HYPERSPECTRAL ESTIMATION OF APPLE CANOPY CHLOROPHYLL CONTENT USING AN ENSEMBLE LEARNING APPROACH
文献类型： 外文期刊
作者： Bai, Xueyuan 1 ; Song, Yingqiang 1 ; Yu, Ruiyang 1 ; Xiong, Jingling 1 ; Peng, Yufeng 1 ; Jiang, Yuanmao 2 ; Yang, Gui 1 ;
作者机构： 1.Shandong Agr Univ, Coll Resources & Environm, Tai An 271018, Shandong, Peoples R China
2.Shandong Agr Univ, Coll Hort Sci & Engn, Natl Apple Engn & Technol Res Ctr, Tai An 271018, Shandong, Peoples R China
3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
4.Shandong Agr Univ, Coll Resources & Environm, Natl Engn Lab Efficient Utilizat Soil & Fertilize, Tai An 271018, Shandong, Peoples R China
关键词： Apple tree canopy; Chlorophyll content; Crop stress monitoring; Ensemble learning; Hyperspectral; Vegetation index
期刊名称：APPLIED ENGINEERING IN AGRICULTURE （ 影响因子：0.985； 五年影响因子：1.02 ）
ISSN： 0883-8542
年卷期： 2021 年 37 卷 3 期
页码：
收录情况： SCI
摘要： Rapidly and effective monitoring of the canopy chlorophyll content (CCC) of apple trees is of great significance for crop stress monitoring in precision agriculture. This study attempted to use hyperspectral vegetation indices (VIs) to estimate the CCC of apple trees based on ensemble learning approach. In this study, vegetation indices combined by any two wavelengths from 400 to 1100 nm were constructed to calculate the correlation coefficient with the CCC in apple. We constructed a partial least squares regression model (PLSR), artificial neural network regression model (ANN), support vector machine regression (SVR), random forest regression (RF) model and support vector machine combination regression model (C-SVR) based on combinations of VIs to improve the estimation accuracy in apple CCC. The results showed that the correlation coefficients between NDVI (949,695), OSAVI (828,705), RDVI (741,725), RVI (716,707), DVI (572,532), and apple CCC were all above 0.76. The CCC estimation model using the RF and C-SVR approach constructed by the NDVI (949,695), OSAVI (828,705), RDVI (741,725), RVI (716,707), and DVI (572,532) achieved the better estimation results, and the R-V(2), RMSEV, and RPDV values of models were 0.76, 0.131(mg.g(-1)), 2.04 and 0.78, 0.127(mg.g(-1)), 2.12, respectively. Compared with the PLSR, ANN, and SVR model, the R-V(2) and RPDV values of C-SVR model were increased by 4%, 1.2%, 3.8%, and 5.0%, 28.4%, 7.1%, respectively. The results show that using C-SVR approach to estimating the apple CCC can realize high accuracy of quantitative estimation. Ensemble learning approach is an effective method for monitoring the nutrient status of fruit trees based on hyperspectral technique.
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