Automatic optimization of regions of interest in hyperspectral images for detecting vegetative indices in soybeans.

ABSTRACT: Vegetative indices (VIs) are widely used in high-throughput phenotyping (HTP) for the assessment of plant growth conditions; however, a range of VIs among diverse soybeans is still an unexplored research area. For this reason, we investigated a range of four major VIs: normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), anthocyanin reflectance index (ARI), and change to carotenoid reflectance index (CRI) in diverse soybean accessions. Furthermore, we ensured the correct positioning of the region of interest (ROI) on the soybean leaf and clarified the effect of choosing different ROI sizes. We also developed a Python algorithm for ROI selection and automatic VIs calculation. According to our results, each VI showed diverse ranges (NDVI: 0.60-0.84, PRI: -0.0

SUBMITTER: Lee S 

PROVIDER: S-EPMC11922918 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature