Crop Performance Evaluation of Chickpea and Dry Pea Breeding Lines Across Seasons and Locations Using Phenomics Data - Zhang, Chongyuan; McGee, Rebecca J.; Vandemark, George J.; Sankaran, Sindhuja (Frontiers in Plant Science, 2021)
The Pacific Northwest is an important pulse production region in the USA. 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 performance and agronomic traits in two pulse (chickpea and dry pea) breeding programs using data acquired over multiple seasons and locations. An unmanned aircraft system-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017 to 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 < 0.05) between image-based features (e.g. canopy area and sum normalized difference vegetation index) and yield (r up to 0.93 and 0.85 for chickpea and dry pea, respectively) and 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 LASSO regression models, during which, the coefficient of determination as high as 0.91 and 0.80 during model testing for chickpea and dry pea, respectively was 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 performance of breeding materials more efficiently and accelerate breeding programs.