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Analysis of crop leaf area index, leaf chlorophyll content, and canopy chlorophyll content based on deep learning and hyperspectral remote sensing

文献类型： 外文期刊

作者： Leng, Mengdie 1 ; Liu, Yang 2 ; Li, Bing 3 ; Che, Yinchao 1 ; Feng, Haikuan 4 ; Shu, Meiyan 1 ; Xu, Xin 1 ; Qiao, Hongbo 1 ; Yue, Jibo 1 ;

作者机构： 1.Henan Agr Univ, Coll Informat & Management Sci, 63 Agr Rd, Zhengzhou 450002, Henan, Peoples R China

2.China Agr Univ, Minist Educ, Key Lab Smart Agr Syst, Beijing, Peoples R China

3.Henan Univ, Key Res Inst Yellow River Civilizat & Sustainable, Kaifeng, Peoples R China

4.Nanjing Agr Univ, Coll Agr, Nanjing, Peoples R China

5.Beijing Res Ctr Informat Technol Agr, Minist Agr, Key Lab Quantitat Remote Sensing Agr, Beijing, Peoples R China

关键词： Deep learning; hyperspectral remote sensing; canopy chlorophyll content; convolutional neural network

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：2.6； 五年影响因子：2.9 ）

ISSN： 0143-1161

年卷期： 2025 年

页码：

收录情况： SCI

摘要： Canopy chlorophyll content (CCC) is a critical indicator for assessing crop photosynthetic capacity, nitrogen status, and the occurrence of diseases. Accurate estimation of CCC holds significant importance for precision agriculture, providing a scientific basis for crop management, yield prediction, and stress detection. CCC is commonly defined as the product of leaf area index (LAI) and leaf chlorophyll content (LCC). Traditional methods of acquiring CCC rely on destructive sampling, which limits large-scale application. Hyperspectral remote sensing enables non-destructive acquisition of rich spectral information from the crop canopy across the visible to near-infrared spectrum, offering a promising approach for CCC estimation. This study proposes a convolutional neural network-based model, CanopyChlNet, to jointly estimate LAI and LCC, thereby deriving CCC. The model utilizes a one-dimensional CNN structure to effectively extract deep spectral features from hyperspectral data, improving estimation accuracy. Field-measured canopy hyperspectral reflectance and corresponding LAI and LCC data from winter wheat and potato were used to train and validate the model. The CanopyChlNet model outperformed both Random Forest (RF) and Partial Least Squares Regression (PLSR) in estimating LAI, LCC, and CCC, achieving R2 values of 0.709, 0.775, and 0.718, with RMSE values of 0.803 m2 m-2, 5.288 mu gcm- 2, and 34.938 mu gcm- 2, respectively. In comparison, RF yielded R2 values of 0.636, 0.685, and 0.667, and RMSE values of 0.896 m2 m- 2, 6.396 mu gcm- 2, and 37.901 mu gcm- 2. PLSR achieved R2 values of 0.522, 0.709, and 0.586, with RMSE values of 1.029 m2 m- 2, 6.042 mu gcm- 2, and 42.332 mu gcm- 2.These results demonstrate that CanopyChlNet is a high-precision model for estimating crop LCC and CCC. This study demonstrates that integrating deep learning with hyperspectral remote sensing significantly enhances the estimation accuracy of key crop parameters, providing an effective tool for crop growth monitoring.

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