Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.

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 pe

SUBMITTER: Liu J 

PROVIDER: S-EPMC10773816 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature