Evaluating the Canopy Chlorophyll Density of Maize at the Whole Growth Stage Based on Multi-Scale UAV Image Feature Fusion and Machine Learning Methods

文献类型： 外文期刊

第一作者： Zhou, Lili

作者： Zhou, Lili;Su, Tao;Zhou, Lili;Nie, Chenwei;Song, Yang;Yin, Dameng;Liu, Shuaibing;Liu, Yadong;Bai, Yi;Jia, Xiao;Jin, Xiuliang;Zhou, Lili;Nie, Chenwei;Song, Yang;Yin, Dameng;Liu, Shuaibing;Liu, Yadong;Bai, Yi;Jia, Xiao;Jin, Xiuliang;Xu, Xiaobin

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关键词： machine learning; canopy chlorophyll density; multi-scale feature fusion; maize

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2023 年 13 卷 4 期

页码：

收录情况： SCI

摘要： Maize is one of the main grain reserve crops, which directly affects the food security of the country. It is extremely important to evaluate the growth status of maize in a timely and accurate manner. Canopy Chlorophyll Density (CCD) is closely related to crop health status. A timely and accurate estimation of CCD is helpful for managers to take measures to avoid yield loss. Thus, many methods have been developed to estimate CCD with remote sensing data. However, the relationship between the CCD and the features used in these CCD estimation methods at different growth stages is unclear. In addition, the CCD was directly estimated from remote sensing data in most previous studies. If the CCD can be accurately estimated from the estimation results of Leaf Chlorophyll Density (LCD) and Leaf Area Index (LAI) remains to be explored. In this study, Random Forest (RF), Support Vector Machines (SVM), and Multivariable Linear Regression (MLR) were used to develop CCD, LCD, and LAI estimation models by integrating multiple features derived from unmanned aerial vehicle (UAV) multispectral images. Firstly, the performances of the RF, SVM, and MLR trained over spectral features (including vegetation indices and band reflectance; dataset I), texture features (dataset II), wavelet coefficient features (dataset III), and multiple features (dataset IV, including all the above datasets) were analyzed, respectively. Secondly, the CCDP was calculated from the estimated LCD and estimated LAI, and then the CCD was estimated based on multiple features and the CCDP was compared. The results show that the correlation between CCD and different features is significantly different at every growth stage. The RF model trained over dataset IV yielded the best performance for the estimation of LCD, LAI, and CCD (R-2 values were 0.91, 0.97, and 0.97, and RMSE values were 6.59 mu g/cm(2), 0.35, and 24.85 mu g/cm(2)). The CCD directly estimated from dataset IV is slightly closer to the ground truth CCD than the CCDP (R-2 = 0.96, RMSE = 26.85 mu g/cm(2)) calculated from LCD and LAI. The results indicated that the CCD of maize can be accurately estimated from multiple multispectral image features at the whole growth stage, and both CCD estimation strategies can be used to estimate the CCD accurately. This study provides a new reference for accurate CCD evaluation in precision agriculture.

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