Industry 4.0 Technologies in Circular Food Supply Chains: A Systematic Literature Review

Pankhuri  Bansal; Orr Karassin; Manoj Dora

doi:10.55845/joce-2025-32101

Authors

Pankhuri Bansal United Nations (CEFACT) & ISO, United Kingdom Author https://orcid.org/0009-0005-4563-3690
Orr Karassin Environment and Sustainability Research Centre at The Open University of Israel Author https://orcid.org/0000-0001-9527-583X
Manoj Dora Anglia Ruskin University, Cambridge, CB1 1PT, United Kingdom Author https://orcid.org/0000-0003-4730-8144

DOI:

https://doi.org/10.55845/joce-2025-32101

Keywords:

Industry 4.0 (I4), Food supply chain (FSC), Circular Economy (CE), Sustainability, Food Waste, Blockchain, Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics (BDA), Smart Factories, Robotics

Abstract

This paper explores the transformative role of Industry 4.0 (I4) technologies—specifically Blockchain, Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics (BDA), Smart Factories, Robotics—in enabling the circular food supply chain (FSC). Through a systematic literature review, we identify how these technologies contribute to key circular economy (CE) strategies in FSC, comprising reduction, redistribution, valorisation, and nutrient recovery. We also examine implementation barriers, including digital infrastructure gaps, regulatory misalignments, and data interoperability. A conceptual framework is proposed to map digital solutions to CE principles in food systems. Our findings offer actionable insights for scaling up digital-enabled circular practices in the agri-food sector.

Author Biography

Pankhuri Bansal , United Nations (CEFACT) & ISO, United Kingdom

Blockchain & AI Expert

References

Abbate, S., Centobelli, P., & Cerchione, R. (2023). The digital and sustainable transition of the agri-food sector. Technological Forecasting and Social Change, 187, Article 122222. https://doi.org/10.1016/j.techfore.2022.122222 DOI: https://doi.org/10.1016/j.techfore.2022.122222

Abideen, A. Z., Sundram, V. P. K., Pyeman, J., Othman, A. K., & Sorooshian, S. (2021). Food Supply Chain Transformation through Technology and Future Research Directions—A Systematic Review. Logistics, 5(4), 83. https://doi.org/10.3390/logistics5040083 DOI: https://doi.org/10.3390/logistics5040083

Afresh Technologies. (2023). Afresh deploys AI-powered store ordering and inventory platform to Albertsons Companies meat and seafood departments chainwide. https://www.afresh.com/news/afresh-deploys-ai-powered-store-ordering-and-inventory-platform-to-albertsons-companies

Agarwal, H., Ahir, B., Jain, S., Barot, H., & Bide, P. (2020). Minimisation of food waste in retail sector using time-series analysis and object detection algorithm. 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 1–5. https://doi.org/10.1109/INCET49848.2020.9154101 DOI: https://doi.org/10.1109/INCET49848.2020.9154156

Ahmadzadeh, S., Ajmal, T., Ramanathan, R., & Duan, Y. (2023). A comprehensive review on food waste reduction based on IoT and big data technologies. Sustainability, 15(4), 3482. https://doi.org/10.3390/su15043482 DOI: https://doi.org/10.3390/su15043482

Ahmi, A., Elbardan, H., & Raja Mohd Ali, R. H. (2019). Bibliometric analysis of published literature on Industry 4.0. 2019 International Conference on Electronics, Information, and Communication (ICEIC), 1–6. https://doi.org/10.23919/ELINFOCOM.2019.8706445 DOI: https://doi.org/10.23919/ELINFOCOM.2019.8706445

Akyazi, T., Goti, A., Oyarbide, A., Alberdi, E., & Bayon, F. (2020). A guide for the food industry to meet the future skills requirements emerging with Industry 4.0. Foods, 9(4), 492. https://doi.org/10.3390/foods9040492 DOI: https://doi.org/10.3390/foods9040492

Alfian, G., Syafrudin, M., Farooq, U., Ma’arif, M., Syaekhoni, M., Fitriyani, N., Lee, J., & Rhee, J. (2020). Improving the efficiency of an RFID-based traceability system for perishable food by utilising IoT sensors and a machine learning model. Food Control, 110, Article 107016. https://doi.org/10.1016/j.foodcont.2019.107016 DOI: https://doi.org/10.1016/j.foodcont.2019.107016

Alonso-Muñoz, S., García-Muiña, F., Medina-Salgado, M.-S., & González-Sánchez, R. (2022). Towards circular economy practices in food waste management: A retrospective overview and a research agenda. British Food Journal, 124(13), 478–500. https://doi.org/10.1108/BFJ-01-2022-0072 DOI: https://doi.org/10.1108/BFJ-01-2022-0072

Altay, A., Learney, R., Güder, F., & Dincer, C. (2022). Sensors in blockchain. Trends in Biotechnology, 40(2), 141–144. https://doi.org/10.1016/j.tibtech.2021.04.011 DOI: https://doi.org/10.1016/j.tibtech.2021.04.011

Annosi, M. C., Brunetta, F., Bimbo, F., & Kostoula, M. (2021). Digitalisation within FSC to prevent food waste: Drivers, barriers and collaboration practices. Industrial Marketing Management, 93, 208–220. https://doi.org/10.1016/j.indmarman.2021.01.005 DOI: https://doi.org/10.1016/j.indmarman.2021.01.005

Thomas, M., & Sachin, K. (2021). IoT based smart warehousing using machine learning. 2021 IEEE Asian Conference on Innovation in Technology (ASIANCON), 1–6. https://doi.org/10.1109/ASIANCON51346.2021.9544579 DOI: https://doi.org/10.1109/ASIANCON51346.2021.9544579

Aschemann-Witzel, J., de Hooge, I. E., & Normann, A. (2016). Consumer-related food waste: Role of food marketing and retailers and potential for action. Journal of International Food & Agribusiness Marketing, 28(3), 271–285. https://doi.org/10.1080/08974438.2015.1110549 DOI: https://doi.org/10.1080/08974438.2015.1110549

Awan, S., Ahmed, S., Ullah, F., Nawaz, A., Khan, A., Uddin, M. I., Alharbi, A., Alosaimi, W., & Alyami, H. (2021). IoT with blockchain: A futuristic approach in agriculture and FSC. Wireless Communications and Mobile Computing, 2021, 1–14. https://doi.org/10.1155/2021/5580171 DOI: https://doi.org/10.1155/2021/5580179

Balińska, A., Werenowska, A., & Jaska, E. (2024). The importance of mobile applications in reducing food waste—The example of the TooGoodToGo application. Journal of Modern Science, 56(2), 190–214. https://doi.org/10.13166/jms/188442 DOI: https://doi.org/10.13166/jms/188723

Barasa, S., & Etene, Y. (2023). Robotics in the food manufacturing industry in the Industry 4.0 era. International Journal of Computer Science and Mobile Computing, 12(8), 11–17. https://doi.org/10.47760/ijcsmc.2023.v12i08.002 DOI: https://doi.org/10.47760/ijcsmc.2023.v12i08.009

Beretta, C., Stoessel, F., Baier, U., & Hellweg, S. (2013). Quantifying food losses and the potential for reduction in Switzerland. Waste Management, 33(3), 764–773. https://doi.org/10.1016/j.wasman.2012.11.007 DOI: https://doi.org/10.1016/j.wasman.2012.11.007

Casado-Vara, R., Prieto, J., la Prieta, F. D., & Corchado, J. M. (2018). How blockchain improves the supply chain: Case study of the alimentary supply chain. Procedia Computer Science, 134, 393–398. https://doi.org/10.1016/j.procs.2018.07.198 DOI: https://doi.org/10.1016/j.procs.2018.07.193

Casino, F., Kanakaris, V., Dasaklis, T. K., Moschuris, S., Stachtiaris, S., Pagoni, M., & Rachaniotis, N. P. (2021). Blockchain-based food supply chain traceability: A case study in the dairy sector. International Journal of Production Research, 59(19), 5758–5770. https://doi.org/10.1080/00207543.2020.1789238 DOI: https://doi.org/10.1080/00207543.2020.1789238

Chauhan, A., Brouwer, B., & Westra, E. (2022). Robotics for a quality-driven post-harvest supply chain. Current Robotics Reports, 3, 39–48. https://doi.org/10.1007/s43154-022-00078-0 DOI: https://doi.org/10.1007/s43154-022-00075-8

Chauhan, A., Jakhar, S. K., & Chauhan, C. (2021). The interplay of CE with Industry 4.0 enabled smart city drivers of healthcare waste disposal. Journal of Cleaner Production, 279, 123854. https://doi.org/10.1016/j.jclepro.2020.123854 DOI: https://doi.org/10.1016/j.jclepro.2020.123854

Chen, S., Brahma, S., Mackay, J., Cao, C., & Aliakbarian, B. (2020). The role of the smart packaging system in FSC. Journal of Food Science, 85(3), 517–525. https://doi.org/10.1111/1750-3841.15046 DOI: https://doi.org/10.1111/1750-3841.15046

Choe, H. O., & Lee, M. (2023). Artificial intelligence-based fault diagnosis and prediction for smart farm information and communication technology equipment. Agriculture, 13(11), 2124. https://doi.org/10.3390/agriculture13112124 DOI: https://doi.org/10.3390/agriculture13112124

Ciccullo, F., Fabbri, M., Abdelkafi, N., & Pero, M. (2022). Exploring the potential of business models for sustainability and big data for food waste reduction. Journal of Cleaner Production, 340, Article 130673. https://doi.org/10.1016/j.jclepro.2022.130673 DOI: https://doi.org/10.1016/j.jclepro.2022.130673

Cooney, R., Baptista de Sousa, D., Fernández-Ríos, A., Mellett, S., Rowan, N., Morse, A. P., Hayes, M., Laso, J., Regueiro, L., Wan, A. H., & Clifford, E. (2023). A CE framework for seafood waste valorisation to meet challenges and opportunities for intensive production and sustainability. Journal of Cleaner Production, 392, Article 136283. https://doi.org/10.1016/j.jclepro.2023.136283 DOI: https://doi.org/10.1016/j.jclepro.2023.136283

Culot, G., Orzes, G., Sartor, M., & Nassimbeni, G. (2020). The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0. Technological Forecasting and Social Change, 157, Article 120092. https://doi.org/10.1016/j.techfore.2020.120092 DOI: https://doi.org/10.1016/j.techfore.2020.120092

Da Costa, T. P., Gillespie, J., Cama-Moncunill, X., Ward, S., Condell, J., Ramanathan, R., & Murphy, F. (2023). A systematic review of real-time monitoring technologies and their potential application to reduce food loss and waste: Key elements of food supply chains and IoT technologies. Sustainability, 15(1), 614. https://doi.org/10.3390/su15010614 DOI: https://doi.org/10.3390/su15010614

Dantas, T. E. T., de-Souza, E. D., Destro, I. R., Hammes, G., Rodriguez, C. M. T., & Soares, S. R. (2021). How the combination of CE and Industry 4.0 can contribute towards achieving the Sustainable Development Goals. Sustainable Production and Consumption, 26, 213–227. https://doi.org/10.1016/j.spc.2020.10.005 DOI: https://doi.org/10.1016/j.spc.2020.10.005

Despoudi, S. (2021). Challenges in reducing food losses at producers’ level: The case of Greek agricultural supply chain producers. Industrial Marketing Management, 93, 520–532. https://doi.org/10.1016/j.indmarman.2020.09.022 DOI: https://doi.org/10.1016/j.indmarman.2020.09.022

Dey, S., Saha, S., Singh, A. K., & McDonald-Maier, K. (2022). SmartNoshWaste: Using blockchain, machine learning, cloud computing and QR code to reduce food waste in decentralised Web 3.0 enabled smart cities. Smart Cities, 5(1), 170–176. https://doi.org/10.3390/smartcities5010011 DOI: https://doi.org/10.3390/smartcities5010011

DigitalDefynd. (2025). 5 ways Nestlé is using AI – Case study. https://digitaldefynd.com/case-study/nestle-ai-strategy/

FAO, IFAD, UNICEF, WFP, & WHO. (2023). The state of food security and nutrition in the world 2023: Urbanisation, agrifood systems transformation and healthy diets across the rural–urban continuum. FAO. https://doi.org/10.4060/cc3017en DOI: https://doi.org/10.4060/cc3017en

Feng, H., Wang, W., Chen, B., & Zhang, X. (2020). Evaluation on frozen shellfish quality by blockchain based multi-sensors monitoring and SVM algorithm during cold storage. IEEE Access, 8, 54361–54371. https://doi.org/10.1109/ACCESS.2020.2977723 DOI: https://doi.org/10.1109/ACCESS.2020.2977723

Fresho. (2024). AI-powered order automation. https://fresho.com/ai-powered-order-automation/

Govindan, K. (2018). Sustainable consumption and production in the FSC: A conceptual framework. International Journal of Production Economics, 195, 419–431. https://doi.org/10.1016/j.ijpe.2017.03.003 DOI: https://doi.org/10.1016/j.ijpe.2017.03.003

Hurst, W., & Luttikhold, A. J. (2022). Harnessing augmented reality for increasing the awareness of food waste amongst Dutch consumers. Augmented Human Research, 7(1), Article 2. https://doi.org/10.1007/s41133-021-00053-5 DOI: https://doi.org/10.1007/s41133-022-00057-7

Jabbour, A. B. L. S., Jabbour, C. J. C., Foropon, C., & Filho, M. G. (2018). When titans meet – Can Industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technological Forecasting and Social Change, 132, 18–25. https://doi.org/10.1016/j.techfore.2018.01.017 DOI: https://doi.org/10.1016/j.techfore.2018.01.017

Jagtap, S., & Rahimifard, S. (2019). The digitisation of food manufacturing to reduce waste – Case study of a ready meal factory. Waste Management, 87, 387–397. https://doi.org/10.1016/j.wasman.2019.02.017 DOI: https://doi.org/10.1016/j.wasman.2019.02.017

Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 3, 150–164. https://doi.org/10.1016/j.ijin.2022.09.004 DOI: https://doi.org/10.1016/j.ijin.2022.09.004

Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in the food industry. Journal of Agriculture and Food Research, 2, Article 100033. https://doi.org/10.1016/j.jafr.2020.100033 DOI: https://doi.org/10.1016/j.jafr.2020.100033

Khan, S., & Prasetyo, J. (2023). Humanoid robot to help reduce food waste by detecting food quality using artificial intelligence. Journal of Applied Science and Advanced Engineering, 1(1), 35–41. https://doi.org/10.59097/jasae.v1i1.12 DOI: https://doi.org/10.59097/jasae.v1i1.12

Konur, S., Lan, Y., Thakker, D., Mokryani, G., Polovina, N., & Sharp, J. (2021). Towards design and implementation of Industry 4.0 for food manufacturing. Neural Computing and Applications, 35, 1–13. https://doi.org/10.1007/s00521-021-05726-z DOI: https://doi.org/10.1007/s00521-021-05726-z

Kutyauripo, I., Rushambwa, M., & Chiwazi, L. (2023). Artificial intelligence applications in the agri-food sectors. Journal of Agriculture and Food Research, 11, Article 100502. https://doi.org/10.1016/j.jafr.2023.100502 DOI: https://doi.org/10.1016/j.jafr.2023.100502

L’heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. (2017). Machine learning with big data: Challenges and approaches. IEEE Access, 5, 7776–7797. https://doi.org/10.1109/ACCESS.2017.2696365 DOI: https://doi.org/10.1109/ACCESS.2017.2696365

