Crop Performance Evaluation of Chickpea and Dry Pea Breeding Lines Across Seasons and Locations Using Phenomics Data

Abstract

The Pacific Northwest is an important pulse production region in the United States. Currently, pulse crop (chickpea, lentil, and dry pea) breeders rely on traditional phenotyping approaches to collect performance and agronomic data to support decision making. Traditional phenotyping poses constraints on data availability (e.g., number of locations and frequency of data acquisition) and throughput. In this study, phenomics technologies were applied to evaluate the performance and agronomic traits in two pulse (chickpea and dry pea) breeding programs using data acquired over multiple seasons and locations. An unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017–2019. The images were analyzed semi-automatically with custom image processing algorithm and features were extracted, such as canopy area and summary statistics associated with vegetation indices. The study demonstrated significant correlations ( P r up to 0.93 and 0.85 for chickpea and dry pea, respectively), days to 50% flowering ( r up to 0.76 and 0.85, respectively), and days to physiological maturity ( r up to 0.58 and 0.84, respectively). Using image-based features as predictors, seed yield was estimated using least absolute shrinkage and selection operator regression models, during which, coefficients of determination as high as 0.91 and 0.80 during model testing for chickpea and dry pea, respectively, were achieved. The study demonstrated the feasibility to monitor agronomic traits and predict seed yield in chickpea and dry pea breeding trials across multiple locations and seasons using phenomics tools. Phenomics technologies can assist plant breeders to evaluate the performance of breeding materials more efficiently and accelerate breeding programs.

Plant phenotyping relevance

UAVマルチスペクトル画像と半自動画像処理により、作物形質を抽出・推定し、複数環境で検証するフェノタイピング手法の実質的応用である。

Code and data availability

The paper reports UAV multispectral phenotyping of chickpea and dry pea breeding trials (2017–2019) with custom MATLAB image-processing algorithms and LASSO yield prediction. No public repository deposit of the phenotype data, images, or author code is stated; the data availability statement only promises raw data from

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