A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is
Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model
Frontiers in Plant Science
Accurate and rapid identification of the effective number of panicles per unit area is crucial for the assessment of rice yield. As part of agricultural development, manual observation of effective panicles in the paddy field is being replaced by unmanned aerial vehicle (UAV) imaging combined with target detection modeling. However, UAV images of panicles of curved hybrid Indica rice in complex field environments are characterized by overlapping, blocking, and dense distribution, imposingdoi:10.3389/fpls.2022.1021398 pmid:36420030 pmcid:PMC9676644 fatcat:gvtpgl35z5g3dasxfa257drzsa