Artificial intelligence in fruit and vegetable preservation: innovations and applications of intelligent technology.

Fruits and vegetables are highly susceptible to post-harvest quality deterioration, and traditional preservation technologies can hardly meet the modern industry's demands for precision management and green development. By deeply integrating with sensors, the Internet of Things (IoT) and big data technologies, artificial intelligence (AI) enables full-link refined management covering signal collection, intelligent analysis, scheme formulation and automatic regulation, and facilitates precise control under controlled postharvest experimental conditions. Although research on AI-assisted postharvest handling is expanding rapidly, few studies systematically connect technological innovation to full-scale industrial application scenarios. Existing reviews have largely focused on individual technologies such as machine learning (ML) or only analyzed separate segments of postharvest workflows; rare efforts comprehensively analyze the integrated application of these tools across sorting, storage, cold chain monitoring and shelf-life prediction to realize targeted whole-process preservation regulation. In addition, prior literature has not fully summarized the industrial bottlenecks limiting large-scale on-site deployment. This paper sorts out targeted AI solutions for typical postharvest quality degradation of fruits and vegetables, including water loss, chilling injury, microbial contamination, and senescence caused by respiration and ethylene accumulation. Nevertheless, industrial promotion still faces multiple obstacles: inconsistent data quality, poor algorithm adaptability, high hardware costs, and the lack of unified industry standards and specifications. Looking forward, breaking bottlenecks such as cross-variety generalizable models, lightweight edge hardware and reliable data ecosystems will transform this field from scattered, experience-reliant operations into integrated intelligent decision-making systems. This can also provide technical references for global food security and sustainable agricultural development.