# 2025 photonics for agrifood roadmap: towards a sustainable and healthier planet

Lien Smeesters, Francesca Venturini, Stefan Paulus, Anne-Katrin Mahlein, David Perpetuini, Daniela Cardone, Arcangelo Merla, Benjamin Gac, Stephane Perrin, Denis Trégoat, et al.

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## To cite this version:

Lien Smeesters, Francesca Venturini, Stefan Paulus, Anne-Katrin Mahlein, David Perpetuini, et al. 2025 photonics for agrifood roadmap: towards a sustainable and healthier planet. Journal of Physics: Photonics, 2025, 7 (3), pp.032501. 10.1088/2515-7647/adbea9. hal-04987546

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Submitted on 19 Aug 2025

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# 2025 photonics for agrifood roadmap: towards a sustainable and healthier planet

To cite this article: Lien Smeesters et al 2025 J. Phys. Photonics 7 032501

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ROADMAP

2025 photonics for agrifood roadmap: towards a sustainable and healthier planet

Lien Smeesters<fnref n="1" />,*, Francesca Venturini<fnref n="2" />,<fnref n="3" />,* ID, Stefan Paulus<fnref n="4" />, Anne-Katrin Mahlein<fnref n="4" />, David Perpetuini<fnref n="7" /> ID,
Daniela Cardone<fnref n="5" />, Arcangelo Merla<fnref n="5" />, Benjamin Gac<fnref n="6" />,<fnref n="7" />, Stephane Perrin<fnref n="8" /> ID, Denis Trégoat<fnref n="8" />, Luiz Poffo<fnref n="9" /> ID,
Antoine Fournier<fnref n="6" /> ID, Giuseppe Bonifazi<fnref n="10" />, Silvia Serranti<fnref n="10" />, Wenzhi Liao<fnref n="11" />,<fnref n="12" />, Noureddin Osseiran<fnref n="13" />,
Mathias Vanwolleghem<fnref n="13" /> ID, Romain Peretti<fnref n="13" />, Aparajita Bandyopadhyay<fnref n="14" /> ID, Karl Bertling<fnref n="15" /> ID,
Krishnan Parameswaran<fnref n="16" />, Aleksandar D Rákic<fnref n="15" />, Amartya Sengupta<fnref n="15" />,<fnref n="17" />, Sune Svanberg<fnref n="18" />,
Małgorzata Kujawinska<fnref n="19" />,<fnref n="20" />, Daniel Cozzolino<fnref n="21" />, Dolores Pérez Marín<fnref n="22" /> ID, José A Entrenas de León<fnref n="22" />,
Irina Torres Rodríguez<fnref n="22" />, Marietta Fodor<fnref n="23" /> ID, Eszter Benes<fnref n="23" />, Anna Matkovits<fnref n="23" />, Zsuzsa Jókai<fnref n="23" />,
Simona M Cristescu<fnref n="24" /> ID, Amir Khodabakhsh<fnref n="24" />, Khalil Jahromi<fnref n="25" />, Mohammadreza Nematollahi<fnref n="26" />,
Frans H Harren<fnref n="24" />, Eugene Rokx<fnref n="27" />, Ewa Sikorska<fnref n="28" /> ID, Kay Sowoidnich<fnref n="29" /> ID, Martin Maiwald<fnref n="29" /> ID,
Bernd Sumpf<fnref n="29" /> ID, Matthieu Roussey<fnref n="30" />, Kai-Erik Peiponen<fnref n="30" />, Antoni Femenias³¹, Polina Fomina³¹,
Vjekoslav Kokoric³², Robert Stach³², Boris Mizaikoff<fnref n="1" />,³² ID, Maria Konstantaki³³, Stavros Pissadakis³³,
Umberto Michelucci<fnref n="3" />,³⁴, Sławomir Paśko<fnref n="19" />, Anna Pakula<fnref n="19" />, Richard A Crocombe³⁵, Ellen V Miseo³⁶,
Andreas C Liapis³⁷, Faisal Ahmed³⁷ ID, Zhipei Sun³⁷, Chrysoula Chandrinou³⁸, Marianneza Chatzipetrou³⁸,
Ioanna Zergioti³⁸ ID, Ulrich Trog³⁹, Anja Haase³⁹, Martin Smolka³⁹, Lex Oosterveld⁴⁰, Chris Van Hoof⁴⁰,
André Müller<fnref n="29" /> ID, Michael Hlavatsch⁴¹, Diellza Bajrami³¹ ID, Ernesto Olvera-Gonzalez⁴¹,
Nivia Escalante-Garcia⁴¹, Arturo B Toro⁴²,⁴³ ID, Koenraad Van Hoorde⁴⁴, Brijesh K Tiwari⁴⁵,
Thomas Westerhoff⁴⁶, Michael Kneissl<fnref n="29" />,⁴⁷ and Anna Grazia Mignani⁴⁸,*

<footnote n="1">¹ Department of Applied Physics and Photonics, Vrije Universiteit Brussel and Flanders Make, Brussels Photonics (B-PHOT), Pleinlaan 2, B-1050 Brussels, Belgium</footnote>

<footnote n="2">² School of Engineering, ZHAW Zurich University of Applied Sciences, 8401 Winterthur, Switzerland</footnote>

<footnote n="3">³ TOELT LLC, Research and Development, 8600 Duebendorf, Switzerland</footnote>

<footnote n="4">⁴ Institute of Sugar Beet Research, Holtenser Landstr. 77, 37079 Göttingen, Germany</footnote>

<footnote n="5">⁵ Department of Engineering and Geology, University 'D'Annunzio' of Chieti-Pescara, Pescara 65127, Italy</footnote>

<footnote n="6">⁶ Arvalis, 45 Voie Romaine, 41240 Beauce la Romaine, France</footnote>

<footnote n="7">⁷ Université de Rennes, CNRS, Institut FOTON—UMR 6082, F-22305 Lannion, France</footnote>

<footnote n="8">⁸ Photonics Bretagne, 4 Rue Louis de Broglie, 22300 Lannion, France</footnote>

<footnote n="9">⁹ University of Limoges, XLIM UMR CNRS, 7252, Limoges, France</footnote>

<footnote n="10">¹⁰ Department of Chemical Engineering, Materials and Environment, Sapienza—University of Rome, via Eudossiana 18, 00184 Rome, Italy</footnote>

<footnote n="11">¹¹ Flanders Make, 8500 Kortrijk, Belgium</footnote>

<footnote n="12">¹² Department of Telecommunications and Information Processing, Ghent University, 9000 Ghent, Belgium</footnote>

<footnote n="13">¹³ Institut d'Electronique de Microélectronique et de Nanotechnologie (IEMN), CNRS-UMR 8520, Université de Lille, 59652 Villeneuve d'Ascq, France</footnote>

<footnote n="14">¹⁴ DRDO-Industry-Academia Centre of Excellence (DIA-CoE), Indian Institute of Technology Delhi, New Delhi 110016, India</footnote>

<footnote n="15">¹⁵ School of Electrical Engineering and Computer Science, University of Queensland, Brisbane QLD 4072, Australia</footnote>

<footnote n="16">¹⁶ Cambridge Consultants, Boston, MA, United States of America</footnote>

<footnote n="17">¹⁷ Department of Physics, Indian Institute of Technology Delhi, New Delhi 110016, India</footnote>

<footnote n="18">¹⁸ Physics Department, Lund University, SE-221 00 Lund, Sweden</footnote>

<footnote n="19">¹⁹ Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. Andrzeja Boboli St, 02-525 Warsaw, Poland</footnote>

<footnote n="20">²⁰ Central Office of Measures, 2 Elektoralna St., 00-139 Warsaw, Poland</footnote>

<footnote n="21">²¹ Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St.Lucia, Brisbane QLD 4072, Australia</footnote>

<footnote n="22">²² Non-Destructive Spectral Sensors Unit, Department of Animal Production, Faculty of Agriculture and Forestry Engineering (ETSIAM), University of Cordoba, Campus de Rabanales, 14071 Cordoba, Spain</footnote>

<footnote n="23">²³ Department of Food and Analytical Chemistry H-1118, Hungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology, Villányi st. 29-31, Budapest, Hungary</footnote>

<footnote n="24">²⁴ Life Science Trace Detection Laboratory, (TDLab), Institute of Molecules and Materials, Faculty of Science, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands</footnote>

<footnote n="25">²⁵ Intero Integrity Services, Research and Development Group, Steenoven 2-6, 4196 HG Tricht, The Netherlands</footnote>

<footnote n="26">²⁶ R&D Department, ASMPT ALSI B.V., Platinawerf 20, 6641 TL Beuningen, The Netherlands</footnote>

<footnote n="27">²⁷ Storex B.V. Beneden Havendijk 89, NL-3295 XB's Gravendeel, The Netherlands</footnote>

<footnote n="28">²⁸ Institute of Quality Science, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland</footnote>

<footnote n="29">²⁹ Ferdinand-Braun-Institut (FBH), Leibniz-Institut für Höchstfrequenztechnik, Gustav-Kirchhoff-Str. 4, 12489 Berlin, Germany</footnote>

<footnote n="30">³⁰ Department of Physics and Mathematics, Center for Photonics Sciences, University of Eastern Finland, PO Box 111, 80101 Joensuu, Finland</footnote>
31 Institute of Analytical and Bioanalytical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany

32 Hahn-Schickard, Sedanstraße 14, 89077 Ulm, Germany

33 Institute of Electronic Structure and Laser (IESL), Foundation for Research and Technology- Hellas (FORTH), 70013 Heraklion, Crete, Greece

34 Computer Science Department, Lucerne University of Applied Sciences and Arts, 6343 Rotkreuz, Switzerland

35 Crocombe Spectroscopic Consulting, Greater Boston, MA, United States of America

36 Consultant, Greater Boston, MA, United States of America

37 QTF Centre of Excellence, Department of Electronics and Nanoengineering, Aalto University, Espoo, Finland

38 School of Mathematical and Physical Sciences, Physics Department, National Technical University of Athens, Heroon Polytehneiou 9, 157 72 Athens, Greece

39 JOANNEUM RESEARCH Forschungsgesellschaft mbH, Leonhardstrasse 59, 8010 Graz, Austria

40 OnePlanet Research Center and imec, Bronland 10, 6708 WH, Wageningen, The Netherlands

41 Laboratorio de Iluminación Artificial (LIA), Tecnológico Nacional de México Campus Pabellón de Arteaga, Carretera a la Estación de Rincón Km. 1, Pabellón de Arteaga, Aguascalientes 20670, Mexico

42 Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Facultat de Farmàcia i Ciències de l'Alimentació, Campus de l'Alimentació de Torribera, University of Barcelona, Barcelona, Spain

43 Institut de Recerca en Nutrició i Seguretat Alimentària (INSA·UB), University of Barcelona, Barcelona, Spain

44 Department of Infectious Diseases in Humans, Foodborne Pathogens Unit, Sciensano, Brussels, Belgium

45 Teagasc Ashtown Food Research Centre, Dublin, Ireland

46 Smart UV Systems at Fraunhofer Institute of Optronics, System Technologies, and Image Exploitation, Am Vogelherd 90, 98693 Ilmenau, Germany

47 Institute of Solid State Physics, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany

48 CNR—Istituto di Fisica Applicata ‘Nello Carrara’, 50019 Sesto Fiorentino (FI), Italy

* Authors to whom any correspondence should be addressed.

E-mail: lien.smeesters@vub.be, vent@zhaw.ch and a.g.mignani@ifac.cnr.it

Keywords: agriculture, spectroscopy, photonics, agrifood industry, optical sensors, machine learning, AI

Abstract

Photonics technologies play a crucial role in driving technological advancements within the agrifood industry, aiming to deliver a sustainable food and agriculture, and offering healthy, nutritious and safe food for all of us. Particularly, optical sensors and imaging systems, together with machine-learning processing and advanced lighting, play a pivotal role in monitoring crop and soil health with unprecedented precision, while safeguarding the food supply chain. This roadmap aims to provide an overview of the state-of-the-art photonics technologies benefitting agrifood applications, including a view on their current limitations, challenges and future potential, while addressing practical case studies. Future trends towards multimodal sensors and sensor fusion, artificial intelligence and digital twins, miniaturization and controlled farming are highlighted. The revolutionizing capabilities of the photonics technologies are indicated, inspiring future applications and developments, and paving the way towards optimized resource utilization, increased crop yields, stopping land degradation and reduction of food waste.
Contents

<table><tr><td>1. Introduction</td><td>4</td></tr><tr><td>Chapter 1: Image-based monitoring and machine learning</td><td>7</td></tr><tr><td>2. Automated routines based on optical sensors and ground-based or airborne robots for monitoring crop plants in experimental trials and agricultural fields—what are the needed framework conditions</td><td>7</td></tr><tr><td>3. Thermal IR imaging in agriculture. Recent advancements and future trends</td><td>11</td></tr><tr><td>4. In-situ fluorescence responses for agriculture</td><td>15</td></tr><tr><td>5. Hyperspectral imaging applications in the agri-food sector</td><td>18</td></tr><tr><td>6. Digital twin for smart multispectral camera design</td><td>22</td></tr><tr><td>7. TeraHertz spectroscopy application in agriculture and food technology</td><td>25</td></tr><tr><td>8. THz sensing in agrifood: towards optimization of agricultural resources and industrial processing of food</td><td>28</td></tr><tr><td>9. Lidar—remote sensing for assessments in the agro-food sector</td><td>32</td></tr><tr><td>10. Digitization and digital twins in agriculture</td><td>35</td></tr><tr><td>Chapter 2: Spectrometry and spectral sensors</td><td>39</td></tr><tr><td>11. Ensuring food for all: state of the art and future trends</td><td>39</td></tr><tr><td>12. NIR spectral sensors to ensure the integrity along the entire food chain</td><td>42</td></tr><tr><td>13. Application of near infrared spectroscopy in the analysis of cultivated mushrooms</td><td>46</td></tr><tr><td>14. Fruit quality control by mid-infrared multi-species trace gas sensing</td><td>49</td></tr><tr><td>15. Fluorescence spectroscopy in agri-food chain</td><td>52</td></tr><tr><td>16. Laser-induced fluorescence spectroscopy for mycotoxin detection in solid food products</td><td>55</td></tr><tr><td>17. Raman spectroscopy for precise food quality assessment</td><td>58</td></tr><tr><td>18. Raman spectroscopy for soil inspection—pathway towards on-site field measurements</td><td>61</td></tr><tr><td>19. On-site detection of water pollutants: the potential and limitations of optical spectroscopy</td><td>64</td></tr><tr><td>20. FTIR-based quality control of commodities</td><td>67</td></tr><tr><td>21. Optical fiber sensors for precision agriculture</td><td>71</td></tr><tr><td>22. Machine learning techniques applied to spectral data</td><td>74</td></tr><tr><td>23. Methods of data classification in spectral studies of food</td><td>77</td></tr><tr><td>Chapter 3: Miniaturization and handheld units</td><td>79</td></tr><tr><td>24. Miniature spectrometers, multispectral sensors and the food chain</td><td>79</td></tr><tr><td>25. Ultra-miniaturized spectrometers and their agro-food applications</td><td>82</td></tr><tr><td>26. Portable biophotonic systems for agro-food analysis</td><td>85</td></tr><tr><td>27. Lab-on-a-chip (LoC) systems for agriculture</td><td>88</td></tr><tr><td>28. Opportunities for photonic integrated circuits in agriculture and food processing</td><td>92</td></tr><tr><td>Chapter 4: Optimized lighting</td><td>95</td></tr><tr><td>29. Wavelength-stabilized and tunable diode laser-based light sources for sensing applications in the agro-food sector</td><td>95</td></tr><tr><td>30. Advanced mid-infrared light sources for food quality/safety applications</td><td>99</td></tr><tr><td>31. LED light spectrum and its power on the nutrient content in closed plant production CPPS</td><td>103</td></tr><tr><td>32. UV light-based technologies for potential application in the agri-food sector</td><td>106</td></tr><tr><td>33. UVC-LEDs for water treatment</td><td>110</td></tr><tr><td>Concluding remarks</td><td>113</td></tr><tr><td>Data availability statement</td><td>113</td></tr><tr><td>References</td><td>114</td></tr></table>
# 1. Introduction

Lien Smeesters<fnref n="1" />, Francesca Venturini<fnref n="2" />,<fnref n="3" /> and Anna Grazia Mignani<fnref n="4" />

E-mail: lien.smeesters@vub.be, vent@zhaw.ch and a.g.mignani@ifac.cnr.it

The agrifood industry has been subject to strong digitalization and technological advances during the last decades, including the introduction of Internet of Things (IoT), smart machinery, machine vision inspection systems, and the introduction of precision farming [1, 2]. However, continuous technological developments remain indispensable to tackle the current and future challenges, aiming to deliver a sustainable food and agriculture production, and to offer healthy, nutritious and safe food for all of us. Key challenges include population growth, food waste, food and feed quality, global warming, stopping land degradation and minimizing the use of resources and pesticides. The world population is expected to grow to 10 billion people by 2050, as estimated by the Food and Agriculture Organization (FAO), requiring a dramatic food and feed production increase of 60% [3]. On the other hand, one third of all food produced is currently wasted during production, processing, distribution or at the consumer [4]. In addition, food scandals linked to safety and traceability have been marking headlines for centuries, impacting consumer trust, as reported by the EIT Food Trust Report indicating a consumer confidence of only 55% regarding product safety and 43% for product authenticity [5]. Different food contaminants still sneak into the food chain, among others, pesticide residues, pathogenic microorganisms, and mycotoxins [6]. Food fraud, including mislabeling, dilution, and adulteration is still affecting a wide range of products, among others, olive oil, milk, seafood, coffee and sugar [7]. Finally, in view of mitigating climate change, a transition to sustainable agriculture is indispensable, since agrifood accounts for 70% of the global water use and 24% of the greenhouse gas emissions [8], while climate change is envisioned to strongly challenge the agrifood industry increasing the presence of toxins and affecting the soil and harvest quality.

Photonics technologies play a crucial role in tackling these challenges, by the development of novel optical sensors, imaging systems, smart labels, and lighting [9, 10]. As main advantages, optical spectroscopic sensors and imaging systems offer a non-destructive and chemical-free evaluation, suitable for both individual spot checks and in-line autonomous monitoring. In general, photonics technologies have the potential to impact the whole food supply chain (figure 1), revolutionizing the industry [1, 2]. On the field, drones and agricultural machines (e.g. tractors, plucking robots) supplemented with multi-/hyper-spectral imaging technologies are being deployed for irrigation, fertilizer monitoring, pest and diseases detection. Visible cameras feature shape inspection, and the detection of foreign objects and anomalies, while near-infrared spectroscopy (NIRS) enables measuring nutrients, proteins and sugars. Soil evaluation benefits from distributed fiber Bragg detectors, which offer root and moisture monitoring capabilities. Light detection and ranging (Lidar) is a valuable technology for the mapping of plants. Fluorescence spectroscopy is useful to monitor toxins, amino acids, vitamins, allergens, and pigments, while Raman spectroscopy (RS) enables determining biochemical components like sugars, lipids, water, and proteins. Terahertz spectroscopy is employed, among others, to monitor the leaf moisture content. Handheld and smartphone-connected spectrometers are entering the food chain, with the potential for deployment along the whole supply chain. The global market for photonics for precision agriculture (PA) is currently worth around 4.6 billion euros and is expected to virtually double by 2027 to a value of 9.1 billion euros, corresponding to an annual growth of around 15% [2].

## Future trends

Continued monitoring and digitalization of the entire food chain, from farm to fork, is indispensable to enhance sustainability, both environmentally and socially, while offering full transparency and traceability. Photonics technology advances are further required to enable a broader deployment, addressing the cost, size, efficiency and sensitivity, while also considering sample handling, technology useability, robustness, system calibration, seasonal and biological variation. In addition, these technologies are also envisioned to contribute to the EU Soil Deal aiming to manage and safeguard soils for future generations [11], as well as to

<footnote n="1">¹ Department of Applied Physics and Photonics, Vrije Universiteit Brussel and Flanders Make, Brussels Photonics (B-PHOT), Pleinlaan 2, B-1050 Brussels, Belgium</footnote>

<footnote n="2">² ZHAW Zurich University of Applied Sciences, School of Engineering, ZHAW Zurich University of Applied Sciences, 8401 Winterthur, Switzerland</footnote>

<footnote n="3">³ TOELT LLC, Research and Development, 8600 Duebendorf, Switzerland</footnote>

<footnote n="4">⁴ CNR—Istituto di Fisica Applicata ‘Nello Carrara’, 50 019 Sesto Fiorentino (FI), Italy</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Photonics technologies benefitting the full agrifood supply chain.</figcaption></figure>

the Farm to Fork Strategy that is defined within the European Green Deal and aims for sustainable, environmental-friendly food systems [12], and to the United Nations Sustainable Development Goals, and in particularly Goal 2 on Zero Hunger.

Driven by the novel developments, the following key trends can be identified:

1. Multimodal sensors combining different sensing technologies within a single unit or device. Combining multiple optical sensor technologies enables to extend the sensing capabilities, enabling multi-element detection, while offering an improved sensitivity and accuracy [10]. For example, combination of absorption and light scattering can provide both insight into the starch and moisture content while also giving insights in the firmness of the product, and combination of absorption and fluorescence spectroscopy might enable a multi-mycotoxin sensing covering both fluorescent and non-fluorescent toxins. In addition, sensor fusion can be exploited to improve the calibration, standardization, and robustness of the current sensors.

2. Miniaturization towards handheld devices and photonic integrated circuits (PICs) [13]. The availability of miniaturized and handheld spectrometer units has been empowering mobile spectroscopy [2]. PICs are driving further miniaturization, offering the potential of adding highly precise sensing functionalities on single low-power and miniaturized chips, giving rise to a cost-effective and scalable technology.

3. Advanced data processing based on machine learning (ML) and artificial intelligence (AI), benefitting the sensitivity and selectivity within predictive, prescriptive and adaptive processes within the whole food supply chain, including soil monitoring, weed management, disease detection, product sorting and food water management [14–16]. The generation of adequate and complete training sets is of indispensable importance to ensure a robust model, while the processing and sensing technologies are ideally being optimized in synergy. Digital twins (DTs), in combination with AI, might enable the generation of digital plants and animals, helping the prediction algorithm.

4. Sensor-data fusion and enhanced interoperability of data. Future efforts should be made towards the standardization of photonics data, and a network for data transfer and fusion is requested to ensure its implementation in smart agriculture.

5. Indoor, controlled farming, including vertical urban farming, driven by new sensor and lighting technologies. The controlled environment enables continuous monitoring of the plant's health, maximizing yield and reducing economic losses. State-of-the-art sensing technologies, making use of hyperspectral imaging (HSI), have indicated a 100× higher crop yield than traditional farming, and using 98% less soil and 95% less water [17], while offering the potential to measure macro-elements such as nitrogen, phosphorus, and potassium. Specialized energy-efficient LED and lighting algorithms can furthermore optimize growth and yield. Additionally, a trend towards the application of quantum dots
(QD) for spectroscopy and vertical farming can be observed, optimizing e.g. the sunlight spectrum in greenhouses and contributing to a more efficient light use [18, 19].

6. The journey of space exploration is expected to expand significantly in the near future. This expansion carries the potential to reshape our understanding of the cosmos while offering tangible benefits for life on our home planet. Challenges arise in meeting the nutritional needs of astronauts and future space colonists in the extreme space conditions, given the objectives of returning humans to the Moon, the long-term exploration of Mars, the growth of space tourism, and the continued operation of the International Space Station. The roadmap outlined here shows photonic technologies as highly suitable for various space food applications. Examples include the use of UV lighting for plant growth systems in space, as well as for water purification. Also, spectroscopic and imaging techniques are instrumental for identifying contaminants and monitoring nutritional content, and sensors are essential to detect spoilage or degradation of food products during storage [20].

Photonics for agriculture and food processing is today still emerging, but represents a fast-growing segment with a compound annual growth rate (CAGR) of 14% [20]. Lighting and UV disinfection are dominant segments, since lighting advancements enhance growth in greenhouses and vertical farms, and UV is crucial for e.g. irrigation and plant immunity. Second, imaging systems are of major importance, which can be used on drones and agriculture robots. Hyper—and multi-spectral imaging systems have been increasingly implemented, as well as thermal camera systems. We expect this will be further boosted by emerging imaging technologies, including polarization multispectral imaging, x-ray imaging, and THz imaging [21]. Additionally, we believe the miniaturization of the spectroscopic sensing technologies towards handheld and pocket-size devices has a huge potential that will revolutionize the agriculture and food industry in the coming years. In general, an increased use of photonics technologies along the full supply chain is envisioned, where photonics sensors are implemented in a wide range of devices and infrastructures, such as tractors, drones, robotic arms and mobile robotic modules, machine vision and inspection systems, and storage and transport. The increased availability of real-time and continuous monitoring tools will benefit an improved food quality and safety for all of us.

## Roadmap objectives

The roadmap aims to provide an overview of the state-of-the-art photonics technologies benefitting agrifood applications, including a view of their current challenges and future potential. Chapter 1 discusses image-based monitoring and ML processing, while chapter 2 focuses on spectrometry and spectral sensors. Chapter 3 gives a view of the miniaturization of optical sensors, including handheld devices and PICs. Chapter 4 discusses lighting and light source optimization, both for indoor farming and spectroscopy applications. Each chapter includes both general contributions that provide a general overview of the technology, and case-studies that illustrate the technology for a certain challenge within the agrifood industry. Finally, concluding remarks summarize the key messages of this roadmap.
# Chapter 1: Image-based monitoring and machine learning

## 2. Automated routines based on optical sensors and ground-based or airborne robots for monitoring crop plants in experimental trials and agricultural fields—what are the needed framework conditions

Stefan Paulus and Anne-Katrin Mahlein

Institute of Sugar Beet Research, Holtenser Landstr. 77, 37079 Göttingen, Germany

E-mail: paulus@ifz-goettingen.de and mahlein@ifz-goettingen.de

### Status

With the rise of digital plant phenotyping [22, 23] the use of sensors to measure plant traits in greenhouse screening and experimental field sites has become state of the art. By this, the situation of plants in a specific environment can be assessed and described using explainable parameters in high throughput, objectively and reproducibly. By using machine or deep learning approaches, relevant parameters, such as the reaction to a specific stress can be evaluated qualitatively and quantitatively [24].

Commonly used sensor technologies range from simple RGB sensors to more complex multispectral, hyperspectral, thermal sensors or 3D technologies [25]. For all digital phenotyping technologies, data quality and compromise between spatial and spectral resolution are crucial. To bring sensors into the field, commonly they are mounted on uncrewed ground (UGV) or unmanned aerial vehicles (UAV). These vehicles usually are remotely controlled or follow a predefined route over the field. During the last few years, these systems have greatly improved in terms of the level of autonomous operation for data collection [26].

All these technologies assess an enormous amount of data and adequate technologies are required for interpretation and subsequent decision making. The analysis of the datasets is mainly performed by using state-of-the-art AI models that are based on huge human-annotated datasets [27] (see figure 2). A typical workflow therefore combines (i) implementation of a field experiment to acquire training data (ii) data acquisition (iii) data annotation by experts, (iv) training of the ML and deep learning models with feature importance reporting and finally (vi) explanation and result interpretation using agronomic knowledge [28]. With focus on plant pathology and assessment of plant diseases, significant steps have been made regarding fully automated counting of plants and plant coverage description [29], autonomous rating on single plant and on plot area [30] and integration of data acquisition, interpretation and decision making [31].

However, most of the methods developed from research did not make it beyond the proof-of-concept level and need intense reworking to enter a product level which can be used and integrated by practical farmers. Nevertheless, these developments are an impressive demonstration of what can be expected in the field of smart farming and PA. Future development will focus on sensor and analysis in a short term. Long term developments will target online processing of the data in the field as well as integration of these routines into practical agriculture.

### Current and future challenges

A distinction is made between short-term and long-term approaches for plant phenotyping. Short-term means a period within five to ten years, while long-term describes a time horizon of more than ten years.

Imagine the scenario, that sensors are moved to and within the field by using unmanned vehicles like drones or robots. In recent years, huge effort has been made to increase the automation level of these vehicles in parallel with the car industry's motivation to produce autonomous cars. The big picture of an agricultural field managed by machines, autonomous flying drones [32] driving or walking robots [33] and the fully and integration of high resolution satellite data is hindered by incompatible regulations and laws as well as from unsolved problems in navigation and communication between actors on the field.

Today, researchers use different types of cameras, each camera type has different advantages and disadvantages. Complete camera-carrier systems are available as well as carriers with interchangeable sensors like RGB, multispectral or thermal cameras. Each system requires, in particular when multimodal data (data from different sources) is collected, internal and external calibration to provide proper and valid data that can be linked to further sensor data and provide georeferencing. While spectral signatures were used to recognize stress symptoms [34] the 3D geometry is described by stereo vision or 3D laser-scanners [35]. Each dataset is interpreted by sophisticated AI models. This adds semantics to the data and aims for the definition of a static or dynamic trait describing the plant status or behavior. AI means commonly deep learning approaches, which are much more difficult to explain, compared to traditional ML methods like SVM or Random Forest. However this explanation is needed when targeting or tailoring the hardware to the
<figure><img src="image_6.png" /><figcaption>**Figure 2.** The workflow of an agricultural experiment. It includes the agricultural question, the data acquisition, the AI processing pipeline including the human expert's input and the target variables like disease or biomass estimation, digital rating or yield prediction.</figcaption></figure>

application requirements. Finally, and besides all the complex research approaches, application within the field and in agricultural practice are demanded. A common example is the transfer from HSI in the greenhouse using hundreds of scanned channels to a cheaper and more robust multispectral open-field approach with five to six spectral channels [28]. If this transfer is successful, the next step of integrating the AI model into the sensor hardware, called edge AI can be tackled. Another aspect and challenge that needs to be addressed is the issue of data availability and data ownership. Current field experiments collect terabytes of data stored in ‘closed silos’ with metadata described in heterogeneous defined data formats. This makes it impossible to reuse or even to share the data within the community. Acknowledged standards are missing and we need approaches for data harmonization and sharing on an international level [36]). The current situation slows down transdisciplinary collaborations and the adaption of state-of-the-art algorithms to data of existing experiments.

Long-term challenges will need more time and attention as the user group is more heterogeneous. The biggest challenge is the integration of monitoring routines into practical agriculture as sensor based monitoring at different scales (ground (UGV), air (UAV) and orbit (satellites)), AI data analysis, strengthening the trust in AI solutions, defining evaluation routines for algorithms and standards, adapting rules and laws for the use of robots with a high degree of automation, improving the acceptance of digital methods and finally implementing digital flagship showcases with high radiance into the population (figure 3). Furthermore, the integration of multiple heterogeneous data collection stations for field parameters and microclimate into a (inter-) national forecast system for plant diseases and yield is required. This will lead to AI-supported decision-support systems for application recommendations and if used for an automated documentation system for agriculture. Finally, the main challenge will be to define management decisions and adopt daily routines based on the above-described automated approaches.

### Advances in science and technology to meet challenges

UGV and UAV are dominant tools in plant science and are used especially on experimental field sites where the area is limited, and a high imaging resolution is required. However, their application in practical agriculture is so far very limited. Nevertheless, during the last years robots have been established especially for row crops with a high cost recovery contribution like sugar beet, salad, and vegetables. These robots are mainly used for weed control but integrate sensor racks and on-board analysis systems for a direct decision-making and application like the control of a spraying nozzle. The extension to other non-row crops, to other plant diseases or even the integration of modern field management strategies such as spot farming are possible [37].

All methods that use online processing on the field require a proper AI model that is capable to interpret the data. The ability to explain the model generally has become the gold standard for building trust and deployment of AI systems [38]. Thus, explainable AI (XAI) developed in recent years describes a suite of ML methods that enable humans to understand, trust and better explain models. Nevertheless, the model interpretability must be considered in combination with constraints and requirements like data- and model explainability, fairness and accountability.
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Challenges for monitoring crops in the field. While robots, explainable AI, edge AI and data sharing are short-term challenges, the integration of monitoring routines into practical agriculture, the implementation of national prognosis systems and the use of automated monitoring systems are depicted to be long-term goals.</figcaption></figure>

As all AI methods rely on available data, data sharing is essential to encourage a multi-disciplinary cooperation. More and more funding organizations have integrated and made mandatory the FAIR principles (findability, accessibility, interoperability, and reusability) for data management [39]. In addition, the MIAPPE protocol has been published, an approach for Minimum Information About a Plant Phenotyping Experiment [40]. Although these tools exist, data sharing lacks problems like data misuse and missing harmonization, non-existing standards and the risk of eroding properties. Nevertheless, approaches based on blockchain ecosystems have been described in the literature, but a solid implementation is still missing [41].

Edge AI devices that integrate the model directly in the hardware just entered the market and enable the collection of semantic data instead of raw data. Providing sensors with a pre-trained model offers the chance to provide highly customized sensors tailored to the problem [42].

Nevertheless, most field experiments focus on a simple experimental setup to investigate methods for one specific factor or information of interest. We need more trials for data assessment in real world situations with mixed infections and combinations with abiotic stress like nutrient or water deficiencies, which increases the complexity of the problem. This complicates the transfer and integration of methods from research and experimental field sites into the demands of practical agriculture but is closer to the real situation.

Furthermore, local laws and regulations should make it possible to validate new applications and technologies in special test fields with limited scope, even beyond previously tested and permitted scenarios. This includes the utilization of larger payloads for research purposes, the use of innovative application technology, and deployment on prototype carrier platforms. This would decisively strengthen the innovative power and, above all, the contemporary adaptation of regulation.

A further aspect is the availability of climate data for the agricultural fields. In the case of plant diseases, for example, decision making can be supported by a proper microclimate forecast of temperature and humidity of adjacent weather stations. By integration of small microclimate stations distributed across the country and provided vendor-independent by companies and individuals, this disease forecast can be fundamentally enhanced in its quality by a national prognosis system. This also requires, an improved infrastructure and availability of specific technologies in rural areas.
Finally, monitoring and an automated decision-support system can help to automate the farmers documentation obligations. This requires a harmonized field and farm management system similar to ISO-bus which controls the devices of a tractor.

## Concluding remarks

This summary shows potential technical fields where a disruptive change in the way sensors, data and analysis models will be used in future can be expected. It has been shown, that either prototypes or at least concepts for the changes have been published or currently entered the marked.

Monitoring crop plants in the field is today a multidisciplinary approach covering technicians, farmers, AI-specialists, and engineers. The shown approaches for data sharing highlighted have perhaps the greatest impact, as data availability will encourage researchers worldwide to work on problems with existing data. The limitation here is not technical, but more a motivation problem. Data harmonization and the definition of obligatory standards is the first step to not just combine different disciplines in a team, but to motivate groups of different disciplines to work together internationally.

This, together with the expected development of new technologies such as sensor fusion, Edge AI, and robots will transform agriculture (see figure 3). Here, the idea of PA, doing the right at the right place at the right time, will be leading for all applications.

## Acknowledgments

This study was partially funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) under Germany's Excellence Strategy—EXC 2070-390732324.

In addition the study is partially funded by the German Federal Ministry of Food and Agriculture (BMEL) on the basis of a resolution of the German Bundestag. The project is sponsored by the Federal Agency for Agriculture and Food (Bundesanstalt für Landwirtschaft und Ernährung, BLE) within the framework of the digitization strategy of the BMEL, call funding code 28DE104A18.
## 3. Thermal IR imaging in agriculture. Recent advancements and future trends

David Perpetuini, Daniela Cardone and Arcangelo Merla

Department of Engineering and Geology, University ‘D’Annunzio’ of Chieti-Pescara, Pescara, 65127, Italy

E-mail: david.perpetuini@unich.it, d.cardone@unich.it and arcangelo.merla@unich.it

### Status

Thermal IR imaging encompasses the collection, analysis, and interpretation of data predominantly obtained within the thermal infrared IR portion of the electromagnetic spectrum, and it could be employed in agriculture both on the field or in a remote sensing configuration. IR remote sensing commonly acknowledged in vegetation research spans from 3 to 14 µm, which is further categorized into the mid-wave infrared (MWIR) range of 3–5 µm and the long-wave infrared (LWIR) range of 8–14 µm. The MWIR sensor detects radiation that is emitted by the Earth as well as radiation that is reflected from the Sun. On the other hand, the LWIR sensor is primarily influenced by the emitted radiation [43]. Extensive research has been conducted on the remote sensing of vegetation in the visible and near-infrared (VNIR) range (0.3–1.0 µm) and shortwave infrared (SWIR) range (1.0–2.5 µm), with a specific focus on the analysis of biochemical and biophysical properties of vegetation [44]. Nevertheless, the spectral data obtained from the VNIR and SWIR domains are insufficient in capturing the complete range of structural and chemical attributes exhibited by plants. To face this issue, hyperspectral and multispectral imaging can be employed. Specifically, HSI provides a detailed spectral analysis across a wide range of wavelengths, enabling the detection of specific biochemical properties of plants [45], whereas multispectral imaging, while less detailed than HSI, strikes a balance by providing sufficient information for various agricultural assessments without the complexity of hyperspectral data, which requires complicated processing for its interpretation [45]. Notably, it should be highlighted that the key absorption qualities of specific vegetative components, such as polysaccharides (e.g. cellulose) and leaf surface attributes (e.g. waxes and hairs), are situated in the thermal infrared domain [46]. The advancement of technology in the field of thermal IR spectrometers and sensors has facilitated the increase in the utilization of thermal IR imaging in the agricultural sector in recent years [47]. This can be attributed to the decreased costs of the equipment and the straightforward operational procedures, which have opened up possibilities for its implementation in various areas of the agricultural and food industries. Furthermore, efforts are currently being made to enhance its compatibility with precision farming practices [48]. The thermal characteristics of plant leaves are influenced by a multifaceted and heterogeneous interior structure, which encompasses a specific quantity of water per unit area. Therefore, the potential for conducting research on individual plants using thermal remote sensing is feasible due to the versatile, accurate, and high-resolution capabilities of infrared thermography [47]. However, the precision of thermal measurements is contingent upon the prevailing environmental circumstances, as these factors have a direct impact on the thermal characteristics of the observed crop. Hence, it is imperative to calibrate pictures based on meteorological conditions in order to facilitate the comparison of image data acquired throughout distinct measurement intervals and growth seasons. Therefore, calibration of images according to weather conditions is necessary for comparison between image data obtained during different measuring periods and growth seasons [47]. Moreover, in order to compare images, the soil water content (SWC) should be estimated [49]. In this perspective, an effective approach based on the use of multivariate statistical analysis, such as partial least squares (PLSs) regression, has been proposed for adjusting thermal measurements based on environmental variables [50]. This technique allows for the development of models that can predict thermal responses under varying conditions, thus enhancing the reliability of thermal imaging in agricultural applications. Thermal remote sensing technology has the potential to be applied across various agricultural materials and processes, encompassing situations where heat is either generated or lost throughout spatial and temporal dimensions [51]. The potential application of thermography in the field of agriculture encompasses various areas such as monitoring nurseries, scheduling irrigation and harvesting, detecting, identifying diseases and pathogens, crop health monitoring, evaluating ripeness, and detecting bruises, as reported in figure 4.

