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

ABSTRACT: 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

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11666358 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature