Journal Article

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits

Choi, Myoung-Goo, Jeong, Seok Won, Kim, Kyoung-Hwan, Lee, Chaewon, Lyu, Jae Il, Baek, Jeongho, Moon, Jung-Kyung, Jeong, HwangWeon, Park, Youn-Il

2024

Overview

Key message Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes . Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Agricultural production / agricultural productivity / Algorithms / Biomedical and Life Sciences / Biotechnology / Cell Biology / color / data collection / Discriminant analysis / Genes / Genome-wide association studies / genome-wide association study / Genome-Wide Association Study - methods / Genomic analysis / Glycine max - genetics / Hyperspectral imaging / Hyperspectral Imaging - methods / Image analysis / Image processing / Image Processing, Computer-Assisted - methods / Life Sciences / Machine Learning / Original / Original Article / Phenotype / Phenotyping / Pigmentation / Pigmentation - genetics / Plant Biochemistry / Plant Sciences / Precision agriculture / Reflectance / seed coat / seed quality / seed size / Seeds / Seeds - anatomy & histology / Seeds - genetics / Soybeans / Spectra