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Abstract <p>Post-harvest losses remain a major challenge in agri-food systems, particularly in the Global South, where poor handling, storage, and distribution practices cause significant crop waste. These losses weaken food security and create economic and environmental pressure. Artificial Intelligence is increasingly explored as a solution to improve post-harvest efficiency. This review summarizes current applications of AI in post-harvest loss management, focusing on machine learning, deep learning, computer vision, predictive analytics, and Internet of Things technologies. Their roles in real-time monitoring, quality evaluation, spoilage prediction, sorting, and supply chain optimization are highlighted.&#xa0;AI-based tools improve the accuracy of detecting deterioration, forecasting shelf life, and optimizing storage and logistics, leading to reduced losses and better product quality. However, high costs, limited expertise, infrastructure gaps, and data concerns hinder adoption, especially in developing regions. Overall, AI integration offers clear benefits for reducing waste, improving efficiency, and enhancing food security.</p>

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Use of Artificial Intelligence in post-harvest losses management: a current insight

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Hafiz Muhammad Abdullah,
  • Farhan Saeed,
  • Muhammad Bilal Hussain,
  • Ali Raza,
  • Amar Shankar,
  • Atreyi Pramanik,
  • Gunjan Garg,
  • Muhammad Wasiq,
  • Muhammad Usman Butt,
  • Muhammad Shameel Raheem,
  • Samra Rasheed,
  • Marriam Azhar,
  • Laraib Soukat,
  • Fakhar Islam,
  • Muhammad Afzaal

摘要

Abstract

Post-harvest losses remain a major challenge in agri-food systems, particularly in the Global South, where poor handling, storage, and distribution practices cause significant crop waste. These losses weaken food security and create economic and environmental pressure. Artificial Intelligence is increasingly explored as a solution to improve post-harvest efficiency. This review summarizes current applications of AI in post-harvest loss management, focusing on machine learning, deep learning, computer vision, predictive analytics, and Internet of Things technologies. Their roles in real-time monitoring, quality evaluation, spoilage prediction, sorting, and supply chain optimization are highlighted. AI-based tools improve the accuracy of detecting deterioration, forecasting shelf life, and optimizing storage and logistics, leading to reduced losses and better product quality. However, high costs, limited expertise, infrastructure gaps, and data concerns hinder adoption, especially in developing regions. Overall, AI integration offers clear benefits for reducing waste, improving efficiency, and enhancing food security.