果蔬成熟度检测关键技术研究进展

doi:10.15933/j.cnki.1004-3268.2026.01.001

摘要/Abstract

摘要： 果蔬的成熟度与生产者和消费者紧密相关，其检测在提升消费者体验、提高收获时机准确性、延长货架期、减少浪费、提高产量与质量方面均发挥重要作用。然而，传统的人工检测果蔬成熟度的方式存在劳动强度大、成本高、效率低下等问题。近年来，随着计算机技术快速发展，数据驱动的果蔬成熟度检测方法使果蔬成熟度检测的自动化、智能化成为可能。从数据出发，对常用于果蔬成熟度检测的数据形式进行了全面的梳理，然后从传统图像处理算法、机器学习算法和深度学习算法3个方面阐述了果蔬成熟度检测算法的研究进展，并对现阶段农业领域中果蔬成熟度检测工程应用进行了系统性总结。在此基础上，进一步分析了数据驱动的果蔬成熟度检测方法在实际应用中面临的困难，并针对关键问题给出了可行的应对措施与研究思路，最后，对果蔬成熟度检测技术在农业领域中未来的发展方向进行了分析和展望，旨在为果蔬成熟度检测技术的进一步研究与应用提供理论参考和实践借鉴。

关键词: 果蔬, 数据形式, 成熟度检测, 深度学习, 模型部署

Abstract: The maturity of fruits and vegetables is closely related to both producers and consumers.It plays a crucial role in enhancing consumer experience，improving harvest timing accuracy，extending shelf life，reducing waste，and increasing yield and quality. However，traditional manual methods of detecting fruit and vegetable maturity have issues such as high labor intensity，high costs，and low efficiency.In recent years，with the rapid development of computer technology，data‐driven methods for detecting fruit and vegetable maturity have made automation and intelligence in this process possible.This study begins with a comprehensive review of the commonly used data formats for fruit and vegetable maturity detection.It then discusses the research progress of maturity detection algorithms from three perspectives：traditional image processing algorithms，machine learning algorithms，and deep learning algorithms.A systematic summary of the current engineering applications of maturity detection in the agricultural field is also provided. Based on this，the study further analyzes the challenges faced by data‐driven maturity detection methods in real‐world applications and offers feasible solutions and research approaches to address these key issues.Finally，the study presents an analysis and outlook on the future development directions of fruit and vegetable maturity detection technologies in agriculture，aiming to provide theoretical references and practical insights for the further research and application of these technologies.

Key words: Fruits and vegetables, Data format, Maturity detection, Deep learning, Model deployment

中图分类号:

S126

江明泽, 闫建伟, 邹传筑. 果蔬成熟度检测关键技术研究进展[J]. 河南农业科学, 2026, 55(1): 1-15.

JIANG Mingze, YAN Jianwei, ZOU Chuanzhu. A Review of Key Technologies in Fruits and Vegetables Maturity Detection[J]. Journal of Henan Agricultural Sciences, 2026, 55(1): 1-15.

图/表 4

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