Estimation of spectral responses and chlorophyll based on growth stage effects explored by machine learning methods

查看全文 作　　者：Dehua [1]Gao;Lang [1]Qiao;Lulu [1]An;Ruomei [1]Zhao;Hong [1,3]Sun;Minzan [1,3]Li;Weijie [1]Tang;Nan [2]Wang

机构地区：[1]Key Laboratory of Modern Precision Agriculture System Integration Research,Ministry of Education,China Agricultural University,Beijing 100083,China;[2]Key Laboratory of Agricultural Information Acquisition Technology,Ministry of Agriculture and Rural Affairs,China Agricultural University,Beijing 100083,China;[3]Yantai Institute of China Agricultural University,Yantai 264670,Shandong,China

出　　处：《The Crop Journal》索引2022年第10卷第5期,共11页

基　　金：supported by the National Key Research and Development Program (2019YFE0125500);University-Locality Integrative Development Project of Yantai (2020XDRHXMPT35);the National Natural Science Foundation of China (31971785 and41801245);the Graduate Training Project of China Agricultural University (JG2019004, JG202026, YW2020007, QYJC202101, and JG202102)。 摘　　要：Estimation of leaf chlorophyll content(LCC) by proximal sensing is an important tool for photosynthesis evaluation in high-throughput phenotyping. The temporal variability of crop biochemical properties and canopy structure across different growth stages has great impacts on wheat LCC estimation, known as growth stage effects. It will result in the heterogeneity of crop canopy at different growth stages, which would mask subtle spectral response of biochemistry variations. This study aims to explore spectral responses on the growth stage effects and establish LCC models suited for different growth stages. A total number of 864 pairwise samples of wheat canopy spectra and LCC values with 216 observations of each stage were sampled at the tillering, jointing, booting and heading stages in 2021. Firstly, statistical analysis of LCC and spectral response presented different distribution traits and typical spectral variations peak at 470, 520 and 680 nm. Correlation analysis between LCC and reflectance showed typical red edge shifts. Secondly, the testing model of partial least square(PLS) established by the entire datasets to validate the predictive performance at each stage yielded poor LCC estimation accuracy. The spectral wavelengths of red edge(RE) and blue edge(BE) shifts and the poor estimation capability motivated us to further explore the growth stage effects by establishing LCC models at respective growth periods.Finally, competitive adaptive reweighted sampling PLS(CARS-PLS), decision tree(DT) and random forest(RF) were used to select sensitive bands and establish LCC models at specific stages. Bayes optimisation was used to tune the hyperparameters of DT and RF regression. The modelling results indicated that CARS-PLS and DT did not extract specific wavelengths that could decrease the influences of growth stage effects. From the RF out-of-bag(OOB) evaluation, the sensitive wavelengths displayed consistent spectral shifts from BE to GP and from RE to RV from tillering to heading stages. Compared with CARS-PLS and DT,results of RF modelling yielded an estimation accuracy with deviation to performance(RPD) of 2.11, 2.02,3.21 and 3.02, which can accommodate the growth stage effects. Thus, this study explores spectral response on growth stage effects and provides models for chlorophyll content estimation to satisfy the requirement of high-throughput phenotyping. 关键词：Wheat chlorophyll content Growth stage effects Sensitive wavelengths Spectral response Random forest

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Estimation of spectral responses and chlorophyll based on growth stage effects explored by machine learning methods

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