as the corporate agriculture described by Gasselin et al. (2015) and Hervieu and Purseigle (2013). In the first case, capital and labour are in the hands of the family whereas in the second, capital is held by non-agricultural actors and all workers are employees. The latter situation prefigures the megafarm,<fnref n="26" /> which is underpinned by very high productivity made possible by extremely large-scale operations, mechanisation and, increasingly, adapted automation.

There is some contrast between these two types of models, because agroecology is more family-based while sustainable intensification is more capital-based. However, the diversity of systems cannot be reduced to these archetypes. There

<footnote n="26">26. The largest farm in the world is located in China with approximately 40,000 dairy cows; an even larger one (100,000 cows) is under construction... way beyond our controversial 1,000-cow farm!</footnote>
are also many hybrid situations of what is known as “agriculture of the middle”. In addition, the opposition between these two types in the societal or professional debate does not exclude forms of coexistence in the territories – which may be spatial or work-based (exchanges) – and the urban food supply chain. Whatever the model, farmers want a decent income and working conditions (Ghai, 2003), that is, conditions that protect their health, offer social protection and preserve their ability to influence their future. They also want work that is meaningful, useful, attractive and maintains relationships with others.

## 2.5 The territorial nature of agriculture

Another element to be positioned in the debate on agricultural models is the territorial nature of agriculture. The question of the territorial inclusion of the models addresses certain needs: the economic and social development of these territories, their increased resilience, their environmental quality and the availability of opportunities to integrate activities that use by-products in local applications. How can agricultural models reduce material and energy flows? At what scales should production and activities be relocated and rediversified? How can city-countryside relations, short chains and agriculture near cities meet these needs? What strategies and tools should be mobilised (geographical quality indications such as PDO, AOC<fnref n="27" />, etc.)? How can employment be promoted and sufficient economic value be ensured? These questions all position the territory (composed of its spaces, activities and actors) as an essential entity in addressing the challenges that agriculture must face, in particular through:

* the analysis of modes of coexistence and border sharing between agricultural models (Gasselin et al., 2021) in food systems;

* the design of processes for the agroecological transition at the territorial level (Bergez et al., 2019);

* the provision of information and decision-making tools for stakeholders, local authorities and government services concerned with the environmental and health dimensions that link agriculture to the other components of ecosystems, hydrosystems and pathosystems.

<footnote n="27">27. Appellation d'Origine Contrôlée.</footnote>
Conclusion

Agriculture is currently facing critical challenges in terms of food security, pollution and resources that call into question the productive dimensions of the activity and profession of the farmer. They raise questions about which agricul- tural models could provide a response and about the territories themselves in which the concrete variations of these new models interact, contrast or coexist. It should be noted that they are subject to different dynamics, depending on the socio-technical regimes to which they are attached. Sustainable intensification is often associated with large farms with a small workforce and is linked to the evolution of the predominant regime inherited which relies on (large) private upstream and downstream companies, large cooperatives, advisory services from chambers of agriculture, the CAP and research leading to the prescription of technical solutions applicable in most environments. Agroecology is still a socio- technical niche, but a real change in priorities has been observed among multiple actors, including in research, public policy making and education. Agroecology is also associated with the idea of transition and radical transformation. It calls for the development of knowledge, methods and tools exploring levers to enhance the coherence and performance of agroecological systems. It also accompanies processes of change and facilitates the exploration of desirable situations, learning, adapted mechanisations. It supports the step-by-step reconfiguration of systems in a context of incomplete knowledge and uncertainty about the impact of actions.

In this context, digital technologies are considered above all from the pers-
pective, for the precision of the information they provides and the new decision
support regimes that they support. In this way, they can reverse the dynamics
of simplified reasoning and actions brought about by the increase in farm's size.
By providing tools for observing and managing increasingly large areas, digital
technology acts as a lever for sustainable intensification and the expansion of
structures, which becomes compatible with precision and individualisation. At
the same time, digital technologies could also contribute to the development of
the agroecological model on family farms. Indeed, part of this model is based on
dialogue and learning among peers and on direct links with consumers: forums,
online stores and social networks could become effective tools. More digital
technology could help understand and manage the biotechnical, ecological
and socioeconomic complexity of systems based on agroecological farming,
although this remains to be confirmed. It could also "equip" the farmer to detect
malfunctions sooner and help decision making (decision "support" information).
The territorial level is also of interest from the digital perspective, due to the ability of digital technology to deal with complex processes connecting spaces, activities and actors and to explore useful scenarios for multi-actor decision making, whether this concerns food systems, environmental issues or, increasingly, health issues (infectious animal or zoonotic diseases) (Charrier et al., 2019).

Ultimately, to allow a quick transformation capable of meeting the challenges, it is necessary to question the capacity of digital technologies to respond to the demands of the different actors and stakeholders (public or private) and their urgent needs for:

* information, understanding of the complexity of the systems, risks and uncertainties;

* support for the development of strategies and policies and the multi-criteria assessment of agricultural production/food system scenarios at different levels (European, national, regional and territorial);

* support for decision making and managing compromises in single and multi-actor situations;

* and lastly, support for the components of concrete action that link humans, machines and tasks on the one hand and experiential dialogue between peers on the other.
3

Foundations
and state of play

**Authors** – Nathalie Miton, Ludovic Brossard, Tassadit Bouadi, Frédérick Garcia, Romain Gautron, Nadine Hilgert, Dino Ienco, Christine Largouët, Evelyne Lutton, Véronique Masson, Roger Martin-Clouaire, Marie-Laure Mugnier, Pascal Neveu, Philippe Preux, Hélène Raynal, Catherine Rousset, Alexandre Termier, Véronique Bellon-Maurel.

Acknowledgements (contribution, proofreading, editing) – Isabelle Piot-Lepetit.

<figure><img src="image_8.png" /></figure>
Having described the different challenges facing agriculture in the previous chapter, in particular those of agroecology and sustainable food systems, which will be the “target” of our reflections, in this chapter we will address the foundational aspects of digital technology, their use in agriculture and current research. In the Introduction, we reviewed the pillars of digital agriculture, which can be summarised as data, processing capacities, connectivity to allow data and information exchange and, finally, automation. The challenges facing agriculture concern all levels of the data cycle, from capture to use *via* collection, traceability, processing, storage, interpretation, provision and application in automatic and robotic systems.

## 3.1 Data

The use of digital technology in agriculture produces large volumes of highly heterogeneous data that can be qualified as “big data” (Bellon-Maurel et al., 2018). It is uniquely complex because it includes observations of complex objects and environments of different natures and operating at very different spatio-temporal levels (for example from the gene to the field), with strong intra- and inter-level interactions and involving numerous actors. This complexity leads to questions about what data to collect (nature, frequency, objective, etc.) to guide the deployment of a technical solution at all levels (hardware, software, interface, etc.).

### Data capture (what, why, where and how)

The challenges of data capture are both hardware- and software-related. Knowing what the data is intended for helps to determine the choice of measurement equipment.

First, the nature of the measurement (temperature, air or soil humidity, condition of a plant's leaves, weight of an animal, etc.) and the accuracy required must be specified. These requirements, which depend on the needs defined, vary greatly from one use to another. The second issue is how to capture the data. The nature, size, weight, bulk and robustness of the sensor will also depend on the nature of the measurement, the object to which it is applied and the environment in which it will be placed: a sensor worn by an animal will be chosen according to the weight and bulk of the equipment and the size of the animal. Similarly, a sensor for field measurements on soil or plants will require protection to make it resistant to the surrounding environment (humidity, temperature variations, shock resistance, etc.). Finally, how the data will be used will define the sampling method, in particular the collection location and spatial and temporal resolution (Brun-Laguna et al., 2018). For example, should the sensors be positioned per m²
or per km²? If the aim is to monitor animals, should all or just a few of the animals
in the herd be equipped (Jabbar et al., 2017)? What time frequency is required
and should it be constant? Some applications require high spatial and temporal
frequency, offered by satellite remote sensing. Others need less frequent mea-
surements, such as those obtained from participatory data (Minet et al., 2017).

The decision of which technology and equipment to use and which methodologies to implement for the deployment of sensors has been the subject of numerous studies in recent decades, with applications in both cropping and livestock production: identification and geolocation using RFID (Ruiz-Garcia and Lunadei, 2011) or GPS; imagery (2D, 3D, infrared, hyperspectral), accelerometry, acoustics, biochemical measurements on fluids (including biomarkers), measurement robots such as weighing scales, water or milk meters, feed dispensers, etc. (Chastant-Maillard and Saint-Dizier, 2016; Halachmi et al., 2019). In most cases, trade-offs must be made between cost, resolution, precision and practicality (Foubert and Mitton, 2019). Research aims to limit these concessions, either by developing sensors that are increasingly precise, energy-efficient, smaller, less intrusive and less costly, or by designing massive data acquisition devices (using satellite images, drones, etc.). The deployment of new satellite constellations (Sentinel-2),<fnref n="28" /> which produce high-resolution images (both spatial and temporal) made available free of charge, offers new monitoring opportunities.

In conclusion, the work to develop acquisition systems is inherently multidisci-
plinary and requires collaboration between agronomists, biologists, zootechnicians,
geneticists, computer scientists, electronic engineers and end users to ensure
that the requirements of users (who are sometimes researchers themselves in
another field) are met and to combine knowledge of the objects of study, their
specificities and their constraints with knowledge of digital technology.

<footnote n="28">28. https://sentinel.esa.int/web/sentinel/missions/sentinel-2</footnote>
<figure><img src="image_9.png" /></figure>

For example...

Several INRAE units are developing such acquisition devices for phenotyping or monitoring animals or crops. Examples include: a high-speed 3D image acquisition device and its associated processing methods to measure the physical condition and morphology of dairy cows, developed by the **PEGASE** Mixed Research Unit (UMR) in collaboration with the Agricultural Technical Institute **IDELE** and the company **3D Ouest**. An automatic feeder has been developed by the **PEAT** Experimental Unit (UE) and the **BOA** UMR for studying the feed intake and individual feeding behaviour of poultry reared in groups. The electronic mounting detector "ALPHA" (the company Wallace), based on an automatic RFID reader worn by a ram, was designed by the **SELMET** UMR to automatically detect heat in sheep, particularly in large-scale farming. In the plant field, the **ITAP** UMR and the **CAPTE** Mixed Technology Unit (UMT) are developing optical sensors for phenotyping or early detection of plant diseases. The **TETIS** UMR uses satellite remote sensing to detect plot defects. The acquisition of phenotypic data using sensors is being addressed by large-scale programmes and infrastructures such as **PHENOME** on the characterisation of crops grown in greenhouses and in the field and **IN SYLVA** on forests. The resulting data can be used to improve the predictive capacities of models and how they take into account interactions between genotypes and the environment. More broadly, high-speed phenotyping systems are also being developed in plant and animal experimental units at INRAE.

Data collection and transmission (What data to send, when and how)

Once the data has been acquired, it must be transmitted. Some systems use wired communication (Ethernet, serial, etc.), but this is not always possible and sensors have to be equipped for wireless communication, which poses different challenges. Data capture and transmission in agriculture increasingly use Internet of Things technology (Zhao et al., 2010), especially RFID and wireless sensor networks with specific features for agriculture.
<figure><img src="image_10.png" /><figcaption>Monitoring of animals in extensive breeding. © Selmet - CIRAD.</figcaption></figure>

Most wireless sensors rely on energy sources that are either limited (e.g. batteries) and/or variable (e.g. via a solar collector) and must therefore be preserved. Data transmission is often the most energy-consuming factor and presents a major challenge, so the aim is to limit the amount of data sent while maintaining the data accuracy required for the application to function. Research therefore focuses on data processing in the sensor, which itself is limited in computing and memory capacity, using spatial and/or temporal data aggregation (Salim et al., 2020) and simplified artificial intelligence methods. For example, researchers use the correlation between two quantities (such as temperature and humidity) to transmit just one of the two values and interpret the second. Another option is to locally predict the next value to be measured and only transmit the data if it does not match the predicted value. The more demanding an application is in terms of temporal resolution or accuracy, the more data transfer is required. This also requires a trade-off between efficiency, accuracy and cost.
<figure><img src="image_11.png" /></figure>

For example...

The **FUN** and **EVA** project-teams at Inria are working on data collection for agriculture using wireless sensor networks. Their work concerns both specific network protocols and the question of which data to transmit to avoid saturating the communication media and reduce the energy consumption of transmissions. The **FUN** project-team is installing sensors in vineyards in South Africa to improve watering and water management. They also collaborate with Sencrop, which uses sensors in cereal and potato farming. **EVA** installs sensors on peach trees in Argentina to protect against frost.

