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

Bo Chen, Xianju Lu, Shuan Yu, Shenghao Gu, Guanmin Huang, Xinyu Guo () and Chunjiang Zhao ()
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Bo Chen: College of Resources and Environment, Jilin Agricultural University, Changchun 130118, China
Xianju Lu: Beijing Key Laboratory of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Shuan Yu: Beijing Key Laboratory of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Shenghao Gu: Beijing Key Laboratory of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Guanmin Huang: Beijing Key Laboratory of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Xinyu Guo: Beijing Key Laboratory of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Chunjiang Zhao: College of Resources and Environment, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2022, vol. 12, issue 11, 1-18

Abstract: 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 2 val ), 0.925, and the lowest root mean square error (RMSE val ), 0.068, followed by the CARS-SVM model ( R 2 val , 0.895; RMSE val , 0.081), and the CARS-ANN model ( R 2 val , 0.814; RMSE val , 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.

Keywords: summer maize; nitrogen nutrient index; leaf spectral reflection; effective band; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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