# Animating the transition: How agriculture 5.0 revitalises agroecological principles

Mohammad Naim, A. Boukhizzou

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## To cite this version:

Mohammad Naim, A. Boukhizzou. Animating the transition: How agriculture 5.0 revitalises agroecological principles. Smart Agricultural Technology, 2026, 13, pp.101788. 10.1016/j.atech.2026.101788 hal-05446899

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Submitted on 7 Jan 2026

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# Animating the transition: How agriculture 5.0 revitalises agroecological principles

M. Naim<sup>a,*</sup> <sup>●</sup>, A. Boukhizzou<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Agroecological transition

Sustainable development

Smart farming

Human-machine interaction

Digital transformation

## ABSTRACT

Agriculture is undergoing a rapid digital transformation that challenges its ecological, social, and ethical foundations. This study explores how the transition from two revolutions, from Agriculture 4.0 (A4.0) to Agriculture 5.0 (A5.0), redefines the relationship between technology and agroecology. The dominant approach of A4.0, driven by automation, big data, and artificial intelligence, has enhanced efficiency but missed many agroecological principles, mainly those contributing to secure social equity and responsibility. Emerging as a corrective paradigm, A5.0 seeks to integrate technological progress with agroecological principles that value the social and human dimension. Adopting a scoping review following PRISMA-ScR guidelines, scientific publications indexed in Scopus and CABI up to October 2025 were screened and coded to assess how current A5.0 research embeds the thirteen agroecological principles defined by the High-Level Panel of Experts in 2019. A total of 136 documents were analysed through bibliometric and thematic synthesis. Results show that A5.0 represents a philosophical and structural evolution beyond the efficiency-oriented logic of A4.0, integrating distributed computing, explainable artificial intelligence, digital twins, and collaborative robotics within ecologically restorative and socially inclusive frameworks. However, while A5.0 strengthens resource efficiency, resilience, and certain social segments through open-source technologies and participatory design, gaps remain in policy coherence, emotional engagement, and human-machine co-learning. To address these, the study proposes two complementary agroecological principles, cognitive symbiosis and emotional ecology, emphasising shared intelligence and affective stewardship between humans, machines, and ecosystems. Overall, Agriculture 5.0 reframes digitalisation as a human-ecological partnership that can operationalise agroecology's ethical goals if governed by inclusion, transparency, and regeneration rather than control and optimisation.

## 1. Introduction

The accelerating transformation of global food systems is unfolding amid unprecedented technological and socio-economic pressures [1,2]. Agriculture, long the backbone of human civilisation, must reconcile environmental limits with rapid digital transformation, as digital technologies shape sustainable practices and influence agriculture's environmental footprint [3-7]. For instance, the global agri-food sector contributes roughly one-third of anthropogenic greenhouse gas emissions and is the primary driver of land-use change, freshwater withdrawal, and biodiversity loss [8,9]. The dominant model of industrial agriculture has already pushed many ecological boundaries, contributing to soil degradation, water stress, greenhouse gas emissions, and losses in agro-biodiversity [10]. Simultaneously, rising food insecurity,

soil degradation, and social inequalities have revealed the fragility of current production and distribution models [11]. These converging crises have incited scholars and policymakers to call for a “great food transformation”, one that couples technological innovation with ecological restoration and social inclusion [12,13].

So modern agriculture is facing a paradox, it must produce more food to feed a growing global population while simultaneously reducing its environmental footprint, preserving biodiversity, and building resilience to climate change. A profound transformation is required also not only in practices but in the socio-technical regimes that govern food systems. Feeding a projected global population of nearly 10 billion by mid-century and safeguarding ecological integrity demands more than gradual improvements, it requires a paradigmatic transformation of agrifood systems [14,15].

<footnote><sup>a</sup> InTerACT (UP 2018.C102), Institut Polytechnique UniLaSalle, Beauvais, France</footnote>

<footnote><sup>b</sup> Institut Polytechnique UniLaSalle, Beauvais, France</footnote>

<footnote>* Corresponding author.
E-mail address: mohammad.naim@unilasalle.fr (M. Naim).</footnote>
Within this context, digitalisation and automation have emerged as a promise. The so-called “Fourth Agricultural Revolution” or “Agriculture 4.0” (hereafter A4.0) has been propelled by advances in cyber-physical systems, artificial intelligence, and data-driven agronomy (e.g. agricultural robots, variable rate application (VRA) systems, etc.) [16–20]. Yet, evidence shows that these technologies often reinforce existing power asymmetries and techno-economic lock-ins, privileging capital-intensive actors and marginalising smallholders [21–23]. The framing of sustainability through efficiency metrics alone (e.g. yield per hectare, litres per kilogram), or input optimisation, risks neglecting deeper systemic dimensions of equity, resilience, and ecological integrity [24–26]. Consequently, an emerging body of literature envisions a new stage “Agriculture 5.0” (hereafter A5.0) where technological progress claims to be explicitly aligned with human-centred innovation [27–29]. In fact, a wave of research has already started mentioning an even more futuristic agricultural revolution “Agriculture 6.0” [30–32].

On the other side lies agroecology, a transdisciplinary, value-laden dimension combining ecological science, local knowledge, and social justice, it emphasises on various principles and elements, having the objective of recycling of nutrients, conservation of biodiversity, resilience through functional redundancy, and participatory governance [10,33] shed the light on the transition from conventional to agroecological systems, showing resilience to shocks, greater self-organisation, and co-innovation among farmers, yet they highlighted that agroecology is not just a set of farming practices; it can act as a transformative vision of food systems, contesting power asymmetries, supply chain dependencies, and monocultural logics, which have a significant negative impact on the ecosystem [34,35].

So, agroecology is best understood as an ethic of design rather than a shopping list of techniques, it treats farms and food webs as living, co-evolving systems, and it insists that knowledge, values, and power are part of the ecology to be managed [10,36]. Canonical definitions already frame it as a science, a set of practices, and a social movement, with transformation, rather than mere optimisation, as the endgame [37–39]. Indeed, policy frameworks have converged on this systemic view: the FAO’s Ten Elements [40] and the High-Level Panel of Experts “CFS-HLPE’s” Thirteen Principles [41] (Fig. 1) turn that ethos into an operational compass across field, farm, and system or territory [10,40, 41]. In practice, this implies moving along the five levels defined by Gilessman in 2007 [42,43], from input efficiency and substitution of alternative inputs toward full redesign of agroecosystem and global food-system re-embedding, where the higher levels explicitly rewire

governance and social relations, not just agronomy [10,44]. The design logic is ecological, shift from controlling nature to collaborating with it. A large body of evidence now shows that diversified systems (rotations, cover crops, intercropping, agroforestry, crop-livestock integration) improve multiple ecosystem services such as pollination, natural pest regulation, nutrient cycling, water regulation, usually without sacrificing yields and often improving stability [45–51].

None of this romanticises the transition. Reviews document the friction between ideals and practice, labour demands, knowledge intensity, market access, and policy incoherence, and stress the need for context-specific redesign, participatory research, and indicators that reward public goods, not just private output [25,41]. Yet the direction of travel is clear, when agroecology is treated as a techno-ecological constitution, principles that govern which tools, incentives, and data practices are legitimate, whereas organisational innovations can be recruited to serve living systems rather than subdue them. That is the spirit in which an agriculture grounded in agroecology becomes plausible, technology curated by principles of the instance of diversity, co-creation, justice, and responsible governance, and evaluated by multi-criteria performance rather than throughput alone [10,52].

In parallel, there is a relationship between A5.0 and what are noted as Industry 4.0 & 5.0 frameworks. While A4.0 draws directly from Industry 4.0's focus on automation, interconnected systems, and digitalisation, its core aim has been to enhance productivity and operational efficiencies by embedding cyber-physical systems, the Internet of Things (IoT), and artificial intelligence into farming practices [20,53]. In contrast, A5.0 extends this foundation by integrating technological innovation with broader societal and environmental goals, such as ecological sustainability, equity, and improved human-technology collaboration, which are central to the human-centred paradigms of Industry 5.0 [54].

Industry 4.0, characterised by cyber-physical systems, automation, and real-time data analytics, has laid the technological groundwork for smart agriculture, enabling improvements in precision farming [55]. Industry 5.0 advances these technologies by emphasising human-machine collaboration and sustainability, advocating for the incorporation of human creativity and ethical considerations into the technological ecosystem [56]. Thus, Agriculture 5.0 shares a conceptual framework with Industry 5.0, wherein technology serves not only productivity but also environmental stewardship and social well-being [57].

A critical question in positioning Agriculture 5.0 is whether it

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Agroecological principles set by [41]. The question mark “?” indicates the open and adaptive nature of the framework, acknowledging the potential inclusion of additional principles or dimensions as agroecological transitions evolve. Illustration and conceptualization by the authors.</figcaption></figure>
represents a linear evolution of Agriculture/Industry 4.0 or a discon-
tinuous paradigm shift. Related research suggests that for instance, the
transition from Industry 4.0 to Industry 5.0 does not occur through an
abrupt rupture but through an evolutionary process of assimilation and
enhancement of existing technologies, values, and organisational
structures [58]. In [58] review of the food and agri-food sectors, pre-
liminary evidence indicates that Industry 5.0 emerges from the tech-
nological base of Industry 4.0 while progressively incorporating broader
human-centric and sustainability-oriented objectives rather than over-
turning the preceding paradigm overnight. This suggests along with
common sense that A5.0 can be interpreted as an evolutionary trans-
formation of A4.0, one in which digital, biological, and cooperative
technologies are reconfigured to foreground environmental stewardship
and social inclusion, while still maintaining continuity with the digital
infrastructures and data logics established under A4.0. However, the
degree to which this shift is purely evolutionary versus genuinely
paradigmatic could depend on how deeply agroecological principles are
embedded in the design and governance of technologies, rather than
simply layered onto pre-existing precision-agriculture frameworks.

Some recent works already position “Agriculture 5.0” as the inte-
gration of digital, biological, and green-energy technologies toward
human-centred, sustainable agriculture [27,29,59]. For instance, [27]

propose that the A5.0 agenda must embed societal challenges into the
design of farming systems. The review by [59] emphasises that A5.0
must transcend mere mechanistic control and incorporate adaptive,
co-operative human-machine partnerships, and to "consider societal
challenges, in terms of technology lock-ins and the need for behaviour shifts
among stakeholders for faster adoption and customisation of the solutions".
While the literature on A5.0 increasingly describes a transition toward
sustainability, the extent to which its technological shift already em-
bodies, or departs from, core agroecological principles remains under-
studied. Other research focused on a specific technology, for example,
[60] highlighted the animation of agroecological principles with agri-
cultural robots at the field scale of application.

This study examines which agroecological principles defined by
HLPE [41] (reflected along the elements of FAO [40]), are explicitly or
implicitly embedded in current A5.0 research. The goal is to identify
where A5.0 did progress towards an agroecological farming and food
system, and what is still lacking in this transition. Thus, this study
compares how both A4.0 and A5.0 interpret the relationship between
technology and agroecology. A4.0, grounded in precision farming and
automation, emphasised optimisation using sensors, data analytics, and
basic robotics to maximise efficiency within existing production models.
By contrast, A5.0 gestures toward redesign: hybridising digital,

<figure><img src="image_5.png" /><figcaption>Fig. 2. PRISMA-ScR flowchart describing the selection process of the reviewed documents.</figcaption></figure>
biological, and renewable technologies with social and ecological intelligence. Yet this claim must be verified through evidence, not rhetoric. In short, the study would clarify whether A5.0 represents a genuine reflection shift toward an agroecological ethos, or merely an ecological rebranding of digital intensification. By identifying where A5.0 technologies align with or diverge from the principles of agroecology, such research could provide an evidence-based framework for designing technology that truly serves living systems rather than abstract productivity metrics.

## 2. Materials and methods

For this study, the scoping review design was selected for its methodological flexibility and suitability for emerging research areas, allowing broader inclusion criteria and a comprehensive mapping of literature without the restrictive appraisal protocols characteristic of systematic reviews [61]. The review process followed the PRISMA-ScR (Fig. 2) reporting guidelines to ensure transparency and methodological rigor [62]. Inclusion and exclusion criteria, defined a priori, guided the final corpus selection.

### 2.1. Review approach and search strategy

A document search was conducted using the Scopus and CABI databases to capture the literature relevant to the agroecological transition from A4.0 to A5.0. The search encompassed all publications available up to October 2025. Scopus was selected due to its extensive multidisciplinary coverage of peer-reviewed scientific reports, including journal articles, conference proceedings, and book chapters, an advantage over databases with narrower disciplinary or format scopes [63]. The inclusion of proceedings and book sections was methodologically justified to intercept emergent research trends in rapidly evolving technological domains such as agricultural robotics, artificial intelligence, and digital agriculture, which often appear first in these formats prior to journal publication. Complementarily, CABI database was incorporated for its domain-specific specialisation in agriculture, environmental sciences, and life sciences, ensuring comprehensive coverage of the applied and agroecological dimensions of the topic, even though it was expected that the reports' exclusion rate would be noticeably high due to the absence of the technological aspect in most reports retrieved.

