AI-driven technologies in biochemical quality assessment and postharvest metabolic monitoring of horticultural crops

Abstract

Horticultural crops play a vital role in global nutrition, providing essential vitamins, minerals, and bioactive compounds that support human health. However, their high perishability leads to significant postharvest losses, particularly in developing countries lacking adequate storage infrastructure. Traditional postharvest practices, such as manual grading and static storage, are often inefficient and fail to address crop-specific requirements. Artificial Intelligence (AI) offers a transformative solution by enabling predictive, data-driven management of postharvest systems. Through the integration of machine learning, sensors, imaging technologies, robotics, and cloud computing, AI facilitates real-time monitoring and decision-making to reduce spoilage and maintain quality. Additionally, the convergence of AI with IoT, big data, and blockchain enhances traceability, supply chain coordination, and resource efficiency. These intelligent systems shift postharvest management from reactive to proactive approaches, minimizing losses, improving food safety, and promoting sustainability in horticultural production and distribution systems.