Development of a field-based high-throughput mobile phenotyping platform

Master of ScienceDepartment of Biological and Agricultural EngineeringNaiqian ZhangIn order to meet food, fiber, and bio-fuel needs of a growing world population, crop-breeding 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, high-throughput 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