The search strategy was conceptually structured around two thematic components: (i) the agricultural dimension ("agriculture") and (ii) the sustainability or ecological dimension ("agroecology" or "sustainability"). To ensure conceptual precision, the search was confined to document titles, abstracts, and author-supplied keywords, excluding database-generated indexing terms. This exclusion was deliberate to avoid the semantic distortion that may arise from controlled vocabularies (e.g. MeSH, Etree), which are harmonised for cross-domain retrieval but do not necessarily reflect the authorial intent or explicit terminology of the source documents. The final query was formulated as follows:

TITLE-ABS-KEY (agroecolog* OR sustainabl*) AND ("agricultur* 4.0") AND ("agricultur* 5.0") AND ORIG-LOAD-DATE < 20251005.

Preliminary retrievals using only the term "agroecology" yielded a limited corpus of four documents, indicating insufficient representation of the agroecological dimension in the indexed literature. Consequently, the broader construct "sustainability" was introduced as a complementary descriptor. On another hand, using the operator "OR" between "agricultur* 4.0" and "agricultur* 5.0" yielded 2720 documents, implying that it's extremely broad to be considered, however all documents were in the temporal range of 2018-2025, proving the novelty of the subject.

### 2.2. Eligibility criteria and coding process

All document types (i.e. journal articles, conferences papers, and

book chapters) and subject areas included in the databases were retained. The rationale was to refine the selection upon thematic criteria and to maintain the focus on agroecology and agricultural transition. To ensure the relevance and quality of the literature reviewed in the context of agroecological transition from A4.0 to A5.0, specific eligibility criteria were applied to exclude certain studies that did not align with the scope of this work. Table 1 shows the categories of papers excluded.

To ensure a thorough and focused analysis of the full text content, we used a deductive coding approach based on three main themes: contextual information, agricultural revolutions (4.0 and 5.0), and agroecological principles (Fig. 3). Deductive coding was chosen because it allows us to work with clearly defined categories aligned with our research objectives. In contrast, inductive coding is typically used when there are no predefined codes, where the goal is to identify patterns that emerge directly from the data while staying true to the original material [64]. Throughout the article, agroecological principles were examined and grouped based on their contribution (i.e. improve resource efficiency, strengthen resilience, secure social equity/responsibility) in the model presented by HLPE in its report ([41], page 41, Table 1). Whereas technological evolution was analysed to define patterns of change that better describe the transition from A4.0 to A5.0. In parallel, a binary dataset was prepared to analyse quantitatively the animation of each agroecological principle in each record.

### 2.3. Sample characterisation

To identify the conceptual structure and research dynamics underlying the transition from A4.0 to A5.0, a bibliometric co-occurrence analysis was conducted using VOSviewer (version 1.6.20). This approach allows for the visualisation of scientific landscapes through network mapping based on the frequency and co-association of key terms across the selected corpus [65, Eq. 6]. A total of 3794 unique words were identified across the 136 documents' titles and abstracts, with 52 occurring >22 times, out of which 31 (60 %) were retained for the map (Fig. 4). The resulting network map delineates clusters of closely related terms that represent thematic domains within the literature. In this analysis, distinct color-coded clusters emerged, corresponding to the technological, computational, and data-driven dimensions of agricultural innovation. This visualisation-based synthesis aligns with recommended methodological standards for scoping reviews employing bibliometric and network-based analyses to map conceptual evolution and research frontiers [61,66,67].

<table><caption>**Table 1**
Exclusion criteria and the rationale for each criterion.</caption><thead><tr><td>Criterion</td><td>Rationale</td></tr></thead><tbody><tr><td>Non-agricultural Studies</td><td>Papers that focused on non-agricultural sectors were excluded as they do not contribute directly to understanding technological or ecological transitions within agriculture.</td></tr><tr><td>Lack of Agroecological Nuances</td><td>Studies that did not address agroecological principles or failed to incorporate ecological, social, or sustainability aspects relevant to agroecology were excluded. This ensured the review remained focused on approaches that integrate ecological knowledge and sustainable practices.</td></tr><tr><td>No Technologies Mentioned</td><td>Since the transition from A4.0 to A5.0 involves the integration of advanced technologies within agroecological systems, papers that did not mention any relevant technologies were excluded. This criterion guaranteed the inclusion of studies that explicitly addressed technological innovations driving the transition.</td></tr><tr><td>Country-specific Studies</td><td>Papers that focused solely on a single country without broader applicability or transferable concepts were excluded to maintain the global relevance of the review. This approach helped avoid bias toward localised contexts that may not reflect the wider trends and challenges in agroecological transitions globally.</td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Schematic representation of the methodology to retrieve the needed information through the deductive coding of the literature body on three areas: context, technologies/ frameworks and principles.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** VOSviewer network illustrating terms co-occurrence in the analysed documents' title and abstract. Only terms with a minimum occurrence of 22 are included. Label and dot sizes are proportional to keyword frequency, while colours distinguish four clusters based on association strength.</figcaption></figure>

The map (Fig. 4) shows four main clusters. The red cluster centres on smart agriculture, sustainability, and digital integration; the yellow cluster focuses on automation, robotics, and system implementation; the green cluster deals with sensors, environmental monitoring, and

resource management; and the blue cluster emphasises AI, data analytics, and crop modelling. Together, these clusters reveal an interdisciplinary but still technology-driven field where sustainability and efficiency dominate the agenda, while deeper agroecological and social
dimensions remain only marginally developed.

The temporal distribution of publications illustrates the rapid ac-
celeration of scientific interest in A5.0 and related agroecological
technologies (Fig. 5). Between 2018 and 2020, research activity was
almost negligible, reflecting the dominance of centralised A4.0 para-
digms focused on precision farming and cloud-based analytics. From
2021 onward, however, the volume of publications began to grow
steadily, proving an increased differentiation between what A4.0 rep-
resented and the new offerings of A5.0.

A marked inflection occurred between 2023 and 2025, with total
publications increasing more than sixfold. The expansion was driven
primarily by peer-reviewed journal articles, indicating that the research
on the field has moved from exploratory to mature, evidence-based in-
quiry. The concurrent rise in book sections and conference proceedings
suggests consolidation of conceptual frameworks and a growing
consensus on A5.0's theoretical foundations. The recent publication
surge signals the institutional recognition of A5.0 as a distinct research
and innovation domain rather than a mere extension of A4.0.

The global distribution of publications related to the agroecological transition from A4.0 to A5.0, shows a widespread engagement with the topic across all continents (Fig. 6). While publications are present globally, there is a clear concentration in North America, Europe, and parts of Southeastern Asia, where research has gained substantial momentum. These regions are leading the way in terms of both volume and depth of research on the agroecological transition.

However, the map also reveals significant regional disparities,
particularly in Africa, where the number of publications is notably
lower. Despite this, the map proves global interest, which could lead to
more balanced knowledge-sharing and a more inclusive transition to
agroecological practices in the future.

3. Findings

3.1. *Technologies, the powerhouse of the transition*

The literature shows a clear evolution from the data-driven optimi-
sation of A4.0 toward the human and ecosystem-centred approaches
that define A5.0. Three main technological evolutions shape this tran-
sition and best differentiate between the two generations (Fig. 7).

3.1.1. *Intelligent architecture foundation*

The technological foundations of A4.0 were based mainly on sensor
deployment, connectivity infrastructure, and centralised data analytics.
A5.0, by contrast, represents a clear architectural shift toward distrib-
uted, resilient, and interoperable systems. This evolution is not only
about increasing digital capacity but also about reorganising how
perception, reasoning, and control operate within agricultural
environments.

<figure><img src="image_8.png" /><figcaption>Fig. 5. Temporal distribution of the documents retained after screening (n = 136).</figcaption></figure>

In A4.0, dense wireless sensor networks, proximal and remote sensing, and cloud-based analytics enabled high-resolution measurement of soil, plant, and atmospheric interactions [68–71]. For instance, data collected from an “unmanned aerial vehicle” (hereafter UAV) imagery, multispectral canopy sensors, and soil probes were typically transmitted to remote servers, where machine learning models produced agronomic recommendations [72,73]. Although this setup improved decision-making precision, it depended heavily on cloud connectivity and high-bandwidth communication [74]. As a result, these systems suffered from latency, vendor lock-in, and poor interoperability, limitations that made them fragile under real-world conditions and environmental influences [75,76].

A5.0 addresses these structural weaknesses through edge, fog, and
cloud co-processing architectures, rather than sending all raw data to
remote servers, computation now occurs near data sources, allowing
sensing, prediction, and control to function even under unstable
network conditions [76–78]. In this architecture, edge/fog computing is
proposed to reduce latency and bandwidth constraints in remote-sensing
driven agriculture [76]. This distributed structure prioritises resilience
and responsiveness over raw data throughput, marking a significant
departure from A4.0’s centralised logic.

The sensor layer has also evolved. A4.0 largely focused on expanding the number of sensors, while A5.0 emphasises smart sensors that are part of modular data platforms [73]. These sensors no longer act as passive data collectors but as active agents capable of executing predefined control actions based on human-defined rules [70,79]. Communication systems now combine low-power wide-area networks for energy-efficient telemetry with 5 G networks for high-speed robotic perception. Together with built-in cybersecurity, these hybrid communication systems ensure continued operation even under degraded conditions [69,71].

In terms of artificial intelligence (AI), A4.0 relied mainly on cen-
tralised supervised learning for yield prediction, pest detection, and
variable-rate application (VRA) [70,71]. These models improved
analytical accuracy but often lacked transparency, adaptability, and
user trust [7,77].

A5.0 redefines AI as a collaborative and explainable partner that supports human reasoning rather than replacing it, new neurosymbolic pipelines (e.g. “FarmSense-NeuroRF”) integrate rule-based logic with machine learning to classify plant stress, combining interpretability with predictive power [80,81]. Meanwhile, federated learning systems train models directly on farms, protecting data privacy while improving local adaptability [7,77]. Together, these advances demonstrate both technical and ethical progress by embedding transparency and privacy within AI design. Emerging work in quantum-accelerated optimisation further extends AI capabilities, enabling real-time, multi-objective decision-making for complex resource management [82,83].

3.1.2. *Operational digital twins*

In A4.0, remote sensing primarily served diagnostic purposes, and
most decisions were made manually after off-site data analysis [71,72].
A5.0 transforms this approach by coupling remote sensing with dynamic
modelling frameworks that integrate data from multiple spatial and
temporal scales into continuously updated digital twins [76,84]. These
digital representations combine satellite, UAV, and ground sensor data
to simulate real-time field conditions. For example, [84] developed a
digital twin system for irrigation and fertigation that automatically ad-
justs control parameters based on uncertainty in crop, soil, and equip-
ment behaviour. This demonstrates that digital twins now function not
only as visualisation tools but as operational systems capable of auton-
omous feedback control.

Recent studies further show that digital twins are expanding in both
scope and dimensionality. A5.0 integrates biological, mechanical, and
economic models within unified digital frameworks, enabling coordi-
nated optimisation across processes that were previously isolated [85,
86]. This convergence moves agriculture from episodic, model-based
<figure><img src="image_9.png" /><figcaption>Fig. 6. World map representing the countries of authors' affiliations in the documents found (Source: elaborated from metadata).</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 7. Evolution of the technological approach from Agriculture 4.0 to Agriculture 5.0 on 3 levels; architecture, sensing and automation. Source: elaborated by the author.</figcaption></figure>

management toward continuous, data-driven control embedded in everyday operations. The result is a more adaptive, self-organising agricultural system, one that balances efficiency, resilience, and

sustainability.
### 3.1.3. Automation and control evolution

Reviewed literature proposes that the A4.0 automation model has evolved in A5.0 into a distributed, cooperative network of autonomous actors. Early systems such as auto-steering tractors, single-purpose pickers, and vision-guided weeders improved local efficiency but struggled in unstructured environments and demanded frequent operator calibration; in practice, they served as mechanical assistance rather than true autonomy [70,87].

A5.0 advances toward collaborative autonomy, sharing control between humans and robots via coordination frameworks that prioritise safety, transparency, and alignment to agronomic intent. Robotic platforms now integrate perception and planning while exposing their decision processes to operators for real-time intervention; mission-planning systems explicitly tie actions to agronomic objectives [88, 89]. The result is partnership, not substitution: robotics amplifies skilled labour instead of replacing it.

UAVs exemplify the shift from sensing to manipulation. Modern drones use adaptive control grounded in aerodynamic models and real-time multispectral feedback to adjust spraying and seeding autonomously, moving from passive observation to active microclimate and input management [90]. This role change is coordinated across air and ground robots over heterogeneous networks (e.g., 5 G for high-rate perception; Low-Power Wide Area Network (LPWAN) for energy-efficient telemetry), flattening the spatial hierarchy and enabling cooperative operations [69,76].