Laskurain-Iturbe, I., Arana-Landín, G., Landeta-Manzano, B., & Uriarte-Gallastegi, N. (2021). Exploring the influence of Industry 4.0 technologies on the CE. Journal of Cleaner Production, 321, Article 128944. https://doi.org/10.1016/j.jclepro.2021.128944 DOI: https://doi.org/10.1016/j.jclepro.2021.128944

Latino, M. E., Corallo, A., Menegoli, M., & Nuzzo, B. (2021). Agriculture 4.0 as enabler of sustainable agri-food: A proposed taxonomy. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3101548 DOI: https://doi.org/10.1109/TEM.2021.3101548

Leffler, L., Bräck, N., & Ayele, W. (2023). Robotic process automation for reducing food wastage in Swedish grocery stores. In M. Rossi, R. Müller, & K. Marton (Eds.), Information Systems (pp. 875–892). Springer Nature. https://doi.org/10.1007/978-3-031-28073-3_58 DOI: https://doi.org/10.1007/978-3-031-28073-3_58

Maffezzoli, F., Ardolino, M., Bacchetti, A., Perona, M., & Renga, F. (2022). Agriculture 4.0: A systematic literature review on the paradigm, technologies and benefits. Futures, 142, Article 102998. https://doi.org/10.1016/j.futures.2022.102998 DOI: https://doi.org/10.1016/j.futures.2022.102998

Michelini, L., Principato, L., & Iasevoli, G. (2018). Understanding food sharing models to tackle sustainability challenges. Ecological Economics, 145, 205–217. https://doi.org/10.1016/j.ecolecon.2017.09.009 DOI: https://doi.org/10.1016/j.ecolecon.2017.09.009

Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal, 9(9), 6305–6324. https://doi.org/10.1109/JIOT.2020.2998584 DOI: https://doi.org/10.1109/JIOT.2020.2998584

Monteiro, A. L., de Freitas Souza, M., Lins, H. A., da Silva Teófilo, T. M., Barros Júnior, A. P., Silva, D. V., & Mendonça, V. (2021). A new alternative to determine weed control in agricultural systems based on artificial neural networks (ANNs). Field Crops Research, 263, Article 108075. https://doi.org/10.1016/j.fcr.2021.108075 DOI: https://doi.org/10.1016/j.fcr.2021.108075

Morseletto, P. (2020). Targets for a circular economy: Developing a common framework based on 10R strategies. Resources, Conservation and Recycling, 153, Article 104553. https://doi.org/10.1016/j.resconrec.2019.104553 DOI: https://doi.org/10.1016/j.resconrec.2019.104553

Mota-Grajales, R., Torres-Peña, J. C., Camas-Anzueto, J. L., Pérez-Patricio, M., Grajales Coutiño, R., López-Estrada, F. R., Escobar-Gómez, E. N., & Guerra-Crespo, H. (2019). Defect detection in eggshell using a vision system to ensure the incubation in poultry production. Measurement, 135, 39–46. https://doi.org/10.1016/j.measurement.2018.09.059 DOI: https://doi.org/10.1016/j.measurement.2018.09.059

Iqbal, W., & Kang, Y. (2024). Circular economy of food: A secondary supply chain model on food waste management incorporating IoT based technology. Journal of Cleaner Production, 435, 140566. https://doi.org/10.1016/j.jclepro.2024.140566 DOI: https://doi.org/10.1016/j.jclepro.2024.140566

Ojha, S., Bußler, S., & Schlüter, O. K. (2020). Food waste valorisation and CE concepts in insect production and processing. Waste Management, 118, 600–609. https://doi.org/10.1016/j.wasman.2020.09.010 DOI: https://doi.org/10.1016/j.wasman.2020.09.010

Ojo, O. O., Shah, S., Coutroubis, A., Jiménez, M. T., & Munoz Ocana, Y. (2018). Potential impact of Industry 4.0 in sustainable food supply chain environment. 2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), 172–177. https://doi.org/10.1109/ITMC.2018.8691223 DOI: https://doi.org/10.1109/ITMC.2018.8691223

Oliveira, R. T., Ghobakhloo, M., & Figueira, S. (2023). Industry 4.0 towards social and environmental sustainability in multinationals: Enabling CE, organisational social practices, and corporate purpose. Journal of Cleaner Production, 430, Article 139712. https://doi.org/10.1016/j.jclepro.2023.139712 DOI: https://doi.org/10.1016/j.jclepro.2023.139712

Orange. (2023). Artificial intelligence against food waste. Hello Future. https://hellofuture.orange.com/en/artificial-intelligence-against-food-waste/

Orbisk. (2024). About Orbisk - on a mission to create a sustainable food system. https://orbisk.com/about-orbisk/

Pakseresht, A., Yavari, A., Ahmadi Kaliji, S., & Hakelius, K. (2023). The intersection of blockchain technology and circular economy in the agri-food sector. Sustainable Production and Consumption, 35, 260–274. https://doi.org/10.1016/j.spc.2022.11.002 DOI: https://doi.org/10.1016/j.spc.2022.11.002

Panetto, H., Lezoche, M., Hernández, J., Diaz, M., & Kacprzyk, J. (2020). Special issue on AgriFood 4.0 and digitalisation in agriculture supply chains: New directions, challenges and applications. Computers in Industry, 116, Article 103188. https://doi.org/10.1016/j.compind.2020.103188 DOI: https://doi.org/10.1016/j.compind.2020.103188

Pang, Z., Chen, Q., Han, W., & Zheng, L. (2012). Value-centric design of the internet-of-things solution for food supply chain: Value creation, sensor portfolio and information fusion. Information Systems Frontiers, 17, 289–319. https://doi.org/10.1007/s10796-012-9374-9 DOI: https://doi.org/10.1007/s10796-012-9374-9

Papargyropoulou, E., Lozano, R., Steinberger, J. K., Wright, N., & Ujang, Z. B. (2014). The food waste hierarchy as a framework for the management of food surplus and food waste. Journal of Cleaner Production, 76, 106–115. https://doi.org/10.1016/j.jclepro.2014.04.020 DOI: https://doi.org/10.1016/j.jclepro.2014.04.020

Secondi, L., Principato, L., & Pratesi, C. (2015). Reducing food waste: An investigation on the behavior of Italian youths. British Food Journal, 117(2), 336–355. https://doi.org/10.1108/BFJ-10-2013-0314 DOI: https://doi.org/10.1108/BFJ-10-2013-0314

Rajesh, P., Zaveri, D., & Kale, S. (2022). Big data analytics in agriculture. International Journal of Scientific Research in Engineering and Management, 6(4), 1–9. https://doi.org/10.55041/IJSREM12456

Ramanathan, R., Duan, Y., Ajmal, T., Pelc, K., Gillespie, J., Ahmadzadeh, S., Condell, J., Hermens, I., & Ramanathan, U. (2023). Motivations and Challenges for Food Companies in Using IoT Sensors for Reducing Food Waste: Some Insights and a Road Map for the Future. Sustainability, 15(2), 1665. https://doi.org/10.3390/su15021665 DOI: https://doi.org/10.3390/su15021665

Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of Cleaner Production, 224, 10–24. https://doi.org/10.1016/j.jclepro.2019.03.181 DOI: https://doi.org/10.1016/j.jclepro.2019.03.181

Rogerson, M., & Parry, G. C. (2020). Blockchain: Case studies in FSC visibility. Supply Chain Management: An International Journal, 25(5), 601–614. https://doi.org/10.1108/SCM-08-2019-0300 DOI: https://doi.org/10.1108/SCM-08-2019-0300

Salim, O. O., Guarnieri, P., & Leitão, F. (2021). Food waste from the view of circular economy: A systematic review of international literature. Revista de Gestão Social e Ambiental, 15, Article e02579. https://doi.org/10.24857/rgsa.v15.2579 DOI: https://doi.org/10.24857/rgsa.v15.2579

Savvie. (2024). Savvie features. https://savvie.io/features/

Scherhaufer, S., Moates, G., Hartikainen, H., Waldron, K., & Obersteiner, G. (2018). Environmental impacts of food waste in Europe. Waste Management, 77, 98–113. https://doi.org/10.1016/j.wasman.2018.04.038 DOI: https://doi.org/10.1016/j.wasman.2018.04.038

