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Enhanced Estimation of Rice Leaf Nitrogen Content via the Integration of Hybrid Preferred Features and Deep Learning Methodologies

文献类型： 外文期刊

作者： Peng, Yiping 1 ; Zhong, Wenliang 1 ; Peng, Zhiping 1 ; Tu, Yuting 1 ; Xu, Yanggui 1 ; Li, Zhuxian 1 ; Liang, Jianyi 1 ; Huang, Jichuan 1 ; Liu, Xu 4 ; Fu, Youqiang 5 ;

作者机构： 1.Guangdong Acad Agr Sci, Inst Agr Resources & Environm, Guangzhou 510640, Peoples R China

2.Minist Agr, Key Lab Plant Nutr & Fertilizer South Reg, Guangzhou 510640, Peoples R China

3.Guangdong Key Lab Nutrient Cycling & Farmland Cons, Guangzhou 510640, Peoples R China

4.Guangdong Acad Agr Sci, Inst Agr Econ & Informat, Guangzhou 510640, Peoples R China

5.Guangdong Acad Agr Sci, Rice Res Inst, Guangzhou 510640, Peoples R China

关键词： UAV hyperspectral; leaf nitrogen content (LNC); feature optimization; deep learning

期刊名称：AGRONOMY-BASEL （ 影响因子：3.3； 五年影响因子：3.7 ）

ISSN：

年卷期： 2024 年 14 卷 6 期

页码：

收录情况： SCI

摘要： Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial in achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology is an important tool for determining LNC. However, the intricate coupling between spectral information and nitrogen remains elusive. To address this, this study proposed an estimation method for LNC that integrates hybrid preferred features with deep learning modeling algorithms based on UAV hyperspectral imagery. The proposed approach leverages XGBoost, Pearson correlation coefficient (PCC), and a synergistic combination of both to identify the characteristic variables for LNC estimation. We then construct estimation models of LNC using statistical regression methods (partial least-squares regression (PLSR)) and machine learning algorithms (random forest (RF); deep neural networks (DNN)). The optimal model is utilized to map the spatial distribution of LNC at the field scale. The study was conducted at the National Agricultural Science and Technology Park, Guangzhou, located in Baiyun District of Guangdong, China. The results reveal that the combined PCC-XGBoost algorithm significantly enhances the accuracy of rice nitrogen inversion compared to the standalone screening approach. Notably, the model built with the DNN algorithm exhibits the highest predictive performance and demonstrates great potential in mapping the spatial distribution of LNC. This indicates the potential role of the proposed model in precision fertilization and the enhancement of nitrogen utilization efficiency in rice cultivation. The outcomes of this study offer a valuable reference for enhancing agricultural practices and sustainable crop management.

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