The choice of communication technology depends on the quantity of data to be retrieved as well as the distance to cover and location of the sensors. For data to be collected over long distance and requiring greater intervals between transmission (such as once-a-day temperature readings) one would uses long-range technology with low data rate and power consumption, whereas high-frequency readings (such as animal video tracking) require high data rate. The measurement points may be located in areas not covered by cellular technology (such as 3G/4G/5G or LPWAN – Low Power Wide Area Network), which would require specific network mechanisms to be put in place such as routing (relaying information to the destination station). This must take account of the constraints and requirements of the applications and the material limitations and characteristics of existing radio technology (Foubert and Mitton, 2021) and the environment in which the sensors are deployed (Ferreira et al., 2020). An additional difficulty is the heterogeneity of technologies required that must to coexist and sometimes cooperate, as well as the more general challenges of the Internet of Things (IoT), addressed in the Inria White Paper on the Internet of Things.<fnref n="29" />

Lastly, mobile data collection solutions are emerging for blackspots, ranging from simplified solutions (portable data devices that can be carried in a rucksack, as in the case of the COWSHED project in Africa<fnref n="30" />) to high-tech solutions with "aerial" devices (drones or nanosatellites). The latter can collect data from thousands of connected objects at a low data rate (LoRa protocol) or high data rate (i.e. 100 kB per transmission) using a smaller number of terminals on the

<footnote n="29">29. Scientific challenges of the Internet of Things. Inria White Paper. https://www.inria.fr/en/inria-white-paper-internet-things-iot</footnote>

<footnote n="30">30. https://hal.archives-ouvertes.fr/hal-03102190/document</footnote>
ground (around 100) (UHF protocol). Applications are developing in the field of agriculture, such as in Australia where farmers remotely monitor the level of irrigation tanks using nanosatellites.<fnref n="31" />

<figure><img src="image_12.png" /></figure>

## Definition

The Internet of Things (IoT) is the interconnection of the Internet with things, places and physical environments. The term refers to a growing number of objects that are connected to the Internet, allowing communication between the physical and digital aspects of our possessions. The IoT combines a wide range of technologies from simple RFID tags to mobile phone applications and wireless sensor networks. Radio communication technologies are diverse with different characteristics with regard to data rate, consumption, range, etc. Sensors can be equipped with microcontrollers with varying levels of power and energy consumption.

## Data storage and exchange, traceability

Once the data has been captured and transmitted, it can be used for a variety of purposes. Firstly, it can be stored and processed to extract knowledge, anticipate malfunctions, etc. This data can be very heterogeneous and of varying levels of quality. It can also have very different sampling rates due to it coming from different sources (physical sensors, "human" sensors or even simulation results) and can be very large in volume (many capture points, potentially high temporal frequency). Methods derived from multivariate data management and now big data offer a response to the challenges of volume, processing speed and the diversity of formats and sources (Bellon-Maurel et al., 2018). The prerequisite for successfully using this data is that it meets the guiding principles of "FAIR", which are Findability, Accessibility, Interoperability and Reusability,<fnref n="32" /> with minimal human intervention. There is therefore a demand for a new generation of information systems adapted to agriculture in order to manage and structure this complex mass of data using the FAIR principles. Metadata and data must be well described using semantic resources (ontology, taxonomy, thesaurus) to make them understandable and facilitate access via standardised protocols.

<footnote n="31">31. https://which-50.com/world-first-australian-iot-uses-satellites-to-monitor-farmers-tanks-rain-levels/</footnote>

<footnote n="32">32. Wilkinson et al. *The FAIR Guiding Principles for scientific data management and stewardship*. Scientific Data 3, 160018. doi:10.1038/sdata.2016.18.</footnote>
<figure><img src="image_13.png" /></figure>

For example...

At INRAE, automated data processing centres (CATI), federate and structure skills, methodologies and technologies to facilitate the reuse of data, such as the CATI SICPA (Information Systems and Computation for Animal Phenotyping), the CATI Codex (plant phenotyping) and the CATI GEDEOP (Management of Experimentation, Observation and Practice Data on Agro-socioecosystems).

Finally, the question of data validation is becoming a central issue with the increase in the quantities of data collected: is the measured value correct, for what applications and under what conditions?

A variety of DBMS (Database Management Systems) exist depending on the data model used, with the relational model currently being the most common. *Benchini et al.* (2007), for example, coupled a dynamic cropping system simulation model with a relational database to achieve efficient storage and analysis of model data at farm level. But when it comes to dealing with very large volumes (of the order of petabytes) or complex and heterogeneous data (graphs, documents, etc.) in highly distributed contexts (remote servers, Internet of Things, etc.), NoSQL-type databases using a different data model – which has fewer conceptual constraints than the relational model – are more pertinent.

In some cases, the primary aim is to share the data among multiple actors, for example to limit fraud or validate certain processes (correctly following a given route, compliance with the cold chain, local production or insecticide-free requirements). To achieve this, one of the most promising digital tools currently available is the blockchain (*Bermeo-Almeida et al.*, 2018). Blockchains, which use a distributed database that requires no control entity, date the components they contain and guarantee their unalterability. In agriculture, this technology makes it possible to record the life stages of a product and thus ensure traceability (*Kamilaris et al.*, 2019). It offers multiple advantages (see Chapter 4: Opportunities) including transparency and records of transactions between farmers, suppliers, buyers, consumers etc. In some sectors, using blockchains makes it possible to avoid long and costly certification processes (*Lin et al.*, 2017).

However, blockchains also pose new digital and organisational challenges. In any information system, there is always an inherent risk of hacking. Also, blockchain technology was initially designed for transferring and sharing intangible goods
(funds, certificates, diplomas) and is not infallible in cases where information flows must be coupled with physical flows, such as in agriculture and agrifood where the digital data must be an exact representation of the physical flow. Furthermore, the perceived security of a blockchain is based on a mechanism known as "proof of work" (of computer operations), which validates the new blocks to be incorporated. Public blockchains rely on large numbers of "proofs of work", which is very energy intensive, and research currently focuses on reducing the complexity of these cryptographic algorithms to reduce their energy consumption.

## 3.2 Modelling, simulation and optimisation

If data is one of the driving forces of digital agriculture, modelling is also essential for linking measurements and observations to interpretations and recommendations to help actors in the agricultural sector better understand, manage and improve their production systems.

In the field of agriculture and agronomic research, the scientific approach of modelling to predict harvests emerged as early on as the reign of Egyptian Pharaoh Sesostris I, when river levels were used to make crop forecasts (Gros de Belet, 1998), or among Inca farmers who knew several months in advance what agricultural cycles to expect by observing nature (Gutiérrez, 2008). Much later, the pioneering work of Mendel (Mendel, 1907) and then Fisher (Street, 1990) definitively legitimised the use of statistical models in the fields of genetics and agronomy. In the latter half of the 20th century, agricultural modelling developed in particular in rural economies to rationalise and optimise production, agronomy and zootechnics, crop management, animal nutrition and genetic selection in plants or animals. With the development of computers and the first calculators, modelling gradually went beyond statistics and operations research and increasingly used symbolic and algorithmic formalisms to produce models expressed in mathematical and computer terms and in which simulation plays a key role.

The general function of a model is often called a mediation function: "to an observer B, an object A* is a model of an object A to the extent that B can use A* to answer questions that interest him about A" (Minsky, 1965). This mediation can help meet different cognitive objectives: facilitating experience, intelligible formulation, theorisation, communication and the coconstruction of knowledge, decision and action (Varenne and Silberstein, 2013). Today, agricultural modelling concerns a very broad spectrum of objects and has four main purposes: analysis, communication, predicting and controlling the evolution of various components in an agricultural system and designing and optimising the system under consideration. In the rest of this section, we will present a few of the major types of models and how they can be used in digital agriculture thanks to simulation and optimisation.
## What to model, for what purposes and with which tools

**What to model?** – In agriculture, the objects of study – or subjects of the model – are anthropised natural systems that can involve multiple scales and levels of organisation. Modelling focuses on the components of these systems as well as the processes that govern their dynamics, the events that activate or inhibit these processes and the exogenous factors that influence them (such as weather conditions) (Martin et al., 2011). Some of the components are biophysical (such as crops with growth processes, diseases) (Kumar and Sinhg, 2003) while others are centred on the roles played by human actors. In the latter case, modelling can concern either a single individual (Martin-Clouaire and Rellier, 2004; 2009) or a group of individuals (such as the members of a cooperative) and the social processes of activity coordination carried out by different individuals in the collective (Drewniak et al., 2013; Manson et al., 2016).

<figure><img src="image_14.png" /></figure>

### For example...

Models of processes, flows and interactions are being developed by many teams and units. Below are a few examples at different scales.

At INRAE, several teams are working on crop or livestock modelling, whether on a plot-by plot basis (multispecies crop models such as STICS, which describe growth according to climate and environmental variables), at the individual level (animal growth models according to their diet and environment), or at larger scales (plant epidemiology models including inter-plot dispersal and animal epidemiology models describing inter-herd transmissions, etc.). These models are hosted by modelling platforms such as **RECORD**, **OPEN ALEA** and **OPEN FLUID** and form the basis of simulations run according to different climate and contextual scenarios.

The **STEEP** project-team (Inria, CNRS, Université Grenoble Alpes) develops mathematical models for analysing material flows (production, transformation, exchanges, consumption, waste) in agriculture and the forestry-wood sector in order to 1) understand the upstream/downstream vulnerabilities in the sectors, 2) question the use of natural resources and potential problems caused by competition for use and, finally, 3) assess environmental impacts. The tools developed are based on the modelling of chains in terms of products and sectors and the existing flows between them. One of the major difficulties is the particularly patchy and inconsistent nature of the data.
The **EASE** project-team (Inria, Ecole Nationale Supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, Université de Rennes 1) is developing a complete series of new interaction models, offering tools for augmenting and describing information from complex systems. The work has been applied to energy management in agriculture. In particular, the model helps define how to reduce the environmental impact of energy consumption when optimising an existing site or installing new facilities. It has shown that the optimisation of a single parameter alone (local production, storage or process transfer) is not enough to maximise self-consumption and minimise energy requirements.

**What purposes to model for?** – The level of detail of the modelling systems at the heart of a study is defined by the purpose of the study and the tool under consideration. The most commonly applied objectives range from identifying the aims and means of managing agroecosystems to predicting performance (Rio et al., 2019) in the light of different scenarios going through the identification of risks and the critical analysis of the functioning and conduct of agricultural production systems (Li et al., 2019). Modelling can also allow the design of new systems such as the configuration and sizing of a logistics chain (Taghikhah et al., 2021).

<figure><img src="image_15.png" /></figure>

For example...

Many models concern agroecosystem management. The joint **INRIA / INRAE** project-team **BIOCORE** (CNRS, Sorbonne Université-UPMC) focuses on modelling and control in epidemiology for tropical agriculture. At INRAE, the **MIAT UR** and **MISTEA** UMR develop simulation models and optimisation methods for managing agroecosystems at farm level. For animals, UMRs such as **BIOEPAR**, **SELMET**, **MoSAR**, **UMRH** and **PEGASE** are developing models on animal health and epidemiology, dynamic ingestive, digestive and metabolic phenomena and livestock farming systems. For example, the **PEGASE** UMR has developed new models for adjusting daily feed according to the nutritional needs of each animal for individualised feeding for pregnant and lactating sows (Gauthier et al., 2019).
<figure><img src="image_16.png" /><figcaption>Building an agronomic model. © INRAE</figcaption></figure>

**How to model?** – Computer modelling to support the analysis, design and management of agroecosystems is combined with approaches using either simulation or optimisation (Li et al., 2020). In dynamic simulation, the modelling of phenomena considered important for the objectives of the study is centred on realism (Kaghazchi et al., 2021), whereas optimisation involves algorithmic exploration of the space of alternatives that efficiently searches for an optimal solution according to one or more explicitly formulated criteria using reductionist mathematical models (Ezanno et al., 2020; Casagli et al., 2020). The two methods have relatively antagonistic objectives (modelling realism versus computational efficiency) and therefore generally use different modelling approaches.

## Representation frameworks

Agroecosystems are complex objects of which the models concern, on the one hand, the functioning of the biophysical entities that compose them (soil, plants, animals, mineral and water flows, etc.) and, on the other hand, human decision making and action on these biophysical entities (Zabala et al., 2021). Models convey knowledge that mainly comes from scientific disciplines such as agronomy, zootechnics, environmental science, management science and the humanities.

Biophysical models can be classified into three main fields: mechanistic, empirical and hybrid (Reyniers, 1996). Mechanistic modelling focuses on events, causal relationships and processes, whereas empirical models treat systems as
"black boxes" and only generally describe the underlying biophysical phenomena. These models represent the input-output dynamics of a system component in terms of observation data. In reality, there are few truly mechanistic or empirical models. Models are generally hybrids or classified in one or the other category according to whether they possess mainly mechanistic or empirical components. The overall understanding and level of information required to build these models increases as we move from empirical to mechanistic models. By making causality explicit, mechanistic models can be more complex, while empirical models are generally simpler but have a more limited scope of application due to statistical data availability issues.

