植物学报2026，Vol.61Issue(4)：657-670,14.DOI:10.11983/CBB25224
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
摘要
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.关键词
无损检测/水果成熟度/多模态融合/传感器/机器学习Key words
non-destructive testing/fruit ripeness/multimodal fusion/sensors/machine learning引用本文复制引用
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[J].植物学报,2026,61(4):657-670,14.基金项目
国家自然科学基金(No.32502338,No.32501780)、中央高校基本科研业务费专项(No.2662025GXPY010,No.2662023GXQ-D001)和江苏省普通高校自然科学研究(No.24KJB210014) （No.32502338,No.32501780）
