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The objective is to quantify effectiveness at multiple stages, from production to consumption, to strengthen food security and sustainability. A mixed-methods design combines IoT sensor data from field studies with stakeholder insights from farmers and retailers. Results show AI-driven cold storage reduces post-harvest losses by 60%, while ML-optimized logistics decrease transportation-related waste by 20%. Challenges include inaccurate market-demand prediction causing occasional overproduction, indicating a need for improved algorithms to handle volatility. Future directions include real-time adaptive logistics, blockchain traceability, and predictive demand forecasting.","University of Dundee  \nSustainable farming with machine learning solutions for minimizing food waste  \nOlawale, Rukayat Abisola; Olawumi, Mattew A. ; Oladapo, Bankole I.  \nDOI:  \n10.1016/j.jspr.2025.102611  \nPublication date:  \n2025  \nLicence: CC BY  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in Discovery Research Portal  \nCitation for published version (APA):  \nOlawale, R. A. , Olawumi, M. A. , & Oladapo, B. I. (2025) . Sustainable farming with machine learning solutions for minimizing food waste. Journal of Stored Products Research, 112 , Article 102611.  \n[https://doi.org/10.1016/j.jspr.2025.102611](https://doi.org/10.1016/j.jspr.2025.102611)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 04. Aug. 2026  \nJournal of Stored Products Research 112 (2025) 102611  \nContents lists available at ScienceDirect  \nJournal of Stored Products Research  \njournal [homepage:](homepage: www.elsevier.com/locate/jspr)[ www.elsevier.com/locate/jspr](homepage: www.elsevier.com/locate/jspr)  \n| Sustainable farming with machine learning solutions for minimizing food waste |  |  |  |\n| --- | --- | --- | --- |\n| Rukayat Abisola Olawalea, Mattew A. Olawumib, Bankole I. Oladapoc,* a School of Management Sciences, Babcock University, Ilishan Remo, Ogun State, Nigeria\u003Cbr>b Computing, Engineering and Media, De Montfort University, Leicester, UK\u003Cbr>c School of Science and Engineering, University of Dundee, Dundee, UK |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Handling Editor: Dr Manoj Nayak |  | This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) in mitigating post-harvest losses and reducing food waste within the agricultural supply chain. Our objective is to rigorously quantify the effectiveness of these technologies at various stages of food handling, from production to consumption, to improve food security and sustainability. The study employs a mixed-methods approach, integrating quantitative data from IoT sensors deployed in field studies and qualitative insights from stakeholders, including farmers and retailers. The study’s findings reveal that AI-driven cold storage interventions led to a 60% reduction in post-harvest losses for perishable items. Meanwhile, ML-optimized logistics resulted in a 20% decrease in transportation-related food waste. Despite these improvements, challenges remain in accurately predicting market demands, occasionally leading to overproduction. This highlights the need for further refinement in AI algorithms to handle market volatility. Integrating AI and ML in agricultural practices offers substantial benefits, demonstrating the potential to transform food supply chain management. However, additional improvements are required to maximize accuracy and efficiency. Future applications of the models include real-time adaptive logistics, blockchain integration for traceability, and AI-powered predictive demand forecasting. |  |\n| Keywords:\u003Cbr>Post-harvest losses Food waste reduction Artificial intelligence Machine learning Agricultural supply chain Sustainable agriculture |  |  |  |\n\nAbbreviations & Symbols  \n\n| Abbreviation | Full Form |\n| --- | --- |\n| AI | Artificial Intelligence |\n| ML | Machine Learning |\n| IoT | Internet of Things |\n| FW | Food Waste |\n| Lf | On-farm loss |\n| Lh | Post-harvest handling losses |\n| Lt | Transportation losses |\n| Lm | Market losses |\n| Ts | Technological interventions |\n\n1. Introduction  \nIn the evolving landscape of global ","cbCaimsOVZe5ENpI","https://ap.wps.com/l/cbCaimsOVZe5ENpI","pdf",5599084,11,1,12,"English","en",105,"# Introduction\n## Definitions of post-harvest losses and food waste\n## Role of AI and ML in agricultural practices\n# Abbreviations & Symbols","[{\"question\":\"What problems does the study target in agriculture?\",\"answer\":\"The study targets post-harvest losses and food waste, treating them as distinct issues that harm food security, economic stability, and environmental sustainability.\"},{\"question\":\"How does the paper evaluate AI/ML effectiveness across the supply chain?\",\"answer\":\"It quantifies impact at different stages from production to consumption using a mixed-methods approach that combines IoT sensor data with stakeholder insights.\"},{\"question\":\"What improvements were reported for cold storage and logistics?\",\"answer\":\"AI-driven cold storage interventions reduced post-harvest losses for perishable 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