Decision modelling varies depending on the modeller's hypothesis about the decision maker. In a first type of hypothesis, the decision maker is assumed to be perfectly rational (in the economist's sense) and, when making a decision, determines the mathematically optimal choice according to theoretically defined functions of utility. In a second type of hypothesis, known as a bounded rationality hypothesis, the agent makes a decision that leads to an outcome that they consider satisfactory given the information available and their level of aspiration. Mental models of varying levels of sophistication are often used, including models based on decision rules that associate situations with decisions or actions (Martin-Clouaire, 2017). To facilitate and standardise the development of these models, ontologies can be used to define the concepts, relationships and other distinctions relevant to the areas concerned (major crops, livestock production, etc.) (Roussey et al., 2011). An ontology (see Section 3.4) is an abstract model (metamodel) of the area and provides the representation primitives allowing the instantiation of models for specific systems in the form of knowledge bases (Martin-Clouaire and Rellier, 2004; Fishwick, 2007).

<figure><img src="image_17.png" /></figure>

## Definition

An ontology defines a vocabulary and the semantic links between the elements of the vocabulary. The vocabulary is composed of names of concepts (or "classes"), which are types of entities known by the system and names of possible relationships (or "roles") between these entities (for example, the relationship of "pest" links two "living organism"-type entities). The ontology is described in a logical computer language that expresses the representation of knowledge to different degrees of expression. It can range from a simple taxonomy (a set of concepts structured by progressive specialisation) to complex descriptions of vocabulary elements and their semantic links. The language used allows the implementation of automatic reasoning.
**Discrete event, discrete time and continuous time systems** – A discrete event simulation model allows the representation of a dynamic system using variables whose evolution depends entirely on the occurrence of asynchronous events over time. One particular case is when the time progression is in fixed increments. The discrete event approach contrasts with (but does not oppose) the “system dynamics” approach in which the state of the system is modified continuously over time based on a set of differential equations defining the rates of change and state variables. In each of these different cases, researchers are interested in the representation of causal relations (i.e., for the biophysical aspect, mechanistic models). One of the best known formalisms is Discrete Event System Specification – DEVS (Zeigler *et al.*, 2000), which is based on a generic framework allowing different adaptations to specific formalisms such as Petri nets, cellular automata and, more generally, models with fixed time intervals. Petri nets are a particularly popular mathematical formalism because of their ability to represent the synchronisation of processes running in parallel and offer possibilities for rigorous model analysis. A cellular automaton is built using a network of discrete cells and is well suited to representing spatial dynamics (such as the propagation of an infestation) and self-organising phenomena (such as the landscape dynamics of natural reforestation). Some formalisms such as statecharts (Léger and Naud, 2009), timed automata (Hélias *et al.*, 2008) and Petri nets (Guan *et al.*, 2008) can also suit processes for verifying the behaviour of the model (e.g. to ensure that it cannot lock up) or its temporal properties.

<figure><img src="image_18.png" /></figure>

Automatic detection of mangoes by artificial intelligence from a smartphone photo. Hortsys.

© Hortsys - CIRAD.
**Individual- or agent-based models** – These models focus on systems that can be broken down into a set of entities (such as plants, animals, zones) that act or interact (Daudé, 2004; DeAngelis and Diaz, 2019). When combined with a cellular automaton-based approach, the individual-based approach allows the spatial representation and simulation of biophysical processes on a territory divided into plots. When the modelled entities acquire more elaborate cognitive and decision-making capacities (Bahri et al., 2020), we speak of agent models that allow, for example, simulation of the decision-making behaviour of a group of agents (such as farmers) operating in a given territory (Huber et al., 2018). Farm management has often been modelled using simple mechanisms for triggering decision-making rules associated with possible situations. With this approach, however, it has proved difficult to control the order in which rules are used and to maintain the rule base once it reaches a certain size. An improvement was introduced by the BDI (Belief, Desire, Intention) approach (Georgeff et al., 1970; Bratman, 1987), which makes it possible to model the process by which an agent makes decisions based on a perception of the current situation (Belief), the declared objectives (Desire) and decisions on how to proceed toward the objectives (Intention).

<figure><img src="image_19.png" /></figure>

For example...

The **INRAE-MIAT** UR has developed several formalisms to represent and simulate the decision-making behaviour of farmers when managing their farms using the BDI approach, temporal planning and uncertainty in artificial intelligence. For example, Martin-Clouaire and Rellier address the problem of production management as one of coordinating a set of activities organised in flexible plans for which it is possible to simulate the implementation in a particular context (Martin-Clouaire and Rellier, 2009). For application examples in dairy farming see Martin et al. (2011) and Martin-Clouaire et al. (2016) in viticulture.

**Constraint-based models** – Constraint-based models use varied range of formalisms that are mainly based on the concept of graphs modelling binary relationships between variables (Hurley et al., 2016). These relationships can model correlations and causal influences, whether deterministic or probabilistic, as in the case of Bayesian networks and Markov chains. These networks can also describe constraints between variables in terms of combinations of acceptable or unacceptable values, leading to a Constraint Satisfaction Problem (CSP)
(Moummadi et al., 2011). In a similar vein, linear programming methods are based on the optimisation of a linear combination of multiple variables connected by linear relationships called constraints (Maqrot et al., 2017).

<figure><img src="image_20.png" /></figure>

For example...

At INRAE, the **BAGAP** UMR works on modelling the problem of dynamic crop allocation on a farm, based on the use of spatial and temporal constraints and the toolbar2 solver (Akplogan et al., 2013). For example, the team analysed the wooded countryside of the Charolais-Brionnais region to show the uniqueness of this landscape and its suitability for the different structures and functions of hedges. Thanks to this analysis, the countryside was added to the list of potential sites for submission to UNESCO for heritage protection.

Modelling and simulation

The primary advantage of modelling approaches is no doubt the ability to model and simulate complex behaviours in agricultural systems and, more broadly, socioecological systems such as agroecosystems (Peart and Curry, 1998). Models, especially agent-based ones, are often complex due to the number and heterogeneity of components and interactions and their sensitivity to variations affecting the systems. Their behaviour is difficult to study because the phenomena involved are non-linear with multiple discontinuities and feedback between levels of organisation and scales. Some of these models represent cognitive agents with bounded rationality behaviour. Numerous agricultural applications have been developed based on the CORMAS (Bommel et al., 2015) and GAMA (Taillandier et al., 2010) platforms, such as for studying water management, the reform of the Common Agricultural Policy, reducing the use of pesticides and developing organic farming.

<figure><img src="image_21.png" /></figure>

For example...

At INRAE, the **AGROECOLOGIE** UMR coordinates the development of the MAELIA platform for the integrated modelling and evaluation of socio-agroecological systems. It aims to produce knowledge on the structure, functioning and performance of these forms of agriculture at plot, countryside and/or territorial level.
In practice, modelling-simulation approaches offer a variety of uses, ranging from laboratory analysis by scientists, decision support (Huber et al., 2018), real-time decision making by farmers or agricultural advisors and support for negotiations between stakeholders (e.g. support models for joint water management in a territory) to the co-design of new production systems by a group of farmers and training. Individual or groups of farmers can thus improve their understanding of biophysical functioning and obtain ideas for improving the system studied in terms of product quality, system vulnerability, environmental consequences of the practices implemented, reduction of work overload and drudgery and, finally, economic performance linked to the application of agroecological principles.

<figure><img src="image_22.png" /></figure>

For example...

At INRAE, skills are grouped in **CATIs** for modelling large-scale systems, such as the **IMOTEP CATI** (Information, Models and Data Processing in Epidemiology and Population Dynamics) and the **IUMAN CATI** (Computerisation and Use of Models for Digital Agroecosystems). The work covers both modelling of the spread of epidemics in plants or animals and software development for platforms and proofs of concept allowing the sharing and computerisation of these new models at multiple scales.

Modelling and optimisation

By definition, optimisation explores possible solutions to a given problem using different methods to find an optimum or optima according to a criterion or set of criteria (Zelinka et al., 2013). It is used in different areas of agriculture and at different scales (Plà-Aragonés, 2015). At the farm level, optimisation is implemented either explicitly or implicitly, whether in feed formulation, herd management, animal slaughter planning, crop and land use planning or water management. It is also used at different scales, including groups of farms, territories, regions and countries, for managing land use, water and economic trade and market issues (Carpentier et al., 2015). In these cases, bioeconomic models are employed according to an analytical approach, in which the primary objective is to evaluate the impact of the applying constraints and criteria to optimal solutions.

Due to the complexity of agricultural systems and changes in questions relating to agriculture, optimisation has also evolved in agriculture (Jones et al., 2016). The early economic models of the 1950s focussed above all on maximising income.
Today, the formulation of low-cost feed still primarily aims to obtain the cheapest feed possible while meeting nutritional criteria. Optimisation has gradually become multi-objective to combine different aims: productive (e.g. animal or plant production, working time), economic (e.g. income, cost) or environmental (nutrient levels, environmental impact calculated by life cycle analysis, ecosystem services, etc.). In "constrained" optimisations, the constraints are also varied and can be biological, structural, regulatory, environmental or linked to decision-making.

Model optimisation in agriculture has also benefited from developments in optimisation methods, using a diverse range of methods. Deterministic linear programming methods are still very common, with adaptations to solve multi-objective problems. Stochastic metaheuristic methods are applied alone or in combination with the previous ones. These metaheuristic methods make it possible to address multicriteria optimisation and obtain a set of optimal solutions considered admissible in the context (called Pareto Front); they include, for example, evolutionary algorithms (such as genetic algorithms or differential evolution) that work on a population of solutions, particle swarm optimisation, taboo search, simulated annealing, etc. (Kaim et al., 2018; Memmah et al., 2015).

Current issues surrounding optimisation concern, in particular, how to adapt methods to increasingly complex models, and in particular how to take account of uncertainty (Crespo et al., 2010) and the temporal aspect in the formulation of the optimisation problem (Akplogan et al., 2013). These issues echo those traditionally addressed in the automatic control and optimal control community. Another major topic of research is the coupling between optimisation and simulation (Borodin, 2014), in particular in connection with reinforcement learning methods (Gosavi, 2015). Despite technological advances in computing power, the processing time of optimisation processes is still an important factor to be considered due to the increasing complexity of the models in question. Recent developments in metamodelling offer a possible simplification strategy to reduce these processing times.

<figure><img src="image_23.png" /></figure>

For example...

At INRAE, the **PEGASE** and **SMART-LERECO** UMRs develop multi-criteria optimisation approaches (zootechnical and economic performance, environmental impact) for feeding strategies in pig farming, based on a pig farm model.

At Inria, there are more than twenty project-teams working on the development of optimisation, operational research or control algorithms.
## 3.3 Multi-scale learning and knowledge extraction

The two previous subchapters presented the approaches used to collect data, followed by modelling techniques based mainly on human analysis. In this subchapter, we will focus on the main families of approaches to building models directly from data and thereby automatically extracting knowledge. The resultant knowledge can either be presented to human experts or remain within a learning context for prediction or identification tasks, for example.

We will first show that the “raw” data sent by the sensors cannot generally be used in its initial state and that its pre-processing represents a challenge in itself. Let’s start by presenting the types of data frequently used in digital agriculture, and which could constitute a Big Data.

### Massive data in agriculture

In agriculture, the most “massive” data comes from sensors with high temporal or spatial resolution, such as time series and remote sensing or mapping data from embedded sensors.

**Time series** – A time series is a sequence of numerical values representing the evolution of a variable measured on an individual over time. Such sequences of variables can be modelled individually to understand their past evolution and predict future behaviour using ARMA-type models (Box et al., 2015). Today, experiments in agronomy make it possible to observe the same variable on thousands of individuals (e.g. leaf area on thousands of plants in a greenhouse, the temperature of livestock) over long periods. The aim of analysing these time series has therefore evolved toward the search for common characteristics between series, major differences or the acquisition of more detailed knowledge about the internal (e.g. effect of genotypes) or external (e.g. linked to environmental variables) mechanisms that influence the observed variables. Time series are thus studied more generally as functions of time. Their data is also known as “functional” or “longitudinal” data.

**Remote sensing data** – Remote sensing data can be images of a given area, taken by satellite or by drones. Satellite images – which we will focus on next – can be recorded at different periods, these sequences constituting time series. They can also, for the same period, be taken from different satellites, each with a different radiometric content (i.e. radar information, optical information). Thanks to recent
space missions such as *Copernicus*, plant dynamics can now be monitored with a spatial resolution compatible with the size of the objects of interest and short revisit time intervals. The *Sentinel-1* satellite mission acquires radar information (two bands) every five or six days over the same area at a spatial resolution of ten metres. This source of data provides access to information on the structure of objects (i.e. forest or agricultural biomass) and makes it possible to monitor and assess wetland areas and the area of land that has been irrigated over a certain period. Another equally interesting satellite mission is *Sentinel-2*, which provides multispectral imaging information (thirteen bands), again delivered every five or six days and at a spatial resolution of ten metres. This optical sensor is particularly suitable for mapping land cover and land use, monitoring the biodiversity of natural states and for large-scale yield estimation over large areas (Lambert et al., 2018).