Robotics integrate tightly with digital twins in A5.0. Autonomous machines interact with virtual farm models that assimilate field observations, update digital states, and dispatch control actions; the twin functions as an operational controller rather than an advisory visualisation, closing a continuous loop between prediction and execution [84, 86].

Control logic shifts from reactive rules to predictive, multi-objective optimisation. A4.0 irrigation and nutrient systems relied on thresholds and presets [71,73]. A5.0 employs controllers that jointly balance yield stability, water efficiency, and environmental impact, combining weather modelling with AI to reduce water use without yield loss while maintaining operation under network interruptions through fog-native designs [78,91].

Comparative evidence across studies indicates three defining features of A5.0 automation absent or underdeveloped in A4.0: situational awareness via continuous contextual sensing for real-time adaptation [76]; semantic interoperability so control data follow shared standards across platforms [88]; and human-centred safety and learning, where explainable interfaces support operator oversight and corrective feedback that improves subsequent performance [89].

## 3.2. What A4.0 has already attained in agroecology

A4.0 has introduced a digitally mediated reconfiguration of agricultural systems that aligns partially with the agroecological principles. The integration of artificial intelligence (AI), the Internet of Things (IoT), robotics, remote sensing (RS), and big data has increased efficiency and environmental awareness, yet these same technologies expose tensions between ecological integration and technological optimisation. Examining the HLPE principles sequentially shows that A4.0 has operationalised some dimensions effectively, while others remain only superficially addressed, revealing the boundaries of a purely digital transition.

### 3.2.1. Optimised resource efficiency

Recycling within A4.0 operates mainly through engineered loops rather than biological cycles. Energy harvesting systems that recover solar or kinetic power sustain autonomous sensing networks, while IoT-based water reuse frameworks in hydroponics and aquaponics achieve up to 99 percent recycling efficiency [92–94]. This signifies that recycling is interpreted as efficient throughput rather than ecological

regeneration. The circulation of data replaces the cycling of biomass, revealing a mechanistic rather than ecological understanding of resource loops. Agroecologically, this remains incomplete because nutrient and energy recycling depend on living processes that technology alone cannot reproduce.

The second principle, input reduction, is where A4.0 shows its strongest alignment with agroecology. Precision farming, variable-rate technologies, and AI-driven monitoring systems have lowered fertiliser and pesticide use by 15 to 30 percent, reducing costs and pollution while maintaining yields [93]. Sensor-based irrigation systems cut water use by up to 75 percent, and robotic weeding reduced herbicide demand by 90 percent [88]. UAV sprayers achieved 30 to 50 percent pesticide reduction [95]. This evidence indicates that technological precision is sufficient for agroecological efficiency on this aspect.

### 3.2.2. Partially resilient systems

Soil health management has advanced through digital diagnostics rather than regenerative practices. IoT sensors and AI algorithms now measure nutrient profiles, pH, and soil moisture in real time [96,97], while quantum and multispectral imaging reveal early stress or disease [76,82]. These tools allow precise fertilisation and irrigation schedules that minimise degradation [90,98]. Yet soil is treated as a managed substrate rather than a living ecosystem since digital surveillance optimises conditions but seldom restores microbial networks or organic matter. Therefore, it means that A4.0 views soil health through a lens of control, translating ecological vitality into data variables. Agroecology, in contrast, treats soil as an active partner, this ontological gap limits the depth of the transition.

However, biodiversity, remains marginal in digital farming. Robotics and field sensors can identify and spare non-target plants, reducing chemical damage [99]. Precision technologies lower pollution loads that indirectly protect species richness [95,100]. Nevertheless, the system architecture of A4.0 continues to favour monocultures [101,102]. This proposes that technology tolerates biodiversity only insofar as it does not disturb efficiency metrics. The agroecological function of biodiversity as the engine of resilience and ecological interaction, remains unexpressed. A4.0 thus conserves somehow biodiversity statistically but not structurally.

Synergies between elements of production are reinterpreted in A4.0 as interoperability among devices. Integration of AI, IoT, robotics, and 5 G creates machine-to-machine coordination for irrigation, fertilisation, and pest control [75,103]. Predictive analytics fuse heterogeneous data sources to optimise resource use [96]. These networks generate cybernetic synergy that increases responsiveness and reduces waste [104]. Yet synergy remains peripheral. The network functions without biology. This suggests that A4.0 has replaced ecological interdependence with digital connectivity. The result is efficiency without emergent resilience, a key difference from agroecological design.

Animal health has benefited from digitisation. Wearable sensors, computer vision, and UAV surveillance monitor livestock behaviour, disease, and nutrition with unprecedented precision [103,105]. These systems reduce mortality and improve welfare standards [97,105]. Nevertheless, animal welfare remains instrumental to productivity; ethical and ecological reintegration of livestock into farm cycles is still rare. This suggests that the digital management of animal health enhances oversight but not systemic empathy. Agroecology calls for co-evolution between animals and landscapes, something data cannot substitute.

Finally, economic diversification has emerged as a by-product of digital expansion, A4.0 stimulates new markets in data analytics, sensor manufacturing, and precision-farming services [106]. These sectors create employment and financial growth, yet they diversify economies rather than agroecosystems. This implies that diversification is occurring vertically, within technological value chains, not horizontally within farms. Agroecological resilience requires diversity in production, not only in capital streams, highlighting a conceptual misalignment
between economic innovation and ecological sustainability.

### 3.2.3. A gap in social equity and responsibility

The principle of co-creation of knowledge finds both empowerment and limitation in A4.0. Mobile applications, cloud platforms, and AI-assisted advisory services enable the rapid exchange of agronomic data [73,75]. Digital extension services create links between researchers, farmers, and policymakers [107]. Human-in-the-loop systems merge experiential knowledge with algorithmic precision [88]. However, data ownership and literacy barriers constrain inclusivity [106]. This proposes that knowledge democratisation in A4.0 is uneven: while information circulates widely, control over meaning and innovation remains centralised. Co-creation has become data-sharing rather than participatory science, highlighting the need for social institutions that translate information into empowerment.

Social values and diets, which include respect for culture, equity, and well-being, are only indirectly present. Some initiatives (e.g. open data platforms for agriculture, participatory data gathering, farmer-led digital literacy programs, and the integration of indigenous knowledge into AI training datasets) emphasise human-centred AI and ethical governance [107,108], yet the dominant discourse concerns productivity and technical efficiency. Social well-being and dietary diversity receive limited attention [102]. This suggests that A4.0 treats social values as constraints to be managed rather than as goals to be realised. Agroecology's cultural and ethical dimensions remain external to the digital framework, producing a sustainability model that is ecological in metrics but not in meaning. Culture and food traditions are minimally integrated, digital agriculture optimises supply chains but rarely addresses the transformation of diets or community food practices [109]. This signals that A4.0 operates mainly at the production level, neglecting the consumption and cultural dimensions of sustainability that agroecology considers essential for systemic change.

Fairness within A4.0 is compromised by unequal access. High entry costs and infrastructure requirements exclude smallholders, intensifying disparities between technologically advanced and resource-poor farmers [96,104]. Programs that provide subsidies or training mitigate but do not eliminate this digital divide [100,103]. This implies that the fairness achieved is distributive, not structural. The digital revolution amplifies efficiency while entrenching asymmetry. Agroecological fairness demands collective governance of knowledge and resources, an aspect missing in corporate-led digitisation.

Closely related, participation in A4.0 is mediated through technology rather than community institutions. Although collaborative platforms and government-industry partnerships exist [75,110], farmers are often end-users rather than co-designers. Their experiential feedback is collected as data but seldom shapes technological development [107]. This suggests that participation has become instrumental: farmers participate to validate models, not to govern them. The implication is that empowerment remains rhetorical until design authority and decision power are redistributed.

The principle of connectivity has been realised technically but not socially, which is not the true background of this principle [41]. IoT networks connect sensors across landscapes, creating real-time awareness of production systems [69]. Yet these connections are digital rather than relational; they do not necessarily foster collaboration or solidarity. This indicates that A4.0 has achieved the infrastructure for connectivity but not the community that agroecology envisions. Information travels fast, but trust and shared governance lag behind.

Land and natural resource governance has improved monitoring capacity but remains centralised. Remote sensing and Geographic Information Systems (GIS) support better spatial management and efficient irrigation scheduling [90,97]. Yet, Local stewardship and collective land management, the governance forms favoured by agroecology, are rarely included. This implies that digital transparency does not equal democratic governance. A4.0 makes land visible but not necessarily justly governed.

Taken together, these observations reveal that A4.0 integrates many agroecological principles in a functional sense but rarely in a systemic one. The digital transition has demonstrated that sustainability can be optimised, but optimisation is not transformation. This implies that agroecology within a technological framework risk becoming a managerial paradigm rather than a socio-ecological one. A4.0 therefore stands as an intermediary phase, a powerful but incomplete translation of agroecology into digital language. Its gaps define the agenda for Agriculture 5.0, which must reintroduce human agency, ecological reciprocity, and social inclusion into the algorithmic heart of modern farming.

### 3.3. Agroecological principles galvanising the transition

Agriculture 5.0 emerges as a corrective evolution to the technocentric model of Agriculture 4.0 by integrating human intelligence, social inclusion, and ecological reciprocity into digital systems, extending agroecological principles from efficiency toward regeneration and equity [27,28,111]. This suggests a shift from precision as an end in itself to purpose, technology serving restoration and co-creation within complex agroecosystems [84,109].

#### 3.3.1. Next-gen resource efficiency

Recycling expands beyond energy and data loops to biological, economic, and social cycles, for instance, digital twins and blockchain trace input streams and waste flows, enabling closed-loop nutrient and water management and circular bio-economy interventions [84,86,110, 111]. Energy harvesting and urban AgTech combine with AI/IoT to support resource recovery at farm and city scales [93,112]. This suggests digital oversight becomes ecological accountability when it is applied to material renewal rather than throughput metrics [7,83].

Input reduction, already animated in A4.0, progresses from engineering optimisation to context-aware, human-centred decision systems. AI-guided irrigation, fertilisation, and plant protection operate under environmental thresholds and long-term sustainability goals, with robots adapting in real time to field feedback via 5G/6 G connectivity and remote sensing [28,84,111]. Model Predictive Control (hereafter MPC) integrated with AI/IoT/UAVs strengthens site-specific scheduling to reduce inputs while maintaining yields [113]. This implies A5.0 reframes automation as collaboration, humans set guardrails; machines execute with sensitivity to ecological thresholds [27,114].

#### 3.3.2. Strengthened resilience

Soil health becomes a living infrastructure priority. New quantum/bio-sensing and proximal-remote data streams resolve microbial activity, nutrient cycling, and soil carbon dynamics to guide restorative practices such as cover cropping, reduced tillage, and organic amendments [97,111]. AI models translate these diagnostics into adaptive plans that co-optimise carbon retention, fertility, and productivity [115]. This indicates monitoring is no longer a substitute for life but a scaffold for regeneration, data supports biology rather than overriding it [29,116].

Biodiversity moves from externality to design variable. Robotics and sensor networks identify and preserve non-target flora/fauna, while diversified rotations and intercropping are optimised via predictive models to sustain pollinators and natural enemies, which was already seen in A4.0 [88,99,111]. However, A5.0 adds digital twins that simulate ecosystem services and quantify payoffs from habitat mosaics and reduced pesticide dependence [84]. This proposes biodiversity to become a computational asset for resilience and yield stability rather than a constraint on efficiency.

Synergies extend beyond device interoperability toward ecological co-functionality. Multi-intelligence systems (AI, IoT, robotics, blockchain) interact with human expertise and environmental feedback to coordinate water-nutrient-pest decisions as an integrated whole [27, 96]. Cobots adjust operations based on human cues and sensor signals, embodying mixed human-machine synergies in the field [27]. MPC and
UAV/AI swarms add anticipatory control, reducing lags and losses across operations [103,113]. This suggests synergy is reinterpreted as emergent behaviour among ecological processes and layered in-telligences, not just a network configuration.

Animal health acquires ethical and ecological depth. Precision wel-fare monitoring through wearables, UAV thermal imaging, and behav-ioural analytics reduces mortality and enhances well-being, while aligning grazing and housing with biodiversity and pasture regeneration goals [27,103]. Big-data emissions accounting integrates livestock into climate strategies without sacrificing welfare [106]. This may transform animals to become indicators and agents of landscape health, not just production units.

Economic diversification expands horizontally and vertically. New markets arise in robotics, data services, renewable energy, and envi-ronmental monitoring, while data cooperatives and circular bio-economies diversify farm income [106,114]. Lower labour and mate-rial costs, more productivity for the same amount of labour, and improved environmental sustainability are just a few of the many pos-itive outcomes of technological adoption on farms [115,117]. In addi-tion, integrating smallholder farmers into innovative trends contributes to keeping young people in the countryside with rural tourism and social entrepreneurship activities, diversifying household income [118].

### 3.3.3. Socially better but not perfect

Co-creation and sharing of knowledge evolve into reciprocal learning ecosystems. Digital extension and open platforms are co-developed with farmers, feeding experiential data back into models and tailoring advi-sories to local contexts [28,106]. Citizen-science, training, and com-munity innovation hubs strengthen data literacy and sovereignty to prevent one-way, top-down dissemination [93,109]. However, what's lacking is supposedly a knowledge which is no longer merely distrib-uted, but co-governed, with algorithms learning from farmer practice as much as farmers learn from analytics.

Fairness is pursued through structural inclusion. Agriculture 5.0 strategies prioritise affordability and smallholder access via open-source models, subsidised shared infrastructure, and cooperative/cloud ser-vices [84,111]. Job creation and skills development through cobots and new service models address unequal benefits observed under 4.0 [27, 104]. Data ownership and bias auditing are treated as fairness levers, not afterthoughts [106,110]. This suggests equity becomes a design parameter embedded in governance, addressing the digital divide that entrenched inequality in A4.0.

Participation deepens from training to co-governance, multi-actor coalitions (farmers, researchers, policymakers, civil society, and in-dustry) co-design platforms, hardware, and rules, aligning algorithmic priorities with local sustainability goals [27,111]. Participatory MPC deployments and blockchain pilots show the value of shared oversight for adoption and trust [110,113]. This hints participation in smart agriculture remains primarily instrumental, focusing on user adoption, data sharing, and collaborative implementation rather than co-authorship in the design of socio-technical systems.

Connectivity becomes both infrastructural and relational. 6G-ready IoT, edge computing, and satellite links extend real-time awareness to remote areas, while traceability builds consumer trust and aligns in-centives with ecological performance [7,83,84,102]. Urban-rural ties and local food webs are strengthened through vertically integrated, renewable-powered systems that shorten supply chains [112]. This may reflect that connectivity now rebuilds community and ecological links, not just device networks (see e.g. [83]).

Land and natural resource governance advances via transparent monitoring plus participatory oversight. Remote sensing with AI de-livers early warnings on soil and water stress [97], while blockchain smart contracts improve tenure security and compliance across supply chains [110]. Community-level stewardship is supported by open data and shared analytics that redistribute environmental agency [109,114].

Social values and diets, long neglected, are pulled into scope by

transparent supply chains and urban/vertical farming that reduce car-bon footprint and reconnect consumers to agricultural products [102, 112]. Smart-city agro-systems, powered by renewables and informed by AI/IoT, embed nutrition, culture, and equity into digital food systems [109,112]. This hints that the farm becomes the starting point of food citizenship: production, distribution, and consumption are treated as one agroecological metabolism. Social values shift from peripheral compliance to central design constraints. Human-centred AI from the Industry 5.0 paradigm emphasises augmentation over automation, integrating ethical reasoning, safety, and dignity into deployment [28]. Social metrics such as rural employment, occupational health, mean-ingful work, enter sustainability dashboards alongside biophysical in-dicators [29,106]. So, modernisation is being re-aligned with its cultural roots: sustaining people is inseparable from sustaining production.

Agriculture 5.0 builds on the digital tools of previous farming sys-tems but aims to create a closer balance between humans, technology, and the environment. It promotes ideas of fairness, participation, co-creation of knowledge and land governance (Fig. 8). However, while these developments represent progress toward more sustainable and inclusive farming, they do not yet amount to a full agroecological transformation. The focus often remains on technological improvement rather than on fundamentally reshaping the social and ecological re-lationships that define agriculture.

## 4. Discussion

The transition from Agriculture 4.0 to Agriculture 5.0 has undeniably advanced the technological frontier of agri-food systems (Fig. 9), espe-cially in terms of the approach towards the technologies present in modern day agriculture. However, these systems continue to under-perform in achieving a holistic agroecological transition (Fig. 8), as their architectures and policy frameworks remain primarily optimised for technical efficiency.

### 4.1. What does agroecology lack?

It is evident from the results obtained that the traditional agroeco-logical principles (e.g., those synthesised by the HLPE) are neither totally fulfilled nor cover the cognitive and affective complexities introduced by such socio-technical systems. Two emergent dimensions deserve explicit recognition: *cognitive symbiosis* (the co-governance of human and artificial decision-agents in agroecosystems) and *emotional ecology* (the formal acknowledgement of affective, relational and meaning-laden variables in farming systems). The literature reveals clear gaps in current frameworks and indicates that these dimensions are essential for ensuring that Agriculture 5.0 remains consistent with the normative ends of agroecology, namely ecological stewardship, farmer autonomy, justice and socio-ecological resilience [119].

#### 4.1.1. Cognitive symbiosis

Despite their emphasis on resilience and interoperability shown above, some A5.0 systems are still developed under data-centric (i.e. data becomes the main driver of decision-making) approach inherited from A4.0 [70,71]70. So systems are technology-driven rather than farmer-oriented, meaning that farmers must adapt to digital tools rather than those tools being tailored to the farmer's tacit, local knowledge and context [120]. This asymmetry may be described as the “cognitive gap” of digital agriculture, where intelligence is distributed but understand-ing is not shared [121,122]. Cognitive symbiosis requires that decision systems not only process environmental data but also enable participa-tory co-learning, embedding local ecological knowledge, sensory expe-rience, and adaptive reasoning into algorithmic feedback loops [123–125] show in their review that although human-robot synergy can deliver performance gains, major challenges remain around trans-parency, shared mental models, situational awareness and trust in the human-machine system. Moreover, research on cognitive factors
<figure><img src="image_11.png" /><figcaption>Fig. 8. A radar chart analysis for the alignment of agriculture 4.0 and 5.0 with the agroecological principles of the HLPE [41].</figcaption></figure>

affecting adoption of autonomous agricultural technologies emphasises the risks of reduced farmer situational awareness, loss of tacit knowledge and increased cognitive load when autonomy increases [126]. These findings show that adding new technologies to agroecological systems without clear governance of how humans and machines share decision-making can weaken farmers' control, reduce their ecological awareness, and create new system risks. The literature on AI in agriculture further identifies that trust, explainability, accountability and alignment with farmer values are prerequisites for meaningful adoption [127,128]. The absence of a principle that explicitly places the human-machine decision ecology at the core of agroecological transition therefore constitutes a conceptual blind spot, and without it, the high-tech transition may inadvertently replicate extractive, black-box, centralised decision-making rather than decentralised, context-sensitive, synergetic agroecological governance.

Departing from this logic, and based on the results obtained, a new principle might be introduced to guide better through the agroecological transition, the principle of “cognitive symbiosis” which can be defined as “*design agricultural intelligence as a co-evolving partnership between human and machine perception, where learning itself becomes an ecological process.*” In Agriculture 5.0 and later evolutions, humans and machines should form a mutually adaptive learning ecosystem in which each enhances, and not replaces, the other’s modes of perception, interpretation, and decision. The farm becomes a cognitive common where biological and artificial intelligence co-evolve.

### 4.1.2. Emotional ecology

Second, turning to the emotional dimension, the literature increasingly shows that farming practices are embedded not only in material and ecological conditions but also in emotions, identity, place-attachment, and relational values [129–131]. Studies of farmer well-being and emotional regulation in sustainability-oriented farming show that emotions such as sense of meaning, pride, frustration, guilt and hope shape adaptive practices and resilience [132,133]. Moreover, research on sustainable economic behaviour reveals that mindfulness, the management of emotional labour, and mental health mediate the link between farmer cognition and sustainable action [134,135]. Yet, agroecological frameworks rarely articulate emotional variables as system-level levers or design parameters [136]. In the context of Agriculture 5.0, when technologies alter not only labour patterns but also social relations, meanings and affective bonds with land and

community, emotional disruption becomes a risk: for example, automation might undermine meaningful work, reduce sense of place, erode intergenerational ties or create anxiety about displacement. By neglecting emotional ecology, transitions may fail not due to technological deficiency but because of relational dis-engagement or emotional misalignment, thereby undermining agroecological goals of care, resilience and justice.

Therefore, emotional ecology can be considered as an agroecological principle to better cover all dimensions in the current transition. This principle might be defined as “*Acknowledge, understand, and integrate the emotional, psychological, and affective relationships between humans and the living ecosystem they steward.*” By explicitly integrating the emotional and relational factors, emotional ecology might support more effective, socially grounded, and sustainable transitions in agroecological systems.

In sum, without formalising how human and machine cognitive agents co-operate and how affective dimensions shape practice, agroecological transitions may achieve technological gains at the expense of autonomy, resilience and equity. Recognising these two dimensions ensures that high-tech farming does not merely optimise resource use but sustains agroecology’s deeper commitments, to ecological integrity, democratic knowledge, relational justice and human flourishing.

### 4.2. Policies hindering agriculture 5.0

The policy framing modern agriculture remains poorly aligned with the institutional, epistemic and distributive requirements of an agroecological transition augmented by Agriculture 5.0 technologies [137,138]. Public finance and subsidy systems continue to stabilise high-input, large-scale production models, creating strong economic lock-ins that raise the transaction costs and perceived risk of switching to diversified, ecological farming even when digital tools could technically support diversification [139,140]. Policy inertia in subsidy and support instruments therefore both preserves the structural advantages of incumbents and channels investments and digitalisation toward scale-intensive pathways rather than enabling smallholder-friendly, place-sensitive agroecological designs [10,140].

Regulatory and governance fragmentation is a second, evidence-based barrier. Agroecological change requires multi-scale, cross-sectoral governance that integrates environmental, social and technological objectives; existing policies remain siloed, agriculture, trade, environment and digital/telecommunications are regulated in separate logics,
<figure><img src="image_12.png" /></figure>

*THE AGROECOLOGICAL PRINCIPLES ARE POSITIONED APPROXIMATELY WITH RESPECT TO THE AXIS, BASED ON THE RESULTS OF THE LITERATURE ANALYSIS.

SOURCE: HLPE (2019), ILLUSTRATION AND CONCEPTUALIZATION ARE DONE BY THE AUTHOR (M. NAIM, 2025).

<caption>**Fig. 9.** This diagram outlines the agroecological transition from Agriculture 4.0 to 5.0, according to the definitions of ([41], page 41, Table 1) and the analysed literature.</caption>

producing incoherence in incentives and rules [141–145]. Where digital governance is weak or misaligned, emergent Agriculture 5.0 components (data platforms, algorithms, autonomous machines) are governed by disparate or voluntaristic standards, undermining accountability, farmer agency and the capacity of public policy to steer technology toward sustainable outcomes [146,147].

Third, digital/data policy deficits, unclear data ownership, opacity of proprietary algorithms, uneven infrastructure and low digital literacy, generate distributional risks that can entrench inequality rather than democratise innovation [122,147]. Research documents how current data governance arrangements concentrate power with technology providers, reduce farmers' bargaining power, and risk algorithmic bias that privileges high-volume, industrial datasets over heterogeneous agroecological contexts [121,146,147]. Without enforceable frameworks ensuring data portability, algorithmic transparency, and equitable access to digital infrastructure, the transition toward Agriculture 5.0 may intensify data concentration and corporate consolidation, thereby constraining diverse agroecological and smallholder-led innovation pathways [3].

Finally, market and trade policies, certification costs, and weak local value-chain support limit the economic viability of agroecological products, even where digital platforms could facilitate market linkages [148–151]. Research reveals that value-chain and trade arrangements

continue to favour commodity throughput, depressing prices for diversified, ecological outputs and discouraging producers from bearing the short-term costs of transition [152,153]. Consequently, policy reform that would align procurement, public purchasing, and market-building with agroecological outputs is necessary to pair technological possibility with economic sustainability [154].

### 4.3. Limitations of the study and future research

The major limitation lies in agroecology itself, since it appears to be still a highly polysemic term with different interpretations, especially in the context of ecological and technological intensification [155], which may even reflect power unbalance and misuse of the terms. On one hand, these differences led to different implementations across studies, which were difficult to consider. On the other hand, the literature sample may have been biased by an overemphasis on agroecology and sustainability, similar to the referencing of climate change for example even in research where it is not a central analytical focus. Second, although paywall restrictions reduced the available literature body, the overall limited number of documents addressing the agricultural revolutions (4.0 and 5.0) and agroecology also reflects the nascent nature of this field. The selected database and screening criteria may have excluded some relevant studies. For example, the choice of a databases
(i.e. Scopus, CABI) can have inherently biased the results, as some relevant studies may not be indexed in them. However, other databases are either more restricted (e.g. Web of Science), unstructured (e.g. Google Scholar), or non-relevant for the subject (e.g. PubMed) [60]. Concerning the screening, the first phase based on keywords and abstracts may have overlooked pertinent research due to misclassification or lack of detail in the text. Finally, the rapid pace of technological advances in related technologies creates a risk of obsolescence of the studies reviewed, although the review criteria remain valid. Another limitation is the analysis bias, especially that this research highly depends on the personal knowledge of the authors, and interpretation of principles and keywords may differ between researchers, for instance, “human centred” may be interpreted differently and it may be socially seen differently between societies. Finally, a conceptual limitation related to the nature of the study is inevitable, agricultural revolutions encompass even lower scale of applications than those explicitly mentioned by HLPE, a revolution can reach the cellular/molecular scale of application (e.g. genetic engineering, biotechnology, etc.) which isn’t directly tackled.

