title: Computer vision and machine learning for robust phenotyping in genome-wide studies.
abstract: Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (IDC) of soybean is reported. IDC classification and severity for an association panel of 461 diverse plant-introduction accessions was evaluated using an end-to-end phenotyping workflow. The workflow consisted of a multi-stage procedure including: (1) optimized protocols for consistent image capture across plant canopies, (2) canopy identification and registration from cluttered backgrounds, (3) extraction of domain expert informed features from the processed images to accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automatically extracted features with expert-rating equivalent IDC scores. ML-generated phenotypic data were subsequently utilized for the genome-wide association study and genomic prediction. The results illustrate the reliability and advantage of ML-enabled image-phenotyping pipeline by identifying previously reported locus and a novel locus harboring a gene homolog involved in iron acquisition. This study demonstrates a promising path for integrating the phenotyping pipeline into genomic prediction, and provides a systematic framework enabling robust and quicker phenotyping through ground-based systems.
authors.affiliation: Department of Agronomy, Iowa State University, Ames, IA, USA.
authors.forename: Jiaoping
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.
authors.forename: Hsiang Sing
authors.initials: HS
authors.lastname: Naik
authors.affiliation: Department of Agronomy, Iowa State University, Ames, IA, USA.
authors.forename: Teshale
authors.initials: T
authors.lastname: Assefa
authors.affiliation: Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.
authors.forename: Soumik
authors.initials: S
authors.lastname: Sarkar
authors.affiliation: Department of Agronomy, Iowa State University, Ames, IA, USA.
authors.forename: R V Chowda
authors.initials: RV
authors.lastname: Reddy
authors.affiliation: Department of Agronomy, Iowa State University, Ames, IA, USA.
authors.forename: Arti
authors.initials: A
authors.lastname: Singh
authors.affiliation: Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.
authors.forename: Baskar
authors.initials: B
authors.lastname: Ganapathysubramanian
authors.affiliation: Department of Agronomy, Iowa State University, Ames, IA, USA.
authors.forename: Asheesh K
authors.initials: AK
authors.lastname: Singh
mesh_terms: D001185:Artificial Intelligence; D055106:Genome-Wide Association Study / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D010641:Phenotype; D040641:Quantitative Trait Loci; D013025:Glycine max / Q000235:genetics* / Q000378:metabolism; D013312:Stress, Physiological