### Current and future challenges

Thermal IR technology has proven very beneficial in PA due to its ability to non-invasively and efficiently monitor and assess many aspects of crop health and environmental conditions. Specifically, thermal IR imaging allows academics and practitioners to get valuable information about how plants react to stress, improve the way resources are managed, and increase agricultural production. Research has highlighted the significance of thermal imaging in studying the relationship between plants and their environment, making
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Examples of the most spread applications of thermal IR imaging in agriculture. This technology could help in crop monitoring, ripeness and bruises detection, soil conditions evaluation and irrigation management. This figure is created in Biorender.com.</figcaption></figure>

it a widely used technology in the fields of agronomy and environmental sciences [52]. In addition, thermal IR imaging is widely recognized as an essential technique in PA for monitoring crop health, identifying stress, and analyzing the environment [53]. Concerning the evaluation of plant stress, thermal IR imaging has played a crucial role in evaluating plant stress in several scenarios, including those with high salt levels and water scarcity in rice farming [54] and citrus trees in greenhouses [55], showing its ability to evaluate the water condition of plants. Particularly, thermal IR imaging is able to assess plant stress responses by tracking fluctuations in leaf temperature, which indicate changes in plant physiology under different stress scenarios. Research has shown the efficacy of thermal imaging in identifying plant distress caused by reduced rates of photosynthesis and transpiration or scarce water condition [52, 56]. Particularly, thermal imaging allows for the real-time evaluation of plant responses to stress factors such as drought by monitoring leaf temperature and angle, which serve as early-stage markers of plant stress [57, 58]. Importantly, the surface temperature of plants is regarded as a highly responsive indication of stress, frequently anticipating the manifestation of apparent symptoms [59]. By integrating thermal imaging with multispectral imaging, researchers may get a holistic comprehension of the physiological reactions of plants under stress [60].

It is worth to highlight that thermal IR imaging was used to evaluate not only the temperature of the leaves, but also of the soil. Particularly, thermal imaging may be used to evaluate SWC and its distribution, and this technology provides extensive insights for this purpose. For instance, Tian *et al* developed a thermo-time domain reflectometry (T-TDR) probe capable of monitoring the water content and thermal characteristics of unfrozen soil, as well as measuring the amount of ice in partly frozen soils [61]. Similarly, Qiu & Zhao devised an algorithm using thermal imaging and the three-temperatures model (3 T model) to gauge soil evaporation and soil water status [62]. This technique showcases the capacity of thermal imaging to track the movement of water in soil and the pace at which it evaporates.

Thermal IR imaging has emerged as a helpful technique in assessing seeds in nurseries because of its non-destructive characteristics and capacity to provide important information on seed quality and viability. Research has shown that thermal imaging may be used to observe several factors associated with the health and quality of seeds. These factors include seed viability, the health of transplants, the quality of graft unions, and the identification of physiological diseases [52, 63]. Moreover, thermal imaging methods have been used to diagnose seed viability without causing harm during the process of seed absorption, through image processing and statistical algorithms [64]. This demonstrates the efficiency of thermal imaging in evaluating the health of seeds under controlled environmental settings [65, 66]. To examine the disparities between the coldest and hottest seedlings using thermal imaging, it is essential to take into account the thermal reactions of the seedlings under different environmental circumstances. Prior research has emphasized the importance of thermal imaging in observing how plants react to fluctuations in temperature and stressors, demonstrating its capability to assess identify physiological processes such as the closing of stomata and water deficiency stress [67, 68]. Regarding seedlings, the thermal response refers to the impact of temperature on the growth and development of plants. Studies have shown that exposing seedlings to warm temperature conditions during their first development stage might affect their ability to withstand stressors such as oxidative stress and transpirational water usage in the future [69].
<figure><img src="image_9.png" /><figcaption>**Figure 5.** A possible technological hub integration for agriculture management. This framework encompasses the integration of IR imaging, eventually mounted on UAV, with AI and IoT algorithms.</figcaption></figure>

Thermal IR imaging has been demonstrated to be a powerful method for identifying diseases in plants because it can record temperature differences linked to pathogen infections. Research has emphasized the capacity of thermal imaging to identify and monitor plant diseases by evaluating temperature fluctuations caused by stress imposed by pathogens such as pathogenic bacteria, fungus, nematodes, and viruses [70, 71]. Moreover, thermal imaging has shown potential in identifying the ripeness and quality of fruits and vegetables in agriculture and the food business [72]. Particularly, thermal IR imaging has been employed to discern various phases of fruit ripeness, particularly in unblemished tomato fruits [73], but also to identify excellence of fruits, as well as identifying injuries in fruits and vegetables [74].

**Advances in science and technology to meet challenges**

Concerning a spread and proper application of this technology in agriculture, it is worth to highlight that some limitations should be addressed and overcome. For instance, the accuracy of thermal imagery can be affected by environmental factors such as weather conditions. To meet this challenge, it is essential to allow the camera to acclimatize to local weather conditions, and to protect it with a casing in order to reduce air temperature effects on the sensor when mounted on UAV [75]. Moreover, algorithms that can adjust the data for weather influences should be developed. Another relevant issue is related to the data management. In fact, handling the vast amount of data generated by thermal imaging can be challenging, hence data management systems should be implemented exploiting IoT solutions. Concerning the improvements of the sensors and data analysis, it should be highlighted that a good spatial resolution should be provided by IR cameras when they are mounted on an UAV. In this perspective, more sensitive optics should be developed, and algorithms able to generate fine spatial resolution thermal images from coarse spatial resolution [76]. To this aim, also the integration of IR imaging with other techniques could help to overcome this limitation. In fact, combining thermal imaging data with geographic information system (GIS) can help in mapping and monitoring the spatial variability of crop fields, aiding in more precise agricultural planning and management [77]. Moreover, the employment of AI algorithms could aid in enhancing the quality of thermal images, by filtering noise and improving the clarity, which could lead to more accurate analysis [78]. AI could also help automate the data analysis process, saving time and reducing the workload on farmers. Moreover, AI models able to forecast crop yields or identify potential pest infestations before they might produce significant problems could be developed. AI can help in the implementation of PA, where farmers can make data-driven decisions to manage their crops more efficiently, and it can facilitate remote monitoring of crop fields, allowing farmers to detect issues without having to physically inspect the fields, thus saving time and resources. In addition, AI systems can provide real-time alerts for potential issues detected through thermal imagery, helping farmers take timely actions to prevent losses. A possible technological hub integration is proposed in figure 5.

**Concluding remarks**

IR thermal imaging has the capability to delineate subtle variations in temperature, providing several advantages in agriculture. The most relevant applications of such a technology are related to detect plant
stresses even before physical symptoms manifest, evaluating the condition of the soil, to manage the irrigation and to detect bruises, thereby heralding an era of preventative and proactive agriculture management. Leveraging advancements in sensor technology, coupled with the integration of AI, can potentially morph the current challenges of the employment of thermal IR imaging in agriculture into catalysts for further innovation, paving the way for intelligent agricultural solutions characterized by automation, predictive analytics, and real-time monitoring.
# 4. In-situ fluorescence responses for agriculture

Benjamin Gac<fnref n="1" />,<fnref n="2" />, Stephane Perrin<fnref n="3" />, Denis Trégoat<fnref n="3" />, Luiz Poffo<fnref n="4" /> and Antoine Fournier<fnref n="1" />

E-mail: b.gac@arvalis.fr, sperrin@photonics-bretagne.com, dtregoat@photonics-bretagne.com, luiz.poffo@unilim.fr and a.fournier@arvalis.fr

## Status

Chlorophyll fluorescence is a phenomenon based on the ability of chlorophyll molecules in the plants and algae to release a part of absorbed energy in form of light emission at specific wavelength. This release of absorbed energy is in direct competition with heat dissipation and with the photosynthesis process. Fluorescence measurements can be classified as passive (e.g. natural sunlight) or active excitation methods. Both passive [79] and active [80, 81] chlorophyll fluorescence measurement methods have been investigated using unmanned aerial, ground and manual systems [82]. By analysing the fluorescence emission light, the physiological status, stress level and environmental interactions of plants can be retrieved at different scales [83].

At the leaf level, chlorophyll fluorescence measurements allow the analysis of the plant photosystem II efficiency (i.e. conversion of absorbed light energy into chemical energy). Key parameters can then be extracted, among them, the effective quantum of the photosynthesis process [83]. Furthermore, these measurements have informed on the ability of plants to manage the absorption of light under various stresses, either biotic or abiotic [84].

At the plant scale, the global health of the whole plant has been estimated through the stress-induced changes in fluorescence patterns [85]. In addition, the photosynthetic capacity and efficiency have been studied at different phenological stages. Moreover, depending on the environmental conditions, the fluorescence response can provide information on the adaptation of plants to their surroundings [86].

At the canopy level, chlorophyll fluorescence has been used to probe the photosynthetic activity (PSA) over a large area and to reveal changes in ecosystem dynamics (e.g. responses to climate change and shifts in plant communities) [79]. Also, it allowed to map stress patterns for assessment of regional environmental conditions as well as potential agricultural or ecological challenges (figure 6).

Plants organs contain not only chlorophyll molecules, but also several other pigments (e.g. carotenoid, anthocyanin, and flavonoid). They play a role in metabolic processes such as flowering, light harvesting and photo-protection. These other pigments can also provide information about daily and weekly regulation processes but with a lower quantum capacity than chlorophyll molecules. Thus, the chlorophyll fluorescence signal must be strongly reduced to detect the pigments' emission. For this, field and indoor systems have been developed, enabling the detection of chlorophyll-fluorescence-compensated signals. The physiological interpretation of fluorescence signals from pigments other than chlorophyll has been useful to determine some stresses which do not impact the PSA [87].

## Current and future challenges

Plant physiology studies are generally conducted in controlled environments such as greenhouses and growth chambers [82, 88], to constrain most of experimental conditions. By conducting these studies outdoors using agro-equipment and automated platform [82, 89], it would be possible to investigate the influence of the natural environment on the complex response of genetics features to real agricultural management practices [90].

In the field and indoor, active chlorophyll fluorescence can be measured at the leaf scale using a leaf clip. At the plant and canopy scales, a phenotyping cabinet or a robot-mounted sensor can also be used. However, the duration of a full protocol of chlorophyll fluorescence active measurement is currently too long for high throughput plant phenotyping. To cope with this limit, the instruments thus require low photon noise and, when performed outdoor, low wind to avoid any leaf motions [89]. Greater integration of fluorescence measurement systems on field robots would increase the measurement throughput over agriculture experimental or production fields.

In addition, the influence of the leaf angle on the chlorophyll fluorescence signal is currently not fully considered. A fluorescence measurement combined with a geometric distribution of the canopy would enable the determination of the spatial distribution of PSA over the canopy [91].

<footnote n="1">¹ Arvalis, 45 Voie Romaine, 41240 Beauce la Romaine, France</footnote>

<footnote n="2">² Université de Rennes, CNRS, Institut FOTON—UMR 6082, F-22305 Lannion, France</footnote>

<footnote n="3">³ Photonics Bretagne, 4 Rue Louis de Broglie, 22300 Lannion, France</footnote>

<footnote n="4">⁴ XLIM UMR CNRS 7252, University of Limoges, Limoges, France</footnote>
<figure><img src="image_10.png" /></figure>

Active and passive measurements of the chlorophyll fluorescence provide complementary physiological information. Until now, the different measurement protocols, instruments and correlation of their signals are not yet compatible. Thus, instrument calibration and data fusion from different measuring methods stay a major challenge [92].

Chlorophyll fluorescence signals need to be accurately interpreted to obtain a quantitative estimate of the plant phenotyping traits, as well as for their physiological significance. For example, physiological imaging performed outdoor could significantly contribute to a better understanding of the spatial distribution within the canopy of the radiation use efficiency (RUE) [93]. RUE is an indicator that quantifies the ability of plants to convert solar radiation into biomass. Fluorescence-based physiological imaging could reveal the spatial distribution of PSA over the canopy, the impact of stress conditions and environmental interactions on the RUE, the influence of the canopy structure on the light penetration and the light-to-biomass conversion. Moreover, long-term outdoor studies of RUE could be conducted, leading to records of temporal dynamics of light with seasonal variations [94]. These experimental measurements could then improve RUE-assisted models for simulating plant growth and crop productivity.

At the cellular scale, biochemical, anatomical and ontogenetic properties of leaves could be investigated [95]. Interpretation of these results would allow to better understand the microscopic origins of the *in-situ* macroscopic behaviors of leaves parameterized by environmental interactions and photosynthetic properties. For this, handheld fluorescence microscopes should be developed for field use.

Pigments other than chlorophyll are easily detected and analyzed using leaf clips. However, outdoor acquisition of fluorescence signals of these pigments using contactless tools seems difficult due to their low emission intensity and overlapping emission spectra [87].
## Advances in science and technology to meet challenges

Recent advances on phenotyping platforms have allowed to quickly and automatically collect morphological and physiological traits of plants in the field [82]. Further increasing the acquisition rate of chlorophyll fluorescence signals would be beneficial for breeding (i.e. selecting cultivars for their tolerance to different stresses [96]). Nowadays, a multitude of instruments are embedded on these robotic platforms, such as RGB and hyperspectral cameras, or even lidar sensors. Eventually, they will be used to retrieve the orientation and height of the leaves and data from optical sensors will be combined, reinforcing the concept of multi-sensor data fusion [97].

Recent technological advances in illumination (e.g. laser diode) and collection (e.g. fast avalanche photodiodes and cameras) components appear promising for chlorophyll fluorescence measurements. Indeed, they will not only make it possible to increase the acquisition rate, but also to improve the signal-to-noise ratio of measurements in the field. These advances will minimize the influence of sunlight that occurs at the leaf, plant and canopy levels. Furthermore, in some applications, high optical power sources will allow to saturate the photosynthesis process activated by the absorbed light energy. The working distance (currently around 70 cm) and the measurement area (currently around 1 m²) [89] will thus both be increased.

Harmonic analysis of chlorophyll fluorescence in plants is a novel method based on the decomposition of the fluorescence signal into its harmonic components, providing a more detailed and nuanced assessment of the various physiological and photochemical processes occurring within photosynthetic organisms [98]. The advantage of harmonic analysis lies in its ability to disentangle different physiological processes that contribute to the overall chlorophyll fluorescence signal. By analysing the amplitude and phase relationships of these harmonics, one can better understand the dynamic responses of photosynthetic systems to changing environmental conditions, stressors, and various regulatory mechanisms.

Recently, the use of sensor-plants has been investigated on detached leaf in controlled-environment laboratories [99]. These genetically modified plants produce specific fluorophores when they are exposed to specific stresses. At given phase and amplitude interrogations, these fluorophores generate specific fluorescence signals, improving the early detection of stresses.

## Concluding remarks

Chlorophyll fluorescence is a promising optical technology, enabling to retrieve crucial *in-situ* information in agriculture, such as the health, physiological status and environmental interactions of crop plants. Optical and robotic advances do not yet permit high-throughput phenotyping fluorescence measurements in both passive and active excitation modes. Integrated in numerical models, they enable improvement in crop productivity and they strengthen the capacity to adapt to climate change. In addition, studying the *in-situ* behavior of leaves at the microscopic scale is needed to understand environmental interactions and photosynthetic properties.

## Acknowledgments

This work was partly funded by a National Association for Research in Technology (ANRT, France) Grant [No 2022/1856] and a FEDER (Fonds Européens de Développement Regional) [No EU001292] Grants. The authors thank both the Biophotonics Team of Photonics Bretagne and Department of Crop Phenotyping of Arvalis which contributed to the work described in the section, as well as Dr Gwenaëlle Lefeuvre for their constructive contribution to the drafting of the manuscript.
# 5. Hyperspectral imaging applications in the agri-food sector

Giuseppe Bonifazi and Silvia Serranti

Department of Chemical Engineering, Materials and Environment, Sapienza—University of Rome, via Eudossiana 18, 00184 Rome, Italy

E-mail: giuseppe.bonifazi@uniroma1.it and silvia.serranti@uniroma1.it

## Status

HSI is an advanced detection technique allowing to acquire the spectral reflectance associated to each pixel of the image [100]. HSI dates to the late 1970s when it was developed by NASA's Jet Propulsion Laboratory. Originally utilized in several remote sensing applications, in the last year's it has been made available for laboratory, industrial and 'ground-based' applications. Hyperspectral devices acquire hundreds, or even thousands of spectral bands, at very narrow intervals across the electromagnetic spectrum. In agri-food sector the spectra range usually spans between the ultraviolet (UV) (100-400 nm) and the SWIR regions (1000 and 3000 nm), passing through the VIS-NIR (400-1000 nm), representing these intervals the right balance between meaningfulness of the acquired data and device costs [101]. Following this approach, and starting from the collected spectral signatures, several actions can be performed, they are synthetically reported in the following:

* *crop health monitoring*, for disease early detection (i.e. fungal infections), or nutrient deficiencies, as well as water stress and pest infestations, thus allowing to adopt timely and correct pest control, irrigation, and fertilization strategies, thus minimizing the environmental impact [102, 103];

* *weed detection*, through the distinction of crop plants and weeds according to their spectral signatures [104];

* *soil analysis*, allowing to define maps embedding information on soil composition, moisture content, and nutrient levels across a specific area, to be used in precision farming for a rational and sustainable use of fertilizers and conditioners, reducing waste and improving soil health [105, 106];

* *fruit and vegetables harvesting quality*, detecting and quantitatively assess bruising, ripeness and defects (figure 7) [107–109];

* *food quality assessment*, real-time detection and identification of contaminants, foreign objects, or spoiled items inside the food manipulation chain for safety and process control [110].

All the previous mentioned actions will be object of further development not only through the *improvement of sensing devices*, and *strengthening detection architectures*, but also *implementing AI based logics*, instead of the chemometric approaches [111], currently adopted for data handling, analysis and consequent recognition/classification algorithms set up. Chemometrics, in fact, even until today remains a powerful analytical tool, presents some potential 'practical/operative' disadvantages mainly linked to the required sophisticated algorithms and computational power, the existing intrinsic lack of HSI knowledge and integration inside the existing agri-food systems and practices, the access to HSI technology especially in developing countries, the need to adopt standardized protocols, to collect, analyze and interpret data and, finally, the need of an extensive education and training.

## Current and future challenges

It is well known as HSI is powerful tool to acquire detailed information about crops status, soil composition, and quality of agricultural products (figure 8) [112]. The potentialities of HSI as a link in the different agri-food sector production/control stages, even if correctly addressed, have not been yet fully explored and implemented, especially with reference to the field. Current and future challenges are related to the development of further analytical and technological approaches and the resulting implementations.

* Analytical and technological developments will be mainly addressed to:

* Acquisition architectures (i.e. satellites, drones, handheld, or fixed units) [113] are expensive to acquire and maintain and can represent a barrier for smaller farms, thus they cost decrease represents one of the main future challenges.

* Miniaturization, allowing an easier integration inside already existing operative units and platforms. The fulfillment of this goal will enable a larger utilization of HSI detection and analytical approaches [114, 115].

