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The Application of Machine Learning Models Based on Leaf Spectral Reflectance for Estimating the Nitrogen Nutrient Index in Maize

文献类型： 外文期刊

作者： Chen, Bo 1 ; Lu, Xianju 2 ; Yu, Shuan 2 ; Gu, Shenghao 2 ; Huang, Guanmin 2 ; Guo, Xinyu 2 ; Zhao, Chunjiang 1 ;

作者机构： 1.Jilin Agr Univ, Coll Resources & Environm, Changchun 130118, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

关键词： summer maize; nitrogen nutrient index; leaf spectral reflection; effective band; machine learning

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）

ISSN：

年卷期： 2022 年 12 卷 11 期

页码：

收录情况： SCI

摘要： Non-destructive acquisition and accurate real-time assessment of nitrogen (N) nutritional status are crucial for nitrogen management and yield prediction in maize production. The objective of this study was to develop a method for estimating the nitrogen nutrient index (NNI) of maize using in situ leaf spectroscopy. Field trials with six nitrogen fertilizer levels (0, 75, 150, 225, 300, and 375 kg N ha(-1)) were performed using eight summer maize cultivars. The leaf reflectance spectrum was acquired at different growth stages, with simultaneous measurements of leaf nitrogen content (LNC) and leaf dry matter (LDW). The competitive adaptive reweighted sampling (CARS) algorithm was used to screen the raw spectrum's effective bands related to the NNI during the maize critical growth period (from the 12th fully expanded leaf stage to the milk ripening stage). Three machine learning methods-partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM)-were used to validate the NNI estimation model. These methods indicated that the NNI first increased and then decreased (from the 12th fully expanded leaf stage to the milk ripening stage) and was positively correlated with nitrogen application. The results showed that combining effective bands and PLS (CARS-PLS) achieved the best model for NNI estimation, which yielded the highest coefficient of determination (R-va(l)2), 0.925, and the lowest root mean square error (RMSE val), 0.068, followed by the CARS-SVM model (R-val(2),0.895; RMSEval, 0.081), and the CARS-ANN model (R-val(2), 0.814; RMSEval, 0.108), which performed the worst. The CARS-PLS model was used to successfully predict the variation in the NNI among cultivars and different growth stages. The estimated R-2 of eight cultivars by the NNI was between 0.86 and 0.97; the estimated R-2 of the NNI at different growth stages was between 0.92 and 0.94. The overall results indicated that the CARS-PLS allows for rapid, accurate, and non-destructive estimation of the NNI during maize growth, providing an efficient tool for accurately monitoring nitrogen nutrition.

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