Future research should systematically investigate how emerging agricultural technologies translate agroecological principles into practice across different scales of application and operation (see e.g. [60]). It would be valuable to explore how the functional logic of each technological domain, such as sensing, automation, or data analytics embeds, supports, or potentially conflicts with ecology or agroecology (see e.g. [156]). Further studies should also assess farmers’ perceptions, acceptance, and trust toward these technologies, identifying the social, economic, and cognitive determinants that influence their adoption and sustained use. Understanding these human-technology interactions will be essential for ensuring that the digital transition in agriculture remains aligned with agroecological objectives and is both socially legitimate and technically resilient. Additionally, research should examine the business models underpinning these technologies and conduct market studies to understand how they are introduced, positioned, and scaled within the agricultural market and systems, especially those with a diverse market (e.g. agricultural robots [157]). Indeed, there’s already ongoing research and increasing interest on this subject (e.g. [158]).

## 5. Conclusion

The transition from Agriculture 4.0 to Agriculture 5.0 marks a pivotal reorientation in how technology, ecology, and society interact within modern agri-food systems. While Agriculture 5.0 introduces distributed intelligence, digital twins, and collaborative robotics aligning more with agroecological principles, it was shown that it remains an incomplete agroecological transformation. The human dimension of agroecology, embodying participation, ethics, emotion, and meaning, has not yet been fully integrated into the technological framework. This study demonstrates that progress toward regenerative and equitable food systems depends not only on innovation but also on rethinking how intelligence, agency, and responsibility are shared between humans and machines. To guide this evolution, two new principles are proposed: *cognitive symbiosis*, emphasising co-learning between human and artificial agents, and *emotional ecology*, recognising affective and relational bonds as functional components of sustainable systems. In final words, achieving a true agroecological revolution requires as well coordinated policy reforms, inclusive governance, and ethical oversight to ensure that digitalisation serves people and ecosystems rather than abstract productivity. Only by embedding human and ecological values at the core of its technological design can future agricultural systems move from optimisation to genuine transformation.

## Ethical statement

This study does not involve any experiments on humans or animals, and no ethical approval was required. Therefore, an ethical statement is

not applicable.

## Consent to participate

Not Applicable.

## Consent for publication

Not Applicable.

## CRediT authorship contribution statement

M. Naim: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. A. Boukhizzou: Investigation, Formal analysis, Data curation.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements / Funding

M. Naim was financed in the framework of NINSAR project by ANR under France 2030 (grant n. ANR-22-PEAE-0007). The authors applied a CC-BY public copyright license to the present document and will apply to all subsequent versions up to the author accepted manuscript arising from this submission, under the grant’s open access conditions. The authors used generative AI tools to assist in grammar correction, clarity improvement, and refinement of language throughout this manuscript. All content was reviewed and validated by the authors to ensure accuracy, originality, and adherence to ethical standards.

## Data availability

Data will be made available on request.

## References

[1] M. Herrero, P.K. Thornton, D. Mason-D’Croz, J. Palmer, T.G. Benton, B. L. Bodirsky, J.R. Bogard, A. Hall, B. Lee, K. Nyborg, P. Pradhan, G.D. Bonnett, B. A. Bryan, B.M. Campbell, S. Christensen, M. Clark, M.T. Cook, I.J.M. De Boer, C. Downs, K. Dizyee, C. Folberth, C.M. Godde, J.S. Gerber, M. Grundy, P. Havlik, A. Jarvis, R. King, A.M. Loboguerrero, M.A. Lopes, C.L. McIntyre, R. Naylor, J. Navarro, M. Obersteiner, A. Parodi, M.B. Peoples, I. Pikaar, A. Popp, J. Rockström, M.J. Robertson, P. Smith, E. Stehfest, S.M. Swain, H. Valin, M. Van Wijk, H.H.E. Van Zanten, S. Vermeulen, J. Vervoort, P.C. West, Innovation can accelerate the transition towards a sustainable food system, Nat. Food 1 (2020) 266–272, https://doi.org/10.1038/s43016-020-0074-1.

[2] X. Jia, Agro-food innovation and sustainability transition: a conceptual synthesis, Sustainability 13 (2021) 6897, https://doi.org/10.3390/su13126897.

[3] R. Finger, Digital innovations for sustainable and resilient agricultural systems, Eur. Rev. Agric. Econ. 50 (2023) 1277–1309, https://doi.org/10.1093/erae/jbad021.

[4] J.A. Foley, N. Ramankutty, K.A. Brauman, E.S. Cassidy, J.S. Gerber, M. Johnston, N.D. Mueller, C. O’Connell, D.K. Ray, P.C. West, C. Balzer, E.M. Bennett, S. R. Carpenter, J. Hill, C. Monfreda, S. Polasky, J. Rockström, J. Sheehan, S. Siebert, D. Tilman, D.P.M. Zaks, Solutions for a cultivated planet, Nature 478 (2011) 337–342, https://doi.org/10.1038/nature10452.

[5] A. Hassoun, F. Boukid, F. Ozogul, Editorial: food sustainability and Food Industry 4.0: unveiling the relationship, Front. Sustain. Food Syst. 8 (2024) 1440023, https://doi.org/10.3389/fsufs.2024.1440023.

[6] J. Rockström, W. Steffen, K. Noone, Å. Persson, F.S. Chapin, E.F. Lambin, T. M. Lenton, M. Scheffer, C. Folke, H.J. Schellnhuber, B. Nykvist, C.A. De Wit, T. Hughes, S. Van Der Leeuw, H. Rodhe, S. Sörlin, P.K. Snyder, R. Costanza, U. Svedin, M. Falkenmark, L. Karlberg, R.W. Corell, V.J. Fabry, J. Hansen, B. Walker, D. Liverman, K. Richardson, P. Crutzen, J.A. Foley, A safe operating space for humanity, Nature 461 (2009) 472–475, https://doi.org/10.1038/461472a.
[7] J. Xu, Y. Li, M. Zhang, S. Zhang, Sustainable agriculture in the digital era: past, present, and future trends by bibliometric analysis, Heliyon 10 (2024) e34612, https://doi.org/10.1016/j.heliyon.2024.03.4612.

[8] M. Crippa, E. Solazzo, D. Guizzardi, F. Monforti-Ferrario, F.N. Tubiello, A. Leip, Food systems are responsible for a third of global anthropogenic GHG emissions, Nat. Food 2 (2021) 198-209, https://doi.org/10.1038/s43016-021-00225-9.

[9] Intergovernmental Panel on Climate Change (Ipcc), Climate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel On Climate Change, 1st ed, ed, Cambridge University Press, 2023, https://doi.org/10.1017/9781009157926.

[10] A. Wezel, B.G. Herren, R.B. Kerr, E. Barrios, A.L.R. Gonçalves, F. Sinclair, Agroecological principles and elements and their implications for transitioning to sustainable food systems. A review, Agron. Sustain. Dev. 40 (2020) 40, https://doi.org/10.1007/s13593-020-00646-z.

[11] FAO, Revealing the True Cost of Food to Transform Agrifood Systems, ed., Food and Agriculture Organization of the United Nations, Rome, 2023.

[12] Food Security and nutrition: Building a Global Narrative Towards 2030, HLPE, 2020. https://openknowledge.fao.org/server/api/core/bitstreams/2a2bdf7d-596e485c-9521-f4227db5c6aa/content (accessed 4 January 2026).

[13] United Nations Environment Programme, Making Peace with Nature: A Scientific Blueprint to Tackle the Climate, Biodiversity and Pollution Emergencies, United Nations, Erscheinungsort nicht Ermittelbar, 2021.

[14] W. Willett, J. Rockström, B. Loken, M. Springmann, T. Lang, S. Vermeulen, T. Garnett, D. Tilman, F. DeClerck, A. Wood, M. Jonell, M. Clark, L.J. Gordon, J. Fanzo, C. Hawkes, R. Zurayk, J.A. Rivera, W. De Vries, L. Majege Sibanda, A. Afshin, A. Chaudhary, M. Herrero, R. Agustina, F. Branca, A. Lartey, S. Fan, B. Crona, E. Fox, V. Bignet, M. Troell, T. Lindahl, S. Singh, S.E. Cornell, K. Sinath Reddy, S. Narain, S. Nishtar, C.J.L. Murray, Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems, The Lancet 393 (2019) 447-492, https://doi.org/10.1016/S0140-6736(18)31788-4.

[15] J. Rockström, O. Edenhofer, J. Gaertner, F. DeClerck, Planet-proofing the global food system, Nat. Food 1 (2020) 3-5, https://doi.org/10.1038/s43016-019-0010-4.

[16] C. Eastwood, L. Klerkx, M. Ayre, B.D. Rue, Managing socio-ethical challenges in the development of smart farming: from a fragmented to a comprehensive approach for responsible research and innovation, J. Agric. Environ. Ethics 32 (2019) 741-768, https://doi.org/10.1007/s10806-017-9704-5.

[17] S. Gupta, W. Hasan, S. Singh, D. Kumar, M.J. Ansari, S. Nisar, Agriculture 4.0: Smart Farming With IoT and Artificial Intelligence, CRC Press, London, 2024, https://doi.org/10.1201/9781003570219, 1st ed.

[18] D.C. Rose, R. Wheeler, M. Winter, M. Lobley, C.-A. Chivers, Agriculture 4.0: making it work for people, production, and the planet, Land Use Policy 100 (2021) 104933, https://doi.org/10.1016/j.landusepol.2020.104933.

[19] D. Rose, J. Chilvers, Agriculture 4.0: broadening responsible innovation in an era of smart farming, Front. Sustain. Food Syst. 2 (2018) 87, https://doi.org/10.3389/fsufs.2018.00087.

[20] S. Wolfert, L. Ge, C. Verdouw, M.-J. Bogaardt, Big Data in Smart Farming - A review, Agric. Syst. 153 (2017) 69-80, https://doi.org/10.1016/j.agsy.2017.01.023.

[21] K. Bronson, Looking through a responsible innovation lens at uneven engagements with digital farming, NJAS Wageningen. J. Life Sci. 90-91 (2019) 1-6, https://doi.org/10.1016/j.njas.2019.03.001.

[22] L. Klerkx, E. Jakku, P. Labarthe, A review of social science on digital agriculture, smart farming and agriculture 4.0: new contributions and a future research agenda, NJAS Wageningen. J. Life Sci. 90-91 (2019) 1-16, https://doi.org/10.1016/j.njas.2019.100315.

[23] S. Rotz, E. Gravely, I. Mosby, E. Duncan, E. Finnis, M. Horgan, J. LeBlanc, R. Martin, H.T. Neufeld, A. Nixon, L. Pant, V. Shalla, E. Fraser, Automated pastures and the digital divide: how agricultural technologies are shaping labour and rural communities, J. Rural Stud. 68 (2019) 112-122, https://doi.org/10.1016/j.jrurstud.2019.01.023.

[24] A.M. Dumont, G. Vanloqueren, P.M. Stassart, P.V. Baret, Clarifying the socioeconomic dimensions of agroecology: between principles and practices, Agroecol. Sustain. Food Syst. 40 (2016) 24-47, https://doi.org/10.1080/21683565.2015.1089967.

[25] A.M. Dumont, A.C. Wartenberg, P.V. Baret, Bridging the gap between the agroecological ideal and its implementation into practice. A review, Agron. Sustain. Dev. 41 (2021) 32, https://doi.org/10.1007/s13593-021-00666-3.

[26] M.P. Pimbert, Food sovereignty, Agroecology and Biocultural diversity: Constructing and Contesting Knowledge, ed., Routledge, Taylor & Francis Group, London ; New York, 2018.

[27] K.D. Bissadu, S. Sonko, G. Hossain, Society 5.0 enabled agriculture: drivers, enabling technologies, architectures, opportunities, and challenges, Inf. Process. Agric. 12 (2025) 112-124, https://doi.org/10.1016/j.inpa.2024.04.003.

[28] A. Holzinger, I. Fister, I. Fister, H.-P. Kaul, S. Asseng, Human-centered AI in smart farming: toward agriculture 5.0, IEEE Access 12 (2024) 62199-62214, https://doi.org/10.1109/ACCESS.2024.3395532.

[29] K. Ragazou, A. Garefalakis, E. Zafeiriou, I. Passas, Agriculture 5.0: a new strategic management mode for a cut cost and an energy efficient Agriculture sector, Energies 15 (2022), https://doi.org/10.3390/en15093113.

[30] M. Munawar, T.M. Singh, S. Fatima, R.M. Mohana, A. Nag, Agriculture 6.0: advancing intelligent and sustainable Agriculture through the convergence of IoT and machine learning, Eds, in: K.K. Reddy, C.B. Saju, A. Hulus, T. Sibalija. H. Nakouri (Eds.), Adv. Comput. Intell. Robot., IGI Global Scientific Publishing, 2025, pp. 225-256, https://doi.org/10.4018/979-8-3373-4207-8.ch009.

[31] M.F. Neves, B.P. Casagrande, V. Cambaúva, G.D.O. Teixeira, P.J.F. Toledo, Agriculture 6.0: a new proposal for the future of agribusiness, Rev. Gest. Soc. E Ambient. 17 (2023) e04004, https://doi.org/10.24857/rgsa.v17n9-021.

[32] Y.H. Patil, R.Y. Patil, O. Ozer, Next-gen Agriculture 6.0: a smart solution pioneering the future of farming. Evol. Adv. Comput. Ind. 60 Technol. Pract. Chall., CRC Press, Boca Raton, 2024, https://doi.org/10.1201/9781003593943, 1st ed.

[33] E. Sachet, O. Mertz, J.-F. Le Coq, G.S. Cruz-Garcia, W. Francesconi, M. Bonin, M. Quintero, Agroecological Transitions: a systematic review of research approaches and prospects for participatory action methods, Front. Sustain. Food Syst. 5 (2021) 709401, https://doi.org/10.3389/fsufs.2021.709401.

[34] G.S. De Groot, M.A. Aizen, A. Sáez, C.L. Morales, Large-scale monoculture reduces honey yield: the case of soybean expansion in Argentina, Agric. Ecosyst. Environ. 306 (2021) 107203, https://doi.org/10.1016/j.agee.2020.107203.

[35] H. Wu, W. Yan, H. Wu, J. Zhang, Z. Zhang, Z. Zhang, C. Rensing, W. Lin, Consecutive monoculture regimes differently affected the diversity of the rhizosphere soil viral community and accumulated soil-borne plant viruses, Agric. Ecosyst. Environ. 337 (2022) 108076, https://doi.org/10.1016/j.agee.2022.108076.

[36] M.A. Altieri, C.I. Nicholls, Agroecology: a brief account of its origins and currents of thought in Latin America, Agroecol. Sustain. Food Syst. 41 (2017) 231-237, https://doi.org/10.1080/21683565.2017.1287147.

[37] C. Francis, G. Lieblein, S. Gliessman, T.A. Brelan, N. Creamer, R. Harwood, L. Salomonsson, J. Helenius, D. Rickerl, R. Salvador, M. Wiedenhoeft, S. Simmons, P. Allen, M. Altieri, C. Flora, R. Poincelot, Agroecology: the ecology of food systems 10, J. Sustain. Agric. 22 (2003) 99-118, https://doi.org/10.1300/J064v22n03.10.

[38] S. Gliessman, Defining agroecology, Agroecol. Sustain. Food Syst. 42 (2018) 599-600, https://doi.org/10.1080/21683565.2018.1432329.

[39] A. Wezel, S. Bellon, T. Doré, C. Francis, D. Vallod, C. David, Agroecology as a science, a movement and a practice. A review, Agron. Sustain. Dev. 29 (2009) 503-515, https://doi.org/10.1051/agro/2009004.

[40] The 10 Elements of Agroecology, FAO, 2018.

[41] Agroecological and Other Innovative Approaches For Sustainable Agriculture and Food Systems That Enhance Food Security and Nutrition, HLPE, 2019. https://openknowledge.fao.org/server/api/core/bitstreams/ff385e60-0693-40fe-9a6b-79bbef05202c/content (accessed 4 January 2026).

[42] S.R. Gliessman, Agroecology: the Ecology of Sustainable Food Systems, CRC press, Boca Raton (Fla.), 2007, 2nd ed.

[43] S.R. Gliessman, Agroecology: The Ecology of Sustainable Food Systems, CRC Press, 2014, https://doi.org/10.1201/b17881. Third Edition, 0 ed.

[44] S. Gliessman, Transforming food systems with agroecology, Agroecol. Sustain. Food Syst. 40 (2016) 187-189, https://doi.org/10.1080/21683565.2015.1130765.

[45] H. Blanco-Canqui, T.M. Shaver, J.L. Lindquist, C.A. Shapiro, R.W. Elmore, C. A. Francis, G.W. Hergert, Cover crops and ecosystem services: insights from studies in temperate soils, Agron. J. 107 (2015) 2449-2474, https://doi.org/10.2134/agronj15.0086.

[46] R.W. Brooker, A.E. Bennett, W. Cong, T.J. Daniell, T.S. George, P.D. Hallett, C. Hawes, P.P.M. Iannetta, H.G. Jones, A.J. Karley, L. Li, B.M. McKenzie, R. J. Pakeman, E. Paterson, C. Schób, J. Shen, G. Squire, C.A. Watson, C. Zhang, F. Zhang, J. Zhang, P.J. White, Improving intercropping: a synthesis of research in agronomy, plant physiology and ecology, New Phytol 206 (2015) 107-117, https://doi.org/10.1111/nph.13132.

[47] A.S. Davis, J.D. Hill, C.A. Chase, A.M. Johanns, M. Liebman, Increasing cropping system diversity balances productivity, profitability and environmental health, PLoS ONE 7 (2012) e47149, https://doi.org/10.1371/journal.pone.0047149.

[48] A.J. Franzluebbers, L.K. Paine, J.R. Winsten, M. Krome, M.A. Sanderson, K. Ogles, D. Thompson, Well-managed grazing systems: a forgotten hero of conservation, J. Soil Water Conserv. 67 (2012) 3, https://doi.org/10.2489/jswc.67.4.100A.

[49] S. Jose, Agroforestry for ecosystem services and environmental benefits: an overview, Agrofor. Syst. 76 (2009) 1-10, https://doi.org/10.1007/s10457-009-9229-7.

[50] C. Kremen, A. Iles, C. Bacon, Diversified Farming Systems: an agroecological, systems-based alternative to modern Industrial agriculture, Ecol. Soc. 17 (2012) art44, https://doi.org/10.5751/ES-0513-170444.

[51] G. Tamburini, R. Bommarco, T.C. Wanger, C. Kremen, M.G.A. Van Der Heijden, M. Liebman, S. Hallin, Agricultural diversification promotes multiple ecosystem services without compromising yield, Sci. Adv. 6 (2020) eaba1715, https://doi.org/10.1126/sciadv.aba1715.

[52] F. Ewert, R. Baatz, R. Finger, Agroecology for a sustainable agriculture and food system: from local solutions to large-scale adoption, Annu. Rev. Resour. Econ. 15 (2023) 351-381, https://doi.org/10.1146/annurev-resource-102422-090105.

[53] D. Uztürk, G. Büyüközkan, Industry 4.0 technologies in Smart Agriculture: a review and a technology assessment model proposition, Technol. Forecast. Soc. Change 208 (2024) 123640, https://doi.org/10.1016/j.techfore.2024.123640.

[54] A. Tóth, L. Nagy, R. Kennedy, B. Bohus, J. Abonyi, T. Ruppert, The human-centric industry 5.0 collaboration architecture, MethodsX 11 (2023) 102260, https://doi.org/10.1016/j.mex.2023.102260.

[55] A. Hassoun, A. Aït-Kaddour, A.M. Abu-Mahfouz, N.B. Rathod, F. Bader, F. J. Barba, A. Biancolillo, J. Cropotova, C.M. Galanakis, A.R. Jambrak, J. M. Lorenzo, I. Måge, F. Ozogul, J. Regenstein, The fourth industrial revolution in the food industry—part I: industry 4.0 technologies, Crit. Rev. Food Sci. Nutr. 63 (2023) 6547-6563, https://doi.org/10.1080/10408398.2022.2034735.
[56] G. Narkhede, B. Pasi, N. Rajhans, A. Kulkarni, Industry 5.0 and the future of sustainable manufacturing: a systematic literature review, Bus. Strategy Dev. 6 (2023) 704–723, https://doi.org/10.1002/bsd2.272.

[57] D. Haloui, K. Oufaska, M. Oudani, K. El Yassini, Bridging industry 5.0 and Agriculture 5.0: historical perspectives, opportunities, and future perspectives, Sustainability 16 (2024) 3507, https://doi.org/10.3390/su16093507.

[58] A. Hassoun, S. Jagtap, H. Trollman, G. Garcia-Garcia, L.N.K. Duong, P. Saxena, Y. Bouzembrak, H. Treiblmaier, C. Para-López, C. Carmona-Torres, K. Dev, D. Mhlanga, A. Aït-Kaddour, From Food Industry 4.0 to Food Industry 5.0: identifying technological enablers and potential future applications in the food sector, Compr. Rev. Food Sci. Food Saf. 23 (2024) e370040, https://doi.org/10.1111/1541-4337.70040.

[59] S. Fountas, B. Espejo-García, A. Kasimati, M. Gemtou, H. Panoutsopoulos, E. Anastasiou, Agriculture 5.0: cutting-edge technologies, trends, and challenges, IT Prof. 26 (2024) 40–47, https://doi.org/10.1109/MITP.2024.3358972.

[60] M. Naim, D. Rizzo, L. Sauvée, M. Medici, Advancing agroecology and sustainability with agricultural robots at field level: a scoping review, Comput. Electron. Agric. 237 (2025) 110650, https://doi.org/10.1016/j.compag.2025.110650.

[61] H. Arksey, L. O'Malley, Scoping studies: towards a methodological framework, Int. J. Soc. Res. Methodol. 8 (2005) 19–32, https://doi.org/10.1080/136455703200011961.

[62] J. McGowan, S. Straus, D. Moher, E.V. Langlois, K.K. O'Brien, T. Horsley, A. Aldcroft, W. Zarin, C.M. Garitty, S. Hempel, E. Lillie, Ö. Tunçalp, A.C. Tricco, Reporting scoping reviews—PRISMA ScR extension, J. Clin. Epidemiol. 123 (2020) 177–179, https://doi.org/10.1016/j.jclinepi.2020.03.016.

[63] V.K. Singh, P. Singh, M. Karmakar, J. Leta, P. Mayr, The journal coverage of Web of Science, Scopus and dimensions: a comparative analysis, Scientometrics 126 (2021) 5113–5142, https://doi.org/10.1007/s11192-021-03948-5.

[64] M. Skjott Linneberg, S. Korsgaard, Coding qualitative data: a synthesis guiding the novice, Qual. Res. J. 19 (2019) 259–270, https://doi.org/10.1108/QRJ-12-2018-0012.

[65] N.J.V. Eck, L. Waltman, How to normalize coc occurrence data? An analysis of some well-known similarity measures, J. Am. Soc. Inf. Sci. Technol. 60 (2009) 1635–1651, https://doi.org/10.1002/asi.21075.

[66] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, W.M. Lim, How to conduct a bibliometric analysis: an overview and guidelines, J. Bus. Res. 133 (2021) 285–296, https://doi.org/10.1016/j.jbusres.2021.04.070.

[67] A.C. Tricco, E. Lillie, W. Zarin, K.K. O'Brien, H. Colquhoun, D. Levac, D. Moher, M.D.J. Peters, T. Horsley, L. Weeks, S. Hempel, E.A. Akl, C. Chang, J. McGowan, L. Stewart, L. Hartling, A. Aldcroft, M.G. Wilson, C. Garitty, S. Lewin, M. Godfrey, M.T. Macdonald, E.V. Langlois, K. Soares-Weiser, J. Moriarty, T. Clifford, Ö. Tunçalp, S.E. Straus, PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation, Ann. Intern. Med. 169 (2018) 467–473, https://doi.org/10.7326/M18-0850.

[68] A. Alexopoulos, K. Koutras, S.B. Ali, S. Puccio, A. Carella, R. Ottaviano, A. Kalogeras, Complementary use of ground-based proximal sensing and airborne/spaceborne remote sensing techniques in precision agriculture: a systematic review, Agronomy 13 (2023), https://doi.org/10.3390/agronomy13071942.

[69] M.N. Mowla, N. Mowla, A.F.M.S. Shah, K.M. Rabie, T. Shongwe, Internet of things and wireless sensor networks for smart agriculture applications: a survey, IEEE Access 11 (2023) 145813–145852, https://doi.org/10.1109/ACCESS.2023.3346299.

[70] V. Sáiz-Rubio, F. Rovira-Más, From smart farming towards agriculture 5.0: a review on crop data management, Agronomy 10 (2020) 207, https://doi.org/10.3390/agronomy10020207.

