As the global population continues to grow, fruit and vegetable (F&V) products play an increasingly vital role in the agricultural economy, while postharvest phase faces pressing challenges in operational efficiency, cost control, and quality assurance. The rapid development of artificial intelligence (AI) technology provides new opportunities to solve these problems. This paper systematically outlines the application of AI in the postharvest process of F&V products, covering key technologies such as machine learning, deep learning, computer vision, and fuzzy logic. It examines AI's contributions across various domains including quality inspection, safety management, intelligent packaging, and integration with the Internet of Things and automated equipment. AI facilitates non-destructive, real-time monitoring and accurate prediction of key quality attributes, including contaminants, nutritional content, and adulteration. AI-driven smart packaging systems dynamically adjust storage conditions, while automated robots enhance sorting precision and packaging efficiency. These advancements significantly enhanced the level of automation in operations, reduce postharvest losses, and optimize resource allocation through data-driven approaches. Nevertheless, challenges persist in data quality, model interpretability, cost-effectiveness, and multi-technology integration. Collaborative efforts between researchers and decision-makers are crucial for addressing these issues. In summary, this paper clarifies current applications and future potential of AI in the postharvest industry, highlighting its role in advancing sustainability within the F&V sectors.
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