激光技术2024，Vol.48Issue(4)：521-526,6.DOI:10.7510/jgjs.issn.1001-3806.2024.04.009
基于高光谱成像的蓝莓微腐烂检测研究
Study of blueberry micro-rot detection based on hyperspectral imaging
摘要
Abstract
In order to investigate the effects of time and temperature changes on blueberries after early decay,hyperspectral imaging technology combined with partial least squares and back-propagation neural network algorithms were used to carry out theoretical analysis and experimental validation,and partial least squares and back-propagation neural networks were used to obtain the time model and the temperature model of blueberry decay,and the modeling effects of these two algorithms were compared.The results show that with the increase of time,the blueberry decay will further deteriorate;along with the increase of temperature,the intensity of blueberry decay gradually increases,the effect of the model established based on the partial least squares method is more suitable for the detection of decayed blueberries,the coefficient of covariance and correlation coefficient of decayed blueberries are 0.131,0.149,0.932 and 0.921,respectively,and the error shows that the error is small and correlation tends to be consistent.The model established by partial least squares method can better show the effect of time and temperature on decayed blueberries,which provides a certain reference for the detection of micro-decay on the surface of blueberries.关键词
光谱学/偏最小二乘法/反向传播神经网络/腐烂蓝莓/温度/时间Key words
spectroscopy/partial least squares/back-propagation neural networks/rotting blueberries/temperature/time分类
物理学引用本文复制引用
刘燕德,李念,崔正淳,严柠晨..基于高光谱成像的蓝莓微腐烂检测研究[J].激光技术,2024,48(4):521-526,6.基金项目
国家重点研发计划资助项目(2022YFD2001804) （2022YFD2001804）
