Improving nitrogen use efficiency in rice by estimating leaf nitrogen content with near-infrared spectroscopy and chemometric modeling.

ABSTRACT: Accurate nitrogen management in rice (Oryza sativa L.) is essential for optimizing both crop productivity and environmental sustainability. This study evaluated the potential of Near-Infrared Spectroscopy (NIRS) combined with chemometric modeling to classify leaf nitrogen content (LNC) in five rice genotypes (Nerica, Rufipogon, IR64, Ciherang, and Curinga) subjected to five nitrogen fertilization levels (0%, 25%, 50%, 75%, 100%). Spectral data (350-2500 nm) were processed using Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) to distinguish nitrogen treatments and explore genotype-specific spectral responses. The 1700-2200 nm spectral region yielded the highest classification accuracy, consistently exceeding 94%, indicating strong sensitivity to nitrogen-rela

SUBMITTER: Quiroga-Mosquera A 

PROVIDER: S-EPMC12706053 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature