Zhang Chongyuan, McGee Rebecca J, Vandemark George J, Sankaran Sindhuja
Department of Biological System Engineering, Washington State University, Pullman, WA, United States.
USDA-ARS, Grain Legume Genetics and Physiology Research, Washington State University, Pullman, WA, United States.
Front Plant Sci. 2021 Feb 25;12:640259. doi: 10.3389/fpls.2021.640259. eCollection 2021.
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 ( < 0.05) between image-based features (e.g., canopy area and sum normalized difference vegetation index) with yield ( up to 0.93 and 0.85 for chickpea and dry pea, respectively), days to 50% flowering ( up to 0.76 and 0.85, respectively), and days to physiological maturity ( 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.
太平洋西北地区是美国重要的豆类生产区。目前，豆类作物（鹰嘴豆、小扁豆和干豌豆）育种者依靠传统的表型分析方法来收集性能和农艺数据，以支持决策制定。传统表型分析在数据可用性（如地点数量和数据采集频率）和通量方面存在限制。在本研究中，利用多季多地获取的数据，应用表型组学技术评估了两个豆类（鹰嘴豆和干豌豆）育种项目的性能和农艺性状。采用基于无人机的多光谱成像系统，获取了2017 - 2019年三个地点鹰嘴豆和干豌豆高级产量试验的图像数据。使用定制图像处理算法对图像进行半自动分析，并提取了诸如冠层面积和与植被指数相关的汇总统计数据等特征。研究表明，基于图像的特征（如冠层面积和总和归一化差异植被指数）与产量（鹰嘴豆和干豌豆分别高达0.93和0.85）、50%开花天数（分别高达0.76和0.85）以及生理成熟天数（分别高达0.58和0.84）之间存在显著相关性（<0.05）。使用基于图像的特征作为预测因子，利用最小绝对收缩和选择算子回归模型估计种子产量，在此过程中，鹰嘴豆和干豌豆模型测试期间的决定系数分别高达0.91和0.80。该研究证明了使用表型组学工具在多个地点和季节的鹰嘴豆和干豌豆育种试验中监测农艺性状和预测种子产量的可行性。表型组学技术可以帮助植物育种者更有效地评估育种材料的性能，并加速育种计划。