* Development of hyperspectral sensors characterized by higher spatial and spectral resolution will allow to acquire more detailed and accurate information about crops, soil, and other agricultural elements [116].
<figure><img src="image_11.png" /><figcaption>**Figure 7.** Examples HSI based analytical strategies applied to olive fruit ripening and olive fruit flies (*Bactrocera oleae*) damage. Average reflectance spectra of olive fruit characterized by different degree of ripening (a) and attacked and un-attacked by mosquito fruit flies (b). Olive ripening prediction following a partial least squares (PLS) regression (c) and results of partial least squares discriminant analysis (PLS-DA) classification for damaged and undamaged (d) olive fruit. Reproduced from [107].</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 8.** Examples of strategies, procedures, and results to perform agricultural products quality assessment following an HSI based approach [112]. Preliminary to classification set-up, hyperspectral images are pre-processed performing a background removal. A combination of pre-processing algorithms is chosen to attenuate un-wanted occurring sources of noise, enhancing spectral differences among samples. Principal component analysis (PCA) is used as an exploratory analysis tool. Occurring outlier are successfully identified and removed from training sets used to calibrate classification models. A classification model is then applied. One of the most utilized is the partial least squares-discriminant analysis (PLS-DA). PLS-DA is a supervised pattern recognition technique. PLS-DA model is calibrated with pre-processed spectra extracted from region of interests (ROIs) depicting known samples, cross-validated and validated on a test set.</figcaption></figure>

<footnote>* Data handling and analysis. The fulfillment of the previous goals will produce as a result extensive data sets and specialized databases (i.e. hyperspectral libraries for different crops, diseases, soil conditions and, agricultural regions) requiring more sophisticated algorithms, and ML techniques (i.e. deep neural network,</footnote>
DNN) to process and analyze, both in terms of quantity and quality, this huge amount of information [117, 118].

* Customized hyperspectral solutions, tailored to specific agri-food applications, (i.e. pest detection, nutrient assessment, presence of bruise and/or molds, etc).

* Applications could be strengthened in many fields as:

* PA, performing a stronger integration with GPS and drones to optimize resource use, reduce environmental impact, and enhance the overall crops efficiency.

* Sensor based irrigation systems, allowing automated watering and oil moisture monitoring.

* Vertical farming, adopting sensing architecture, and related HSI based control logics, in hydroponics, aeroponics, indoor lighting control, allowing year-round production, reduced soils consumption and production waste minimization.

* Automation, HSI driven robots for planting, harvesting and weeding actions.

* Waste identification and classification, allowing finalized agricultural by-products reuse in a circular economy perspective.

Finally, farmers and agricultural professionals' acceptance and education on the applications and interpretation of hyperspectral data, together with the introduction of new legislative rules and protocols, considering HSI base strategies, are fundamental for future successful and sustainable HSI based applications.

## Advances in science and technology to meet challenges

The advances in science and technology, influencing the agri-food sector, will necessarily have to consider climate changes which will increasingly affect the methods of cultivation and handling of products. The development of technologies capable of supporting the control and evaluation of the state of crops in controlled environments (i.e. greenhouses) will certainly become increasingly important, also thanks to the fact of being able to operate in environments where the variability of abiotic factors is minimized. HSI, in such environments, will be able to strongly contribute to the overall optimization of cultivation processes. In this perspective some factors assume preeminent importance, that is:

* Devices miniaturization. Compaction of the optics (i.e. mirrors, lens, and filters), components integration (i.e. energizing source, detectors, and spectrometers) and microscale sensors development are three aspects of paramount importance to reduce not only dimensions but also weight, energy consumption, costs and, at the same time, increasing the possibility of HSI device integration in already existing analytical architectures, and/or customization for specific new applications. The fulfillment of this goal will allow a larger utilization of this technology.

* Data processing improvement, that is the possibility to implement 'on-board' advanced algorithms and signal processing to handle and analyze data or performing 'real-time' remote analysis, transmitting the acquired data, and receiving the result through wireless connectivity. Following this approach, the 'remote integration' with other devices can also be realized.

Both the two factors will contribute to facilitate collaboration, and the advanced scientific understanding, thanks to the possibility to share data at local scale on crop health, nutrient levels, and water requirements or, at larger scale, on diseases and pest monitoring, thus allowing to define and set up shared intervention strategies among the different subjects involved (i.e. farms, cooperative, agri-food industries, food distributors, local, regional or national authorities), optimizing efforts and resources. Data sharing, if from a scientific and a technical point of view presents great advantages, generates, on the other hand, the need to define clear rules on data ownership and a their correct and ethical 'shared' utilization.

## Concluding remarks

The use of HSI in the agri-food sector will play more and more an important role also in an Industry 4.0 perspective. Specific database can be generated and utilized embedding information to utilize to develop comparative evaluation logics (i.e. big data handling/analysis) for a better and faster understanding of new occurring phenomena and/or new process results. Furthermore, it will be necessary to define a sensory-based IOT information architecture where the HSI based information represents the core to define other possible investigation tools, both hardware and software providing information that can be aggregated, stored, and analyzed for more elaborate and holistic reasoning and inference. Finally, the data collected to produce elemental mapping related to crop and products evolutionary status could be also profitably handled using ML algorithms (i.e. DNN) finalized to perform their classification generating knowledge specific on crops growing and cultivation characteristics in a certain area, in respect of different environmental conditions and
variables. Finally, device miniaturization and players awareness will be the other two important aspects that in the next years will strongly influence the application of HSI in the agri-food sector.

Acknowledgments

This study was carried out within the Agritech National Research Center and received funding from the European Union Next-GenerationEU (PNRR—MISSION 4 COMPONENT 2, INVESTMENT 1.4—D.D. 1032 17/06/2022, CN00000022—CUP: B83C22002920007). This manuscript reflects only the authors’ views and opinions; neither the European Union nor the European Commission can be considered responsible for them.
# 6. Digital twin for smart multispectral camera design

Wenzhi Liao

Flanders Make, 8500 Kortrijk, Belgium

Department of Telecommunications and Information Processing, Ghent University, 9000 Ghent, Belgium

E-mail: wenzhi.liao@flandersmake.be

## Status

Spectral imaging allows seeing subtle optical signatures or light wavelengths invisible for human vision. This has potential for numerous industrial applications and a large economic impact by improving quality inspection, increasing automation and the development of innovative applications. Consequently, spectral imaging has become an active field of research in the world, illustrated by the many companies (e.g. Specim, Headwall, etc) and research institutes (IMEC, Flanders Make, etc) involved in this topic via basis research or hardware design and exploitation. Industrial applications can already be found in food sorting, recycling, medical and remote sensing [119]. On the other hands, recent advances in ML algorithms boost the target detection (identify specific objects in your product in real-time), quantification (measure the composition percentage of a component in your product) [120]. DTs are helping the manufacturing industry work smarter and more efficient [121]. By combining the spectral camera and ML methods, one can exploit DTs to optimize the configurations of camera design, decrease downtime and the cost, then perform continuous and automatic quality inspection on your food production line based on chemical analysis (by using the designed camera). This provides a non-destructive solution for chemical analysis and can be integrated with other modules (e.g. machine control and sorting systems), greatly reducing manpower in the production line, as well as increasing the efficiency.

## Current and future challenges

Popular commercially available hyperspectral solutions employ a push-broom approach combined with a grating and a silicon or InGaAs sensor to record the spatial-spectral signal, as shown in figure 9. Solutions that combine multiple sensors can cover wide wavelength ranges. State of the art examples are the Headwall photonics Co-Aligned VNIR-SWIR imager, with a spectral range in the VNIR + SWIR (400-2500 nm), obtained by splitting a single line into two spatial-spectral signals on a silicon CMOS and a mercury cadmium telluride sensor, or the popular Specim FX17 SWIR linescan sensor based on a InGaAs sensor, sensitive in the 900-1700 nm range [115]. When area images are desired, the state-of-the-art solutions employ selective illumination, filtering, or spatial-spectral patterns. An example of the solution (spatial-spectral patterns) is the IMEC sensors, a pixel array based on Si or InGaAs technology is overlayed with a band-pass filter pattern. Interpolation of each band to the full resolution is required and can introduce artifacts.

An alternative is to employ multiple camera systems, each with its own filter. This often results in relatively low-cost solutions for multispectral imaging systems, and is a popular solution for e.g. drone applications and index-based approaches. Alignment of the different images can then become an issue. This issue can be avoided by using a single camera with a rotating filter wheel, but this defers the alignment issue to the temporal instead of the spatial domain. A common challenge in the development and deployment of such industrial spectral imaging systems is the multitude of design decisions that have to be resolved, and the prior unknown performance of the resulting device for a given industrial processing task. A concrete example is the choice of spectral bands that will be integrated in the device. Depending on the technology, a number of bands can be used, e.g. by choosing the filters in a filter wheel or on a number of parallel cameras. The resulting spectral image can then be used for further processing, but obviously the design choices and algorithm details influence the performance of the desired task.

## Advances in science and technology to meet challenges

The design of smart spectral cameras that are optimized for solving certain tasks (e.g. target detection, classification, quantification) is in high demand in industry. As optimization exclusively with respect to task performance would often lead to infeasible or very expensive designs, a trade-off must be included that takes design complexity and price into account. Many technological approaches (as shown in figure 9) can be followed to obtain spectral signals or images, each with its own advantages, drawbacks, constraints and price point. To build a design space for spectral cameras, several topics must be addressed:
<figure><img src="image_13.png" /><figcaption>**Figure 9.** Left: push-broom system with spatial-spectral decomposition via grating [122]; middle: spectral imaging via variable LED illumination and/or tunable optical filter elements [123]; right: hyperspectral sensors from imec [124], with color-coded filter patterns sensitive in the VIS-NIR range.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Figure 10.** Task-driven smart industrial spectral camera design via digital twin and artificial intelligence (VLAIO SISCAI project) [128].</figcaption></figure>

* What are the possible inputs of the space, and in which range are they allowed to vary?

* What are the constraints on the inputs? Certain combinations are not feasible.

* What are the desired outputs of the design, e.g. optical performance, price estimate, design complexity, ...?

* How to model these outputs given a design space input and an 'idealized' known input?

With DT, we can build a multispectral camera in a digital way (before manufacturing a real physical camera), where a number of design choices (size, weight, power usage, processing capacity, framerate, etc.) and high-quality spectral inputs are given, a sensor or camera output signal can be simulated, as indicated in figure 10. This will allow us to find the optimal trade-off between system performance and cost. Recent AI methods will be exploited to solve this type of optimization problems [125, 126]. While many forms exist, genetic algorithms or particle swarm optimization methods have been successfully used for design space
optimization [127]. These algorithms are based on swarm intelligence, and iteratively try to improve a
candidate solution with regard to the objective function by having a population of candidate solutions and
moving this population around in the search-space according to simple rules (e.g. random searches or
mutations, attraction to the best solution, local random search around good candidate solutions,
recombining two good input sets into new input sets, ...).

Concluding remarks

Fast design of a multispectral camera in a smart way (where performances and prices are well balanced) that
can carry out real-time target detection, quantification, etc, is in high demand in industry. Especially for
earlier disease detection of plants and real-time food defect detection, where very high spatial and temporal
resolution spectral cameras are required to better characterize the tiny disease symptoms (or defects) under
very complex environmental conditions. DT can facilitate the design of multispectral cameras, where all the
functions-devices (light sources, lens, filters, etc.) required by the customers can be simulated before
manufacturing a real physical camera. AI can find the best combination of different devices in a faster and
smarter way. This way we can find the trade-off between system performance and cost. Future work will
consider environmental factors (illuminations, humidity, temperature, etc) in DT, to reduce the spectral
variations.

Acknowledgment

This work is financially supported by the VLAIO ICON Project (SISCAI-2021-0599): ‘Smart industrial spectral cameras via artificial intelligence’.
# 7. TeraHertz spectroscopy application in agriculture and food technology

Noureddin Osseiran, Mathias Vanwolleghem and Romain Peretti

Institut d'Electronique de Microélectronique et de Nanotechnologie (IEMN), CNRS-UMR 8520, Université de Lille, 59 652 Villeneuve d'Ascq, France

E-mail: noureddin.osseiran@univ-lille.fr, mathias.vanwolleghem@univ-lille.fr and Romain.peretti@cnrs.fr

## Status

The TeraHertz spectral range (100 GHz–10 THz) lies at the intersection of electronics and photonics, with 1 THz corresponding to a timescale of 1 ps, an energy of 4 meV, and a wavelength of approximately 300 µm (figure 11). THz spectroscopy is a non-destructive and non-invasive technique, well-suited for potential food processing and inspection applications due to the following properties. THz waves are non-ionizing and penetrate many common packaging materials, such as paper, cardboard, and plastic. These advantages have motivated numerous research studies in food science and agriculture involving THz spectroscopy. However, it is still a relatively recent technology and is in its early stages of development.

Historically, the so-called THz gap posed a challenge due to the high cost and limited efficiency of available emitters and detectors. However, advances in the 1990s greatly improved the feasibility of THz applications. Electronic sources, like multiplication chains and vector network analyzers (VNAs), now operate beyond 1 THz, offering extremely high signal-to-noise ratios and frequency resolution. However, these systems are expensive, with costs often approaching €1 million. Such precision makes them ideal for standardization. Meanwhile, advances in photonic sources, including time-domain spectroscopy (TDS) and continuous-wave frequency-domain generation, have enhanced accessibility. These methods rely on laser-based optoelectronic conversion and have progressively improved in performance, compactness, and affordability, with system costs now often below €100 000. This cost reduction and technological refinement have made THz spectroscopy increasingly viable for real-world applications and further integration into commercial instruments.

While THz spectroscopy's capabilities for non-invasive, remote sensing applications in food science and agriculture are promising, it remains in the academic phase of development. Current studies employ THz spectroscopy to investigate the picosecond-scale dynamics in solid, liquid, and gaseous states [129]. For instance, THz methods have been explored to monitor molecular crystal crystallization during sugar processing, detect adulteration in liquids, and identify volatile compounds in gases. However, to date, none of these applications have reached the industrial implementation stage, underscoring that THz technology's readiness for practical use in agronomy and food technology is still nascent.

## Current and future challenges

THz spectroscopy approaches in agrifood have been developed for applications across the three phases of matter: liquids, solids, and gases.

For liquids, THz spectroscopy leverages the strong broadband absorption of polar solvents like water, which has a significant absorbance in the THz range (e.g. ~200 cm⁻¹ at 1 THz). This absorption is highly sensitive to changes in physical or chemical parameters, such as temperature or composition such as the temperature or the composition, making THz spectroscopy a powerful tool for detecting compositional variations in liquids, such as measuring carbohydrate concentration [130] and detecting adulteration in products like acacia honey [131].

In solids, two main THz approaches are utilized. The first approach uses water's high absorption to monitor moisture levels in samples. In this application, samples absorb more THz radiation as their water content increases, enabling effective monitoring of food and agricultural products for evaluating drought stress in plants [132] or general moisture content. The second approach targets molecular crystals, such as carbohydrates, which exhibit specific THz signatures influenced by both molecular conformation and crystal structure [133]. Applications have included studies about detecting melamine in milk powder [134] motivated by the poison milk powder scandal, measuring flavonoids in waste orange peels [135], and assessing protein content in soybean powder [136].

For gases, THz spectroscopy is highly selective for volatile organic compounds (VOCs) [137] and is particularly suited for analyzing complex VOC mixtures in food and beverages, with potential applications in quality control for aroma profiling and safety monitoring. Advances in THz technology have improved sensitivity, allowing for detection of gas-phase contaminants at the ppm level [138]. Applications include ammonia detection in storage and transport settings (as reviewed in [139]), as well as microbial monitoring through gas by-product analysis. Recent THz spectroscopy advancements have achieved sensitivity levels
<figure><img src="image_15.png" /><figcaption>**Figure 11.** TeraHertz spectroscopy probes material in the liquid solid and gaseous phase. Maturity of the approaches differ with the phase but each of them can be used for agriculture and food technology depending on the application.</figcaption></figure>

comparable to SIFT-MS for detecting H₂S contamination in packaged Atlantic salmon (Salmo salar) [140], using cost-effective ultra-high-frequency electronics.

**Advances in science and technology to meet challenges**

The cost and performance of THz systems are crucial factors for their adoption in the agrifood industry. Since THz components and systems are still emerging, performance is steadily improving, and costs are expected to decrease with broader adoption, although certain applications remain beyond immediate reach. A major research focus is on achieving sub-ppm sensitivity without compromising compactness. One promising approach is extending the optical path within gas cells, either through multipass configurations or resonant techniques. A circular gas cell, for instance, can enhance the optical path length by 20 times [141], while ultra-high-finesse Fabry-Pérot cavities, relying on specialized quasi-optic methods such as low-loss corrugated waveguides with highly reflective photonic mirrors, now reach finesse above 3000. This enables effective path lengths of up to 1 km within a 48 cm cell, allowing detection of polar molecules with line strengths as low as $10^{-27}$ cm⁻¹/(molecule cm⁻²) [142].

In terms of practical applications, no current methods assess the freshness of packaged food without opening the package—a significant gap THz spectroscopy has the potential to address. Current THz spectroscopic systems are too expensive and bulky to fulfill the needs of this application. One solution is integrating THz systems with micro/nano technologies. Micromechanical systems (MEMS) are a mature technology that has driven innovations across fields such as photoacoustic spectroscopy. From a more general perspective, one is witnessing a trend where the leverage offered by MEMS is solving a number of key challenges in THz applications. Examples range from MEMS-based THz detectors, reconfigurable switches and filters, and tunable biosensors [143–145]. Besides mechanical microtuning of THz MEMS-metadevices, MEMS principally enhance THz devices by transducing an electromagnetic absorption into a mechanical nano/micro-motion. As such they are highly sensitive transducers, converting photoacoustic signals to electrical ones, as already proven in infrared applications [146].

THz photoacoustic gas spectroscopy is an emerging technique that has the potential to revolutionize food safety and quality control by providing a detectorless, compact, and non-intrusive way to detect trace gases in food packaging. MEMS transducers can be integrated into the packaging material and fabricated cost-effectively using CMOS foundry wafer scale techniques. Although sensitivities in current systems are limited [147], advancements in resonator-enhanced MEMS systems [148, 149] are expected to improve trace gas detection at atmospheric pressures (figure 12). Further sensitivity gains may also come from coupling the system with photonic modes in photonic crystals using advanced concepts like topological photonics or bound-in-continuum (BIC) modes.

Finally, data processing for THz spectroscopy, though still developing, has already shown potential with super-resolution techniques for TDS [150] and more rigorous fitting approaches [151, 152]. The integration of advanced data analysis, particularly ML, is anticipated to enhance sensitivity and leverage THz spectroscopy's high selectivity for complex, real-world applications.

**Concluding remarks**

THz spectroscopy has only recently become practical for research applications beyond its initial community, driven by advances in THz source and detector technology. These advances will continue spurred by 6G and future telecommunications needs, as well as continuous improvements in short-pulse laser sources. Several
<figure><img src="image_16.png" /><figcaption>**Figure 12.** Example of the combination of a MEM's and THz technology proposed in the TERAFOOD project (https://terafood.iemn.fr/). An integrated THz 1D THz photonic crystal cavity combined with a MEM's sensor to probe trace gas for freshness sensing.</figcaption></figure>

challenges remain before THz spectroscopy can fully meet real-world demands in the agrifood industry. Nonetheless, we are confident that current efforts to enhance the sensitivity, compactness, and integration of THz systems—along with modern advancements in signal and data processing and complementary technologies like nanophotonics and microfluidics—will pave the way for this technology's widespread adoption across the agrifood sector. From field to supermarket, THz spectroscopy holds the potential to establish new standards in food quality control, safety assessment, and process monitoring, for both gas-phase and solid-state applications.

