Thompson Alison L, Thorp Kelly R, Conley Matthew, Andrade-Sanchez Pedro, Heun John T, Dyer John M, White Jeffery W
U.S. Arid Land Agricultural Research Center, United States Department of Agriculture, Agricultural Research Service, Maricopa, AZ, United States.
Department of Agriculture and Biosystems Engineering, Maricopa Agricultural Research Center, The University of Arizona, Maricopa, AZ, United States.
Front Plant Sci. 2018 Apr 23;9:507. doi: 10.3389/fpls.2018.00507. eCollection 2018.
Field-based high-throughput phenotyping is an emerging approach to quantify difficult, time-sensitive plant traits in relevant growing conditions. Proximal sensing carts represent an alternative platform to more costly high-clearance tractors for phenotyping dynamic traits in the field. A proximal sensing cart and specifically a deployment protocol, were developed to phenotype traits related to drought tolerance in the field. The cart-sensor package included an infrared thermometer, ultrasonic transducer, multi-spectral reflectance sensor, weather station, and RGB cameras. The cart deployment protocol was evaluated on 35 upland cotton ( L.) entries grown in 2017 at Maricopa, AZ, United States. Experimental plots were grown under well-watered and water-limited conditions using a (0,1) alpha lattice design and evaluated in June and July. Total collection time of the 0.87 hectare field averaged 2 h and 27 min and produced 50.7 MB and 45.7 GB of data from the sensors and RGB cameras, respectively. Canopy temperature, crop water stress index (CWSI), canopy height, normalized difference vegetative index (NDVI), and leaf area index (LAI) differed among entries and showed an interaction with the water regime ( < 0.05). Broad-sense heritability () estimates ranged from 0.097 to 0.574 across all phenotypes and collections. Canopy cover estimated from RGB images increased with counts of established plants ( = 0.747, = 0.033). Based on the cart-derived phenotypes, three entries were found to have improved drought-adaptive traits compared to a local adapted cultivar. These results indicate that the deployment protocol developed for the cart and sensor package can measure multiple traits rapidly and accurately to characterize complex plant traits under drought conditions.
基于田间的高通量表型分析是一种在相关生长条件下对难以测量且对时间敏感的植物性状进行量化的新兴方法。近端传感车是一种替代平台，可替代成本更高的高秆拖拉机，用于田间动态性状的表型分析。开发了一种近端传感车及特定的部署协议，用于对田间与耐旱性相关的性状进行表型分析。该车载传感器套件包括红外温度计、超声波换能器、多光谱反射传感器、气象站和RGB相机。在美国亚利桑那州马里科帕种植的35个陆地棉（L.）品种上对该车的部署协议进行了评估。实验小区采用（0,1）α晶格设计在水分充足和水分受限条件下种植，并于6月和7月进行评估。0.87公顷田地的总采集时间平均为2小时27分钟，传感器和RGB相机分别产生了50.7MB和45.7GB的数据。不同品种间冠层温度、作物水分胁迫指数（CWSI）、冠层高度、归一化植被指数（NDVI）和叶面积指数（LAI）存在差异，并与水分状况存在交互作用（<0.05）。所有表型和采集数据的广义遗传力（）估计值在0.097至0.574之间。从RGB图像估计的冠层覆盖率随已定植植株数量的增加而增加（=0.747，=0.033）。基于车载表型分析，发现有三个品种与当地适应性品种相比具有改善的干旱适应性状。这些结果表明，为该车和传感器套件开发的部署协议可以快速准确地测量多个性状，以表征干旱条件下的复杂植物性状。
