ArticlePloS one2020
A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 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
- Unsupervised semantic label generation in agricultural fields.Frontiers in robotics and AI · 2025Article
- Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation.Sensors (Basel, Switzerland) · 2024Article
- Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots.Sensors (Basel, Switzerland) · 2023Article
- The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization.Frontiers in plant science · 2022Article
- Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index.Frontiers in plant science · 2022Article
- Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops.Sensors (Basel, Switzerland) · 2021Article
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9 authors.
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Abstract
Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.
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