**Acknowledgments**

N O and R P are grateful to the Région Hauts de France for the funding of the DEUS MAROONER project, M V thanks INTERREG France-Wallonie-Vlaanderen TERAFOOD and R P the ERC TUSCany 101089040.
## 8. THz sensing in agrifood: towards optimization of agricultural resources and industrial processing of food

Aparajita Bandyopadhyay¹, Karl Bertling², Krishnan Parameswaran³, Aleksandar D Rákic² and Amartya Sengupta²,⁴,*

¹DRDO-Industry-Academia Centre of Excellence (DIA-CoE), Indian Institute of Technology Delhi, New Delhi 110016, India

²School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, QLD 4072, Australia

³Cambridge Consultants, Boston, MA, United States of America

⁴Department of Physics, Indian Institute of Technology Delhi, New Delhi 110016, India

E-mail: loveofconcept@gmail.com, bertling@eecs.uq.edu.au, Krishnan.Valdes-Warren@cambridgeconsultants.com, a.rakic@uq.edu.au and amartya@physics.iitd.ac.in

### Status

Nestled between microwaves and infrared radiation, the terahertz (THz) range of frequencies (commonly considered as 100 GHz–10 THz) was mostly underutilized until the very end of the previous century, for both sensing and imaging purposes, due to limited availability of sources and detectors [153]. A combination of attributes including spectral specificity for a variety of molecules, high sensitivity to liquid water, non-ionizing radiation with very low energy photons, limited transparency through dry packaging materials, and greater imaging resolution than mm-waves, makes THz waves a suitable choice for several application domains, especially agrifood sensing and imaging. As illustrated in figure 13, some of these parameters are quantified for the most useful THz band between 0.1–3 THz that is easily accessible through electronic, optoelectronic, and photonic emitters/receivers [154]. At present, in the agrifood sensing domain, THz time domain imaging (THz-TDI) has been the predominant technique for proximal sensing [155], followed by tunable THz continuous wave (THz-CW) [156], low frequency THz VNA [157] and high frequency and high-power THz-quantum cascade laser (QCL)-based imaging including laser feedback interferometry (LFI) [158]. Application areas range from hydration monitoring, soil sensing, and plant pathology to food quality monitoring, adulterant detection, and others [155]. As illustrated in figure 13, THz radiation has a penetration depth of only 100–15 µms within liquid water over 0.1–3.0 THz. This shallow depth makes data acquisition, analytics and more importantly, adaptation of models challenging. While long-range applications such as THz-Lidar or radar, especially in the range beyond 1 THz is complicated due to the very high atmospheric water vapor absorption, laboratory-scale analytical applications in genomics [159], concurrent multispectral analysis at the molecular level [160] are becoming cornerstones for precision and efficiency in recent times. The agricultural sector benefits from these capabilities in optimizing crop management, soil analysis, and resource utilization, often based on the fingerprint of a biomolecule or inorganics, but also the corresponding structural and symmetry properties with advanced data analytics beyond the numerical realm and employing more ML algorithms [161]. This trend is essentially universal for probing of biomaterials in general, of which agri-food sensing and imaging is a subset. As in the THz range, experimental acquisitions from biomaterials often produce highly complex and counterintuitive results due to the compound effects of collective excitations, multiple scatterings, surface dynamics, varying hydration potential, and other effects [162].

### Current and future challenges

As highlighted in figure 13, agrifood imaging was one of the first applications shown by the pioneers of modern THz technology field in 1995 [163] and within 5 years, the first commercial THz system was available [164]. By 2006, several commercial systems were offered using THz-TDS and CW techniques. However, it was only in 2011 that the first real-time in-line THz sensor for pharmaceutical applications was deployed [165] and as of 2023, there is still no commercial user platform based on THz imaging technology that could be operated in an agricultural field/facility or as an in-line quality control system in food production. Several excellent reviews [153, 166, 167] are available that discuss various aspects of THz imaging technology in general, and some of salient advantages and limitations of this technology in the specific domain of agri-food sensing and imaging are listed below:

* ✓ THz is a non-ionizing radiation and considered to be safe even over long exposure time with low to moderate intensity to human users as well as to the commodities, such as food and agricultural products.

* ✓ Limited penetration of THz through dry packaging materials, such as, paper, plastic, cloth, jute etc.

* ✓ High sensitivity of THz range to liquid water: a natural ‘chemical tag’ in agrifood domain.
<figure><img src="image_17.png" /></figure>

✓ Better spatial resolution of THz range than mm-wave range; super-resolution imaging possible through near-field (NF) technique.

× High cost of THz imaging system relative to the value addition, in terms of specificity, sensitivity of chemical detection etc.

× Reduced limit of detection (LoD) of chemicals in THz range in the presence of liquid water.

× Slow image formation because of mostly raster scanning image acquisition techniques in vogue.

× Absence of standardized THz database of agrifood materials.

While several industry reports and reviews [168] continue to uphold very positive outlook towards the microwave and THz technology in agri-food applications, the footprint of this technology in this specific domain is still very limited compared to other traditional sensing schemes, such as, Raman, IR, HSI etc [155, 169]. While we note that AI-based analytical techniques are increasingly being employed [158, 161, 166, 169, 170] to overcome some of the challenges of the THz range mentioned above to deliver robust decision dynamics along some specific target objectives/applications, fundamental obstacles remain in this field, as discussed in the next section, which the entire THz community must address in coming years.

### Advances in science and technology to meet challenges

One of the major obstacles in the path of expected and yet underachieved performance of THz technology in agri-food domain, is the absence of reliable and robust traceability of key material characterization/ measurement parameters in the THz frequency range. This serious need for calibration and metrology has been mostly met in an ad hoc fashion with traditional microwave measurements reaching up to ~100 GHz, while IR techniques extending to below ~10 THz, far beyond their specified frequency range, resulting in several inaccurate, conflicting results [162]. Thus, to address this 'serious gap in information' related to metrology, other than the lingering 'THz gap in capabilities' [153] in terms of availability of basic technology enablers, the immediate critical necessity is to produce standardized databases. This work must include quantifiable THz measurements of material properties and the interactions between THz radiation and measurement systems with calibrated tools and instruments with repeatable protocols [171], especially in
<figure><img src="image_18.png" /></figure>

terms of sample preparation and handling [172]. As the entire community will have access to such a database, THz application engineers will have a greater chance of success to solve the other lingering issue: development of advanced THz instrumentation to produce cost-effective, compact, user-driven THz system/platforms customized for agri-food industries. This aspect is not only limited to the domain of agri-food but would also address the bigger cross-section of industrial applications of THz technology. In this way, one more emerging trend to solve several challenges of THz sensing, especially related to the compromised LoD of chemicals, is to develop a platform that could combine several sensing/imaging orthogonal technologies. Other than direct applicability to agri-food production and distribution, THz imaging technologies could also prove critical in shaping future schemes to reduce agri-food wastage, recycling, forming ‘green’ farming and food distribution practices through replacement of traditional irrigation, fertilization, and other resource-hungry practices by ‘smarter’ and ‘more precise’ assessments. It must be considered, however, that any innovative approaches of THz technologies to solve a critical problem in future is likely to be more successful when it could be implemented with the least quantum of addition and alteration to the ‘traditional’ structure and organization of the agricultural practices and food distribution system around the world. These key aspects of future advancements required in the THz science and technology are illustrated in figure 14 in the order of their hierarchical importance.

**Concluding remarks**

In recent years, application of THz sensing and imaging has experienced steep advances both in the areas of agriculture (potential to manage irrigation smartly, soil characteristics monitoring, classify seed varieties etc) and across food (potential to identify defects in production, quality control, process monitoring, testing of target pesticides/microbes/toxins in food etc) production and distribution chain [155]. And yet, this technology domain has not reached its ‘promised peak’ as discussed in the previous section. It is likely that with the development of diverse and novel THz-range enablers (source, detector, active and passive optics etc), techniques as well as other feeder technologies, such as, conveyor belts, robotics, UAVs etc, this technology would be able to solve several current issues: restricted LoD, slow rate of operation, higher cost of implementation, total production cost, ease of customization, data calibration and protocol standardization to establish itself as a mainstream option to enhance or complement other existing technology platforms in the domain. Moreover, a larger footprint of THz technology in future, especially in agri-food sector, is rather expected as this technology is already compatible with some of the other key emerging technologies, such as, AI, IoT, Big Data and others.
Acknowledgments

A B and A S acknowledge support from (a) Defence Research & Development Organization (DRDO) vide Grant # DFTM/03/3203/M/01/JATC, (b) Department of Atomic Energy—Board of Research in Nuclear Sciences (DAE-BRNS) vide Grant # 37(3)/14/01/2016 BRNS/37015, and (c) Department of Science and Technology—Science and Engineering Research Board (DST-SERB) vide Grant # PS-FTP/2013/78. ADR and KB acknowledge support from the Australian Research Council (DP200101948, DP21010334).
# 9. Lidar—remote sensing for assessments in the agro-food sector

Sune Svanberg

Physics Department, Lund University, SE-221 00 Lund, Sweden

E-mail: sune.svanberg@fysik.lth.se

## Status

Lasers and laser spectroscopy have developed tremendously during recent years (see, e.g. [173]). The impact in the agro-food sector has been important. Lidar constitutes one class of applicable techniques in that area. As the name implies, the most common lidar application is to transmit a short laser pulse and measure the time lapse till an echo is recorded and in that way obtain a distance measurement. Such measurements with compact sensors have recently developed tremendously for assistance in autonomous vehicle driving. Generally speaking, a lidar sensor can be mounted as a fixed installation, or it can be made mobile with a vehicle, or airborne for convenient coverage of large areas. Recent developments include wide-spread use of drone-based compact systems operated under GPS control. Using a high repetition rate and fast scanning, powerful height imaging is readily achieved from the point swarm recorded. Terrain topography and canopy structures can be assessed and direct provide important information on terrain features, growth status etc, aspects of immediate relevance to agriculture. Information on soil erosion, moisture levels, leaf-area-index (LAI) and many other parameters can also be assessed [174–179].

In addition to distance/height measurements, spectroscopy can be added to provide higher information contents in the lidar data. The differential absorption lidar method (DIAL), where pulses with two neighboring wavelengths are intermittently transmitted, can provide information on important atmospheric gases. Then one wavelength is chosen to match a strong absorption line of the gas of interest, while a second one, off-resonant, serves as a reference [173]. Local carbon dioxide contents reflects the efficacy of photosynthesis and can be measured by lidar [180]. Fluorescence spectroscopy is a further technique, which can be combined with lidar. Then, a lidar pulse of UV wavelength will induce fluorescence in a remote target, and by using a gated array detector the received fluorescence can be recorded and analyzed. Chlorophyll gives rise to the two clear peaks, around 690 nm and 740 nm, and their intensity ratio bears information on the concentration of chlorophyll. By also analysing blue-green fluorescence, certain stress conditions, due to, e.g. draught can be studied. A review on some experience in vegetation remote spectroscopy and imaging in given in [181], while fluorescence lidar assessment of the fertilization level can be illustrated by experiments on rice [182]. Lidar fluorescence measurements on vegetation using a mobile, pulsed lidar system is illustrated in figure 15(a).

Agricultural pests constitute a serious threat to crops. Again, lidar techniques can be applied to flying insects, and characterize them with regard to number, wing-beat frequency and fluorescence properties [183–185].

## Current and future challenges

Clearly, agriculture and food production face significant challenges to make actual conditions match the needs of an increasing population in the presence of global climate change. There will be strong demands on additional information on crops related to, e.g. fertilization level, and stress due to, e.g. drought and insect attacks. The contribution to the field by lidar will rely on the availability of powerful and cost-efficient systems. As we have noted, drone-based pulsed systems for ground and canopy profiling already have reached a high level of sophistication, but major further improvements are in sight. Data beyond ranging traditionally required complex and heavy pulsed systems. A new promising development is provided by instead using cheap CW lasers in combination with a compact triangulation detection geometry based on the Scheimpflug principle, which allows sharp focusing along the whole transmitted laser beam [187, 188]. The array detector can be read out at a high rate allowing the registration of also fast wing-beats [186, 189]. Such a lidar system is illustrated in figure 15(b), and data on insects and on raindrops, against which discrimination can be performed using polarization properties, are shown in figure 15(c).

A range-resolving fluorescence lidar system carried by a drone is described in [190], and is illustrated in figure 16 [190]. Here, a two-dimensional detector is employed, where pixels corresponding to increasing range along the laser beam are recorded in one direction, and the full fluorescence spectrum at the corresponding measurement point is recorded in the other direction. Such a system is capable to map out the canopy with individual trees and display the fluorescence spectrum with high spatial resolution. This is achieved by using multiple flight passes under GPS control to generate a three-dimensional image, where color coding can display selected fluorescence features.
<figure><img src="image_19.png" /><figcaption>**Figure 15.** (a) Mobile fluorescence lidar system transmitting UV laser pulses, which induce fluorescence in distant vegetation. Two recorded fluorescence spectra illustrate the difference between leaves with high and low chlorophyll contents. Reproduced from [181]. © IOP Publishing Ltd. All rights reserved. (b) CW Scheimpflug lidar system equipped with two lasers, which intermittently transmit light with perpendicularly oriented linear polarization. (c) Insects and raindrop signals distinguished by differences in light depolarization, as recorded using a system of the type shown in b). It can be seen how insects are driven down from higher heights into the laser beam at the onset of rainfall. Reproduced from [186]. CC BY 4.0.</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>**Figure 16.** (a) Drone-based multi-spectral lidar system providing range-resolved fluorescence spectra. (b) Data of the kind shown in (a) recorded along multiple drone flight-lines controlled by GPS. Two trees are imaged in false color, mapping the ratio between the two near-IR chlorophyll fluorescence peaks. Reproduced from [190], with permission from Springer Nature.</figcaption></figure>

Elastic backscattering or laser-induced fluorescence (LIF) can be pursued with low-cost multi-mode semiconductor lasers, which are readily available at power level up to several watts. By instead using a narrow-band single-mode laser, which can be tuned to a specific absorption line of an atmospheric constituent, it is possible to map out the concentration of that gas. While water vapor mapping is much needed for improved weather forecasting, the details of the carbon dioxide distribution is of particular interesting in the agricultural sector. Such monitoring using a Scheimpflug system was recently demonstrated [191].

Lidar can also have important applications in horticulture. Short-range fluorescence assessment of maturation status of fruits has been demonstrated [192], and could help in determine optimum harvest time.

### Advances in science and technology to meet challenges

Lidar techniques can be expected to have a major impact in precision farming, but clearly many challenges have to be addressed. Widespread application will certainly rely on effective sensors being available at affordable price. Here, the new Scheimpflug lidar concept, both in its elastic (one color) and inelastic
(multi-color) variety may be of particular importance. Such systems can be made quite compact and thus be adapted to drone deployment. Generally speaking, drones will become increasingly important in agriculture. The development of the most straight-forward application, ranging, towards ever better performance is benefitting from the tremendous expansion of lidar use in vehicle autonomous driving. Fast lateral scanning also in active, multi-spectral drone-based 3D-lidar systems would constitute a natural but challenging development.

The achievement of full eye safety even in unsupervised systems will become feasible following the future availability of high-power, UV semiconductor lasers.

The demand for ever increasing productivity in agriculture and keeping prices reasonable has led to a strong increase in the use of pesticides, which is a worrying development both in terms of environmental pollution and loss of biodiversity. Powerful monitoring of flying pests would help keep control of invasions, with counter measures reduced to the required minimum. It could also help biological farming to increase its competitiveness.

At the same time as the hard-ware is developing, it is equally important to ensure that software, which produces pedagogical and intuitive representations of the complex raw data is implemented.

It is a disturbing fact that a high percentage of crops, and in particular of fruits, is wasted due to inadequate storage. Foods are frequently stored in a modified atmosphere to increase shelf-life, and the correct operation of packaging machines must be ascertained. Also, it is well known that foods can also be in perfect shape after the printed 'best-before' date has passed. Laser spectroscopy can be used to non-intrusively perform gas analysis of food packages [173]. While now available for packaging machines, a natural development would be a short-range lidar handheld sensor for the customer. Combined with reflectance and fluorescence capability, further powerful assessment of the actual food status could become available. The integration in future smart-phones may become a reality.

**Concluding remarks**

Optics and photonics have had a major impact on most sectors of human activities, and their impact does not seem to diminish. Climate, environmental and health issues are receiving an ever increasing interest from individuals and authorities alike, and in many cases approaches based on light as a potent information carrier are utilized. Active techniques, utilizing an artificial light source as compared to passive ones (utilizing ambient radiation) are generally more potent but also more expensive. Lidar monitoring of targets (including in the agro-food sector) can be performed from very large to very short ranges, corresponding to satellite-born or hand-held sensors. Lidars of different level of complexity/capability are already influencing the agro-food sector substantially, but current trends indicate the possibility of an increasing impact. Miniaturization and cost effectiveness based on drones and CW laser sources, to complement pulsed ones, will be important.

**Acknowledgments**

The author gratefully acknowledges the fruitful collaboration with colleagues and students in the agro-food sector, and the financial support from many agencies.
# 10. Digitization and digital twins in agriculture

Małgorzata Kujawinska

Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. Andrzeja Boboli St.,
02-525 Warsaw, Poland

Central Office of Measures, 2 Elektoralna St., 00-139 Warsaw, Poland

E-mail: malgorzata.kujawinska@pw.edu.pl

## Status

Digitization in agriculture is currently taking place at all steps within a food commodity chain [193, 194],
namely:

- the production of agricultural inputs, including fertilizers, pesticides and plant breeding,

- the farming process, including irrigation and crop growing (precision-agriculture equipment, robotics, farm management platforms and agronomy advice and information)

- the trade of raw agricultural produce;

- food processing, where raw agricultural products are manufactured into finished food products,

- food packaging; transport and storage;

- food retail and consumption, which includes both supermarkets and smaller independent traders selling their goods directly to end consumers.

At the farming level digitization is based on the use of IoT [195], big data analysis, as well as AI [196, 197]. At the food processing level, the main trend in emerging digital technologies involves automation or robotics, such as optical systems that automatically sort fruits and vegetables [198], and IoT technologies such as collaborative robots that communicate with one another. Some companies are also experimenting in smart packaging and food printing [199]. In food transportation, different types of sensors (mainly spectroscopic) and analytics allow for greater control over the conditions of the food transportation and storage in order to monitor the freshness, increase the safety and avoid losses through spoiling [200].

Digitization is creating a platform for introducing into agriculture the concept of DTs, which have been widely adopted in other disciplines. DT, according to Clark's definition is ‘*a dynamic virtual representation of a physical object or system, usually across multiple stages of its lifecycle, that uses real-world data, simulation, or ML models combined with data analysis to enable understanding, learning, and reasoning*’ [201]. In general the benefits of DT applications include reduced production times and costs, hiding the complexity of integrating heterogeneous technologies, creating safer working environments and establishing more environmentally sustainable operations. DTs in agriculture involve the creation of virtual models that mimic the characteristics and behavior of physical farms. The architectural framework for implementing DTs in agriculture, as proposed by Peladarinos et al in [121], is shown in figure 17.

It highlights the key components and interactions of DTs in agriculture, showcasing their role in data collection, preprocessing, modeling, simulation, analytics, decision support, visualization, and control, ultimately aiding in optimizing farm operations and improving decision-making processes. The DTs' models are built using a combination of data from various sources, including sensors, drones, satellites, and historical records. By continuously collecting and analysing data, DTs provide farmers with valuable insights into crop health, soil conditions, weather patterns, and resource utilization. The interconnections between all components play the vital role as they are essential for functioning of the DT system in integrating manner. More details on the possible frameworks for implementation of DTs in agriculture are given in [121, 202–204].

Despite to these benefits DT are presently not widely utilized in agricultural applications as shown in the literature reviews published by the groups from the Wageningen University [205, 206] and TU Wien [207]. The published research in Agricultural DTs in the period from 2017 (the first paper [208]) up to 2022 can be classified within the following applications and use cases [207]:

**Crops: monitoring, resource optimization and cultivation support.** This is the most frequent category with the most common usage of photonics sensors for data collection. The representative examples include:

- a multi-agent approach to create a crop DT by monitoring growth and predicting outcomes through simulation [202] as well as detect anomalous states and recommend appropriate measures for correction [203];
<figure><img src="image_21.png" /><figcaption>Figure 17. The architectural framework for implementing DTs in agriculture. Reproduced from [121]. CC BY 4.0.</figcaption></figure>

- an orchard DT; using 3D lidar and cameras a DT of each tree is created and updated with the goal to provide real-time conditions monitoring and decision support on a large number of trees and reducing the farmers labor requirements [209];

- a farmland DT for plant monitoring and decision making support based on low-powered IoT wireless sensor network and drone imagery [204];

- crop irrigation optimization for yield improvement based on IoT based DT using both localized (sensor on site) and remote sensing methods [210];

- a smart service for potato harvesters by the creation of a ‘Digital (twin of a) potato’, which allows manufacturers and farmers to minimize damage of potatoes and ensure optimal calibration of a potato harvester [211].

**Livestock: Monitoring, management and optimization.** The representative examples include [212]:

- DT of a pig farm to monitor pig health status and prevent diseases through controlling the animals parameters and the working conditions of a farm;

- DT of a cow to predict heat, estruses and health according to their behavior,

- DT of bee colonies based on GPS tracking system along with humidity, temperature and weight sensors.

**Urban, controlled environment and aquaponic farms.** The representative examples include:

- vertical planting IoT-based DT with focus on sensing and controlling the conditions via light and misting [212];

- DT of a soft-shell crab farming based on low cost IoT network with focus on real-time water quality monitoring [208];

- DT for general application within commercial greenhouses with focus on optimization of energy consumption and management [213].

The other applications and use cases in agriculture refers also to such areas as environmental transparency, machinery management (similar to industrial solutions) and improving of meat and fruit value chain [207].

**Current and future challenges**

While DTs in general hold immense potential, in agriculture there are several additional challenges that need to be addressed. The most significant challenge refers to those operations which have to do with living subjects, like animals and plants or perishable products. While DTs are ideal to provide insights into such complex systems, their integration with the physical twin can be difficult. Also the open questions include how model fidelity regarding biological systems can be evaluated and what methods can be leveraged for facilitating interactions between Physical and DTs. In agriculture, human-made systems like agricultural equipment could be easier to synchronize with the virtual system, unlike natural systems like animals or land parcels. The second challenge lies in the spatio-temporal dimension of DTs in agriculture, which range from an individual plant (or even its part such as a root or a leaf) to twins of land parcels, farms, or regions [205]. Therefore it is necessary to introduce a variety of instruments for data collection and consider effects across these scales. On the temporal dimension, agricultural DTs differ due to the slower response rates of their physical twins. Agricultural processes like growing of plants tend to evolve relatively slow, so at least initially
there is no need for high-frequency interactions between physical and DTs. On the other hand, methods to reconcile data with significant temporal differences will be required, in-order to take full advantage of available data sources and provide context to entity behavior at different temporal-scales.

Another ongoing issue is data quality and pre-processing. Without good data, models will inevitably under-perform. Improvements in outlier detection, data generation and integration of diverse data sources as well as scenarios for ensuring data accuracy and reliability are needed. These issues are linked with the need of standardization at different levels of architectural framework (figure 17). In future, in a fully connected and interactive world, different physical assets will be interacting with each other and the corresponding DTs also have to do so. In order to facilitate these interactions there is a need for standards cutting across different domain areas. These standards can range from hardware (IoT sensors, their networks) to the file format of the data storage, data compression and the data protection requirements addressing differences in the laws when spread over different geographical areas and disciplines. Obviously, there is also a need for standardized protocols and frameworks to enable interoperability between different DT platforms [207].

Another aspect affecting the adoption of DTs in agriculture is that the community has to build trust in the interplay of the DT components for its correctness. This trust is essential to create DTs that can accurately represent the inner workings of a system, propose maintenance strategies and alternative ways of management. Yet, building this trust in agriculture is difficult, because many decisions affect living systems where, unlike in other disciplines, consequences can be hard to reverse [196, 214].

Last but not least, the frequent lack of data culture in agriculture and the cost of implementing and maintaining DT systems, including sensors' networks and other data collection means, can become a barrier for small-scale farmers, as well as one of the sources of friction between big and small agriculture producers [194, 214].

## Advances in science and technology to meet challenges

To address the first challenge, a big variety of new, low cost sensors are under development [195, 215] the design and technology of sensors with extended spectral, resolution and precision capabilities are already present in research labs and are step by step transferred to industry. The main directions in photonics sensors in agro-food applications, as shown in this paper, are focused on extension of the applied wavelengths ranging from UV through visible, MIR up to THz. Having this in mind, implementation of a variety of spectroscopic methods (FTIR, fluorescence, Raman) into miniaturized sensors by means of photonics integrated circuits, fiber optics, Lab-on Chip, MEMS/MOEMS equipped with multiplicity of functionalities is on its way.

Other technologies generating the highest interest within agriculture right now are unmanned aerial systems (UAS), fitted with visible, IR, or/and hyperspectral cameras and other imaging sensors (e.g. LIDARs). The improved versions of the existing sensors are able to consistently and quickly collect higher-resolution RGB images over increasingly larger areas, being both ruggedized, with low power consumption and inexpensive (UAVs frequently crash) [204]. Current improvements of detectors' spatio-temporal capabilities of physically larger sensors, such as better resolution and faster shutter speeds, already allow higher and faster UAS flights for PA.

Current research European projects include the DT development for individual crops and their individual biophysiological environment, as can be seen at the University of Natural Resources and Live Sciences [207] and at the University of Wageningen [121, 205]. These examples involve also successful implementation of ML and deep learning techniques. Moreover, it was shown that AI significantly processes IoT data through continuously enhanced algorithms that leverage updated user data. Leveraging and fusing/integrating such time-series data, a user's DT has the potential to suggest actions for controlling or mitigating potentially hazardous situations regarding crop health and productivity, or even failures of safety mechanisms of farm machinery, plant production equipment, post-harvest transportation or storage equipment situations in which DTs may visualize alarm signals to stakeholders.

On the other hand, with progressing digitization and development of DTs there are significant efforts to build entire agriculture ecosystem. The notion of 'farm to plate' involves several entities which work in tandem to provide quality food to the end consumer in a just-in time environment. With IoT technology at each stage of the supply chain, it introduces potential cybersecurity threats since a security breach in just-in-time distribution system could also have a serious cascading effects on the entire supply chain. For this reasons the important works related to cybersecurity in the domain are in progress [216]. This in turn, together with increasing precision and reliability of sensors and algorithms, build the trust among users' community [194, 196].
Concluding remarks

Climate change and increase in the world population are the chief drivers for technology in agriculture. Automation motivated by digital revolution, sensing technologies, 5G, and agricultural robots and aerial systems equipped with 2D/3D machine vision accompanied by resource optimization must be adopted in the agricultural sector in order to influence higher and qualitative crops. DTs have the potential to revolutionize agriculture by optimizing resource utilization, reducing environmental impact, and increasing crop yields. However the close collaboration between technicians, farmers and policy makers is strongly required to drive DT adoption.

As technology advances, DTs role in food production, despite the challenges and unsolved till now problems, will continue to grow. DTs' growth and expansion cannot be imagined without the significant participation of photonics technology in DTs. To successfully develop DT, data capture and delivery through efficient, reliable and low cost sensing and data communication system is evidently necessary. Also the community must become familiar with a variety of related technologies including IoT, AI and big data—most of these technologies are still considered new fields of experimentation in agriculture. Once the community gains confidence around them and adopt best practices for their application, we are likely to see more DTs with innovative photonics solutions emerging in prototype and deployed levels in world agriculture.

Acknowledgment

This work was partly supported by the statutory funds of the Institute of Micromechanics and Photonics, Warsaw University of Technology.
# Chapter 2: Spectrometry and spectral sensors

## 11. Ensuring food for all: state of the art and future trends

*Daniel Cozzolino*

The University of Queensland, Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St. Lucia, Brisbane, QLD 4072, Australia

E-mail: d.cozzolino@uq.edu.au

### Status

Advances in sensing technologies, including those belonging to vibrational spectroscopy [mid (MIR), near (NIR), RS and HSI], have allowed for the development of innovative applications in a wide range of fields, among others, in agriculture and food sciences [217, 218]. The implementation and utilization of vibrational spectroscopy has been possible by the current improvements in non-destructive analysis, developments in handheld instruments and spectrometer miniaturization [219], the ability of analysing low sample volumes, the advances in chemical and pattern recognition algorithms [220], as well as the implementation of automation and remote analysis including at/in/on-line analysis [221].

The use of vibrational spectroscopy not only allows the non-destructive analysis of samples but also provides with information and knowledge about the entire food supply and value chains, including food manufacturing processes [217, 218]. Nevertheless, the utilization of vibrational spectroscopy encounters different challenges, some of them related to how the information can be extracted from the technological application, and to the evaluation of complex data sets [217, 218, 220].

Traditionally, these techniques have been used to measure or predict the chemical composition of different foods (e.g. protein, moisture, starch) instead of relying on cumbersome and time consuming traditional chemical analysis that are unsuitable for evaluating fresh food ingredients and products. Consequently, these techniques have been incorporated as tools into management systems along the food supply and value chains [222] as they are relatively inexpensive, easy to operate, and require little or no sample preparation allowing them to be used in-line or at-line [221].

The utilization of vibrational spectroscopy has been associated with assuring and monitoring food safety to protect consumers from the increased number of issues associated with food security (e.g. fraud, contamination), which are linked with mortality, human distress, wellbeing, and economic burden (figure 18) [223]. In this context, vibrational spectroscopy provides useful information to make informed decisions about the safety of specific foods and food ingredients [222]. These techniques have also been of fundamental importance in the digital economy and the fourth revolution [218].

### Current and future challenges

It is well accepted that vibrational spectroscopy techniques have clear advantages over traditional analytical methods, as these methods can be objective, cost effective and provide rapid, reproducible results, with continuous operation [217, 218, 224]. Public health and consumer safety authorities are demanding the development of rapid screening techniques that can be used beyond the laboratory, such as in the field, manufacturing plants, supermarkets and even at home, to evaluate not only the composition and quality of foods but also to guarantee their safety [217, 218, 224]. Although vibrational spectroscopy has been proven to be of importance along the food supply and value chains, the adoption of these technologies has been slow due to different factors. For example, accessibility to instrumentation due to its cost has been one of the main factors limiting its utilization, as defined by the different stakeholders in the food sector. Other factors, such as the skills available to implement and fully exploit the use of vibrational spectroscopy, are constrained by the lack of instructors and teaching resources to deliver appropriate training. There is a need for training, not only of researchers, but also for different stakeholders including managers and consumers. Challenges associated with assuring confidence in the use of the different vibrational spectroscopy methods, as well as how to effectively use the data, are still main barriers faced by the food industry. Furthermore, is still not clear for many users how these technologies will contribute to sustainability as well as to preserve the identity of the food processes. The implementation of vibrational spectroscopy-based technologies requires the creation of mathematical models that are developed to quantify different chemical constituents of the sample, to monitor composition, contamination, fraud, etc.

Above challenges are in contrast with the advantages that the utilization of vibrational spectroscopy offers, such as a real-time monitoring throughout the food manufacturing process (e.g. dairy, brewing), specificity, sensitivity, and adaptability to almost all the different aspects in the supply and value chains.
<figure><img src="image_22.png" /><figcaption>**Figure 18.** The farm to fork journey of food and food ingredients is monitored using vibrational spectroscopy and chemometrics.</figcaption></figure>

Furthermore, these technologies have demonstrated their contribution as tools in sustainable food systems, contributing to reducing the cost (e.g. less energy, reagents) and time of analysis in the circular economy.

### Advances in science and technology to meet challenges

Different advances and developments in the state-of-the-art of vibrational spectroscopy are contributing to the incorporation of these technologies into the food supply and value chains. Some of these advances are discussed in the section below.

**Handheld and miniaturization.** The miniaturization of spectrometers (NIR and MIR) has evolved through developments in micro-electro-mechanical (MEMS) and micro-opto-electro-mechanical (MOEMS) systems [219]. Handheld and portable spectrometers have been based on different types of detectors (e.g. array detectors, single detector devices) that have contributed to the collection of data using different wavelength ranges (e.g. indium gallium arsenide is the preferred material when manufacturing detectors) [219]. The utilization of single-detector systems has determined the development of alternative and low-cost instruments [219].

**HSI.** Other important technological advances are those associated with developments in HSI techniques. By combining imaging techniques with vibrational spectroscopy, the sample can be analyzed both spatially and spectroscopically [111, 225]. The incorporation of images has enhanced the ability of vibrational spectroscopy to gain in-depth detailed information about the concentration and distribution of the different compounds or metabolites in the sample's composition [111, 225]. The utilization of HSI in food analysis has boosted our capabilities towards a better understanding of the intrinsic properties of food [111, 225]. One of the benefits of the use of HSI is the ability to simultaneously collect information on the structural and chemical composition of the analyzed food [111, 225].

**New algorithms and pre-processing.** Pre-processing is highly important to obtain a balance between the quality of the spectra and the meaningful information available on the food. Pre-processing is required to achieve a trade-off between the data analysis and interpretation, ensuring the accuracy and precision of the final outcomes. Progress in data analytics and chemometrics has contributed to developments in pre-processing techniques where new methods continue to appear, such as multivariate curve resolution (MCR) combined with wrapping, block-scaling and multi-block approaches [220, 226].

**Network of instruments.** The network of instruments will allow to detect different levels of information offering a holistic analysis of food quality and safety, and consequently of the whole food supply and value chains [227]. The utilization of a network of instruments will contribute to the improvements in the analytical ability and efficiency of the current measurement systems. The use of networking also allows for the evaluation and detection of different and multiple analytes where the integration of different instruments is one of the main advantages. In addition, the increased connectivity has made it possible that the user can remotely access the calibration, minimizing the calibration time. This technology has fostered and increased the communication between food producers, retailers, authorities and consumers, in their decision-making process. Overall, these technologies will enable and improve our understanding of food systems allowing for a better management of natural resources, making food production more sustainable.
## Concluding remarks

The routine and commercial implementation of vibrational spectroscopy in the food supply and value chains is in continuous development. Regardless of the advances in technology and instrumentation, the implementation of vibrational spectroscopy requires the incorporation of data mining and ML tools. Although, for some users, the utilization of vibrational spectroscopy is still described as a ‘black box’ approach.

In addition, the skills available to implement and fully exploit the use of vibrational spectroscopy are constrained by the lack of instructors and teaching resources, hampering to deliver appropriate training. There is a need for training, not only of researchers, but also for the users and ultimately the consumers.

A highlight on the use of vibrational spectroscopy is that it fosters the integration of knowledge from many research fields (e.g. spectroscopy, analytical chemistry, data analytics, biology, biochemistry and chemistry). This key characteristic has determined the unique multidisciplinary nature of vibrational spectroscopy in food sciences. Preserving the multidisciplinary approach is critical for further development and implementation of these technologies, moving from the black box approach to a robust analytical method. These techniques are allowing for novel solutions that will challenge the way food is analyzed, as well as increase the efficiency, sustainability, flexibility, agility, and resilience of the food supply and value chains, from the farmer to the consumer (e.g. fork to farm approach).

## Acknowledgment

The support of the University of Queensland is acknowledged.
# 12. NIR spectral sensors to ensure the integrity along the entire food chain

Dolores Pérez Marín, José A Entrenas de León and Irina Torres Rodríguez

Non-Destructive Spectral Sensors Unit, Department of Animal Production, Faculty of Agriculture and Forestry Engineering (ETSIAM), University of Cordoba, Campus de Rabanales, 14071 Cordoba, Spain

E-mail: dcperez@uco.es, p82enlej@uco.es and Irina.Torres@uco.es

## Status

Product and process control, authentication and monitoring are key issues for the food industry, affecting its efficiency, trademark image, economic performance, and sustainability. The increasing complexity of the food supply chains has created more opportunities for food fraud, resulting in many food crises over the years, which have reduced consumer confidence in the industry, inspection bodies and policy makers. This situation has led to an increased focus on developing measures to ensure the food integrity throughout the entire food chain, 'from farm to fork'. To verify the integrity of food products it has been necessary to update the current analytical and sampling control systems, through the development of modern and cost-effective analytical methods. Recent developments in NIRS and advances in big data analytics offer new opportunities to provide solutions to the industry in this challenging area.

Near-infrared (NIR) technology is defined as a vibrational spectroscopic technique, based on the absorption produced when near infrared radiation ↓ between 780 nm and 2500 nm ↓ vibrates at the same specific frequency as the molecular bonds of the product being analyzed [228, 229]. NIR spectroscopy was developed as an analytical technique in the late 1960s by Karl Norris working on agricultural applications, while the first commercially available instrument appeared in the early 1970s [230]. Since then, NIRS technology has evolved enormously and has become increasingly widespread and accepted because it enables non-destructive, rapid, accurate, cost-effective and environmentally friendly analysis of a wide range of food products as well as the determination of numerous chemical-physical and even sensory properties of these products. In addition, this technology provides a sustainable digital signal, which enables NIRS to be used as an IoT device [231, 232], allowing the interconnectivity and interoperability with other signals and other information and communication technologies (ITCs). This last aspect is essential in the current global and food industry situation, where everything is digitalized and connected, now living the transition between Industry 4.0 and 5.0.

In the early days of NIRS, the applications were developed using benchtop instruments operating under controlled laboratory environments. However, over the last few decades, the important advances in the design and construction of a new generation of miniaturized and highly portable NIRS instruments [233] have opened up many new possibilities for the on-site, in-line and on-line use of NIR sensors, which can now be used along the food chain, enabling real-time decision making in the field. Importantly, some of the new applications were unthinkable just a few years ago (such as the analysis of *in vivo* animals, in-field measurements of crops and trees or on-line process control), making food control systems now more robust, intelligent, massive, interoperable, and accessible [234–239]. Figure 19 shows some examples of these applications made possible by recent advances in instrumentation. In (a), the spectral analysis of spinach plants in the field using a portable instrument is shown, carried out to determine their dry matter, soluble solids and nitrate content, facilitating decisions on optimal fertilization strategies, harvesting time and industrial destination. In (b), an analysis of live hens is shown to measure their abdominal fat, allowing on-farm decisions to be made on their photo stimulation and laying induction. This analysis was carried out with an ultra-compact and very low-cost spectrometer, essential characteristics for its final implementation in the industry. Image (c) illustrates the on-line analysis of tenderloins, applied to determine their chemical composition, to authenticate the breed of the animal and determine whether the meat has been previously frozen or not, as key aspects of the quality, safety and traceability of meat products. Finally, (d) the use of a very low-cost portable NIRS sensor suitable for the analysis of olive oils in transmission mode using disposable vials is presented. This example focuses on the determination of quality parameters (acidity, waxes, esters, fatty acid profile, polyphenols or sensory profile) and commercial classification of the product, thus providing industrialists with an affordable analysis methodology for self-control in olive oil mills and to provide consumers with more detailed information on a high-value food product.

## Current and future challenges

As mentioned above, the use of NIRS sensors at different stages of the food production and supply chain is currently gaining particular importance as an integral and essential technology for food traceability, quality, and safety assurance. Although significant progress has been made during this century, it should be
<figure><img src="image_23.png" /><figcaption>**Figure 19.** *In-situ* measurements using the portable NIR instruments.</figcaption></figure>

emphasized that many challenges remain to can exploit the full analytical potential of NIRS for food integrity assessment. The complex nature of food poses significant challenges to the application of NIRS, requiring the development of specific and product-dedicated databases for each type of product, sophisticated mathematical data processing or special considerations regarding sampling and population variability. The main challenges and opportunities of NIRS as an intelligent sensing device are:

* Improvement of on-site analysis, based on new instrumentation or data processing technologies, also enabling new applications. Although NIR sensors are extremely well suited for portable or hand-held use, their potential has only relatively recently been recognized at the research level, and in many cases specific developments and adaptations are still needed before they can be adopted by the food sector. Moreover, the industry needs to understand the benefits and opportunities that this technology can offer before decisions can be made about its deployment.

* The high heterogeneity of agri-food products requires improved sampling to increase the amount of product inspected and reduce the associated risks. The role of NIRS for the massive sampling of bulk products and production batches should be further explored, highlighting one of its main advantages—not fully exploited—which is the possibility of reducing the sampling error and therefore the overall analytical error, by having a more precise response.

* Integration and combination of the NIR signal with other sensors that can provide complementary information, to solve complex issues that cannot be solved with a single sensor.

* The development of NIRS as a non-targeted method, spreading the potential of using the NIR spectrum as a product fingerprint that can provide information on its quality, safety and authenticity, and be essential for process control. This approach is based on exploiting the full potential of NIRS sensors, without the need to train them with other analytical reference methods [240, 241].

* Improve scientific knowledge of methods for establishing, maintaining, and transferring robust calibrations suitable for industrial and commercial use. For this technology to work in the food industry, robust and accurate models are needed that are also reproducible across a network of instruments. In addition, the management and interpretation of the huge amount of data generated by NIR sensors working on-line in continuous operation may require the use of more sophisticated mathematical data processing methods, such as ML and AI.

### Advances in science and technology to meet challenges

The incorporation and adoption of NIRS technology by the food sector as an integral analytical technology to solve food integrity issues is being demanded by more and more companies every day. In order to successfully transfer the knowledge developed at the research level to the commercial and industrial level, intensive research is being carried out to address the aforementioned challenges associated with the use of NIR spectral sensors throughout the food supply chain.
<figure><img src="image_24.png" /><figcaption>**Figure 20.** NIRS analysis integrated in a support decision platform.</figcaption></figure>

One of the most significant and notable advances, as already mentioned, has been the improvement in instrumentation. However, real-time monitoring throughout the food chain can still be limited by equipment limitations and data transmission issues. The development of more sensitive, portable, robust and versatile NIR sensors is crucial to obtaining accurate and reliable data. Today, a wide variety of NIRS sensors with different optical designs and characteristics tailored for different applications are being introduced to the market. A notable trend is the miniaturization of these sensors, using technologies such as linear variable filters (LVFs), Fourier transform (FT) or MEMSs [242], making them more portable and accessible, and enabling on-site analysis, eliminating the need to transport samples to the laboratory. Furthermore, the development of low-cost and user-friendly NIRS software will make it accessible to a wider range of users and companies, in particular SMEs (small and medium-sized enterprises), for whom cheaper technologies are key to access and successful implementation in their production processes.

The role of HSI, including the spatial to spectral dimension, is clear when combining different sensors to obtain more information. HSI can make measurements in different ways and regions of the electromagnetic spectrum (NIR reflectance, Raman, fluorescence and thermal spectral regions), increasing the potential for inspection. The combination of several spectral signals in the same instrument is still under development, with some prototypes appearing, such as the multi-mode handheld instrument combining visible, NIR and fluorescence [243]. These instruments result in a more complex signal, which would imply the need for advances in calibration techniques and chemometrics to extract the valuable information to obtain robust predictive models. The integration of data analytics and ML or AI enables real-time analysis, anomaly detection and prediction of potential problems in food products, thus improving overall quality control.

In addition, new approaches to data processing based on the development of compliance tests, the definition of standards and deviations, and the use of spectral information to design early warning systems provide additional benefits and business cases for implementing NIRS as a non-targeted method.

Finally, another key aspect related to spectral sensors and application development is that the systems have a user-friendly interface that provides only the necessary information to the final user, and can be integrated into the industry dashboard to facilitate automated decision making. In this regard, NIRS companies focus on ease of use and user interface, making them more accessible to a wide range of users and applications. In addition, the combination of the NIRS signal with ITCs, including IoT platforms, has facilitated remote monitoring and data sharing (figure 20). This connectivity enables the development of a more efficient and transparent platform, both for the producer to self-monitor the process and to provide more detailed information to consumers through more comprehensive digital labeling of food products.

**Concluding remarks**

NIR spectroscopy, coupled with chemometrics and, more recently, the combination of NIR data with ITCs and other sensor signals, could play a crucial role in the development of intelligent systems for in-situ food quality control and authentication. Although this technology has made significant progress in recent years, a number of issues and challenges remain to be addressed, mainly related to instrumentation improvement,
data processing and sensor integration and interoperability. Furthermore, exploring the use of NIRS to treat
any non-compliant product, both for process control and fraud detection purposes, based solely on spectral
information, could undoubtedly revolutionize the assessment of food integrity and the design of modern
control systems at an industrial level. In the future, new advances in science and technology, combined with
AI strategies (algorithms, DTs...), will lead to increased accuracy, versatility and accessibility, consolidating
NIR spectroscopy as an invaluable tool in a wide range of applications. These advances will contribute to
improved quality control, safety and efficiency in various industries.

NIRS, coupled with chemometrics and, more recently, the combination of NIR data with ITCs and other
sensor signals, could play a crucial role in the development of smart systems for in-situ food quality control
and authentication. Although this technology has made significant progress in recent years, a number of
issues and challenges remain to be addressed, mainly related to instrument improvement, data processing
and sensor integration and interoperability. Furthermore, exploring the use of NIRS to treat any
non-compliant product, both for process control and fraud detection purposes, based solely on spectral
information, could undoubtedly revolutionize the assessment of food integrity and the design of modern
control systems at an industrial level. In the future, new advances in science and technology, combined with
AI strategies (algorithms, DTs...), will lead to increase accuracy, versatility, and accessibility, consolidating
NIR spectroscopy as an extreme valuable tool in a wide range of applications. These advances will contribute
to improved quality control, enhanced safety, and greater efficiency across various industries.

Acknowledgments

This contribution to the Roadmap has been developed under the projects 'NIRS technology and IoT platforms form for ensuring the integrity of high added value Spanish products: Iberian cured ham and extra-virgin olive oil' (PID2019-111387RB-I00), NIROLEO: Innovation in olive oil quality control in olive mills through the use of NIRS sensors (GOPO-CO-20-0002) and the COST Action 'European Network for assuring food integrity using non-destructive spectral sensors' (SensorFINT, CA19145).
# 13. Application of near infrared spectroscopy in the analysis of cultivated mushrooms

Marietta Fodor, Eszter Benes, Anna Matkovits and Zsuzsa Jókai

Department of Food and Analytical Chemistry, Hungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology, H-1118, Villányi St. 29–31. Budapest, Hungary

E-mail: dr.fodormarietta@gmail.com, Benes.Eszter.Luca@uni-mate.hu, Matkovits.Anna@phd.uni-mate.hu and Jokaine.Szatura.Zsuzsanna@uni-mate.hu

## Status

For thousands of years, people have used edible mushrooms as a tasty food and a vital source of nutrition (figure 21). The biological value of mushroom proteins, their polysaccharide and trace element content, and their specific endogenous antioxidant property play important role in nutrition. The main components of secondary metabolites specific to mushrooms are phenols, phenolic acids, tannins, lignans, terpenoids and flavonoids. They are valuable source of vitamin D, riboflavin, niacin, pantothenic acid, ergothioneine [244]. They have a wide range of physiological effects: stimulate metabolism, reduce serum cholesterol levels due to their polyunsaturated fatty acid content, and regulate blood sugar levels. Many beneficial effects are attributed to mushrooms in enzyme induction, anti-tumor cell action, and in defense against oxidative stress damage [245, 246]. Mushrooms are regarded as a great source of protein in addition to being low in calories. Although plants typically lack the amino acid composition needed by the human body [247], the proteins found in mushrooms and animals have comparable amino acid compositions, although their ratios differ [248].

The global market for mushrooms was worth USD 50.3 billion in 2021, but it is expected to grow by 9.7% annually between 2022 and 2030 due to increased consumer demand. The growth of the vegan population worldwide, requiring a protein-rich diet, is expected to be a major driver of the market over the period forecasted [249].

The Food and Agriculture Organization (FAO) and the World Health Organization (WHO) now suggest mushrooms as ‘super foods,’ and consumers like them because of their high nutritional content [250].

Expectations of quality and, by extension, of the analytical techniques employed in quality control are changing as a result of increased consumer demand. The need to create quick, dependable, eco-friendly, and non-destructive imaging and spectroscopic methods based on NIR light has consequently grown.

## Current and future challenges

The conditions of development, harvest, and storage all affect the color, shape, size, flavor, nutritional value, microbial spoiling, and enzymatic or chemical deterioration of mushrooms. Even before harvest, ongoing quality monitoring is required since the biochemical processes of decomposition and degradation cannot be foreseen. For this, NIR techniques (HSI or Spectroscopy—NIR) are used.

Whole mushrooms can have surface damage detected using HSI. Principal component analysis (PCA) was applied to the hyper-cubes through score image analysis to provide an unsupervised classification method for impact damage diagnosis. It is clear that the HSI method is most likely to highlight the distinctions between tissue that is healthy and tissue that is damaged. The discolored samples might have an overall classification accuracy (OCA) of more than 79% when PCA is applied to the spectral data. Mushrooms exposed to freezing were identified using HSI before the telltale symptoms of freeze-damage, such as browning and shrinkage, became apparent [251]. For this, linear discriminant analysis (LDA), a supervised classification algorithm, was employed. In order to detect brown blotching and enzymatic browning of mushrooms with OCA ranging from 69.4%–95%–100%, chemometric evaluation of HSI data was conducted using PLSs discriminant analysis (PLS-DA). An OCA ranging from 92.84 to 94.19% was attained for mechanical damage identification using support vector machines (SVMs) and artificial neural networks (ANNs). Due to their high water content (more than 90%) and vitamin content, mushrooms are among the most susceptible agricultural products to deterioration. In summary, figuring out the moisture content of mushrooms is a critical matter. Using the obtained hyperspectral data, PLS (R² = 0.89) or MLR and PCR models were developed and effectively used to predict the moisture content of the whole mushroom [251, 252].

Numerous attributes can be quantitatively predicted using analytical approaches based on NIR radiation in conjunction with other chemometric methodologies. Examples include the moisture content [253] which is important for shelf life and freshness, the protein content [254], which is mainly composed of essential amino acids and is mainly responsible for the nutritional value, and the polysaccharide content [255].
<figure><img src="image_25.png" /><figcaption>**Figure 21.** The valuable properties and effects of mushrooms.</figcaption></figure>

Zaukuu *et al* [256] have provided a thorough summary of the fundamentals of molecular spectroscopy techniques, such as HSI and NIR, and how they can be used to identify and adulterate mushrooms.

**Advances in science and technology to meet challenges**

Since the genetic traits of the strains are what mostly define quality in the case of mushrooms, breeding the correct individuals and choosing strains with superior traits are both crucial to the cultivation's success [257]. Unlike cultivated plants that have been grown for generations, the gene pool from which the mushroom can be bred is quite small, giving the breeders a limited base from which to work. Most of the mushroom varieties that are grown today can be traced back to an old breeding variety of oyster and agaricus mushroom hybrids. In addition, intensively cultivated mushroom species, do not have 'gene centres' unlike in the case of plants [258].

In cultivation, there is a huge variability in color, morphology, spore formation, texture, etc... From a taxonomic point of view, this variability poses a challenge in species identification. Due to the difficulties in identifying taxonomic groups, breeders and researchers are often faced with misunderstandings and may encounter difficulties. These challenges include, for example, selecting the right environmental parameters or breeding new strains with good production potential based on the researchers' identification of the variety. For all the above reasons, variety identification is the most important step when it comes to avoiding taxonomic confusion and improve genetic traits [259]. Classification of cultivated mushroom samples is also of key importance regarding the possibility of geographical origin and variety identification [260].

On the one hand, to distinguish edible and non-edible mushrooms, and on the other hand, to detect fraud resulting from the mixing of excellent-, poor quality mushrooms for economic gain are an important objective. Mushrooms contain secondary metabolites, such as polyphenols, which can determine the chemical composition depending on the geographical region, species, and growth conditions, and can help in identifying the origin. These secondary metabolites play a protective role in the survival of the mushroom under various stress conditions and pathogen attacks. Their production and accumulation are related also to light, water and temperature [261].

To satisfy rising consumer demand, it is critical to create cultivars with ever higher nutritional values in addition to geographic and varietal identification. The creation of novel cultivar candidates offers the chance to create a variety with better enzyme activity and favorable free amino acid concentration [262].

The aforementioned examples have significantly increased the demand for analytical techniques to assess the quantity, quality and authenticity of food ingredients. As a result, there is a continuous increase in the design and development of non-destructive, quick, accurate, portable, and/or hand-held HSI and NIR
<figure><img src="image_26.png" /><figcaption>**Figure 22.** Possibilities for non-destructive analysis using near infrared radiation.</figcaption></figure>

instruments. NIR equipment have become widely used in agriculture due to their rapid development (figure 22) [263]

**Concluding remarks**

Growing consumer awareness over the past decade, has prompted food scientists to create new techniques for quality management and monitoring. When it comes to creating innovative methods, efficiency and environmental friendliness are crucial. For both farmers and dealers, keeping an eye on the quality of mushrooms is essential because of their high water content, quick respiration rate, unique surface structure, and therefore quick aging process after harvest. Freshness is frequently monitored using conventional techniques such as sensory analysis, enzyme activity assays, and color and texture determination. Food safety and quality depend on the classification and identification of mushrooms, including species, varieties, and place of origin.

These tests, however, are often time-consuming and involve techniques that destroy the sample. To distinguish and evaluate mushrooms, modern analytical methods like HSI and NIR are frequently employed.

For the scientific analysis of the multidimensional data present in various analytical methods, chemometrics is an effective tool. Furthermore, a thorough assessment of its chemical makeup and concentration can aid in our comprehension of the nutritional qualities of mushrooms.

**Acknowledgment**

We are grateful for the support of Doctoral School of Food Science of the Hungarian University of Agricultural and Life Sciences
# 14. Fruit quality control by mid-infrared multi-species trace gas sensing

Simona M Cristescu<fnref n="1" />, Amir Khodabakhsh<fnref n="1" />, Khalil Jahromi<fnref n="2" />, Mohammadreza Nematollahi<fnref n="3" />, Frans H Harren<fnref n="1" /> and Eugene Rokx<fnref n="4" />

E-mail: simona.cristescu@ru.nl*, amir.khodabakhsh@ru.nl, Khalil.jahromi@intero-integrity.com, Reza.nematollahi@asmpt.com, frans.harren@ru.nl and e.rokx@storex.nl

## Status

A continuous supply of fruits and vegetables throughout the year requires long-term large-scale storage. Fruits are usually stored under low oxygen and high carbon dioxide conditions to maximize their shelf life. Lack of proper storage facilities and infrastructure can lead to significant loss of the stored products, up to 10% for various geographical locations [264]. It is therefore a challenge to preserve the quality of agro-products during long-term storage.

One effective way to reduce losses in commercial-scale storage is to control adaptively and dynamically the storage atmosphere based on certain stress indicators. The current inability to undertake timely and effective actions leads to serious losses that could be prevented if continuous quality monitoring of stored products were possible. Additional information on damage, rotting and ripening stages could further help to determine the optimal moment to sell stored fresh food products. Storage facilities would thus strongly benefit from new methods that could improve the monitoring of these products.

To obtain a deeper insight into the physiological and pathological status, various volatile compounds released by fruits are considered extremely relevant, such as ethylene as ripening indicator [265], ethanol, acetaldehyde and ethylacetate as markers for fermentation [266], methanol and acetone as rotting indicators [267] and ethane as chilling injury marker [268]. In practice, these volatiles are often produced in low concentrations and need a long accumulation period to become detectable. This accumulation period will influence the metabolic processes in the fruits, which in turn will respond with the emission of certain volatiles affecting the neighboring fruits.

When unfavorable storage conditions and/or food processes are detected, it is highly desired to have a monitoring system that can provide automated alerts to enable timely and effective interventions by customers to:

a. reduce fresh food product loss during storage by 50% (absolute reduction of 5% of total food lost in storage);

b. extend the storage lifetime of fresh food products by 20%;

c. reduce the use of chemical treatments of fresh food products by 50% (less need due to optimal storage);

d. reduce storage energy consumption by 10%.

These advantages, if achieved, represent major value propositions for substantially improving the sustainability of the fresh food supply chain. The above target values (a), (c) and (d) are defined by the industry based on scientific research [269–272] and the current practices applied by commercial storage using alternative indirect dynamic controlled atmosphere (CA) technologies (b) [273], respectively.

## Current and future challenges

Optimizing storage conditions without using any disruptive or invasive methods for monitoring the hermetically sealed storage facilities is a major challenge. Existing monitoring systems can detect indirect anaerobic fermentation (a late process when dynamic CA is applied), but earlier occurring processes (e.g. damage, rotting and ripening) cannot be detected. This means that adjustments to the storage conditions or sending the products to the markets are often initiated too late.

Therefore, a multi-species sensor, which can continuously monitor the gas species during storage (figure 23), offers a huge advantage for early detection of undesirable conditions and could prevent a large amount of fruit being wasted.

<footnote n="1">¹Life Science Trace Detection Laboratory, (TDLab), Institute of Molecules and Materials, Faculty of Science, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands</footnote>

<footnote n="2">²Intero Integrity Services, Research and Development Group, Steenoven 2-6, 4196 HG, Tricht, The Netherlands</footnote>

<footnote n="3">³ASMPT ALSI B.V., R&D Department, Platinawerf 20, 6641 TL Beuningen, The Netherlands</footnote>

<footnote n="4">⁴Storex B.V. Beneden, Havendijk 89, NL-3295 XB's Gravendeel, The Netherlands</footnote>
<figure><img src="image_27.png" /><figcaption>**Figure 23.** Interactive monitoring of the multi-species volatile indicators released by fruit in controlled storage conditions is the key to preserve the quality of agro-products and prevent losses during prolonged storage.</figcaption></figure>

* Currently, there are three types of technologies available that can monitor these gases.

* Non-optical-based laboratory instruments: gas chromatography and mass spectrometry (e.g. SIFT-MS, PTR-MS).

* Non-optical-based small-size sensors: electronic noses and electrochemical sensors.

* Optical-based sensing systems: nondispersive infrared (NDIR) gas analyzers, fluorescence detectors, FTIR-based instrumentation and laser-based absorption spectroscopy.

