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 < 0.05) between image-based features (e.g., canopy area and sum normalized difference vegetation index) with yield (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.
Highlights
Crop cultivars are consistently selected based on their productivity, tolerance to biotic and abiotic stressors, and adaptation to local production systems and environments (Acquaah, 2009; Hatfield and Walthall, 2015)
In our previous study (Quirós Vargas et al, 2019), we found that vegetation indices (VIs), including normalized difference vegetation index (NDVI), green red vegetation index (GRVI), and the normalized difference red-edge index (NDRE), were correlated with days to 50% flowering and physiological maturity in two winter pea experiments
There were significant and positive correlations (P < 0.05, r up to 0.74) between image-based features and yield with the plotby-plot chickpea data acquired at the early growth, flowering, and pod/seed development stages across field seasons (2017– 2019) and locations (Figure 4A and Supplementary Figure 1)
Summary
Crop cultivars are consistently selected based on their productivity (quantity and/or quality), tolerance to biotic and abiotic stressors, and adaptation to local production systems and environments (Acquaah, 2009; Hatfield and Walthall, 2015). Pulse crops, including pea (Pisum sativum L.) and chickpea (Cicer arietinum L.), have been bred for their adaptation to the Palouse region in the Pacific Northwest, United States, with the overall goal of developing high-yielding and biotic and abiotic stress-resistant cultivars. The Palouse region, which includes parts of eastern Washington, northern Idaho, and northeastern Oregon, is one of the largest producers of pulse crops in the United States (USDA-NASS, 2020) and is home to several pulse breeding programs. Pulse breeders have developed and released multiple pea and chickpea cultivars with better seed yield, quality, and improved disease resistance (McGee and McPhee, 2012; McGee et al, 2012, 2013; Vandemark et al, 2014, 2015; USDA-ARS, 2018). Plant breeders have primarily relied on traditional methods to collect phenotypic data on breeding lines. Sensing technologies, referred to as phenomics technologies, are needed to overcome these constraints to facilitate progress of plant breeding and provide data for a more accurate and comprehensive evaluation of breeding lines
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