Non-destructive estimation of rice canopy LAI using NIR/PAR: application to four rice cultivars with diverse leaf characteristics and plant architectures

Abstract

Accurate and non-destructive estimation of leaf area index (LAI) is crucial for monitoring rice growth and predicting yield. This study tested the applicability of non-destructive method for estimating rice canopy LAI using the ratio of near-infrared to photosynthetically active radiation (NIR/PAR) transmitted through the rice canopy to four rice cultivars with different leaf characteristics and plant architectures. We further compared the accuracy of the present method with a conventional plant canopy analyzer estimation. The NIR/PAR method accurately estimated LAI across all cultivars regardless of leaf characteristics (nitrogen content, leaf mass per area) or plant architecture (height, stem number, biomass). Furthermore, the NIR/PAR method accurately estimated LAI even in dense canopies (> 8 m 2 m −2 ) where the plant canopy analyzer underestimated LAI. These findings demonstrate the robustness and accuracy of the NIR/PAR method for rice LAI estimation, suggesting its potential for improving growth assessment, yield prediction, and developing smart agriculture technologies.

Code and data availability

The paper's phenotype datasets (NIR/PAR measurements, destructive LAI, leaf N, LMA, morphology for four rice cultivars) are not publicly deposited; the Data availability statement says they are available from the corresponding author on reasonable request. No author code, models, or public repository URLs are provided.

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