基于反射率、吸光度和Kubelka-Munk光谱数据的黄桃早期损伤程度检测  

Detection of early damage level of yellow peaches based on reflectance,absorbance and Kubelka-Munk spectral data

作　　者：殷海 李斌 刘燕德 张烽 苏成涛 欧阳爱国 YIN Hai;LI Bin;LIU Yande;ZHANG Feng;SU Chengtao;OUYANG Aiguo(School of Mechatronics&Vehicle Engineering/National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]华东交通大学机电与车辆工程学院/水果智能光电检测技术与设备国家与地方联合工程研究中心,南昌330013

出　　处：《华中农业大学学报》2023年第3期220-229,共10页Journal of Huazhong Agricultural University

基　　金：国家自然科学基金项目(12103019);国家科技奖后备项目培育计划(20192AEI91007)。

摘　　要：为提高黄桃损伤程度无损检测识别的准确率,采集健康和不同损伤程度黄桃(Amygdalus persica)的反射光谱(R)、吸收光谱(A)、Kubelka-Munk光谱(K-M),并基于反射光谱、吸收光谱、Kubelka-Munk光谱等原始光谱和RAW、BOC、DT、SG、SNV等预处理方法后的光谱建立偏最小二乘判别分析(partial least squares discriminant analysis,PLS-DA)、极限梯度提升(extreme gradient boosting,XGBoost)和随机森林(random forest,RF)模型,比较3种模型检测效果,选出正确率较高模型并构建其特征波长下的模型,并对结果再次进行比较。结果显示,基于3种原始光谱和SG预处理后光谱的RF模型判别效果较优,整体准确率均达到了90.00%以上。利用竞争性自适应重加权(CARS)和无信息变量消除(UVE)算法对3种原始光谱和SG预处理后的光谱进行波长筛选,并再次建立RF模型。结果显示,A-RAW-CARS-RF模型和K-M-SG-CARS-RF模型相比于全光谱下的RF模型判别效果得到了改善,并且在基于特征波长建立的RF模型中,A-RAW-CARS-RF模型的判别效果最好,整体准确率达到了97.12%,对4个子类别的误判数分别为0、1、1、1。Yellow peaches are soft and prone to damage,and the different level of damage can directly affect the end use and sale price of yellow peaches.The reflection(R),absorption(A),and KubelkaMunk spectra(K-M) of yellow peaches were obtained by using hyperspectral techniques and used to detect the early damage level of yellow peaches.Partial least squares discriminant analysis(PLS-DA),extreme gradient boosting(XGBoost) and random forest(RF) models based on three raw spectra and various pretreated spectra were established.The results were compared to select the model with higher correctness.The model with its characteristic wavelength was constructed and compared again.The results showed that RF models based on the three raw spectra and SG pretreated spectra were superior in discriminating,with the overall accuracy rates all above 90.00%.The wavelength screening of the raw spectra and SG pretreated spectra was performed with the competitive adaptive reweighting(CARS) and uninformative variable elimination(UVE) algorithms,and the RF models were established again.The results showed that the ARAW-CARS-RF model and the K-M-SG-CARS-RF model were improved in discriminating compared with the RF model at full spectrum.Among the RF models established based on the characteristic wavelengths,the A-RAW-CARS-RF model had the best discriminating effect with an overall accuracy of 97.12%.The number of misclassifications for the four subcategories were 0,1,1,and 1.It is indicated that the feasibility of detecting the early damage level of yellow peaches based on absorption spectroscopy(A).It will provide some theoretical basis for detecting fruit bruise with hyperspectral techniques in the future.

关 键 词：黄桃 损伤程度 反射光谱 吸收光谱 Kubelka-Munk光谱 无损检测 高光谱检测 表型检测 

分 类 号：T255[一般工业技术]