ArticleScientific reports2026
Rice LAI estimation using UAV-based multi-parameter fusion at the booting stage.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The leaf area index (LAI) is a key parameter for characterizing crop growth and water use efficiency. Therefore, efficient and accurate monitoring of LAI is essential for precision rice management. To overcome the limitations of traditional LAI measurement methods, which are time consuming, labor intensive, and difficult to scale, this study proposes an inversion framework that integrates multi-source UAV remote sensing features with machine learning models. The framework incorporates color indices (CIs) derived from RGB imagery, vegetation indices (VIs) derived from multispectral data, texture features (TIs), and texture feature indices (TFIs), and employs six machine learning algorithms to develop optimized LAI estimation models for the rice booting stage. The results indicate that at a flight altitude of 30 m, the CNN model integrating CIs and TIs achieved an accuracy of R
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