Detection and assessment of nitrogen effect on cold tolerance for tea by hyperspectral reflectance with PLSR, PCR, and LM models

作     者：Eric Amoah Asante Zhe Du Yongzong Lu Yongguang Hu 

作者机构：School of Agricultural Equipment EngineeringInstitute of Agricultural EngineeringJiangsu UniversityZhenjiangJiangsu 212013China Department of Mechanical EngineeringFaculty of EngineeringKoforidua Technical UniversityKoforiduaGhana 

出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)

年 卷 期：2021年第8卷第1期

页      面：96-104页

核心收录：

学科分类：0907[农学-林学] 0908[农学-水产] 08[工学] 09[农学] 090203[农学-茶学] 0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0905[农学-畜牧学] 0707[理学-海洋科学] 081203[工学-计算机应用技术] 0906[农学-兽医学] 0829[工学-林业工程] 0901[农学-作物学] 0835[工学-软件工程] 0902[农学-园艺学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors are grateful to the financial support by the Key R&D program of Jiangsu Province(BE2016354) Jiangsu Agriculture Science and Technology Innovation Fund(CX(16)1045) China and Jiangsu postdoctoral science foundations(2016M600376,1601032C) Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2018-87),China 

主　　题：Cold injury Hyperspectral imaging Reflectance Principal component 

摘      要：Knowledge of nutrients effect on freezing tolerance is vital for protecting tea plants against cold injury(CI).Freezing injury treatments on tea leaves with different low temperature and nitrogen(N)concentration was evaluated by hyperspectral imaging based on the reflectance as potential analytical ***,quantitative evaluation of the CI was improved by comparing analyzed results using partial least squares regression(PLSR),principle component regression(PCR)and linear model(LM)*** of the CI was obtained by hyperspectral imaging system in the band between 871 and 1766 *** results showed that average reflectance increases with the rise of N concentration.A substantial portion of the leaf from the plant with 100%N dosage had the darkest image and resulted in the highest reflectance because the N weakened the negative effect of freezing *** of the five spectral domains tested,the best predictive accuracy for the CI of the tea leaf was achieved by PCR(R^(2)=0.9971,RMSE=0.0609)in 1410–1766 nm wavelength,followed by LM(R^(2)=0.9999,RMSE=0.0805)in 871–1000 *** whole interval had the worse prediction accuracy which could be caused by large variations in the data for a specific treatment and high absorption peak occurring around 1450 nm in the reflectance *** averages,R^(2) and RMSE for all the three statistical models showed that the worse prediction accuracy occurred in 1410–1766 nm,followed by the whole interval 871–1766 *** prediction accuracy was low which could be due to the strong water absorption peaks that appeared in both range of *** models without absorption bands had improved correlation coefficient and reduced RMSE values between the measured and the predicted CI(R^(2)=0.9676;RMSE=0.3067)compared to the model developed with the reflectance values in the entire waveband(R^(2)=0.9504;RMSE=0.6629).The reflectance values where absorption bands occurred had detrimental effect on the model performance,which