AI-based postharvest management of fruits and vegetables: Enhancing quality, reducing losses, and improving supply chain efficiency

Abstract:

Fruit and vegetable post-harvest losses continue to be a significant challenge to the food security, profitability of smallholder operations, nutritional availability and the sustainable performance of supply chains. Fresh horticultural commodities have rapid moisture loss, softening, enzymatic changes, microbial spoilage and mechanical damage/temperature abuse, which are all responsible for the loss of market quality, and the continued respiration after harvest makes them very perishable. Traditional post-harvest practices are based on manual practices, fixed storage standards and routines, and experience-based decision making, which do not always allow for the detection of the early signs of internal degradation or the prediction of shelf life in the context of dynamic supply chains. Artificial intelligence (AI) can be the game-changer, connecting computer vision, hyperspectral imaging, near-infrared spectroscopy, electronic noses, Internet of Things sensors, machine learning, deep learning, and predictive analytics to the postharvest sector. This paper explores the potential of AI applications in the post-harvest sector for better quality assessment, minimizing losses, optimizing cold-chain functions, and providing more precise grade descriptions, shelf-life prediction, and boosting supply-chain efficiency. A secondary-data based analytical framework is provided with global food-loss indicators and conceptual AI-based post-harvest model. The paper proposes a paradigm change in the management of the postharvest stage from reactive quality control to predictive and preventive decision making by the adoption of AI. But, quality data, generalizability of models, low cost, readiness of infrastructure, farmer training, interoperability and ethical governance of the agricultural data are all critical for successful adoption. The study finds that the implementation of AI-driven post-harvest systems can significantly enhance the resilience and sustainability of the fruit and vegetable value chain, coupled with low-cost sensors, local calibration, clear algorithms and policy support.