ArticleSensors (Basel, Switzerland)2025
Mature Rice Biomass Estimation Using UAV-Derived RGB Vegetation Indices and Growth Parameters.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Biomass prediction and shoot growth characterization of single-staked yam plants using UAV imagery.Frontiers in plant science · 2026Article
- Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Frontiers in plant science · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
The biomass of rice at maturity serves as a vital indicator for assessing overall productivity, and its accurate estimation holds significant importance for ensuring food security and promoting sustainable agriculture. To improve the precision of current biomass estimation methods for mature rice, this study employed support vector regression to integrate RGB vegetation indices from rice canopy images with growth parameters, thereby developing a biomass estimation model. The model was validated by applying it to the experimental area. The results indicated that screening RGB vegetation indices and combining them with growth parameters enhanced estimation accuracy. Specifically, the model integrating RGB vegetation indices (g, RGBVI) with rice plant height and moisture content demonstrated high estimation accuracy (
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Registered trials
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