High-Throughput Phenotyping That Improves the Efficiency of a Cotton Plant Breeding System

Unmanned Aerial Vehicles (UAVs) play an important role in agricultural research. Cotton (Gossypium spp.) is the world’s leading natural textile fiber crop. The ability to identify cotton plant height and boll count across a field can serve as an important tool in predicting plant growth and yield. This project evaluated the ability of UAVs to predict plant height and yield. In addition, the planting pattern may influence the accuracy of UAV data. In order to capture three dimensional images, the sensor mounted on the UAV should have access to the view of the nearby soil level, but solid cotton planting pattern may obscure the image. Canopy closure prevents the sensor from measuring plant architecture and boll-loads three dimensionally especially during mid-growing season. Therefore, this project was initiated in 2017 to compare solid vs. skip-row planting patterns in terms of predicting yield and fiber quality since skip row would allow UAV sensors to capture more accurate 3-dimensional data from plots.