ArticleSensors (Basel, Switzerland)2021
Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Runoff Potential Index (RPI): 3D modelling of surface-driven hydrological dynamics for drought resilience.Scientific reports · 2026Article
- Mechanistic modeling for agroecological adaptation: genetic potential, uncertainty, andFrontiers in bioinformatics · 2026Article
- Decoding living systems: Reassessing crop model frontiers via biological dynamics and optimized phenotype.PloS one · 2026Article
- Mature Rice Biomass Estimation Using UAV-Derived RGB Vegetation Indices and Growth Parameters.Sensors (Basel, Switzerland) · 2025Article
- Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Frontiers in plant science · 2025Article
- Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing.Sensors (Basel, Switzerland) · 2021Article
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6 authors.
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Abstract
Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.
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