Computer vision and machine learning enabled soybean root phenotyping pipeline.

BACKGROUND: Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis. RESULTS: This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle. CONCLUSIONS: This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

Machine Learning Approach for Prescriptive Plant Breeding.

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding.

Identification and Genetic Characterization of Soybean Accessions Exhibiting Antibiosis and Antixenosis Resistance to Aphis glycines (Hemiptera: Aphididae).

Cultivation of aphid-resistant soybean varieties can reduce yield losses caused by soybean aphids. However, discovery of aphid biotypes that are virulent on resistant soybean greatly threatens sustained utilization of host plant resistance to control soybean aphids. The objective of this study was to identify and genetically characterize aphid resistant soybean accessions in a diverse collection of 308 plant introductions in maturity groups (MG) I and II. In large-scale screening experiments conducted in the greenhouse, we identified 12 soybean accessions (10 aphid-resistant and 2 moderately resistant), including nine previously not reported for resistance against soybean aphids. Three accessions (PI 578374, PI 612759C, and PI 603546A) and the Rag3 resistant check (PI 567543C) were susceptible when infested with a high initial aphid level but resistant when infested with a low initial aphid level, a phenomenon termed as density-dependent aphid resistance. Six accessions (PI 054854, PI 378663, PI 578374, PI 612759C, PI 540739, and PI 603546A) conferred antibiosis, five (PI 438031, PI 603337A, PI 612711B, PI 437950, and PI 096162) conferred both antibiosis and antixenosis, while one (PI 417513B) had neither when tested in no-choice and pairwise choice experiments. Molecular genotyping of the 12 accessions using single-nucleotide polymorphism (SNP) markers linked to known aphid resistance (Rag) genes revealed that PI 578374 and PI 540739 did not have any tested marker variants and could potentially carry unreported Rag genes. Genome-wide association analyses for MG I accessions identified genomic regions associated with aphid resistance on chromosomes 10 and 12 for each level of initial aphid colonization.