Senturk, S., Senturk, F., & Karaca, H. (2023). Industry 4.0 technologies in agri-food sector and their integration in the global value chain: A review. Journal of Cleaner Production, 408, 137096. https://doi.org/10.1016/j.jclepro.2023.137096 DOI: https://doi.org/10.1016/j.jclepro.2023.137096

Sevic, M., & Keller, P. (2021). Model of smart factory using the principles of Industry 4.0. MM Science Journal, 2021(3), 4238–4243. https://doi.org/10.17973/MMSJ.2021_03_2020067 DOI: https://doi.org/10.17973/MMSJ.2021_03_2020067

Siemens. (2022). Digital twins in agriculture: Sustainable farming solutions. https://www.siemens.com/global/en/company/stories/industry/digital-twin-agriculture.html

Silberling, A. (2023, March 14). Nosh uses AI to help people and businesses cut down on their food waste. TechCrunch. https://techcrunch.com/2023/03/14/nosh-uses-ai-to-help-people-and-businesses-cut-down-on-their-food-waste/

Simeone, A., Woolley, E., Luo, Z., & Jellil, A. (2022). A data-driven approach to reducing household food waste. Sustainable Production and Consumption, 29, 600–613. https://doi.org/10.1016/j.spc.2021.11.007 DOI: https://doi.org/10.1016/j.spc.2021.11.004

Spitalleri, A., Kavasidis, I., Cartelli, V., Mineo, R., Palazzo, S., Spampinato, C., & Giordano, D. (2023). BioTrak: A blockchain-based platform for food chain logistics traceability. arXiv. https://doi.org/10.48550/arXiv.2304.09601 DOI: https://doi.org/10.1109/ICCNS58795.2023.10193341

Tagarakis, A. C., Benos, L., Kateris, D., Tsotsolas, N., & Bochtis, D. (2021). Bridging the gaps in traceability systems for fresh produce supply chains: Overview and development of an integrated IoT-based system. Applied Sciences, 11(16), 7596. https://doi.org/10.3390/app11167596 DOI: https://doi.org/10.3390/app11167596

TCS. (2024). Reducing food waste with smart technology. https://www.tcs.com/what-we-do/industries/retail/white-paper/reducing-food-waste-smart-technology

TE-FOOD. (2021). Farm-to-fork food traceability on blockchain. https://te-food.com/

Truică, C.-O., Boicea, Alexandru, & Trifan, I. (2013). CRUD operations in MongoDB. Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013), 401–404. https://doi.org/10.2991/icacsei.2013.88 DOI: https://doi.org/10.2991/icacsei.2013.88

Wang, Z., Shimizu, M., & Kawamura, S. (2021). Cyber physical system considering physical contacts in robotic manipulation for improving automation in the food industry. Journal of Industrial and Intelligent Information, 9(2), 23–28. https://doi.org/10.18178/jiii.9.2.23-28 DOI: https://doi.org/10.18178/jiii.9.2.23-28

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023 DOI: https://doi.org/10.1016/j.agsy.2017.01.023

World Economic Forum. (2019). This is how blockchain can keep your food safe. https://www.weforum.org/stories/2019/01/walmart-is-betting-on-the-blockchain-to-improve-food-safety/

Yang, X., Li, M., Yu, H., Wang, M., Xu, D., & Sun, C. (2021). A trusted blockchain-based traceability system for fruit and vegetable agricultural products. IEEE Access, 9, 36282–36293. https://doi.org/10.1109/ACCESS.2021.3062845 DOI: https://doi.org/10.1109/ACCESS.2021.3062845

Zhang, D. (2020). The innovation research of contract farming financing mode under the blockchain technology. Journal of Cleaner Production, 270, Article 122194. https://doi.org/10.1016/j.jclepro.2020.122194 DOI: https://doi.org/10.1016/j.jclepro.2020.122194