Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model.

ABSTRACT: 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, imposing challenges on rice panicle detection models. This paper proposes a universal curved panicle detection method by combining UAV images of different types of hybrid Indica rice panicles (leaf-above-spike, spike-above-leaf, and middle type) from four ecological sites using an improved You Only Look Once versi

SUBMITTER: Sun B 

PROVIDER: S-EPMC9676644 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature