Detection of Apple Trees in Orchard Using Monocular Camera - Stephanie Nix; Airi Sato; Hirokazu Madokoro; Satoshi Yamamoto; Yo Nishimura; Kazuhito Sato (Agriculture)
This study proposes an object detector for apple trees as a first step in developing agricultural digital twins. An original dataset of orchard images was created and used to train Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) models. Performance was evaluated using mean Average Precision (mAP). YOLO significantly outperformed SSD, achieving 91.3% mAP compared to the SSD’s 46.7%. Results indicate YOLO’s Darknet-53 backbone extracts more complex features suited to tree detection. This work demonstrates the potential of deep learning for automated data collection in smart farming applications.