Multi-scale monitoring for hazard level classification of brown planthopper damage in rice using hyperspectral technique  

作　　者：Juan Liao Wanyan Tao Yexiong Liang Xinying He Hui Wang Haoqiu Zeng Zaiman Wang Xiwen Luo Jun Sun Pei Wang Ying Zang 

机构地区：[1]Guangdong Laboratory for Lingnan Modern Agriculture,College of Engineering,South China Agricultural University,Guangzhou 510642,China [2]Key Laboratory of Key Technology on Agricultural Machine and Equipment(South China Agricultural University),Ministry of Education,Guangzhou 510642,China [3]Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence(GDKL-AAI),Guangzhou 510642,China [4]State Key Laboratory of Agricultural Equipment Technology [5]Huangpu Innovation Research Institute of SCAU,Guangzhou 510715,China [6]School of Electrical and Information Engineering of Jiangsu University,Zhenjiang 212013,China

出　　处：《International Journal of Agricultural and Biological Engineering》2024年第6期202-211,共10页国际农业与生物工程学报(英文)

基　　金：financially supported by Key Research and Development Project of China(Grant No.2022YFD2002400);the Key Scientific and Technological Projects in Key Areas of Crops(Grant No.2023AB014);the National Natural Science Foundation of China(Grant No.31901401);the Key Area Research and Development Program of Guangdong(Grant No.2023B0202130001);Guangdong Provincial Basic and Applied Basic Research Fund Project(Grant No.2022A1515011528).

摘　　要：The primary aim of this study was to classify the hazard level of brown planthopper(BPH)damage in rice.Three datasets,including spectral reflectance corresponding to the sensitive wavelengths from rice canopy spectral wavelengths,rice stem spectral wavelengths,and fusion information of rice canopy and stem spectral wavelengths were used for BPH hazard level classification by using different algorithms.Datasets and algorithms were optimized by the BPH hazard level classification effects(which was evaluated by indices of accuracy,precision,recall,F_(1),and k-value).The optimized algorithm combination was used to build a hazard level classification model for spectral reflectance corresponding to the sensitive wavelength from the rice canopy spectral images.Results showed that:(1)The spectral reflectance corresponding to the sensitive wavelengths of fusion information dataset performed best in BPH hazard level classification,with the highest accuracy(99.08%),precision(99.31%),recall(98.83%),F_(1)(0.99),and k-value(0.99).(2)The optimum algorithm combination was Savitzky-Golay(S-G)smoothing,principal component analysis(PCA)for sensitive wavelength selection,and broad-learning system(BLS)for modeling.(3)The spectral reflectance corresponding to the sensitive wavelengths dataset of rice canopy spectral images achieved accuracy(80.63%),precision(80.28%),recall(77.03%),F_(1)(0.79),and k-value(0.74)in classifying BPH hazard level by using the optimum algorithm combination.

关 键 词：brown planthopper(BPH) hazard level classification hyperspectral technique rice canopy rice stem fusion information 

分 类 号：S51[农业科学—作物学]