Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning  

作　　者：Hui Feng Yongqi Chen Jingyan Song Bingjie Lu Caixia Shu Jiajun Qiao Yitao Liao Wanneng Yang 

机构地区：[1]National Key Laboratory of Crop Genetic Improvement,National Center of Plant Gene Research(Wuhan),Hubei Hongshan Laboratory,Huazhong Agricultural University,Wuhan,430070 Hubei,PR China [2]Shenzhen Institute of Nutrition and Health,Huazhong Agricultural University,Wuhan,430070 Hubei,PR China [3]College of Engineering,Huazhong Agricultural University,Wuhan,430070 Hubei,PR China

出　　处：《Plant Phenomics》2024年第2期269-280,共12页植物表型组学（英文）

基　　金：supported by grants from the STI2030-Major Projects;National Key Research and Development Program(2022YFD1900701-4);National Natural Science Foundation of China(U21A20205);Key Projects of Natural Science Foundation of Hubei Province(2021CFA059);HZAU-AGIS Cooperation Fund(SZYJY2022014);Fundamental Research Funds for the Central Universities(2021ZKPY006 and 2662021JC008);the National Rape Crop Industry System Special Project Funding(CARS-12).

摘　　要：Oilseed rape is an important oilseed crop planted worldwide.Maturity classification plays a crucial role in enhancing yield and expediting breeding research.Conventional methods of maturity classification are laborious and destructive in nature.In this study,a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms.Initially,hyperspectral images were captured for 3 distinct ripeness stages of rapeseed,and raw spectral data were extracted from the hyperspectral images.The raw spectral data underwent preprocessing using 5 pretreatment methods,namely,Savitzky-Golay,first derivative,second derivative(D2nd),standard normal variate,and detrend,as well as various combinations of these methods.Subsequently,the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling,successive projection algorithm(SPA),iterative spatial shrinkage of interval variables(IVISSA),and their combination algorithms,respectively.The classification models were constructed using the following algorithms:extreme learning machine,k-nearest neighbor,random forest,partial least-squares discriminant analysis,and support vector machine(SVM)algorithms,applied separately to the full wavelength and the feature wavelengths.A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods,feature wavelength selection algorithms,and classification models,and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed.The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance,attaining an accuracy rate of 97.86%.The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.

关 键 词：learning image with CLASSIFICATION COMBINED HYPERSPECTRAL machine MATURITY RAPESEED USING 

分 类 号：S565.4[农业科学—作物学]