Semantic circle detection and circle-inner segmentation for tree-wise citrus summer shoot management in aerial images

This work focuses on a novel agricultural application of monitoring and managing summer shoots on citrus fruit trees. To enhance the management efficiency and reliability, a novel scheme based on aerial image analysis was presented to implement intelligent supervision. To this end, we proposed an end-to-end network containing three main modules, i.e. semantic circle detection, circle-inner segmentation, and decision classification. They are respectively in charge of detecting multiple trees, evaluating each tree's shoot quantity, and predicting management decision for each tree in aerial images. Detailed experiment analysis validates the proposed scheme and network on the aerial images collected from practical citrus orchards.