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.

The ‘PhenoBox’, a flexible, automated, open‐source plant phenotyping solution

“…High-throughput plant phenotyping is increasingly used in crop breeding (Araus & Cairns, 2014;Ghanem et al, 2015;Watanabe et al, 2017). It also empowers research approaches such as quantitative trait loci mapping and genome-wide association study, which aim to close the genotype-phenotype gap by identifying associations between phenotypic traits and genetic markers across a broad panel of genotypes (Slovak et al, 2015;Zhang et al, 2017). Moreover, computational methods that can identify, classify or quantify plant stress symptoms or predict plant trait outcomes based on phenotypic data collected at an earlier stage have been established .…”

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

“…Due to sugarcane's genomic complexity, simplified predictive models involving linear regression cannot capture the unknown nonlinear characteristics present in these datasets 31 , as described for other polyploid species [36][37][38] . To address this issue, machine learning (ML) methodologies represent a promising approach with high accuracy 31,[39][40][41] . Although GS was developed to address the problem of categorizing individuals using different populations, its application in biparental populations is suitable and might be highly efficient due to the significant amount of linkage disequilibrium between loci 42 , which would facilitate the initial cycles of breeding programs.…”

Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm

“…The digital camera has emerged as an essential tool for high-throughput plant phenotyping, and it is widely used to reveal genotypic traits associated with the structure and color of plants. The reliability and repeatability of these traits are highly related to the color constancy of the images [1]. However, images acquired using different digital cameras (or the same camera with changing its settings) produce an inconsistent color, due to the different specifications (e.g., spatial resolution, spectral responses, and signal-to-noise ratio).…”