Automated Detection and Tracking of Infield Cotton Bolls

Cotton fiber accounts for nearly 25% of world-wide textile fiber use. Texas produces more cotton than any other state. It contributes approximately 45% of the U.S. cotton production, with about 25% of the entire U.S. crop, and plants more than 6 million acres. Cotton is the state’s leading cash crop and it ranks third behind the beef and nursery industries. The number of cot- ton bolls on a given farm is arguably the most important phenotyping trait. It provides a better understanding of the physiological and genetic mechanisms of crop growth and development that supports breeding research. Currently, the standard approach to obtain cotton boll counts is by manual sampling via human visual inspection, which is tedious, labor intensive, and error prone. To address the inefficiencies in this approach, we developed an automated vision-based system for cotton boll counting from infield videos where each track uniquely identifies a cotton boll, and the total number of tracks equals the estimated cotton boll count as follows. First, we identify the relationship among the locations of neighboring cotton bolls and model them in a probabilistic framework to handle occlusions. In addition, we exploit dense optical flow and utilize particle filtering to guide each tracker. Then, correspondences between detections and tracks are found through data association via direct observations and indirect cues, which are then combined to obtain an updated observation. We highlight the efficacy our approach in detecting and tracking cotton bolls against other cotton boll counting methods, along with three state-of-the-art tracking methods. TACC’s computational and storage resources were essential for obtaining the results reported in this project.