Enhancing Nitrogen Nutrition Index estimation in rice using multi-leaf SPAD values and machine learning approaches

文献类型： 外文期刊

第一作者： Wang, Yuan

作者： Wang, Yuan;Shi, Weiming;Xiang, Haitao;Shi, Peihua;Qian, Yinfei;Xie, Jiang;Guan, Xianjiao;Chen, Gui

作者机构：

关键词： rice nitrogen diagnosis; multi-leaf SPAD values; machine learning; leaf nitrogen concentration; nitrogen nutrition index; statistical metrics

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2024 年 15 卷

页码：

收录情况： SCI

摘要： Accurate nitrogen diagnosis is essential for optimizing rice yield and sustainability. This study investigates the potential of using multi-leaf SPAD measurements combined with machine learning models to improve nitrogen nutrition diagnostics in rice. Conducted across five locations with 15 rice cultivars, SPAD values from the first to fifth fully expanded leaves were collected at key growth stages. The study demonstrates that integrating multi-leaf SPAD data with advanced machine learning models, particularly Random Forest and Extreme Gradient Boosting, significantly improves the accuracy of Leaf Nitrogen Concentration (LNC) and Nitrogen Nutrition Index (NNI) estimation. The second fully expanded Leaf From the Top (2LFT) emerged as the most critical variable for predicting LNC, while the 3LFT was pivotal for NNI estimation. The inclusion of statistical metrics, such as maximum and median SPAD values, further enhanced model performance, underscoring the importance of considering both original SPAD measurements and derived indices. This approach provides a more precise method for nitrogen assessment, facilitating improved nitrogen use efficiency and contributing to sustainable agricultural practices through targeted and effective nitrogen management strategies in rice cultivation.

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