ArticleFrontiers in plant science2023
Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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6 citing papers in PubMed.
- Improving maize LAI estimation by integrating multispectral imagery and digital surface models with ensemble learning.Frontiers in plant science · 2026Article
- Spatiotemporal transferability of multi-temporal random forest model for mappingFrontiers in plant science · 2026Article
- Monitoring phosphorus content in winter wheat using feature fusion and feature selection from UAV remote sensing imagery.Frontiers in plant science · 2026Article
- Assessment of Water Depth Variability and Rice Farming Using Remote Sensing.Sensors (Basel, Switzerland) · 2025Article
- Computer Vision-Based Fire-Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits.Foods (Basel, Switzerland) · 2024Article
- Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages.Frontiers in plant science · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
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Abstract
Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R
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