The non-optical-based laboratory instruments are complex and too expensive for the real-life application. The NDIR gas analyzers are insensitive and limited to the detection of high concentration levels. Electronic noses, electrochemical sensors and fluorescence detectors can be found in fresh food storage facilities, but these usually have low sensitivity and are not very selective. The laser-based sensing systems using tunable diode laser or quantum cascade (QCL) [274] are a good candidate, although they can only detect one or two species. The detection range can be extended by using more complex sources, such as external cavity QCLs and frequency combs [275].

A mature and inexpensive ultra-broadband light source emitting in the mid-IR wavelength range—where most of the important species demonstrate distinct and strong molecular absorption features—can be the most suitable solution. The mid-IR supercontinuum (SC) light sources are spatially coherent and can deliver higher spectral brightness compare to the alternative thermal sources. These features make them an excellent candidate for monitoring various species simultaneously and with high sensitivity. Using a SC source and a broadband spectrometer (e.g. FT spectrometer—FTS or grating-based spectrometer—GS), one can develop a single analyzer that can cover all targeted species, instead of using a single species sensor for each species. This reduces the overall cost of the sensor system, simplifies the maintenance and unifies the processing control and decision-making. In addition, using such a broadband source, other species can be added to the detection list, thereby increasing the applicability of the system.

### Advances in science and technology to meet challenges

We recently demonstrated a mid-IR SC source (NKT Photonics, Denmark) coupled with a grating-based spectrometer (GS) to achieve a spectral coverage of 950 cm⁻¹ (2550–3500 cm⁻¹) and a spectral resolution of 2.5 cm⁻¹ [276]. The GS is a very popular broadband detection system due to the low cost. Moreover, it is easy to use and provides sufficient spectral resolution for atmospheric gas detection using a single-point detector while scanning the grating. When combined with an absorption multi-pass cell that enhances the effective optical path length and a lock-in detection scheme that increases the signal-to-noise ratio, sub-ppmv sensitivity to various hydrocarbons, alcohols, and aldehydes can be obtained [277].

We developed a multispecies trace gas sensor based on this proof-of-concept and it is currently being tested in small fruit storage facilities. The end product of this multiphase development is an innovative
<figure><img src="image_28.png" /><figcaption>**Figure 24.** The journey from a research idea towards a commercial prototype.</figcaption></figure>

interactive storage system that measures the atmosphere of different storage rooms in a storage facility, obtains the concentration of existing volatile markers (i.e. the seven volatile indicators of the health status of the fruit) and translates it into alarm signals/actions for the fruit owner (figure 24). Reaching this level of technology, from a lab setup up to a commercial prototype, required several iteration steps of the functional parts, each with its own specific challenge.

Recent advances in the development of mid-IR SC sources have enabled the expansion or their bandwidth from 2 µm to 10.5 µm and beyond [278]. This opens up new possibilities for simultaneous detection of even more gas species with minimum spectral interference, especially when combined with a FTS. Compared to a GS, the FTS is more complex and expensive, but it provides a simultaneous ultra-broad spectral coverage and it is inherently calibrated [279]. However, the newly developed SC sources still need to become more robust and less noisy, which is essential for the SC-based sensor to successfully enter the fruit storage market. Equally important is that the SC source price is reduced by nearly 50% by developing new non-PM pump laser technology. Encouraging progress has been made in this direction and is ongoing.

**Concluding remarks**

Detection of relevant volatiles for ripening, fermentation, rotting and damage in fruit storage units allows timely and effective interventions when unfavorable conditions occur.

A multi-species gas sensor, based on a mid-IR SC source combined with a GS, has been developed and has demonstrated its ability to monitor automatically and continuously important volatiles emitted by fruits in real-time. The prototype is currently being commercialized. Multidisciplinary technological developments are underway, to further transform the sensor into a commercial interactive storage monitoring and warning system. This will be the world's first automated multi-species gas sensor for fruit storage facilities.

**Acknowledgment**

The authors thank the Interreg North-West Europe program (Project Number: NWE 363), Horizon 2020 research and innovation programme (Max-Fresh Project No. 958840) and the Netherlands Organization for Scientific Research (Project Number: 14709). The authors would also like to thank Ole Bang from DTU/Norblis and NKT Photonics for the technical support of the mid-IR SC light source.
# 15. Fluorescence spectroscopy in agri-food chain

Ewa Sikorska

Institute of Quality Science, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland

E-mail: ewa.sikorska@ue.poznan.pl

## Status

Fluorescence techniques have been used in agriculture and food research for several decades, and have many successful applications [280]. Fluorescence is emission of radiation by molecules, which after excitation by absorption of photons from UV or visible range, return from electronically excited singlet state S₁ to the ground electronic singlet state, S₀. An emission in the visible spectral range may be observed as a glowing color by the naked eye, therefore the early applications utilized visual observation of fluorescence induced by UV light to assess quality of some food products. Due to the high sensitivity and selectivity, fluorescence is particularly useful for studying minor and trace components in complex matrices.

Fluorescent components (fluorophores) naturally occur in plant and animal tissues, contributing to their autofluorescence [281]. This autofluorescence of biological material provides information not only about fluorophores, but also about other relevant properties of sample matrix and has been increasingly used in agri-food sector. Fluorescence techniques are used to monitor the environment, crops and breeding, food processing and to control products safety and quality in the entire agri-food chain.

Fluorescence spectroscopy has been used in monitoring of dissolved organic matter (DOM) in water from natural reservoirs, drinking water and wastewater and detection of water pollution [282, 283]. Fluorescence enables evaluation of water treatment performance, characterization of DOM and monitoring micropollutants and disinfection by-products [284].

Chlorophyll fluorescence emitted by plants has a wide range of applications in monitoring and controlling vegetation processes [285]. It is used as an indicator of PSA and plant self-protection mechanism. Chlorophyll fluorescence is related to the amount of phenolics in the epidermis of leaves and fruit peels, enabling indirect quantification of these compounds [286]. The fluorescence techniques provide information about plant nitrogen (N) status and are applied for the N fertilization management of agricultural crops [285].

Fluorescence sensors have been used for the non-invasive, on-line monitoring of food processing. The changes in the concentration of biogenic fluorophores, observed during microbial cultivation and processing, are applied in biotechnology and food process engineering for process characterization and optimization [287, 288].

Fluorescent food ingredients include aromatic amino acids, polyphenols, fat-soluble vitamins A and E and water-soluble vitamins B₂ and B₆, as well some coenzymes. Many food additives, synthetic dyes and antioxidants, process-derived compounds, some food contaminants and adulterants also show fluorescence [280, 289]. Fluorescent compounds serve as the quality indicators of food, they are related to nutritional value, bioactive functions, sensory attributes, authenticity, processing and shelf life of products [290].

## Current and future challenges

Some issues and challenges related to the wider use of fluorescence in the agri-food sector are presented in figure 25. They include development of appropriate measurement techniques, instruments, data analysis methods and applications regarding specific analytes in selected matrices of agricultural and food samples.

One of the important issues is the availability of instruments, which enable acquisition of appropriate fluorescence signals on site, in simple, fast and preferably non-destructive way. The limitations of currently used instruments, hinder usage of fluorescence in on site applications [286].

Several indices based on the fluorescence intensity ratio measured at specific excitation and emission wavelengths have proved to be useful for characterizing fluorophores in plants and food product. Such types of measurements are popular in-field sensing applications, which utilized simple devices. However, measuring fluorescence spectra allows obtaining more amount of relevant analytical information about the examined samples. Biological samples usually contain several fluorescent ingredients, and their autofluorescence provides a characteristic fingerprint. More advanced measurement techniques include excitation-emission matrix (EEM) fluorescence spectroscopy, synchronous fluorescence spectroscopy (SFS), and total SFS [291]. An EEM consists of a set of emission spectra, each of which is measured at a specific excitation wavelength. It presents fluorescence intensity as function of excitation and emission wavelengths and provides comprehensive fluorescence landscapes of the sample under study. The SFS method is based on
<figure><img src="image_29.png" /><figcaption>**Figure 25.** Challenges related to the use of fluorescence techniques in the agri-food sector.</figcaption></figure>

the recording of the spectrum by simultaneous scanning of excitation and emission monochromators, most commonly with a constant wavelength offset, $\Delta\lambda$, between them. TSFS relies on the collection of the set of SFSs. Each SFS is measured at its individual $\Delta\lambda$ value, which varies between scans by a constant value.

To enhance the utility of fluorescence in analysis of heterogeneous samples the imaging techniques are developed, that enable simultaneous obtaining the spectral characteristics and spatial distribution of fluorophores [286]. An alternative to steady state fluorescence measurements may be time resolved techniques. Lifetime measurements eliminate problems with spectral band overlap from different fluorophores and the issues of internal filter effects [292].

The analysis of fluorescence spectra is conducted using advanced multivariate or multiway methods. The unique feature of EEM is the possibility of it decomposition using parallel factor analysis (PARAFAC) [293]. This method provides information on the relative concentrations of each fluorophore present in the sample and enables the determination of their excitation and emission spectral profiles. The results obtained from PARAFAC analysis combined with univariate regression enable direct quantification of analytes in the sample, even in the presence of uncalibrated interferences. This is the so-called ‘second-order advantage’.

### Advances in science and technology to meet challenges

The development of new instruments and measurement techniques expands the possibilities of using fluorescence in the agri-food sector. Various sensors and imaging instruments are commercially available for measurements of chlorophyll fluorescence and monitoring PSA [286]. The measurement technique include steady-state and time-resolved fluorescence spectroscopy, and pulse-amplitude modulated (PAM) fluorescence. They measure fluorescence excited by controlled light source and are largely limited to the subcellular and leaf levels. This limitation is overcome by development of solar-induced fluorescence techniques, which enable detection of chlorophyll fluorescence excited by sunlight. These techniques enable remote sensing of large area and open new opportunities in agriculture [294].

The important role in miniaturization of fluorescence instruments for on-site applications has played the development of laser-induced fluorescence (LIF) and light emitting diode-induced fluorescence (LED-IF), which replace the conventional light sources [295]. LEDs are compact, available in a broad range of wavelengths and appropriate power outputs, and affordable. The development of analytical applications of miniaturized devices requires the development of methods for calibration transfer between a master bench top instruments and portable fluorimeters based on light-emitting diodes. New solutions in this area can be brought by the use of deep learning methods [296].

The applications of EEM have been extensively increase in the agri-food sector, but fast recording of EEMs remains a challenge [295]. Different solutions have been proposed to solve this problem. Commercially available, filter-based BioView sensor equipped with optical light guides enables rapid acquisition of EEMs for direct monitoring of bioprocesses, however is characterized by relatively low
resolution [287]. Recently, a new approach has been proposed to reconstruct of high-resolution EEM from low-resolution data using deep learning super-resolution techniques [297].

For fast, high-resolution EEM measurement, an ultrafast CCD detector can be used instead of a standard photomultiplier. This technology is used in the commercially available Aqualog spectrofluorometer. The instrument is designed for measurements using the A-TEEM technique, which allows simultaneous, fast acquisition of absorbance and transmission spectra, and fluorescence EEM [298].

Hadamard-transform (HT) multiplexing of the excitation light using a programmable light source combined with advanced data processing has recently been applied to reduce the EEM acquisition time [299]. Based on the same HT approach, a four-dimensional fluorescence imaging system was developed, in which each of the pixels in the image array contains an EEM [300].

In parallel with the development of instrumentation techniques, new data analysis methods are developed, which allow obtaining analytical information using the unique multidimensional nature of fluorescent signals [301]. An important element in the development of applications of fluorescence methods is data interpretation. For this purpose, it is important to develop and make available databases presenting fluorescence spectra of various relevant fluorophores and categories of agri-food samples in a systematic and standardized way.

## Concluding remarks

Fluorescence, like other spectroscopic techniques, is simple and fast, allows direct and non-destructive measurements and simultaneous determination of many fluorophores. The unique properties of this technique are its high sensitivity, good selectivity and multidimensional nature. The study of plant and animal tissues and raw and processed foods is based on the analysis of their autofluorescence, associated with the presence of fluorescent components. Fluorophores in food include components derived from the raw material, compounds derived from the process, and fluorescent additives, contaminants, and adulterants.

Many studies and some successful applications indicate a great potential for the use of fluorescence in the agri-food sector. However, most of these studies are still in the laboratory phase and there is a large gap between the academic, laboratory scale and industrial applications. Research should focus on better characterization of fluorophores found in different groups of raw and processed food, as well as modeling and understanding the relationship between autofluorescence and the relevant properties of the tested material. The use of fluorescence methods in agriculture, production, processing and food quality control depends on the further development of portable devices and the use of modern ITCs to collect and analyze data from these instruments. Developments in these areas should contribute to the widespread use of fluorescence methods as a valuable analytical tool in managing the agri-food supply chain.
# 16. Laser-induced fluorescence spectroscopy for mycotoxin detection in solid food products

Lien Smeesters

Department of Applied Physics and Photonics, Vrije Universiteit Brussel and Flanders Make, Brussels Photonics (B-PHOT), Pleinlaan 2, 1050 Brussels, Belgium

E-mail: lien.smeesters@vub.be

## Status

The development of rapid, sensitive and non-destructive Mycotoxin sensing techniques is of indispensable importance to enhance food safety and quality. Mycotoxins, being secondary metabolites of toxic fungi, appear on a wide range of food and feed products imposing severe health risks, while they cannot be destroyed or removed by food processing and are invisible for the human eye [302–304]. To safeguard human and animal health, worldwide regulations setting maximum values for mycotoxins in food and feed are in place [305]. These maximum allowed contamination levels show regional variations, as well as vary dependent on the toxicity of the mycotoxin-type and the considered food products. Considering aflatoxins, the European Commission puts a limit of 4–15 µg kg⁻¹, while for ochratoxin and zearalenone the limit ranges between 0.5–10 µg kg⁻¹ and 20–400 µg kg⁻¹, respectively [306]. Traditionally, chemical analysis, including liquid chromatography-dual mass spectrometry (LC-MS/MS), are applied to determine the mycotoxin contamination in food and feed products [303, 304]. Despite being highly sensitive and accurate, these chemical analysis might unfortunately give a truncated view on the contamination within a batch by measuring the mean contamination level instead of the local one, while also being destructive and thus inducing food waste.

Fluorescence spectroscopy has been identified as a promising non-destructive alternative for the detection of fluorescent mycotoxins, including aflatoxin, ochratoxin and zearalenone [307–311]. Traditional fluorescence spectroscopy typically considers one-photon induced fluorescence (OPIF) where the fluorescence emission results from the absorption of a single photon. OPIF has shown a promising detection within a wide range of food applications, not only limited to mycotoxin sensing, but also for foreign object detection and food quality evaluation [289, 312]. As main disadvantage, OPIF typically excites different natural fluorescent elements in the food products causing strong background signals. Two-photon induced fluorescence (TPIF), based on the simultaneous absorbance of two photons to excite the electron that subsequently relaxes to emit the fluorescent photon, has been launched as a possible technology to achieve a more selective excitation. Application of TPIF has been demonstrated for the sensing of mycotoxins in alcoholic beverages and maize kernels, as well as for chlorophyll [313–316]. For both OPIF and TPIF, the electron energy transitions are determined by the bandgap given by the Jablónski diagram, dependent on the chemical structure of the product. OPIF typically requires UV excitation wavelengths, while TPIF uses NIR light. TPIF, due to its non-linear character, requires significantly higher excitation powers than OPIF. In addition, according to the quantum physics selection rules, two-photon absorption is not allowed for every molecular structure, implying that not all molecules emitting OPIF generate a TPIF signal when excited with the double wavelength and sufficiently high excitation power density [317, 318]. Non-centrosymmetric molecules enable both one-and two-photon absorption, while for centrosymmetric molecules this transition is allowed for either OPIF or TPIF. In addition, the molecular structure influences the possible electron transitions. Strong two-photon absorbing molecules typically show strong donor and acceptor groups in combination with a long π-conjugation system, since this enhances the nonlinearity in the system and increases the potential for charge-transfer.

## Current and future challenges on the application of one-and two-photon induced fluorescence

Application of one-and TPIF for food evaluation requires tackling the following challenges: (1) optimization of the excitation wavelength maximizing the fluorescence of the element under study while minimizing the background fluorescence, (2) optimization of the excitation power density maximizing the signal to noise ratio, (3) extensive validation taking the natural variation into account, both harvest and area dependent, (4) sensitivity optimization to comply to the global legislations, (5) careful product sampling tackling the inhomogeneous presence of the toxin on the products.

Aflatoxin shows both OPIF and TPIF (figure 26), as illustrated for pure aflatoxin B1 powder (purity >98%, A. flavus fungi, Sigma-Aldrich). Pure aflatoxin B1 features a strong absorption around 365 nm, motivating the choice for this excitation wavelength during OPIF, while an excitation wavelength of 730 nm is used during TPIF [314]. Both OPIF and TPIF spectra show a maximal fluorescence intensity at 428 nm
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(figure 26(b)). The TPIF spectrum shows a narrower peak than the OPIF spectrum, due to the more selective excitation during two-photon absorption. Furthermore, the TPIF spectrum shows a second-harmonic generation (SHG) signal at 365 nm, created by the recombination of two illumination photons to a new photon with the double energy. The aflatoxin fluorescence signal as function of the excitation power density shows a linear behavior for OPIF, until the saturation point is reached where all electrons are excited to the higher energy state (figure 26(c)), while a quadratic dependence is observed for TPIF (figure 26(d)). Aflatoxin contaminated maize kernels indicate a wavelength shift within the fluorescence emission increasing with the contamination level (figures 26(e) and (f)), while showing a stronger contrast for TPIF than for OPIF [315]. Detuning the excitation wavelength from the optimal aflatoxin absorption, using 405 nm and 810 nm for OPIF and TPIF, results in a decreasing wavelength shift (figures 26(g) and (h)).

Considering pistachios, the aflatoxin presence influences the OPIF background fluorescence around 475 nm (figure 26(i)). To maximize the fluorescence signal, and aflatoxin detection with respect to the natural fluorescence, re-optimization of the excitation wavelength was required [319]. TPIF showed to be less suitable for aflatoxin detection in pistachios, illustrating the complexity of the TPIF selection rules (figure 26(j)), while still showing a strong chlorophyll TPIF signal between 600 nm and 675 nm. Studying the fluorescence intensity as function of the excitation power density confirms the linear and quadratic behavior for OPIF and TPIF, respectively, while indicating the higher illumination power constraints for TPIF (figures 26(k) and (l)).

Ochratoxin A shows a similar behavior as aflatoxin (figures 27(a)–(d)), but featuring the strongest fluorescence signal after excitation using 356 nm and 710 nm, for OPIF and TPIF, due to its difference in absorption characteristics. Similar excitation power constraints can be observed as for aflatoxin (figure 27(d)). Zearalenone only shows OPIF, while two-photon absorption only induces SHG (figures 27(e) and (h)). The absence of the TPIF signal can be explained by the study of the molecular structure of zearalenone, showing a much shorter π-conjugation system than aflatoxin B1 and ochratoxin A (indicated by the dotted curves in figures 26(a), 27(a) and (e)), while featuring a smaller amount of donor and acceptor groups. Aflatoxin B1 and ochratoxin A contain nine donor/acceptor groups, while zearalenone only contains seven donor/acceptor groups [320].

**Advances in science and technology to meet challenges**

Fluorescence spectroscopy is currently benefitting from (1) advances in novel light source developments, (2) advanced optical design, (3) miniaturized sensor technologies and (4) AI/ML data processing algorithms.

(1) Traditionally, fluorescence spectroscopy has been performed using laser light as excitation source, featuring a high cost and complicating the miniaturization of fluorescence measurement units. The