At the other end of the scale, at the microscopic level, metabarcoding<fnref n="33" /> metaomic data allows a better characterisation of the biological environment of crops or animals. This metadata is constructed by assembling the “fingerprints” of the genomes present or of their expressions (RNA, proteins), making it possible to analyse new dimensions of ecosystems, which can better explain the behaviour of crops or animals. We are still only at the beginning of exploring these new data sources, some of which remaining difficult to access (proteomics, metabolomics, etc.).

## Data pre-processing

The major challenges in data pre-processing are: i) identifying outliers or unreliable data: data collected during experiments or in the field is voluminous, very noisy and can be affected by errors from a variety of causes, such as a faulty sensor. Specific tools are therefore needed to annotate this data, rapidly detect faulty sensors and diagnose heterogeneity in the field or greenhouse to improve the quality of the data sets for future analyses; ii) linking data with expert knowledge, such as mimicking an expert's reasoning by an automaton when validating a “small” data set, or using the expert's knowledge to adjust curves (alignment of phenological stage dates).

One particular challenge is data fusion. Information that is difficult to obtain directly can be retrieved by combining data, whether of the same type (for example, the leaf area of a plant can be predicted from the analysis of

<footnote n="33">33. Metabarcoding is a method of identifying species from DNA or RNA segments. Instead of targeting different species, metabarcoding determines the composition of species in a sample, thereby allowing the identification of many taxa in an assembly of populations (of *bacteria* or other microorganisms) within an environmental sample (e.g. soil sample, *sediment*, *excrements*, etc.). It is thus one of the fastest methods for the *environmental assessment* of the biodiversity of ecological systems with a high number of unknown or difficult-to-identify species.</footnote>
fifteen images of this plant taken from different angles), or of different types. To do so, i.e. to monitor the same phenomenon or the same study area, an ever greater volume of heterogenous data of various types (called "multisource" data) is collected. The knowledge contained within this data is a real opportunity for improving our understanding of the complex phenomena associated with modern agricultural practices in order to better monitor and manage them. Within this general framework, one of the main challenges today is knowing how to make the best use of these heterogeneous and complementary sources of information to obtain the maximum amount of information (because, in the science of complexity, "the whole is greater than the sum of its parts"<fnref n="34" />). Depending on the typology of the sources involved in the process, two merging strategies can be used: early and late merging. In the first case, the data is combined at the beginning of the process to form a single new homogeneous dataset. This can be done, for example, by bringing all the available information to the same spatial or temporal resolution or unit of analysis. In this context, once the new dataset has been built, standard single-source analysis techniques can be used. In the second case (late merging), an analysis process is set up by each source specifically and the merge is carried out at the descriptor or decision-making level. For example, specific descriptors can be extracted from each source and then combined to exploit higher-level interactions between the different sources considered. Lastly, the different sources can be combined in what is known as an "end-to-end" process, in which the standard processing stages are replaced by a single system (usually a deep neural network) that takes the raw sources as input and returns the required decisions as output (Charvat et al., 2018; Plaisant, 2004; Tonda et al., 2018).

In the case of time series, merging series with different temporal resolutions is a major challenge, for example if the activity sensor in a collar worn by an animal sends information every five minutes, but the animal is only weighed once a day. In order to compare individuals, it may be necessary to interpolate the time series for the same time period (using linear or polynomial smoothing methods), possibly by matching similarities (dates of phenological stages, growth peaks, etc.). Dynamic Time Warping (Sakoe et al., 1978) is one of the well-known techniques for measuring similarities between two series. However, this technique does not provide answers to all the curve alignment issues encountered when dealing with living beings, when it is essential to take phenological time into account. These questions present challenges that are still largely unresolved in biology.

<footnote n="34">34. http://www.scilogs.fr/complexites/le-tout-est-il-plus-que-la-somme-des-parties/</footnote>
There are also methods able to extract several models from time series at different time scales and then select the most relevant ones using information theory approaches (principle of Minimum Description Length – MDL) (Vespier et al., 2012). The advantage of these approaches is that they allow us to focus on the temporal scale of the observed phenomena instead of the technical sampling value.

In the case of remote-sensing, with the explosion in the number of satellite missions (Sentinel, Spot, Pleiades and PleiadesNeo, PlanetScope, etc.), it is now possible to collect information describing a single study area at a lower cost in different spectral ranges (optical and radar) and at different spatiotemporal scales. This massive volume of multisource information requires new data management and analysis tools to be developed (Schmitt and Zhu, 2016). Typically, in a classical multisource fusion process for Earth observation data, sources are exploited through an early fusion process. For example, in the case of imagery at different spatial scales, a resampling stage is incorporated to bring all the images to the same spatial scale beforehand. Unfortunately, this type of process can introduce bias or error by generating new synthetic information. This is why late fusion approaches are now preferred wherever possible. Early examples in the context of land use mapping are starting to appear but we are still far from a generic solution that can be deployed systematically across different territories and adapted to different agricultural practices.

<figure><img src="image_24.png" /></figure>

For example...

At INRAE, the MISCA team of the TETIS UMR develop information management methods to meet the major societal challenges related to the environment, whether storing, managing, sharing or analysing large volumes of data. In particular, it contributes to soil mapping by applying Deep Learning techniques on very large datasets.

At Inria, several project-teams (GEOSTAT, TITANE, FLUMINANCE (Inria, INRAE, Université de Rennes 1), etc.) and the exploratory action AYANA are working on the analysis of satellite images.

In addition to purely satellite-based multisource information, other types of information are now combined with Earth observation data. For example, "spon- taneous" geolocation information or information from citizen science (Ienco et al., 2019) have much to offer to improve calibration and complement the purely physical information from satellite sensors.
## Supervised approaches

Supervised analysis consists mainly of two tasks: supervised classification and prediction of future values. Supervised classification consists in assigning, for a given time series and set of predetermined classes (e.g. "sick animal" and "healthy animal"), one of these classes to the time series. In practical terms, this can help determine the condition of an animal or plant from sensor data and information on the different conditions possible. Supervised classification methods need to be "trained"; to do this, they must be provided with a large number of correctly labelled examples indicating their class. Using these examples, the classification algorithm builds one (or more) model(s), to assign a class to an unlabelled time series according to its characteristics. The main differences between the major families of supervised classification approaches lie in the way the models are built. The simplest approaches, known as k-Nearest Neighbor (kNN), do not build a model but search for the k examples of the training set closest to the individual to be labelled, and return the majority label. The difficulty lies in choosing a suitable similarity method (Karlsson et al., 2016).

Finally, the very popular deep neural network methods can also be used to classify such data. The most successful method of this type is currently MLSTM-FCN (Karim et al., 2019), which combines a convolutional CNN (Convolutional Neural Network) block with a LSTM (Long Short Term Memory) block. The CNN block, widely used in image analysis, serves as a filter that traverses the time series or spectrum and extracts characteristic attributes at time t. It is combined with the LSTM block, which is widely used in the analysis of sequential data (especially text), and allows connections between past and present values to be made. This type of approach can produce excellent results (Kamilaris and Prenafeta-Boldú, 2018). However, it requires an even greater volume of labelled training data (which can be difficult to acquire in some agronomic contexts), and its parameters can be tricky to define (Zhu et al., 2017).

<figure><img src="image_25.png" /></figure>

### For example...

At Inria, the **STATIFY** project-team (Inria, CNRS, Institut Polytechnique de Grenoble) focuses on the statistical modelling of systems involving complex-structure data. The team develops statistical methods for capturing the variability of the systems studied while ensuring a good level of precision and taking into account extreme values that generally reflect rare phenomena. In particular, they model weather events for agroecology.
An example of the use of supervised classification methods in agriculture can be found in Fauvel *et al.* (2019), in which the authors work with precision breeding sensor data from dairy cows. The cows are equipped with thermometers and collars with an accelerometer. The time series of temperature and physical activity are analysed to allow more accurate oestrus detection than with existing methods or visual observation, even in the frequent cases where cows do not express any particular behaviour in the pre-oestrus phase (30%).

## Unsupervised approaches

Unsupervised approaches are used to reveal certain structures in data, whether groupings with clustering or recurring patterns with pattern mining.

**Clustering** – The aim of the clustering (or unsupervised classification) learning method is to identify relevant classes in the data. Data is grouped by similarity or proximity within each class. To achieve a good classification, it is necessary to minimise the intra-class inertia (to obtain homogeneous classes) and maximise the inter-class inertia (to obtain well differentiated classes). Two main families of methods are commonly used: i) hierarchical ascending classification (HAC), which seeks to group individuals iteratively, starting at the bottom (the two closest) and gradually building a tree, or dendrogram, to finally group all the individuals into a single class, at the root; ii) classification by dynamic reallocation (the k-means algorithm is a well-known example of this). The number k of classes is fixed a priori. After initialising k class centres, all individuals are assigned to the class whose centre is closest in the sense of the chosen distance. The algorithm then calculates the barycenters of these classes which become the new centres. The process (assignment of each individual to a centre, determination of the centres) is iterated until convergence to a fixed (local) minimum or maximum number of iterations.

The main issues to overcome when clustering multivariate data are identifying the "right" number of classes and defining a distance that is adapted to the data, sometimes implying the need to reduce the dimension. One common technique involves performing principal component analysis on the data and then apply clustering on the coordinates of the data in the eigenbasis, with all the difficulties of choosing dimensions that this entails. Clustering by combining Dirichlet processes (Coquet *et al.*, 2002) offers a way to get around these difficulties.
**Patterns mining** – Patterns correspond to implied regularities/irregularities or specificities of the data or subparts of the data. In agronomic applications, an individual can be described through a sequence of characteristics or events. For example, a plot can be described by a sequence of cultivation operations, a plant can be described by a DNA sequence, etc. one of the major challenges with this type of data is the extraction of frequent or rare subsequences.

<figure><img src="image_26.png" /></figure>

For example...

At INRAE, the **TETIS** UMR focuses on extracting frequent/rare subsequences in this type of data and frequent patterns in the form of items and sequences (sequences of events ordered in time) in order to characterise the difference in vegetation growth between different spatial areas. Their work is particularly relevant for wetland area estimation and biodiversity monitoring.

Other approaches aim to highlight sub-parts of the data with very different characteristics to the rest of the data (distribution differences for certain attributes, etc.). For example, in *Millot et al.* (2020), the authors use the notion of discriminating patterns to characterise, from simulation data, sub-families of crop protocols in urban farms where part of the attributes (temperature, light, CO₂, etc.) show an interesting distribution with respect to a given measure of interest.

Unfortunately, these methods are often faced with a number of patterns that prove to be too large to be easily used by experts. A promising and currently much studied avenue is the selection of the most relevant pattern subset. Patterns can be extracted from time series after a pre-processing phase in which the sequence of numerical values is transformed into a sequence of symbolic values, allowing classical pattern discovery methods to then be applied. When the numerical data is kept, methods for extracting representative subsets, called "shapelets", can be used.

<figure><img src="image_27.png" /></figure>

For example...

INRAE units such as the **PEGASE** UMR, **UMRH**, the **Toxalim** UMR and the **GenPhyse** UMR use these different learning approaches for precision feeding, early detection of anomalies in the activity of dairy cows in a herd, detection of pathologies in piglets or analysis of sow behaviour respectively.
Reinforcement learning

Like many types of data, agricultural data is often uncertain (see 3.1). Reinforcement Learning (RL) is concerned with learning to operate in an uncertain environment. One example of a modern use of RL for crop management planning is *Déciblé* (*Chatelin et al.*, 2005), originating from *Garcia* (1999) and based on interaction using a decision rule model for wheat cultivation. This empirical crop simulator is used to evaluate policies expressed as sets of decision rules. In *Ndiaye* (1999), model-free methods – namely Q-learning and R-learning – are mixed with genetic algorithms, decision trees and fuzzy logic to find optimal decision rules for crop management coupled with *Déciblé*. The result was considered to be not as good as the decisions that an expert would expect to see. These early approaches were interesting in that they introduced modern RL techniques for crop management while considering a range of actions. They also expressed an optimised policy in a natural way, i.e. in the form of a set of simple decision rules corresponding to farmers’ reasoning. However, the solutions in Garcia (1999) and *Ndiaye* (1999) are limited in that learning is offline, using an empirical decision model simulator with its own biases and field of validity. Because learning is not in real time, the systems do not use farmers’ feedback to improve the policy learned from the simulator.

