ABSTRACT Approximately one‐third of fresh food is wasted globally throughout the supply chain. Machine learning (ML), a key branch of artificial intelligence, enhances postharvest logistics and preservation of fresh food by enabling intelligent sensing, precise evaluation, and adaptive control. However, its application is challenged by data standardization, sensor limitations, product variability, and limited model generalizability. This review summarizes current advanced ML applications in the food supply chain, emphasizing their transformative potential for quality control. We explore ML's ability to integrate multi‐omics data for deeper insights into molecular changes during transportation and storage, enabling the development and evaluation of management strategies. Practical applications in grading, sensor technology, and intelligent preservation materials are also evaluated. ML models, such as support vector machine (SVM) and convolutional neural network (CNN), enhance precise grading and quality prediction by analyzing sensory attributes and chemical composition. By capturing complex molecular interactions, ML enables innovative sensor surface design with enhanced sensitivity and specificity. ML‐driven sensors further support real‐time environmental monitoring, while intelligent packaging materials powered by ML maintain freshness and reduce spoilage through adaptive responses to internal conditions. To ensure model robustness and generalizability, appropriate validation strategies such as cross‐validation and external validation are essential. Despite its substantial potential, the widespread adoption of ML still faces challenges, including limited varietal and regional generalization, decision‐making transparency, computational demands, limited data availability, and algorithm selection. Addressing these issues is critical for achieving effective and sustainable ML integration in postharvest quality control systems.
Xu et al. (2025) studied this question.