[71] M.K. Sott, L.S. Nascimento, C.R. Foguesatto, L.B. Furstenau, K. Faccin, P. A. Zawislak, B.M. Garcia, J.D. Kong, N.L. Bragazzi, A bibliometric network analysis of recent publications on digital agriculture to depict strategic themes and evolution structure, Sensors 21 (2021), https://doi.org/10.3390/s21237889.

[72] O. Bongomin, J. Lamo, J.M. Guina, C. Okello, G.G. Ocen, M. Obura, S. Alibu, C. A. Owino, A. Akwero, S. Ojok, UAV image acquisition and processing for high- throughput phenotyping in agricultural research and breeding programs, Plant Phenome J 7 (2024), https://doi.org/10.1002/pjv2.20096.

[73] K. Paul, S.S. Chatterjee, P. Pai, A. Varshney, S. Juikar, V. Prasad, B. Bhadra, S. Dasgupta, Viable smart sensors and their application in data driven agriculture, Comput. Electron. Agric. 198 (2022) 107096, https://doi.org/10.1016/j.compag.2022.107096.

[74] R. Bertoglio, C. Corbo, F.M. Renga, M. Matteucci, The Digital Agricultural Revolution: a bibliometric analysis literature review, IEEE Access 9 (2021) 134762–134782, https://doi.org/10.1109/ACCESS.2021.3115258.

[75] W.U. Rehman, M.A. Koondhar, S.K. Afridi, L. Albasha, I.H. Smaili, E. Touti, M. Aoudia, W. Zahrouni, I. Mahariq, M.M.R. Ahmed, The role of 5G network in revolutionizing agriculture for sustainable development: a comprehensive review, Energy Nexus 17 (2025) 100368, https://doi.org/10.1016/j.nexus.2025.100368.

[76] N. Victor, P.K.R. Maddikunta, D.R.K. Mary, R. Murugan, R. Chengoden, T. R. Gadekallu, N. Rakesh, Y. Zhu, J. Paek, Remote sensing for agriculture in the era of industry 5.0—A survey, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 17 (2024) 5920–5945, https://doi.org/10.1109/JSTARS.2024.3370508.

[77] F. Assimakopoulos, C. Vassilakis, D. Dionisis, K. Kotis, D. Spiliotopoulos, The implementation of “smart” technologies in the agricultural sector: a review, Inf. Switz. 15 (2024), https://doi.org/10.3390/info15080466.

[78] H. Shahab, M. Iqbal, A. Sohaib, F. Khan, M. Waqas, IoT-based agriculture management techniques for sustainable farming: a comprehensive review, Comput. Electron. Agric. 220 (2024), https://doi.org/10.1016/j.compag.2024.108851.

[79] F. Fuentes-Penailillo, K. Gutter, R. Vega, G.C. Silva, Transformative technologies in digital agriculture: leveraging internet of things, remote sensing, and artificial intelligence for smart crop management, J. Sens. Actuator Netw. 13 (2024) 39, https://doi.org/10.3390/jsan13040039.

[80] F. Fuentes-Penailillo, K. Gutter, R. Vega, G.C. Carrasco, New generation sustainable technologies for soilless vegetable production, Horticulture 10 (2024), https://doi.org/10.3390/horticulture10010049.

[81] T. Vats, S. Mehra, U. Madan, A. Chhabra, A. Sharma, K. Chhabra, S. Singh, U. Chauhan, NeuroRF FarmSense: IoT-fueled precision agriculture transformed for superior crop care, Int. J. Cogn. Comput. Eng. 5 (2024) 425–435, https://doi.org/10.1016/j.ijcce.2024.09.002.

[82] C. Maraveas, D. Konar, D.K. Michopoulos, K.G. Arvanitis, K.P. Peppas, Harnessing quantum computing for smart agriculture: empowering sustainable crop management and yield optimization, Comput. Electron. Agric. 218 (2024) 108680, https://doi.org/10.1016/j.compag.2024.108680.

[83] S. Polymeni, D.N. Skoutas, P. Sarigiannidis, G. Kormentzas, C. Skianis, Smart agriculture and greenhouse gas emission mitigation: a 6G-IoT perspective, Electronics (Basel) 13 (2024) 1480, https://doi.org/10.3390/electronics13081480.

[84] P. Catala-Roman, E.A. Navarro, J. Segura-Garcia, M. Garcia-Pineda, Harnessing digital twins for agriculture 5.0: a comparative analysis of 3D point cloud tools, Appl. Sci. 14 (2024) 1709, https://doi.org/10.3390/app14051709.

[85] A. Ahsen, P. Di Bitonto, P. Novielli, M. Magarelli, D. Romano, D. Diacono, A. Monaco, N. Amoroso, R. Bellotti, S. Tangaro, Harnessing digital twins for sustainable agricultural water management: a systematic review, Appl. Sci. 15 (2025) 4228, https://doi.org/10.3390/app15084228.

[86] M. Aweis, X. Wang, S. Hussain, F. Aziz, M.Q. Mahmood, Advancing precision agriculture through digital twins and smart farming technologies: a review, AgriEngineering 7 (2025), https://doi.org/10.3390/agriengineering7050137.

[87] V. Sharma, A.K. Tripathi, H. Mittal, Technological revolutions in smart farming: current trends, challenges & future directions, Comput. Electron. Agric. 201 (2022) 107217, https://doi.org/10.1016/j.compag.2022.107217.

[88] D. Gackstetter, M. Von Bloh, V. Hannus, S.T. Meyer, W. Weisser, C. Luksch, S. Asseng, Autonomous field management – An enabler of sustainable future in agriculture, Agric. Syst. 206 (2023) 103607, https://doi.org/10.1016/j.agsy.2023.103607.

[89] L. Liu, F. Yang, X. Liu, Y. Du, X. Li, G. Li, D. Chen, Z. Zhu, Z. Song, A review of the current status and common key technologies for agricultural field robots, Comput. Electron. Agric. 227 (2024) 109630, https://doi.org/10.1016/j.compag.2024.109630.

[90] J.L. Imran, UAV Aerodynamics and Crop Interaction: Revolutionizing Modern Agriculture with Drone, Springer Nature Singapore, Singapore, 2025, https://doi.org/10.1007/978-981-96-8402-1.

[91] I.A. Lakhiar, H. Yan, C. Zhang, G. Wang, B. He, B. Hao, Y. Han, B. Wang, R. Bao, T.N. Syed, J.N. Chaudhary, M. Rakibuzzaman, A review of precision irrigation water-saving technology under changing climate for enhancing water use efficiency, crop yield, and environmental footprints, Agriculture 14 (2024) 1141, https://doi.org/10.3390/agriculture14071141.

[92] A.J. Hati, R.R. Singh, Smart indoor farms: leveraging technological advancements to power a sustainable agriculture revolution, AgriEngineering 3 (2021) 728–767, https://doi.org/10.3390/agriengineering3040047.

[93] M. Seif-Ennasr, M. Naimi, M. Chikhaoui, M. Bellafkih, K. Benabbes, Z. Azough, Z. Abail, Harvesting insights on AI-driven technologies in modern agriculture: a systematic literature review, IEEE Access 13 (2025) 143713–143736, https://doi.org/10.1109/ACCESS.2025.3597844.

[94] U. Shareef, A.U. Rehman, R. Ahmad, A systematic literature review on parameters optimization for smart hydroponic systems, AI 5 (2024) 1517–1533, https://doi.org/10.3390/ai5030073.

[95] S.K. Borah, M. Padhiary, L.N. Sethi, A. Kumar, P. Saikia, Precision farming with drone sprayers: a review of auto navigation and vision-based optimization, J. Biosyst. Eng. 50 (2025) 255–273, https://doi.org/10.1007/s42853-025-00263-2.

[96] M. Padhiary, A. Hoque, G. Prasad, K. Kumar, B. Sahu, Precision agriculture and AI-driven resource optimization for sustainable land and resource management, Eds, in: J.A. Ruiz-Vanoye, O. Díaz-Parra (Eds.), Precision agriculture and AI- driven resource optimization for sustainable land and resource management, Smart Water Technol. Sustain. Manag. Mod. Cities (2025) 197–232, https://doi.org/10.4018/979-8-3693-8074-1.ch009.

[97] C. Parra-López, S. Ben Abdallah, G. Garcia-Garcia, A. Hassoun, H. Trollman, S. Jagtap, S. Gupta, A. Aït-Kaddour, S. Makmuang, C. Carmona-Torres, Digital technologies for water use and management in agriculture: recent applications and future outlook, Agric. Water Manag 309 (2025) 109347, https://doi.org/10.1016/j.agwat.2025.109347.

[98] J.N.K. Wah, The role of ai in transforming agriculture: toward sustainable growth in an era of climate change, (2025), https://doi.org/10.5281/ZENODO.15587933.

[99] T. Krachunova, F. Geppert, N. Lemke, S.D. Bellingrath-Kimura, Digital technologies commercially available in Germany in the context of nature conservation and ecosystem service provisioning in agriculture, Front. Sustain. Food Syst. 9 (2025) 1464020, https://doi.org/10.3389/fsufs.2025.1464020.

[100] R. Fragomeli, W. Zawadzki, M. Czajkowski, A. Annunziata, G. Punzo, Italian farmers' preferences for adopting agriculture 4.0 technologies: a choice experiment analysis, J. Clean. Prod. 525 (2025) 146601, https://doi.org/10.1016/j.jclepro.2025.146601.
[101] L. Klerkx, D. Rose, Dealing with the game-changing technologies of Agriculture 4.0: how do we manage diversity and responsibility in food system transition pathways? Glob. Food Secur. 24 (2020) 100347. https://doi.org/10.1016/j.gfs.2019.100347.

[102] A.K. Srivastava, T. Vanitha, Y.S. Devi, S. Gupta, Revolutionizing agriculture with IoT and artificial intelligence. Agric. 40 Smart Farming IoT Artif. Intell., CRC Press, London, 2024, https://doi.org/10.1201/9781003570219, 1st ed.

[103] B. Zvara, M. Macák, J. Galambošová, Review: unmanned aerial vehicles and artificial intelligence in precision agriculture, (2025) 588.85 KB. https://doi.org/10.15159/AR.25.030.

[104] S. Vijayakumar, V. Murugaiyan, S. Ilakkivi, V. Kumar, R.M. Sundaram, R. M. Kumar, Opportunities, challenges, and interventions for agriculture 4.0 adoption, Discov. Food 5 (2025) 265, https://doi.org/10.1007/s44187-025-00576-3.

[105] A.L. Duguma, X. Bai, How the internet of things technology improves agricultural efficiency, Artif. Intell. Rev. 58 (2024) 63, https://doi.org/10.1007/s10462-024-11046-0.

[106] N. Ahmed, N. Shakoor, Advancing agriculture through IoT, Big Data, and AI: a review of smart technologies enabling sustainability, Smart Agric. Technol. 10 (2025) 100848, https://doi.org/10.1016/j.atech.2025.100848.

[107] A.O. Omotayo, S.A. Adediran, A.B. Omotoso, K.O. Olagunju, O.P. Omotayo, Artificial intelligence in agriculture: ethics, impact possibilities, and pathways for policy, Comput. Electron. Agric. 239 (2025) 110927, https://doi.org/10.1016/j.compag.2025.110927.

[108] X. Wang, H. Zeng, X. Yang, J. Shu, Q. Wu, Y. Que, X. Yang, X. Yi, I. Khalil, A. Y. Zomaya, Remote sensing revolutionizing agriculture: toward a new frontier, Future Gener. Comput. Syst. 166 (2025) 107691, https://doi.org/10.1016/j.future.2024.107691.

[109] J. Xu, Y. Cui, S. Zhang, M. Zhang, The evolution of precision agriculture and food safety: a bibliometric study, Front. Sustain. Food Syst. 8 (2024) 1475602, https://doi.org/10.3389/fsufs.2024.1475602.

[110] A. Morchid, A. Ismail, H.M. Khalid, H. Qijdaa, R.E. Alami, Blockchain and IoT technologies in smart farming to enhance the efficiency of the agri-food supply chain: a review of applications, benefits, and challenges, Internet Things 33 (2025) 101733, https://doi.org/10.1016/j.iot.2025.101733.

[111] M.F. Taha, H. Mao, Z. Zhang, G. Elmasry, M.A. Awad, A. Abdalla, S. Mousa, A. E. El wakeel, O. Elsherbiny, Emerging technologies for precision crop management towards agriculture 5.0: a comprehensive overview, Agriculture 15 (2025) 582, https://doi.org/10.3390/agriculture15060582.

[112] R. Sikka, D. Pal Singh, M.K. Sharma, A. Ojha, Advancing agriculture in smart cities: renewable energy and artificial intelligence-powered IoT, E3S Web Conf. 540 (2024) 13010, https://doi.org/10.1051/e3sconf/202454013010.