These methods were later applied in a more complex context, incorporating an
economic model for oilseed rape management and a pest and disease component
in crop modelling (Trépos et al., 2014). RL methods have been successfully applied
in irrigation planning when water availability is limited (Bergez et al., 2001).
Nevertheless, each management decision must take into account the whole
sequence of choices. Different crop varieties have different water requirements,
so there will be different irrigation costs. Bu and Wang (2019), proposed a general
computer architecture for intelligent decision making in agriculture based on
deep Q-Learning. In practice, deep Q-Learning requires billions of instances of
trial and error. Furthermore, no proposal has been made to integrate specialist
knowledge (e.g. knowledge of plant physiology) into this system; approaches
using expert knowledge could therefore be considered (model-based learning),
allowing the amount of examples needed for training to be reduced by several
orders of magnitude.
<figure><img src="image_28.png" /></figure>

The SCOOL project-team (Inria, CNRS, Université de Lille) specialises in reinforcement learning and is studying the recommendation of practices in agriculture for very small farms, especially in developing countries, and in gardening. The research is carried out with a focus on sustainable development.

Different machine learning and data science methods are implemented in the scikit-learn library, which was principally developed at Inria and is one of the three most downloaded artificial intelligence libraries in the world.

The INRAE MIAT unit is also working on the development of methods based on Markov decision processes and reinforcement learning applied to the management of agroecological systems, with particular focus on issues related to the spatial dimension of problems.

## Data warehouses and OLAP analysis

Data warehouses (DWs) were designed to handle very large volumes of data from heterogeneous sources (Chandra and Gupta, 2018). Multidimensional modelling (where data is characterised across multiple axes of analysis) and hierarchical modelling (where an axis of analysis can be associated with different levels of granularity) form the basis of DWs and multidimensional analysis. For example, the analysis of the amount of pesticides or nitrogen used by farmers can be characterised according to several dimensions (or axes of analysis): temporal, spatial and at crop level (Bouadi et al., 2017). This allows quantities to be represented by crop type, season and plot. These dimensions can be expressed in different levels of detail. For example, spatial information can be defined at the scale of a single plot or at a larger scale such as the watershed, region, etc., since each plot belongs to a watershed, which in turn belongs to a region, which in turn belongs to a country.

Multidimensional analysis uses OLAP (On-Line Analytical Processing) to aggregate, visualise and interactively explore data. If we take the previous example, we could analyse the quantity of pesticides or nitrogen at plot level or at a more aggregated level of spatial dimension such as the watershed. OLAP processing is used to navigate between different granularities of one or more dimensions in a very efficient way (i.e. navigation is instantaneous).
Users can use the data warehouse by combining the different dimensions and different levels of granularity of the corresponding hierarchies. To select the appropriate data at the right scale, users express and submit queries to the data warehouse.

Other works (Palpanas, 2000) describe the coupling of multidimensional analysis with data mining methods (e.g. pattern mining), with the aim of proposing hybrid methods that combine the exploratory and analytical capacities of OLAP with the descriptive capacities of data mining. For example, the ADSS-OLAP tool (Abdullah and Hussain, 2006) combines OLAP and data mining (clustering) and was developed to analyse the impact of mealy bug on cotton crops. To further enhance OLAP analysis and allow geographic data mining, the idea emerged to couple OLAP and GIS (Geographic Information System) technologies. Thus, the new concept of Spatial-OLAP (SOLAP) (Bédard et al, 2007) was introduced to jointly exploit OLAP tools (decision, graphs, etc.) and geographic tools (cartographic representation, geographic aggregators, etc.).

<figure><img src="image_29.png" /></figure>

For example...

At Inria, the **LACODAM** project-team (Inria, Institut national des sciences appliquées de Rennes, Institut national supérieur des sciences agronomiques, agroalimentaires, horticoles et du paysage, Université de Rennes 1) has modelled and built a data warehouse to analyse/explore, in space and time, the effects of agricultural practices on nitrogen emissions to water and the air (Bouadi et al., 2017). The team is also studying the use of machine learning to improve animal welfare (dairy cow health and sow feeding).

At INRAE, the **TSCF** unit focuses on spatial OLAP. Among other things, it contributes to storing and analysing biodiversity data online, in particular through the VGI4BIO project (www.vgi4bio.fr) which proposes methods for analysing biodiversity indicators in an agricultural context centred around data and VGI users.
<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

Optidose® a tool to adapt the dose according to the parameters of the crop and the epidemic risk.
© Le Mas Numérique.

## 3.4 Knowledge management and engineering for decision support in agriculture

The previous sections provided an overview of the state of the art in data collection, management and processing; we also saw how modelling makes it possible to manage and represent knowledge in a mathematical way using measurements and observations to help with interpretation and recommendation. Another important facet of digital agriculture is knowledge management, i.e. higher-level information including both general scientific knowledge (e.g. plant or animal physiological processes) and methods specific to certain actors in the agricultural sector (e.g. a livestock farmer's herd management, methodology for making certain cheeses, etc.). In recent years, significant effort has been made to formalise this knowledge and organise it in ontologies that provide a structured access. Ontologies are a component of computer systems that help users accomplish a task. This assistance can take many forms, from automating an irrigation decision to finding information to help make a decision. Knowledge can also be generated by the analyses presented above. In this case, the difficulty lies in presenting these analyses to human actors in the most intelligible way. Again, recent developments in data analysis are of particular interest to agriculture, whether via visualisation approaches or methods for interpreting learning models. Lastly, the aim of
everything presented in this section is to enable human actors to make better
decisions. Specific tools known as DSS (Decision Support Systems) that use all or
some of the techniques presented in this chapter are available to these actors and
are constantly evolving. This section will conclude with an overview of these tools.

<figure><img src="image_32.png" /></figure>

For example...

The **BIOEPAR** UMR contributed to the development of **EMULSION**, an open source program and DSS based on artificial intelligence. **EMULSION** allows modellers to develop stochastic mechanistic models of complex systems in epidemiology at different scales and using different paradigms, while reducing the amount of computer code that has to be written (Picault *et al.*, 2019). Based on this, the **ATOM** (Automation of decision support Tools based On epidemiological Models) project aims to develop a process for industrialising the DSS generation using mechanistic epidemiological models (https://www6.angers-nantes.inrae.fr/bioepar/Recherche/Projets-en-cours/ATOM).

Knowledge-based systems in agriculture

**From the first expert systems to knowledge-based systems** – The first expert systems emerged in the 1970s as a result of research in artificial intelligence. These systems were dedicated to resolving a specific problem by using the knowledge of one or more experts and mimicking their reasoning, with the ultimate aim of replacing them. In one approach, called the symbolic approach, expert knowledge is formalised using a knowledge representation language based on logical reasoning. This is in contrast to the connectionist approach, which mimics the functioning of the human brain using neural networks.

One particularity of the agricultural sector is that the inherent problems in
crop or herd management require expert knowledge in several fields (soil science,
meteorology, chemistry, biology, etc.). To meet this demand for multidisciplinary
expertise, some expert systems incorporate simulation models as components,
such as those described in Section 3.2. Such an example is the expert system
"CrOp MAnagement Expert" (COMAX), dedicated to cotton cultivation, which aims
to maximise yields while minimising inputs (McKinion and Lemmon, 1985) and
encapsulates a simulation model of cotton development (GOSSYM). Because the
acquisition of expert knowledge is crucial for the development of expert systems,
knowledge engineering has focused on methods for acquiring such knowledge.
These methods have been used to guide cognitive scientists through the complex
tasks of identifying, extracting and formalising expert knowledge from a variety
of sources (expert interviews or other documents describing the task of solving
the problem).

Very popular in the 1980s, expert systems have also been severely criticised
for not being adaptable to applications other than the one they were designed
for and for offering poor potential for development. In the 1990s, expert systems
gradually evolved into knowledge-based systems. The notion of ontology then
appeared in computer science. Ontologies are designed to formalise consensual
and relatively stable knowledge in a given domain to allow it to be reused in other
knowledge-based systems.

A knowledge-based system is composed of two distinct parts: firstly, a
knowledge base including an ontology that structures the knowledge from
the domain, with a fact base that instantiates the ontology to describe specific
situations and sometimes a rule base that enriches the ontology; secondly, a
reasoning engine associated with the knowledge representation language but
independent of any particular knowledge base.

**Evolution of knowledge acquisition and capitalisation methods** – The shift toward knowledge-based systems was accompanied by a change in the conception of the relationship between humans and machines. Knowledge-based systems and their associated intelligent computer system aim to cooperate with the user to help them perform a task requiring different types of knowledge by supplementing the user's knowledge, revealing the consequences of their choices and proposing alternative options to those they would have imagined. Knowledge engineering then evolved into a form of knowledge modelling mediation, producing "knowledge models" – a model here taking on a different and more global meaning than in Section 3.2 since it no longer represents phenomena but knowledge. These models allow the cognitive scientist, in charge of implementation in a computer system, to dialogue with experts to enrich and validate the knowledge to be represented. To help this mediation, several methods have been developed, the best known being "Knowledge Acquisition and Documentation Structuring" (KADS, from which commonKADS was later developed). For example, a system for recommending irrigation dates for mango trees was developed using the commonKADS method (Nada et al., 2014).
<figure><img src="image_33.png" /></figure>

For example...

The **GRAPHIK** project-team (Inria, INRAE, CNRS and Université de Montpellier) studies knowledge representation. Among other things, the team is working on a method for collecting, modelling and formalising knowledge to improve the quality of local cheeses. Data collection is carried out through questionnaires; modelling is done by creating mind maps to facilitate validation by experts and formalisation is carried out using conceptual graph language.

Knowledge models were deployed in other computer systems such as information (source) search systems. This development led to the creation of "organisational memories". An organisational memory is the set of human and material resources – knowledge carriers – which allow an organisation to carry out its tasks. A memory can be composed of a set of text documents, videos, lists of employee skills and one or more knowledge models. The formalisation of these models allows them to be used automatically to help the circulation of resources and knowledge among the members of the organisation. This formalisation often takes the form of a thesaurus: a structured list of standardised terms organised into three types of relationship (equivalence, hierarchy, association), with the aim of indexing and helping to search for different content.

<figure><img src="image_34.png" /></figure>

Find out more

The FAO (Food and Agriculture Organisation of the United Nations) has been instrumental in producing organisational memories in the agricultural field (O'Leary, 2008). Its bibliographic database AGRIS offers different types of resources (scientific documents, data sets, etc.) in several languages. These resources are indexed using the **AGROVOC** thesaurus, which is now one of the most important tools in the agricultural field (Sini et al., 2008). This thesaurus encompasses many aspects of the agricultural domain and can be adapted to the needs of a new organisation. An example of an organisational memory is the Agropedia project, led by a number of Indian agricultural institutes in collaboration with the FAO (Pappu et al., 2010) to transform scientific knowledge from universities into practical knowledge of use to farmers. Agropedia uses Topic Map knowledge models, which aggregate all the knowledge needed for a given crop.
**Semantic access to information sources** – The birth of the Semantic Web in the early 2000s had a strong impact on the field of knowledge representation. Semantic Web technology is a set of standardised languages, protocols and tools under the aegis of the W3C to enable the automated exploitation of Web resources according to their content. Web resources (such as HTML documents or, more broadly, any data available on the Web) are annotated with metadata describing their content in a formal language, constituting a fact base that can be enriched with a thesaurus or ontology that specifies its semantics.

<figure><img src="image_35.png" /></figure>

## Definition

The main formal languages of the Semantic Web are:

* Resource Description Framework (RDF): the language for describing Web resources in the form of a graph made up of triples (subject, predicate, object);

* RDF Schema (RDFS): an extension of RDF that allows a vocabulary to be defined in terms of classes and properties (or binary predicates) organised by specialisation;

* SPARQL Protocol and RDF Query Language (SPARQL): the RDF(S) description query language;

* Ontology Web Language (OWL): the most commonly used language for describing Semantic Web ontologies;

* Semantic Web Rule Language (SWRL): a rule language that can be used to enrich OWL descriptions;

* Simple Knowledge Organization System (SKOS): RDFS specification for formalising terminologies, thesauri, classifications and other vocabularies used in information retrieval systems.

Web ontologies are modular and focus on a specific need to facilitate their reuse and combined use. By making them available on the web, the interoperability between knowledge-based systems can be improved. Of particular interest is the RDA *Agrisemantics* working group initiative which has been exploring the use of this technology and associated resources to improve the exchange and sharing of agricultural data (Aubin *et al.*, 2017). This technology has allowed organisational memories to be transferred to the Web.
<figure><img src="image_36.png" /></figure>

For example...

INRAE is also developing organisational memories on the impact of climate change on agricultural practices and agroecology. An archive of French agricultural information bulletins, the Plant Health Bulletins (BSV), was compiled during the VESP project, which studied epidemiosurveillance networks (Roussey et al., 2017).

