基于机器学习的水果成熟度无损检测技术研究进展

doi:10.11983/CBB25224

植物学报 ›› 2026, Vol. 61 ›› Issue (4): 549-714.DOI: 10.11983/CBB25224

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基于机器学习的水果成熟度无损检测技术研究进展

林卫国¹, 李云川¹, 陈先圣¹, 周大猷¹, 王大臣², 王英力¹^,^*(), 黄远³

¹ 华中农业大学工学院 , 武汉 430070
² 南京林业大学机械电子工程学院 , 南京 210037
³ 华中农业大学园艺林学学院 , 武汉 430070

收稿日期:2025-12-13 接受日期:2026-03-06 出版日期:2026-07-10 发布日期:2026-05-28
通讯作者: 王英力
基金资助:
国家自然科学基金(No.32502338); 国家自然科学基金(No.32501780); 中央高校基本科研业务费专项(No.2662025GXPY010); 中央高校基本科研业务费专项(No.2662023GXQ-D001); 江苏省普通高校自然科学研究(No.24KJB210014)

Advances in Non-destructive Testing Technology for Fruit Ripeness Based on Machine Learning

Weiguo Lin¹, Yunchuan Li¹, Xiansheng Chen¹, Dayou Zhou¹, Dachen Wang², Yingli Wang¹^,^*(), Yuan Huang³

¹ College of Engineering, Huazhong Agricultural University , Wuhan 430070, China
² College of Mechanical and Electronic Engineering, Nanjing Forestry University , Nanjing 210037, China
³ College of Horticulture and Forestry, Huazhong Agricultural University , Wuhan 430070, China

Received:2025-12-13 Accepted:2026-03-06 Online:2026-07-10 Published:2026-05-28
Contact: Yingli Wang

摘要/Abstract

摘要： 水果成熟度的无损检测对于降低水果生产、运输以及存储过程中的损耗有重要意义。在传统检测方法中, 经验判断法主观性强、准确率低; 而有损检测法则会破坏样品完整性, 二者均存在检测效率低的局限。近年来, 随着近红外光谱、高光谱成像、机器视觉、声学振动、电学特性、触觉感知及电子鼻等无损检测技术的迅速发展, 通过检测水果的颜色、硬度、糖酸比以及挥发性气体等与水果成熟度具有高度相关性的关键参数, 利用机器学习方法实现了对水果成熟状态的精准评估。该文以水果成熟过程中生理特性变化为切入点, 系统梳理了无损检测技术的基本原理与应用。此外, 总结了该领域面临的挑战, 如环境干扰、数据标准缺失以及成本、复杂度与多模态集成之间的平衡。最后, 探讨了传感器技术的未来发展方向, 如开发低成本的多模态传感器技术、制定数据标准以及提高机器学习的可解释性。

关键词: 无损检测, 水果成熟度, 多模态融合, 传感器, 机器学习

Abstract: Non-destructive testing of fruit ripeness is of great significance for reducing losses during fruit production, transportation, and storage. Among traditional methods, empirical judgment is highly subjective and less accurate, while destructive testing compromises the integrity of samples. Both methods suffer from low detection efficiency. In recent years, with the rapid development of non-destructive testing technologies such as near-infrared spectroscopy, hyperspectral imaging, machine vision, acoustic vibration, electrical properties, tactile sensing, and electronic noses, the key parameters highly correlated with fruit ripeness-such as color, hardness, sugar-acid ratio, and volatile gases-have been used to accurately assess the maturity state of fruits through machine learning approaches. This study begins with the physiological changes during fruit ripening and systematically reviews the fundamental principles and applications of non-destructive testing technologies. Furthermore, it summarizes current challenges in the field, including environmental interference, lack of data standards, and the balance between cost, complexity, and multimodal integration. The study also explores future directions, such as the development of low-cost multimodal sensor technologies, the establishment of data standards, and improving the interpretability of machine learning.

Key words: non-destructive testing, fruit ripeness, multimodal fusion, sensors, machine learning

中图分类号:

TP212
S-1

林卫国, 李云川, 陈先圣, 周大猷, 王大臣, 王英力, 黄远. 基于机器学习的水果成熟度无损检测技术研究进展. 植物学报, 2026, 61(4): 549-714.

Weiguo Lin, Yunchuan Li, Xiansheng Chen, Dayou Zhou, Dachen Wang, Yingli Wang, Yuan Huang. Advances in Non-destructive Testing Technology for Fruit Ripeness Based on Machine Learning. Chinese Bulletin of Botany, 2026, 61(4): 549-714.

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链接本文: https://www.chinbullbotany.com/CN/10.11983/CBB25224

https://www.chinbullbotany.com/CN/Y2026/V61/I4/549

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基于机器学习的水果成熟度无损检测技术研究进展

Advances in Non-destructive Testing Technology for Fruit Ripeness Based on Machine Learning

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