Skip to main content
Computer vision and machine learning for robust phenotyping in genome-wide studies
Scientific Reports
  • Jiaoping Zhang, Iowa State University
  • Hsiang Sing Naik, Iowa State University
  • Teshale Assefa, Iowa State University
  • Soumik Sarkar, Iowa State University
  • R. V. Chowda Reddy, Iowa State University
  • Arti Singh, Iowa State University
  • Baskar Ganapathysubramanian, Iowa State University
  • Asheesh K. Singh, Iowa State University
Document Type
Publication Version
Published Version
Publication Date

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.


This article is from Scientific Reports 7 (2017): 1, doi:10.1038/srep44048. Posted with permission.

Creative Commons License
Creative Commons Attribution 4.0
Copyright Owner
The Author(s)
File Format
Citation Information
Jiaoping Zhang, Hsiang Sing Naik, Teshale Assefa, Soumik Sarkar, et al.. "Computer vision and machine learning for robust phenotyping in genome-wide studies" Scientific Reports Vol. 7 Iss. 44048 (2017) p. 1 - 11
Available at: