文献类型： 外文期刊

作者： Yue, Jibo ¹ ; Yang, Hao ² ; Feng, Haikuan ² ; Han, Shaoyu ¹ ; Zhou, Chengquan ² ; Fu, Yuanyuan ¹ ; Guo, Wei ¹ ; Ma, Xinming ¹ ; Qiao, Hongbo ¹ ; Yang, Guijun ² ;

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

2.Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr, Minist Agr, Beijing 100097, Peoples R China

3.Zhejiang Acad Agr Sci, Inst Agr Equipment, Hangzhou 31000, Peoples R China

关键词： LCC; Convolutional neural network; Hyperspectral; Remote sensing; UAV

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 211 卷

页码：

收录情况： SCI

摘要： Leaf chlorophyll content (LCC) is a distinct indicator of crop health status used to estimate nutritional stress, diseases, and pests. Thus, accurate LCC information can assist in the monitoring of crop growth. The combined use of hyperspectral and deep learning techniques (e.g., convolutional neural network [CNN] and transfer learning [TL]) can improve the performance of crop LCC estimation. We propose a hyperspectral-to-image transform (HIT) technique for converting canopy hyperspectral reflectance into 2D images. We designed a CNN architecture called LCCNet that fuses the deep and shallow features of CNNs to improve soybean LCC estimation. This study evaluated the combined use of hyperspectral remote sensing (RS), HIT, CNN, and TL techniques to estimate soybean LCC for multiple growth stages. The LCCNet was pre-trained based on a simulated dataset (n = 114,048) from the PROSAIL radiative transfer model (RTM) and used as prior knowledge for this work. The soybean canopy hyperspectral RS dataset (n = 910) was obtained using a FieldSpec 3 spectrometer. The knowledge gained while learning to estimate LCC from PROSAIL RTM was applied when estimating field soybean LCC (Dualex readings). TL was used to enhance the soybean estimation model, called the Soybean-LCCNet (RTM + HIT + CNN + TL) model. We tested the LCC (Dualex readings) estimation performance using (a) HIT + CNN, (b) LCCNet (RTM + HIT + CNN), (c) Soybean-LCCNet (RTM + HIT + CNN + TL), and (d) widely used LCC spectral features + partial least squares regression (PLSR). Four methods were ranked based on their LCC estimation performance: Soybean-LCCNet (R2 = 0.78, RMSE = 4.13 (Dualex readings)) > HIT + CNN (R2 = 0.75, RMSE = 4.41 (Dualex readings)) > PLSR-based method (R2 = 0.61, RMSE = 5.39 (Dualex readings)) > LCCNet (R2 = 0.53, RMSE = 7.11 (Dualex readings)). The main conclusions of this work are as follows: (1) HIT + CNN can provide a more robust LCC estimation performance than the widely used LCC SIs; (2) Fusing the deep and shallow features of CNNs can improve the performance of RS soybean LCC (Dualex readings) estimation; and (3) Soybean-LCCNet can reuse the CNN layer information of a pre-trained LCCNet based on a PROSAIL RTM dataset and improve the soybean LCC estimation performance.