In order to meet food, fiber, and bio-fuel needs of a growing world population, cropbreeding methods must be improved and new technologies must be developed. One area under focus is the decoding of the genetic basis of complex traits, such as yield and drought stress tolerance, and predicting these traits from genetic composition of lines or cultivars. In the last three decades, significant advances in genotyping methods have resulted in a wealth of genomic information; however, little improvement has occurred for methods of collecting corresponding plant trait data, especially for agronomic crops. This study developed a mobile, field-based, highthroughput sensor platform for rapid and repeated measurement of plant characteristics. The platform consisted of three sets of sensors mounted on a high-clearance vehicle. Each set of sensors contained two infrared thermometers (IRT), one ultrasonic sensor, one Crop Circle, and one GreenSeeker. Each sensor set measured canopy temperature, crop height, and spectral reflectance. In addition to the sensors, the platform was equipped with an RTK-GPS system that provided precise, accurate position data for georeferencing sensor measurements. Software for collecting, georeferencing, and logging sensor data was developed using National Instruments LabVIEW and deployed on a laptop computer. Two verification tests were conducted to evaluate the phenotyping system. In the first test, data timestamps were analyzed to determine if the system could collect data at the required rate of 10 Hz and 5 Hz for sensor data and position data, respectively. The determination was made that, on average, IRT, ultrasonic, and Crop Circle data are received in intervals of 100 ms (SD = 10 ms), GreenSeeker data are received in intervals of 122 ms(SD=10 ms), and position data are received in intervals of 200 ms (SD = 32 ms). The second test determined that a statistically significant relationship exists between sensor readings and ambient light intensity and ambient temperatures. Whether the relationship is significant from a practical stand point should be determined based on specific application of the sensors. List of Tables Chapter 1 -IntroductionTwenty-first century plant science and crop improvement is faced with the challenge of meeting worldwide food, fiber, and bio-fuel needs of an increasing population expected to exceed 9 billion by 2050 (http://www.un.org/en/development/desa/population/). Improvements in annual crop yields through traditional breeding programs cannot meet the projected demand of three major cereal crops: rice, maize, and wheat. New plant genotypes with intrinsic high yields and yield stability under drought and salinity stress, adaptable to future climate conditions must be developed (Furbank and Tester 2011). In order to quickly and efficiently achieve this, the genetic basis of complex plant traits, such as yield and drought stress tolerance, must be understood and methods for predicting these traits from genetic composition of lines or cultiva...

Accurate and efficient phenotyping has become the biggest hurdle for evaluating large populations in plant breeding and genetics. Contrary to genotyping, high‐throughput approaches to field‐based phenotyping have not been realized and fully implemented. To address this bottleneck, a novel, low‐cost, flexible phenotyping platform, named Phenocart, was developed and tested on a field trial consisting of 10 historical and current elite wheat (Triticum aestivium L.) breeding lines at the International Maize and Wheat Improvement Center (CIMMYT). The lines were cultivated during the 2013 and 2014 growing cycle in Ciudad Obregon, Mexico, and evaluated multiple times throughout the growing season. The phenotyping platform was developed by integrating several sensors: a GreenSeeker for spectral reflectance, an infrared thermometer (IRT), and a global navigation satellite system (GNSS) receiver into one functional unit. The Phenocart enabled simultaneous collection of normalized difference vegetation index (NDVI) and canopy temperature (CT) with precise assignment of all measurements to plot location by georeferenced data points. Across the set of varieties, the Phenocart temperature measurements were highly correlated to a handheld IRT. In addition, CT and NDVI were both significantly correlated to yield throughout the growing season. The Phenocart is a flexible, low‐cost, and easily deployable platform to increase the amount of phenotypic data that crop breeders obtain as well as provide high‐resolution phenotypic data for genetic discovery.

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