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Hyperspectral Estimation of Leaf Nitrogen Content in White Radish Based on Feature Selection and Integrated Learning

文献类型： 外文期刊

作者： Li, Yafeng 1 ; Xu, Xingang 1 ; Wu, Wenbiao 1 ; Zhu, Yaohui 2 ; Yang, Guijun 1 ; Gao, Lutao 3 ; Meng, Yang 1 ; Jiang, Xiangtai 1 ; Xue, Hanyu 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Peoples R China

3.Yunnan Agr Univ, Coll Big Data, Kunming 650500, Peoples R China

关键词： LNC; leaf hyperspectral; vegetation index; feature selection; machine learning; stacking learning; lasso algorithm

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2024 年 16 卷 23 期

页码：

收录情况： SCI

摘要： Nitrogen is the main nutrient element in the growth process of white radish, and accurate monitoring of radish leaf nitrogen content (LNC) is an important guide for precise fertilization decisions for radish in the field. Using white radish LNC monitoring as an object, research on radish nitrogen hyperspectral estimation methods was carried out based on leaf hyperspectral and field sample nitrogen data at multiple growth stages using feature selection and integrated learning algorithm models. First, the Vegetation Index (VI) was constructed from hyperspectral data. We extracted sensitive features of hyperspectral data and VI response to radish LNC based on Pearson's feature-selection approach. Second, a stacking-integrated learning approach is proposed using machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Ridge and K-Nearest Neighbor (KNN) as the base model in the first layer of the architecture, and the Lasso algorithm as the meta-model in the second layer of the architecture, to realize the hyperspectral estimation of radish LNC. The analysis results show the following: (1) The sensitive bands of the radish LNC are mainly centered around 600-700 nm and 1950 nm, and the constructed sensitive VIs are also concentrated in this band range. (2) The Stacking model with spectral features as inputs achieved good prediction accuracy at the radish spectral leaf, with R2 = 0.7, MAE = 0.16, MSE = 0.05 estimated over the whole growth stage of radish. (3) The Lasso algorithm with variable filtering function was chosen as the meta-model, which has a redundant model-selection effect on the base model and helps to improve the quality of the integrated learning framework. This study demonstrates the potential of the stacking-integrated learning method based on hyperspectral data for spectral estimation of nitrogen content in radish at multiple growth stages.

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