The GECO collaborative web portal (https://geco.ecophytopic.fr/) was developed to improve knowledge sharing around integrated crop protection and agroecology. This portal manages a set of explanatory text sheets to propose means of controlling pests (Soulignac et al., 2017). GECO allows users to perform searches regardless of their level of expertise.

Web ontologies and SKOS thesauri have become reusable resources. Specific portals have been developed for searching for all these resources.

<figure><img src="image_37.png" /></figure>

For example...

At INRAE, the computer science department of the MISTEA UMR has, among other things, developed the AgroPortal (http://agroportal.lirmm.fr/) which lists ontologies and thesauri related to agronomy and agriculture and makes them openly available. AgroPortal also provides services to help annotate text documents and detect links between concepts in two ontologies (ontology alignment). It also contributes to advances in high-throughput plant phenotyping.

**Semantic integration of structured data** – Linked Data refers to a network of linked sets of resources. It was developed in the 2010s and marked a new stage in data sharing using Semantic Web technology by considering a network of interconnected sets of resources. This network is based on the use of shared vocabularies (thesauri, ontologies, etc.), used to describe the data. This development goes hand in hand with the generalisation of the concept of data and encompasses all data, including structured data from different databases.
<figure><img src="image_38.png" /></figure>

Examples of structured data include:
Weather data from an INRAE station is available as Linked Data ([http://meteo.clermont.cemagref.fr/](http://meteo.clermont.cemagref.fr/)). A central Semantic Sensor Network (SSN) ontology provides a design pattern for describing the measurements.

The European project SmartOpenData ([http://www.smartopendata.eu/](http://www.smartopendata.eu/)) proposed an infrastructure (and a SmartOpenData (SMOD) schema) for managing Open and Linked Data in the field of biodiversity and the environment (e.g. in agroforestry data management).

The Agronomic Linked Data project (AgroLD - [http://www.agrold.org](http://www.agrold.org)) integrates 50 databases into a single RDF database. Its objective is to jointly question and link different points of view on cultivated plants (genomic, proteomic and phenomenic) formalised by at least one of the ten Web ontologies used (Gene Ontology, Plant Trait Ontology, etc.).

Hybrid architectures such as OpenSilex ([http://www.opensilex.org/](http://www.opensilex.org/)) that incorporate ontologies, an inference engine and different formats of databases (relational, NoSQL, RDF) are used to develop multiple information systems for high-throughput phenotyping. In this architecture, a scientific object (plant, pot, field, etc.) is identified by a web identifier (URI) and typed by an element of one of the associated ontologies. The RDF database stores the static descriptive metadata while the NoSQL database stores the raw data streams: drone photos, time series from field sensors, etc.
<figure><img src="image_39.png" /></figure>

For example...

The GnpIS information system stores all the structured data from experiments carried out on plant phenotyping (Pommier et al., 2019). The ontologies proposed by the Crop Ontology network (https://www.cropontology.org/) are used as dictionaries for all observable traits in the experiments.
For animals, the descriptions of animal experiments carried out in different research centres can be made compatible using a web ontology network developed at INRAE. The network currently consists of three web ontologies (Salaun et al., 2018): Animal Trait Ontology for Livestock (ATOL, on phenotypic traits in livestock), Environment Ontology for Livestock (EOL, on environmental parameters in livestock farming), and AHOL (for livestock animal health).

**Emerging architectures** – Beyond the Web of Data, the problem of the intelligent exploitation of increasingly large and heterogeneous data has led to very active research combining knowledge representation, data management, the Semantic Web, data mining and learning, etc. It is in this context that a new architecture has been proposed called Ontology-Based Data Access (OBDA) (Xiao et al., 2018), which combines a specific approach to data integration, called mediation, with the concept of the knowledge-based system. OBDA systems are structured in three levels: the conceptual level, organised around an ontology (described for example in RDFS or OWL); the data level, composed of various pre-existing and independent databases; and the mapping level, which translates the data relevant to the target application into a fact base using the ontology vocabulary. Queries to the system (e.g. in SPARQL) use this vocabulary, with the user expressing himself at a conceptual level with no knowledge of the data storage system (for example, a query such as ""*what auxiliaries can control pest X and what are the associated techniques that would limit competition with the main crop?"" would be completely dissociated from the underlying database schemas).

<figure><img src="image_40.png" /></figure>

For example...

At INRAE, the Ecology of Mediterranean Forests URFM unit of the **ECODIV** department conducts multidisciplinary research in ecology. In particular, it implements mature OBDA systems such as Ontop (https://ontop-vkg.org/) and MASTRO (https://www.obdasystems.com/mastro) for the sustainable management of Mediterranean forest ecosystems.
In the context of the Internet of Things, some of these systems also use Semantic Web technologies (OWL ontology, SWRL rules, RDF annotation base).

Standardisation bodies such as W3C and ETSI are currently working on the validation of new standards and ontologies to combine the Internet of Things and the Semantic Web: these are the SAREF ontology by ETSI and the Web of Things (WoT) ontology by W3C. These ontologies have not yet reached a sufficient level of maturity to be used in real-life applications.

<figure><img src="image_41.png" /></figure>

For example...

At INRAE, the TSCF unit has proposed a translation of the IRRINOV manual irrigation method into SWRL rules and a web ontology network to represent knowledge for automating irrigation.

Questions remain about the compatibility of ontologies built on different principles: different uses, different authors, different foundational ontologies etc. Some web ontologies propose data schemas corresponding to reusable patterns (design patterns) centred on a specific need. Other ontologies offer reference classifications to qualify data. Data managers must therefore build a network of ontologies to structure their data, checking that these ontologies remain compatible with each other. Are they based on the same patterns? Do they allow correct inferences to be made? Lastly, current research topics focus on questions concerning the distribution of reasoning over all the components of an “Internet of Things”-type system.

## Knowledge restitution, visualisation and human-machine interaction in agriculture

Data-driven knowledge production methods (section 3.3) have produced results that are not only increasingly accurate and reliable but also increasingly difficult to understand, to the extent that most of these approaches are now described as "black boxes", of which the user is unable to understand the determinants of the result produced (e.g. a decision on the technical process). One solution to this problem consists in using local interpretability approaches such as LIME (Ribeiro et al., 2016) or SHAP (Lundberg and Lee, 2017). Instead of aiming to explain the learned model as a whole, which is too complex, these approaches explain the reasons that led the model to produce such a decision in the specific case provided by the user, such as the attributes that contributed most (positively or
negatively) to the decision. For example, SHAP, used in oestrus detection as seen
above (Fauvel *et al.*, 2019), provides explanations of the type: "*an oestrus was
predicted today based on temperature changes over the last three days and a
significant rest period three days ago.*"

In parallel to the issue of interpretability, the visual representation of data and
information is essential for any computer system designed with user interaction
in mind. "Visualising" consists in producing visual elements (graphs, curves,
maps, images) to help users understand, explore and analyse to make sense of
data, models and information, sometimes present in large quantities and often
complex (Kubicek *et al.*, 2013). Smooth and efficient human-machine interfaces
and visualisations are often essential to the success of digital systems intended
for the general public. The field of agriculture is no exception to the rule.

There is a high demand for visualisation in this sector due to a combination of
several factors: significant growth in the masses of data collected, the existence of
users who are not computer scientists but are often technophiles, and the need
for visibility on private and public data at different spatial and temporal scales.
Visualisation is sometimes even seen as a strategic matter, because mastering
these techniques can offer a competitive advantage or afford a certain power to
some actors in the agrifood chain. Private players (equipment manufacturers)
are heavily involved in the field, but there are also initiatives by universities and
institutes, including INRAE and Inria, available under free (e.g. AQUAPONY,<fnref n="35" />
GeoVisage,<fnref n="36" /> PARCHEMIN)<fnref n="37" /> or participatory licenses (I-EKbase)<fnref n="38" /> (Wachowiak
et al., 2017).

<figure><img src="image_42.png" /></figure>

For example...

At INRAE, the Ecology and Evolution of Zoonoses group of the **CBGP UMR** analyses the viral diversity of hantaviruses and the evolutionary processes that shape it. Among other things, they piloted the development of AQUAPONY, a web-based viewer that allows interactive navigation through a phylogenetic tree and facilitates the objective interpretation of evolutionary scenarios.

<footnote n="35">35. http://www.atgc-montpellier.fr/aquapony/</footnote>

<footnote n="36">36. http://geovisage.nipissingu.ca/</footnote>

<footnote n="37">37. http://www.parchemins.bzh/index.php/outil-de-visualisation-donnees-lagriculture-littorale-bretagne/</footnote>

<footnote n="38">38. http://iekbase.com/hot-spots-monitoring</footnote>
The tools currently deployed for the agricultural world are based on traditional visualisation paradigms: cartography (GIS, or Geographical Information Systems), sensor data visualisation interfaces, collections of linked visualisations (multi-faceted) and reactive tools (dynamic queries). Semantic interfaces and related visualisation strategies (linking views) are major topics of interest, as well as 3D visualisation, which provides a view of the topology of geographical areas (down to field profiles), and the use of image synthesis or even augmented reality.

<figure><img src="image_43.png" /></figure>

For example...

At INRAE, the SAS UMR supports the agroecological transition of livestock systems and territories. In particular, it participated in the *PARCHEMINS* project on the visualisation of coastal agricultural data in Brittany, completed in March 2021. This project developed a map viewer, a web-based map consultation tool that allows the user to interact with geographical data. Designed for users who are not geomatics experts or computer scientists, it allows spatial information to be represented and analysed in an intuitive and easy-to-use way.

The interactivity and speed of response of visualisation tools are key in agriculture (and elsewhere!) as these visualisations must be adapted to lightweight devices (smartphones, tablets) or on-board terminals (connected tractors). The fluidity of data visualisation is closely linked with technical solutions, data exchange protocols and system architecture. Adaptive visualisation, which is currently a major research topic, makes it possible to adapt the visualisation to the context, such as the user's profession, the visualisation terminals or the nature of the data available.

The issue of visualisation and knowledge sharing is not widely addressed in practice and thus remains more in the field of research than application for the time being. In agriculture, however, human expertise traditionally plays a very important role, creating a particularly favourable context for the development of interactive techniques: it is indeed tempting to combine human expert capacities with learning, optimisation or modelling algorithms (Boukhelifa et al., 2018). Depending on the strategy and the system, Human-Computer Interaction (HCI) can be either explicit (the user is regularly asked questions via an interface or visualisation system) or implicit (unbeknownst to the user or non-verbal, with the machine capturing information and using it as a learning base). Current research in visualization and HCI focuses primarily on questions concerning the interpretability, explicability, causality and transparency of interactions.
The HCI/visualisation tandem is also a factor in frameworks in which human expertise is required to manage data uncertainty and decision-making on multi-criteria issues: qualitative approaches are complementary to automatic/statistical approaches (e.g. choice of criteria) for managing ambiguities, knowledge gaps or extrapolations to different crop types. Examples include uses in agroecological zoning - based on clustering and segmentation techniques - or the Crop-GIS web application combining modelling and visualisation for maize crop management.<fnref n="39" /> However, interactive systems can be difficult to evaluate because while the algorithm learns and adapts to the human, the reverse is also true: the user learns to use a system. Understanding these subtle mechanisms of co-adaptation and co-evolution requires the use of experimental science approaches (test plans, reliability of results, biases) and testing on cohorts of volunteers.

In conclusion, the topics of visualization and HCI applied to agriculture are relatively rarely addressed in both agronomic and visualization scientific literature. And yet, the issue is a key factor in the adoption of technology because farmers prefer tools that are less accurate but easy to use to high-performance ones that are difficult to use (Pierpaoli et al., 2013).

## Decision Support Systems (DSS)

In the 1980s, computer programs and electronics began to be used to improve efficiency in agriculture and reasoning in agricultural activities. This saw the emergence of the first digital DSS (Decision Support Systems). This revolutionary development has been fairly well received both by farmers (79% of farmers who use new technologies recognise their usefulness, source: Rapport agriculture et innovation 2025)<fnref n="40" /> and in society where there is a demand for digital innovations for the protection of the environment (47% of those questioned, OpinionWay survey, 2016). Digital DSS are based on "simple" computer programming combined with a relatively small body of reference data, and can be installed on personal computers or used in a web interface that allows access to the application. They are most often developed by research or technical institutes. This generation of DSS includes software such as INRAtion<fnref n="41" /> and InraPorc.<fnref n="42" /> These programs, designed by INRA, are French benchmark tool in terms of assistance for defining feed rations for ruminants and pigs. Many software programs have also been developed in the plant sector to help farmers plan and manage crop fertilisation, pest control or irrigation. Today, with the upsurge in digital technology, a new generation of DSS

<footnote n="39">39. https://www.cropgis.com/</footnote>

<footnote n="40">40. https://agriculture.gouv.fr/sites/minagri/files/rapport-agriculture-innovation2025.pdf</footnote>

<footnote n="41">41. https://www.inration-ruminal.fr/</footnote>

<footnote n="42">42. https://inraporc.inra.fr/inraporc/</footnote>
has emerged that uses contemporary digital technology such as remote sensing, GPS, the Internet of Things, artificial intelligence, etc. These DSS are designed and produced by the AgriTech sector, which involves major agribusinesses and numerous start-ups (Padhy and Satapathy, 2020).