[113] E. Bwambale, J. Wanyama, T.A. Adongo, E. Umukiza, R. Ntole, S. R. Chikavumbwa, D. Sibale, Z. Jeramih, A review of model predictive control in precision agriculture, Smart Agric. Technol. 10 (2025) 100716, https://doi.org/10.1016/j.atech.2024.100716.

[114] N. Fares, C. Anass, V. Shardeo, J.A. Garza-Reyes, Supply chain management in the industry 5.0 era: strategic implications, Benchmarking (2025), https://doi.org/10.1108/BIJ-12-2024-1136.

[115] D. Nimma, J.A. Dhanke, G.S.N. Murthy, S.D. Khandekar, J. Hymavathi, P. Jangir, M. Singh, Optimization of crop yields in sustainable agriculture: application of big data analytics and artificial intelligence, Remote Sens. Earth Syst. Sci. 8 (2025) 375-386, https://doi.org/10.1007/s41976-024-00189-x.

[116] S. Mansoor, S. Iqbal, S.M. Popescu, S.L. Kim, Y.S. Chung, J.-H. Baek, Integration of smart sensors and IOT in precision agriculture: trends, challenges and future prospects, Front. Plant Sci. 16 (2025) 1587869, https://doi.org/10.3389/fpsl.2025.1587869.

[117] J.T. Liberty, E. Habanabakize, P.I. Adamu, S.M. Bata, Advancing food manufacturing: leveraging robotic solutions for enhanced quality assurance and traceability across global supply networks, Trends Food Sci. Technol. 153 (2024) 104705, https://doi.org/10.1016/j.tifs.2024.104705.

[118] W.A. Varella, G.C.D. Oliveira Neto, E. Stefani, I. Costa, R.C. Monteiro, W. Conde, W.DA Silva Junior, R.C. Baptestone, R.D.S. Goes, R. Riccotta, M.A. Gaspar, P. R. Felisoni, F.K. Ohashi, H.D. Nascimento, A.A. Fernandes, J.-F. Martins, Integrated service architecture to promote the circular economy in agriculture 4.0, Sustainability 16 (2024) 2535, https://doi.org/10.3390/su16062535.

[119] V. Fiore, M. Borrello, D. Carlucci, G. Giannoccaro, S. Russo, S. Stempfle, L. Roselli, The socio-economic issues of agroecology: a scoping review, Agric. Food Econ. 12 (2024) 16, https://doi.org/10.1186/s40100-024-00311-z.

[120] E. Duncan, A. Glaros, D.Z. Ross, E. Nost, New but for whom? Discourses of innovation in precision agriculture, Agric. Hum. 38 (2021) 1181-1199, https://doi.org/10.1007/s10460-021-10244-8.

[121] R. Dara, S.M. Hazrati Fard, J. Kaur, Recommendations for ethical and responsible use of artificial intelligence in digital agriculture, Front. Artif. Intell. 5 (2022) 884192, https://doi.org/10.3389/frai.2022.884192.

[122] S. Hackfort, Patterns of inequalities in digital agriculture: a systematic literature review, Sustainability 13 (2021) 12345, https://doi.org/10.3390/su132212345.

[123] N. Andrieu, F. Howland, I. Acosta-Alba, J.-F. Le Coq, A.M. Osorio-Garcia, D. Martinez-Baron, C. Gamba-Trimiño, A.M. Loboguerrero, E. Chia, Co-designing climate-smart farming systems with local stakeholders: a methodological framework for achieving large-scale change, Front. Sustain. Food Syst. 3 (2019) 37, https://doi.org/10.3389/fsufs.2019.00037.

[124] A. Rossi, From Co-learning to shared commitment to agroecology. Some insights from initiatives aimed at reintroducing agrobiodiversity, Sustainability 12 (2020) 7766, https://doi.org/10.3390/su12187766.

[125] L. Benos, V. Moysiadis, D. Kateris, A.C. Tagarakis, P. Busato, S. Pearson, D. Bochtis, Human-Robot Interaction in agriculture: a systematic review, Sensors 23 (2023) 6776, https://doi.org/10.3390/s23156776.

[126] S.K. Devitt, Cognitive factors that affect the adoption of autonomous agriculture, (2023) https://doi.org/10.48550/ARXIV.2111.14092.

[127] H. Runowski, P. Kramarz, Trust in artificial intelligence in agriculture. Trust Artif. Intell. Dev. Appl. AI Technol., Routledge, New York, 2024, p. 13, https://doi.org/10.4324/9781032627236, 1st ed.

[128] S. Van Der Burg, M.-J. Bogaardt, S. Wolfert, Ethics of smart farming: current questions and directions for responsible innovation towards the future, NJAS Wageningen. J. Life Sci. 90-91 (2019) 1-10, https://doi.org/10.1016/j.njas.2019.01.001.

[129] H. Li, Y. Chen, W.-Y. Chang, Place attachment, self-efficacy, and farmers' Farmland quality protection behavior: evidence from China, Land (Basel) 12 (2023) 1711, https://doi.org/10.3390/land12091711.

[130] X. Wang, J. Zhang, K. He, W. Li, Place attachment, environmental cognition and organic fertilizer adoption of farmers: evidence from rural China, Environ. Sci. Pollut. Res. 28 (2021) 41255-41267, https://doi.org/10.1007/s11356-021-13509-1.

[131] G. Xu, Y. Li, I. Hay, X. Zou, X. Tu, B. Wang, Beyond place attachment: land attachment of resettled farmers in Jiangsu, China, Sustainability 11 (2019) 420, https://doi.org/10.3390/su11020420.

[132] V. Johannessen, F. Gjæver, S. Efstathiou, S. Russell, Emotion regulation of sustainability professionals facing adversity, Gr. Interakt. Organ. Z. Für Angew. Organ. GIO 55 (2024) 167-174, https://doi.org/10.1007/s11612-024-00734-8.

[133] T.N. Le, E. Brown, W. Zhang, Sense of meaning and purpose making mitigates the experience of stress among Hawai'i farmers, J. Agromedicine 28 (2023) 746-755, https://doi.org/10.1080/1059924X.2023.2215238.

[134] P. Jansen, M. Rahe, F. Wolff, How does mindfulness relate to sustainable attitude and behavior? The role of possible mediators, Curr. Psychol. 43 (2024) 19708-19720, https://doi.org/10.1007/s12144-024-05741-y.

[135] Y. Qiu, Y. Zhang, M. Liu, Dual process model of farmers' mindfulness and sustainable economic behavior: mediating role of mental health and emotional labor, Front. Psychiatry 13 (2022) 979979, https://doi.org/10.3389/fpsyt.2022.979979.

[136] R. Beznér Kerr, J. Liebert, M. Kansanga, D. Kpienbaareh, Human and social values in agroecology, J. Sci. Sci. Anthr. 10 (2022) 00090, https://doi.org/10.1525/elementa.2021.00090.

[137] R. Ajates Gonzalez, J. Thomas, M. Chang, Translating agroecology into policy: the Case of France and the United Kingdom, Sustainability 10 (2018) 2930, https://doi.org/10.3390/su10082930.

[138] A.F. Fonseca, F. Polita, L. Madureira, How agroecological transition frameworks are reshaping agroecology: a review, Land (Basel) 13 (2024) 1930, https://doi.org/10.3390/land13111930.

[139] A. Navarro, J.V. López-Bao, EU agricultural policy still not green, Nat. Sustain. 2 (2019) 990, https://doi.org/10.1038/s41893-019-02424-x. -990.

[140] T.G. Williams, M. Bürgi, N. Debonne, V. Diogo, J. Helfenstein, C. Levers, F. Mohr, A.E. Stratton, P.H. Verburg, Mapping lock-ins and enabling environments for agri-food sustainability transitions in Europe, Sustain. Sci. 19 (2024) 1221-1242, https://doi.org/10.1007/s11625-024-01480-y.

[141] M. Donner, M. Mamès, H. De Vries, Towards sustainable food systems: a review of governance models and an innovative conceptual framework, Discov. Sustain. 5 (2024) 414, https://doi.org/10.1007/s43621-024-00648-x.

[142] T.S. Jiren, J. Leventon, N.W. Jager, I. Dorresteijn, J. Schultner, F. Senbeta, A. Bergsten, J. Fischer, Governance challenges at the interface of food security and biodiversity conservation: a multi-level case study from Ethiopia, Environ. Manage. 67 (2021) 717-730, https://doi.org/10.1007/s00267-021-01432-7.

[143] V.I. Kraak, K.L. Niewolny, A scoping review of food systems governance frameworks and models to develop a typology for social change movements to transform food systems for people and planetary health, Sustainability 16 (2024) 1469, https://doi.org/10.3390/su16041469.

[144] S. Rotz, E. Duncan, M. Small, J. Botschner, R. Dara, I. Mosby, M. Reed, E.D. G. Fraser, The politics of digital agricultural technologies: a preliminary review, Sociol. Rural. 59 (2019) 203-229, https://doi.org/10.1111/soru.12233.

[145] B.K. Singh, T. Arnold, P. Biermayr-Jenzano, J. Broerse, G. Brunori, P. Caron, O. De Schutter, S. Fan, J. Fanzo, E. Fraser, M. Gurinovic, M. Hugas, J. McGlade, C. Nellemann, J. Njuki, R. Sonnino, H.L. Tuomisto, S. Tutundjian, P. Webb, J. Wesseler, Enhancing science-policy interfaces for food systems transformation, Nat. Food 2 (2021) 838-842, https://doi.org/10.1038/s43016-021-00406-6.

[146] C.S. Alexander, M. Yarborough, A. Smith, Who is responsible for 'responsible AI?: navigating challenges to build trust in AI agriculture and food system technology, Precis. Agric. 25 (2024) 146-185, https://doi.org/10.1007/s11119-023-10063-3.

[147] M.-A. Jouanjean, F. Casalini, L. Wiseman, E. Gray, Issues around data governance in the digital transformation of agriculture: the farmers' perspective, 2020, https://doi.org/10.1787/53ecf2ab-en.

[148] S. Ahmadi, V. Amendolagine, P. LaSala, Unpacking the impacts of digitalization of knowledge transfer in agri-food sector, through sociotechnical systems theory: a systematic literature review, Agric. Food Econ. 13 (2025) 51, https://doi.org/10.1186/s40100-025-00393-3.

[149] A. Loconto, A. Jimenez, E. Vandecandelaere, F. Tartanac, Agroecology, local food systems and their markets, Ager Rev. Estud. Sobre Despoblación Desarro. Rural (2018) 13-42, https://doi.org/10.4422/ager.2018.15.

[150] P. Milone, F. Ventura, Nested markets and the transition of the Agro-Marketing system towards sustainability, Sustainability 16 (2024) 2902, https://doi.org/10.3390/su16072902.
[151] G. O'Connor, K. Reis, C. Desha, I. Burkett, Valuing farmers in transitions to more sustainable food systems: a systematic literature review of local food producers' experiences and contributions in short food supply chains, Agric. Hum. Values 42 (2025) 565–592, https://doi.org/10.1007/s10460-024-10601-3.

[152] L.S.O. Liverpool-Tasie, A. Wineman, S. Young, J. Tambo, C. Vargas, T. Reardon, G.S. Adjognon, J. Porciello, N. Gathoni, L. Bizikova, A. Galiè, A. Celestin, A scoping review of market links between value chain actors and small-scale producers in developing regions, Nat. Sustain. 3 (2020) 799–808, https://doi.org/10.1038/s41893-020-00621-2.

[153] B.M. McKay, R. Nehring, G. Catacora-Vargas, The political economy of agroecological transitions: key analytical dimensions, J. Peasant Stud. 52 (2025) 461–484, https://doi.org/10.1080/03066150.2024.2399138.

[154] S. Barel-Shaked, E. Buda, Exploring the nexus of agricultural policy reforms and food security, Front. Sustain. Food Syst. 8 (2024) 1366807, https://doi.org/10.3389/fsufs.2024.1366807.

[155] P. Tittonell, Ecological intensification of agriculture—Sustainable by nature, Curr. Opin. Environ. Sustain. 8 (2014) 53–61, https://doi.org/10.1016/j.cosust.2014.08.006.

[156] M.L. Zingsheim, T.F. Döring, What weeding robots need to know about ecology, Agric. Ecosyst. Environ. 364 (2024) 108861, https://doi.org/10.1016/j.agee.2023.108861.

[157] M. Naim, D. Rizzo, L. Sauvée, M. Medici, A systematic classification of agrobots to inform farmers’ choice and clarify market development, Ed, in: J.V. Stafford (Ed.), Precis. Agric. 25 SET TWO Vol., Brill | Wageningen Academic, 2025, pp. 991–997, doi:10.1163/9789004725232_130.

[158] M. Naim, Poster: defining agroecological routes using robots, (2025). https://doi.org/10.5281/ZENODO.17700793.