Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery
Enyu Cai Jiaqi Guo Changye Yang Edward J. Delp
The sorghum panicle is an important trait related to grain yield and plant
development. Detecting and counting sorghum panicles can provide significant
information for plant phenotyping. Current deep-learning-based object detection
methods for panicles require a large amount of training data. The data labeling
is time-consuming and not feasible for real application. In this paper, we
present an approach to reduce the amount of training data for sorghum panicle
detection via semi-supervised learning. Results show we can achieve similar
performance as supervised methods for sorghum panicle detection by only using
10\% of original training data.