<figure><img src="image_44.png" /></figure>

## Definition

Agritech is a generic term for agricultural technology. It includes four main areas: 1/ biocontrol, 2/agricultural big data, 3/ robotics and 4/ plant genetics and biotechnology. These four elements are often closely linked and many agricultural technologies are derived from them.

The integration of new technologies into DSS allows the range of services offered by these tools to be expanded as farmers seek to make the most appropriate farm management decisions (Spanaki et al, 2021). Precise knowledge of the state of agricultural plots or herds is essential for the farmer, who can now use data (images, biophysical measurements, etc.) from connected sensors to obtain more information than can be perceived by the naked eye. After various digital processes, the farmer can access this information via a dedicated application online or on a smartphone. In the livestock sector, these new digital tools are readily adopted by farmers if they promise technical and economic gains and can reduce the arduousness of work. First of all, there are DSS based on sensors worn by the animals (externally or internally), which provide real-time measurements of the physiological characteristics of the animal and its activity (temperature, abdominal pressure sensors, movement, etc.). In dairy farming, the farmer can use these tools to monitor the animal's reproductive cycle and reliably detect heat or parturition or health problems, even before any external signs can be detected by a professional. We are also seeing the emergence of DSS prototypes based on image recognition (from cameras installed on the farm) using artificial intelligence methods (deep learning). These allow animals' behaviour and health to be monitored and can even go so far as facial recognition. If an anomaly is detected in a group or animal, an alert can be sent to the farmer's smartphone. Despite the number of initiatives underway, certain issues, which are crucial for making a DSS used and usable by professionals, are still the subject of research carried out in collaboration with the latter. In particular, these concern precision, pertinence (a DSS that provides too many false alerts, for example, risks being rejected), the adequacy and form of the information made available to the farmer according to his expertise and needs, and the ergonomics of the tool, in connection with the notions seen in the visualisation and HCI section (Li et al., 2020). The way in which user knowledge is used is also the topic of ethical questions raised in connection with open innovation more generally.
## 3.5 Automation, control and robotics

As highlighted above, digital farming is far from being limited to data acquisition and processing. The aim is to use this data in decision making and determine actions to be taken, both spatially and temporally, to optimise cultivation techniques capable of reconciling high levels of production, crop quality and environmental conservation. In this sense, precise and potentially frequent work will be required in order to meet such specifications, which is not always possible in terms of human resources and capacities. This is especially true since agricultural tasks are often tedious and sometimes dangerous. Exploiting the full potential of the principles of digital farming could therefore lead to task automation. Today, robotics technology is taking the developments already implemented in the context of automated tools or driver assistance systems for agricultural machinery even further. But, beyond the automation of certain tasks, advances in the field of robotics in the agricultural world must pave the way for a change in practices to accompany the ecological transition.

Farmstar is a DSS based on subcellular spatial images. It was developed by Airbus in collaboration with agricultural technical institutes. The complex processing chain combines the use of spatial images and other data sources such as climatic data, and uses computer simulation for agronomic models. The result can be accessed via APIs (Application Programming Interfaces) which are queried by the user application. The farmer can thus obtain useful information in the form of maps and "dashboard" indicators via an integrated web application that hides the complex computer architecture and data flows involved.

<figure><img src="image_45.png" /><figcaption>Figure 1: Farmstar, from high-resolution spatial imagery to advice maps.</figcaption></figure>
<figure><img src="image_46.png" /></figure>

The **VALSE** project-team (Inria, Ecole Centrale de Lille, Université de Lille) studies problems arising from the analysis of distributed, uncertain and interconnected dynamic systems. Its aim is to design estimation and control algorithms for different fields. In particular, in the field of oyster farming, these algorithms have enabled the design of a biosensor based on the measurement and interpretation of bivalve mollusc behaviour, for the remote detection of coastal water pollution and the consequences of climate change.

## Structured environments: allies of robots

The rise of robotics has historically been rooted in industrial applications, especially automotive, for the automation of production lines (Bahrin et al., 2016). From this, it is possible to design infrastructures that allow robots to be referenced and operate in perfectly known and unchanged environments, as well as to control the conditions of interaction (lighting conditions, handling known objects, creation of specific zones). This greatly helps the design of robust perception and control algorithms based on robot operation models that require strong assumptions (rolling without slipping, object or scene recognition, accurate localisation, etc.). As a result, robotics applications in agriculture have primarily focused on the indoor environment, particularly for livestock production (Bergerman et al., 2016). In this sense, the biggest market for robotics in agriculture is currently in the livestock sector, with feeding and milking robots. These are able to operate using a number of reference points and benefit from special arrangements to maintain high repeatability. They can thus perform demanding tasks (such as milking or feeding animals) and free up the farmer's time. Such developments are increasingly common in agricultural practices, and today half of new French dairy farm facilities are equipped with milking robots (Tse et al., 2018).

In cropping, such infrastructures are more difficult to put in place, with the structure of crop production being inherently changeable and posing detection and referencing issues. Nevertheless, the automation of certain tasks, particularly driving farm machinery, has greatly benefited from the advent of GPS, especially centimetre-precision models which offer absolute referencing. Many devices aimed at automating machine operation under the supervision of a "driver" have thus emerged, sharing a certain number of research challenges with advances in driverless vehicles.
However, the use of GPS sensors alone remains limited for the production of fully autonomous robots (i.e. without on-board human supervision) for several reasons. Firstly, the potential loss of satellite signals near buildings, in greenhouses or near tall vegetation, would require manual intervention. Secondly, farming requires referencing and interaction with plants and not absolute references, even if planting is carried out using GPS referencing. Lastly, the absence of an on-board supervisor means that autonomous machines must be equipped with a means of perception to ensure their safety (avoiding obstacles, traversability management).

<figure><img src="image_47.png" /><figcaption>Study of a robotized electric tractor for agroecology. © INRAE.</figcaption></figure>

Thus, several other strategies including vision (Stefas et al., 2019) and laser technology (Tourrette et al., 2017) are substituting or complementing absolute referencing to achieve autonomous navigation. This is already being used commercially in robots, mainly for mechanical weeding, mowing and surveillance. However, the task efficiency of these robots is currently limited and performance is closely correlated to detectability conditions.

Before envisaging more complex work (pruning, harvesting in the field) performed in a fully autonomous way, there are several scientific and technological obstacles that must be overcome in order to deal with the variability of the environments and the diversity and complexity of the tasks to be carried out while preventing any damage to the robot(s).
## From adaptation to reconfiguration

Unlike mobile robotics in industrial environments or road traffic, mobile robots in natural environments require specific adaptive abilities to deal with the diversity of interaction conditions and their variability (Bergerman et al, 2016). This involves the online modification of perception and control parameters (such as modifying response times as a function of speed (Hill et al, 2020) or adapting detection thresholds to light conditions. Several adaptation and anticipation or robust control mechanisms have been proposed to deal with the variation in these environments and maintain a high level of accuracy, while protecting the robot from damage (Krid et al., 2017, Yandun et al., 2017). This last functionality is defined in a relatively binary way in structured environments: avoid collisions with geometric obstacles, do not operate in out-of-bounds areas, etc. In natural environments, the notion of "obstacle" is less well defined and solutions are more complex. Firstly, encountering an obstacle is not necessarily a failure, as robots do not have to be stopped when passing over vegetation or if they have to push aside a branch. Secondly, some areas can be traversed under certain conditions (speed or load limitation) and the crossing also depends on the ground conditions (especially adhesion) and the properties of the robot (Guastella, 2018). Lastly, operating in some areas may lead to a loss of control or physical stability of the robot (Wolf et al., 2019).

<figure><img src="image_48.png" /></figure>

### For example...

At INRAE, the TSCF UR designs reconfigurable and shared autonomy systems to enhance the performance and safety of machines operating in natural environments, particularly those found in agriculture. For example, the team designs adaptation mechanisms to deal with the diversity of interaction conditions and their variability.

Several approaches allow this complexity to be taken into account through the concept of traversability (the set of conditions allowing a given area in front of the robot to be crossed). Nevertheless, work on this concept illustrates the difficulty of defining a single perception and control approach to allow a robot to perform complex agricultural tasks. Many studies currently focus on the real-time selection or fusion of typical behaviour (see the INRAE Adap2E project^43), which addresses the problem of scene interpretation and behaviour evaluation.

<footnote n="43">43. https://adap2e.inrae.fr/</footnote>
In addition, many strategies in agricultural robotics are based on cooperation between less complex robots able to work together or in the same area. This reduces the risks in terms of the operation of each robot (limited kinetic energy in the case of impact) and their cost, but shifts the issue of complexity to the association and synchronisation of the group (Blender et al., 2016).

Conclusion

In this chapter, we have browsed at the different areas of research addressing the use of digital technology in agriculture. They mainly concentrate on data at all levels of the data cycle, from capture to exploitation via collection, traceability, processing, storage, interpretation, restitution and use in automated or robotic systems. Different skills involving networking, modelling, learning, knowledge management, control and security are used to provide efficient, safe and secure solutions. The key aims are to assist farmers in difficult tasks, allow better management of our resources and promote exchanges and expert knowledge, all while respecting the environment as much as possible.
4

Digital technology
and agroecology:
opportunities to explore,
challenges to overcome

**Authors** – Véronique Bellon-Maurel, Pascal Bonnet, Isabelle Piot-Lepetit, Ludovic Brossard, Pierre Labarthe, Pierre Maurel, Jean-Yves Courtonne.

**Acknowledgements (contributions, proofreading, editing)** – Frédérick Garcia, Nathalie Mitton, Alexandre Termier.

<figure><img src="image_49.png" /></figure>
Digital technology is going to have a significant impact on agriculture. But will this impact be positive or negative? Some, such as Rotz et al., (2019), fear that digital technology will lead to an increase in market integration and corporate concentration; while others, such as Bonny (2017) contest this conflict, provided changes are made to governance and provided there is effective communication with the wider public. At the same time, a number of authors have mooted the possible convergence between agroecology and digital technology (Bellon Maurel and Huyghe, 2016; Biradar et al., 2019; Caquet et al., 2020; Grieve et al., 2019; Klerkx and Rose, 2020; Wegener et al., 2017). The term 'agroecology' is used to refer to both the scientific discipline and an agricultural movement or model based on a set of alternative practices, the aim of which is to build viable food systems which respect both mankind and the environment. As pointed out by Altieri (1989), it incorporates both technical and socio-economic aspects all the way along the production chain (what is produced, how it is produced and for whom). Agroecological production is designed to improve agricultural systems through the use of environmentally-friendly processes, with a particular focus on biological synergy between the component parts of the agroecosystem and balancing out the "inputs and outputs" of the system, a lever also known as "closing the cycle".

This chapter will focus on the opportunities and challenges presented by digital technology for agroecology in its broadest sense, i.e. sustainable food systems. As an "enabling technology", digital is capable of increasing the capacities of farmers to respond to four major challenges:

* improving production, in line with the principles of agroecology, by creating knowledge to support the agroecological transition and by adapting to exogenous factors, namely climate change;

* improving production by assisting farmers with the running of their farms;

* better establishing farmers within the agricultural ecosystem, i.e. regional ecosystems and value chains;

* improving sharing, learning and understanding by supporting the agroecological transition: sharing data, information and knowledge.

The specific challenges facing the Global South will also be explored.
Improving production: creating knowledge to support the transition towards agroecology

The scientific and technological knowledge that will support the transition towards new systems of production (including organic farming, integrated pest management and agroforestry) are still being developed. But in order to ensure the widespread deployment of agroecological models and to enable them to be scaled up, there is an urgent need to understand the mechanisms involved (Altieri et al., 2012) and to establish points of reference (Vanloqueren and Baret, 2009). In agroecology, all levels of diversity and biological regulation – within species, between species or functional (plant-animal interaction, landscape ecology, etc.) – can be deployed in order to make systems resilient (Caquet et al., 2020). The flipside is the abundance of possibilities – of varieties to choose, species assemblages, interaction between crops and livestock – which makes it impossible to create knowledge out following conventional paths. Faced with this challenge, new modes of building knowledge must be developed, and digital technology can contribute to this vital step for the agroecological transition (Leveau et al., 2019) using three interconnected levers: (i) the modelling of agroecological complex systems, which requires a holistic approach; (ii) data collection on these new cropping growing and breeding methods, chiefly through the participatory collection of information; (iii) the inference of models on these new production systems, based on data.

<figure><img src="image_50.png" /><figcaption>Connected insect trap. © Le mas numérique.</figcaption></figure>
Representing complex systems within agroecology

Modelling in agroecology is very much on the rise but it remains a complex subject.<fnref n="44" /> Agroecological modelling can only have any meaning if it incorporates interactions within the farm, or even at a landscape level (Tixier et al., 2013). It is a delicate task. Antle et al. (2017) have identified a number of points which must be addressed in order to build next-generation models for use in agroecology: (i) improving existing modelling in order to factor in uncertainty or extreme events; (ii) transitioning from cropping systems to production systems; (iii) modelling complex rotations and crops; (iv) modelling links between crops and animal production; (v) upscaling, from fields to the wider landscape; (vi) interoperability. “Unit models” used to describe each compartment of the system must also be developed. Furthermore, looking towards the wider landscape leads to the emergence of new scientific frontiers relating to a “greater understanding of population dynamics and the role of interfaces between cropping environments and natural environments, about which little has been written” (Caquet et al., 2020). More, there are a number of challenges linked to modelling within farms, owing to the fact that farms are complex systems, which should be managed using a combination of socioecological and sociotechnical models (Bergez and Thérond, 2019).

Large scale data collection for new agroecosystems

A lack of data and difficulties accessing it can prove a hindrance to improving and using models. However, there has been a phenomenal increase in the quantity of data on agriculture: in 2014 around 190,000 pieces of data were estimated to be produced each day on a farm in the USA, and by 2050 more than 4 million pieces of data could be produced each day (Rotz et al., 2019). This data comes from connected objects (Elijah et al., 2018), fixed sensors (weather stations, connected traps, various different types of alarm, etc.), sensors embedded into machines (used to monitor the machine or crops), sensors worn by animals (activity sensors, boluses for measuring temperature, trackers) or sensors carried by human operators (mobile phones). Given the variety and the volume of agricultural data, it may now become more and more appropriate to use the term “agricultural big data” (Bellon-Maurel et al., 2018). Indeed, data is essential for the purposes of creating models of complex mechanisms within agroecology, which are difficult to model using a deterministic approach. In order to develop such models, systematic quantifications and observations must be carried out within agricultural production systems at different levels (Biradar et al., 2019). Chowdhary et al. (2019) have discussed the issue of the lack of reference points, the phenotyping bottleneck which is

<footnote n="44">44. Caquet et al., (2020) identified no fewer than 107 models at Inra in 2018.</footnote>
holding back agroecology and agroforestry. For this reason, the development of knowledge in these areas will require an increase in high-throughput phenotyping capacities in diverse environments, through the deployment of high-throughput phenotyping on farms (in fields or in herds). This raises questions regarding devices for phenotyping. Phenotyping is currently performed by researchers and farmers using expensive measurement devices such as buggies or other automated platforms in fields<fnref n="45" />. A number of authors (Caquet et al., 2020; Grieve et al., 2019; Ingrand, 2018) have recommended developing phenotyping on a large-scale or the continuous monitoring of crops, animals and environmental conditions. This would require affordable and easy-to-use measurement devices, which either proximate – e.g. the portable sensors developed by the CAPTE unit – or remote sensors – e.g. Sentinel 2 satellites, which deliver resolution of ten metres with a three to five day revisit time (Biradar et al., 2019) – to assess the physical and physiological characteristics of plants and animals (Reynolds et al., 2019).

## Data-based modelling: a step towards new knowledge

The possibilities which artificial intelligence opens up for extracting knowledge from data in agriculture – particularly “big data” or “smart data” – have been well documented (Pham et Stack, 2018; Wolfert et al., 2017), but do not specifically concern agroecology. Those authors who have studied the use of neural networks in agroecology (Jiménez et al., 2008; Schultz et al., 2000) have noted several key aspects: (i) the issue of validating the models obtained and uncertainty; (ii) the need to organise systems into simpler sub-systems which neural networks will be applied to and (iii) the importance of considering inference, often compared to a black box, as a stepping stone towards a more analytical model.

## 4.2 Improving production: using digital technology to assist farmers with the running of their farms

According to Caquet et al. (2020), "the capacity of digital technology and agri-cultural equipment to specifically support agroecology remains a challenge". One of the five "main sectors" to come to terms with concerns "the characterisation of environments, plants or livestock with a view towards improving management and analysis".<fnref n="46" /> The question of decision support is also posed, and all the more

<footnote n="45">45. See for example the FieldScanalyzer from Lemnatec, the Phenomobile or the buggy marketed by Hiphen, the Fieldscan from Phenospex, etc.</footnote>

<footnote n="46">46. Others are: "the sharing of information between regional stakeholders", "agricultural equipment for the specific needs of agroecology", "characterising the response of organisms for phenotyping purposes" and "traceability for operating methods".</footnote>
pressing given the multiple objectives in play on farms. “Scaling up” to a highly transformative form of agroecology (redesigning systems) will require new tools, and digital technologies could play a key role when it comes to (i) improving management along the season (which calls for precision agriculture or precision livestock farming) or at a strategic level (incorporating economic data); and (ii) improving agricultural operations, with agricultural equipment designed for more complex agricultural systems requiring more work.

## Adapting the principles of precision agriculture to agroecology: observing and taking decisions

The principles of precision livestock farming and precision agriculture can be applied to agroecology since they lead to interventions tailored to suit plants and animals needs. They centre around a four-stage: observation (measuring “symptoms”), diagnosis (identifying the status of a plant or an animal), recommendation (determining the action to take), and action. With precision agriculture it is possible to map diversity within crops and to apply different measures to different parts of a plot (Bellon and Huyghe, 2016): nitrogen fertilisation (using satellite sensors from the early 2000s onwards and now tractor-

<figure><img src="image_51.png" /><figcaption>Takeoff of a mapping drone in Senegal. © CIRAD.</figcaption></figure>
embedded technology; precision irrigation (Molden, 2007), drawing on estimates of water scarcity using "proxies" (the temperature of the surface of leaves, visual estimation of physiological characteristics);<fnref n="47" /> and crop protection, which is the most complex aspect given the wide ranging nature of phytosanitary problems (weeds, insects and other pests, and diseases). Precision livestock farming involves tracking environments conditions (measuring the atmosphere within buildings or external conditions) and animals. Over the past twenty years or so, sensors on animals or in their environments have been used, particularly on dairy farms: identification and tracking using RFID and GPS, imaging (2D, 3D, infrared), accelerometers, sounds, automated measurement devices (scales, water meters, milk meters, feed distributors, etc.) (Chastant-Maillard and Saint-Dizier, 2016). Various different parameters are monitored: growth, milk production, food ingestion, physiological status, behaviour, reproduction, health and well-being (detecting lameness, digestive issues, etc.)... (Benjamin and Yik, 2019; Fournel et al., 2017; Halachmi et al., 2019; Knight, 2020; Neethirajan, 2017; Rowe et al., 2019; Veissier et al., 2019; Xin and Liu, 2017). Currently, these techniques are primarily targeted at conventional livestock farming, but solutions are being developed for alternative systems. These include devices for monitoring animals and pasture (Shalloo et al., 2018) in order to improve the efficiency of extensive grazing systems - which would otherwise be limited by a lack of data - and to guarantee consumers responsible livestock breeding (Neethirajan, 2017).

There are two crucial questions when it comes to the management of agro-ecological systems:

(1) Regarding observation, this relates to the early detection of malfunctions. For both cropping (Divya and Santhi, 2019; Johannes et al., 2017) and livestock farming (Ingrand, 2018), this is crucial for alternative farms (agroecology, organic farming, integrated pest management) seeking to scale up without access to the same range of curative measures as conventional farms. Out in the fields, visual observation takes up a lot of time, is dependent on the experience and the availability of the observer (Mul et al., 2016) and sometimes impossible to implement if the problem is undetectable. Technologies are marketed or still in the research phase: (i) optical devices for plants monitoring and detection of winged insects (Brydegaard et al., 2014; Grieve et al., 2019), (ii) quantification of spores using real-time analysis of bioaerosols, not yet satisfactory (Sharma Ghimiri, 2019), (iii) connected insect traps (López et al., 2012), (iv) animal monitoring devices (Li et al., 2020; Moura et al., 2008; Tullo et al., 2018; van Hirtum and Berckmans, 2004) and, more recently, so-called "portable" devices, which are worn by animals (Neethirajan, 2017).

<footnote n="47">47. Apex Vigne- https://www.hdigitag.fr/fr/application-mobile-apex-vigne-facilite-le-suivi-de-la-croissance-de-la-vigne/</footnote>
(2) Regarding decision-support, this relates to building models to supply information which can be used in decision-making. Lepenioti et al. (2020) identified three types of data processing: (i) descriptive analysis, answering questions such as "What is the value of the parameter in question? How do levels compare to other producers or other years? What has happened?" (ii) Predictive analysis, answering questions such as "What is going to happen?" and "Why?" and (iii) prescriptive analysis, answering questions such as "What is the recommended course of action?". The level of complexity for these models is growing, as are problems linked to interpretability and uncertainty. There are methodological bottlenecks linked to how models are built: which symptoms are to be selected to incorporate, into the models?, how are symptoms expressed due to natural variability? and, when it comes to recommendations, what are the other factors inherent to plants or animals, the environment, production or breeding systems (factoring in other individuals from their group), the equipment used and the agricultural strategy employed?

## Multi-objective decision-making in agroecology

Strategic decision-making with regard to how farms are run is quite different in agroecology because farmers objectives tend to be multivariate (optimising the three dimensions of sustainability)<fnref n="48" /> and multitemporal (short and long term). This raises certain questions with regard to modelling such as: (i) determining the optimum in a multi-level, spatio-temporal system; (ii) incorporating the farmer's strategy into optimisation models (Antle et al., 2017; Groot et al., 2010); (iii) dealing with uncertainty. The use of alternative modelling methodologies and risk management protocols is also to be explored: the aim here is not to seek out an optimum compromise but to keep the system within possible desired outcomes.

## Co-designing innovative agricultural equipment and agroecosystems

Technology has the capacity to play a key role when it comes to scaling up within agroecology, where the level of technical complexity is greater than in monoculture farming (Wegener et al., 2017). Mixed culture farming (multiple species, multiple varieties) or intercropping could be implemented on a large scale through high-precision operations (from sowing to harvest) and the characterisation or the sorting of mixed products from harvests. In agroforestry, trunks are an impediment to the mobility of traditional machinery, preventing them from being adopted (Mattia et al., 2018), but there are few technological solutions; Chowdahry et al. (2019) have suggested developing small, inexpensive "soft

<footnote n="48">48. The economic, environmental and social.</footnote>
robots” with flexible arms, operating in networks. For livestock farming, milking robots capable of being transported into pastures could help to bring about a more generalized return to grazing (Cloet et al., 2017). Lastly, in relation to the well-being of farmers or employees, the objective is to reduce tasks which are hazardous, tiring or time-consuming (Vasconez et al., 2019). This concerns vegetable growing and arboriculture in particular: weeding robots sold for use in market gardens; inexpensive, open-source weeding robots for microfarms (Farmbot, LettuceThink); harvesting robots – currently a sticking point for market gardens and in arboriculture because of the expense – and most notably collaborative robots or cobots (Vasconez et al., 2019).

Robots would be capable of overcoming constraints in new crop and livestock farming systems, with productivity equal to that of current practices. Collaborative work, either between small robots operating in swarms or between robots and humans (cobots) is a possible avenue to explore. Bottlenecks are the cost of robots (linked to their multifunctionality), how collaborative work is organised (between robots or with humans), perception and gripping, and safety (mobility, interaction with humans). In order to deploy this technology, challenges linked to its environmental impact (manufacturing, use, end of life) and resiliency (repairability, adaptability and autonomy) will also need to be overcome. Participatory design could provide a means of successfully developing robots for use in agroecology, reducing tensions between approaches based on ecology and those based on the benefits of technology (di Salvo et al., 2014). In Denmark the ITU (IT University of Copenhagen) has sought to alleviate these tensions by considering robots as a part of the ecosystem (“robotics agroecology”).<fnref n="49" />

Lastly, there is the issue of the divide between major farms are likely to adopt robots and smaller, unconventional farms which either do not adopt them or are late to adopt it (Caquet et al., 2020). It could be avoided by combining a frugal approach with a high-tech approach, similar to the ones of the ‘high-low tech’ research group (MIT; <fnref n="50" /> Kadish and Dulic, 2015) and “makers” approaches (Anderson, 2012).

<footnote n="49">49. https://real.itu.dk/projects/robotic-agroecology/</footnote>

<footnote n="50">50. http://highlowtech.org/</footnote>