Rice is a strategic commodity in supporting national food security. However, its productivity remains hindered by manual growth monitoring processes, climate change challenges, and limited human resources. This final project develops a seedling detection and counting system using the YOLO (You Only Look Once) algorithm, with aerial imagery input acquired from UAV (Unmanned Aerial Vehicle), presented through an interactive web-based dashboard. The dataset is enhanced with MIRV (mirror vertical) and MIRH (mirror horizontal) augmentation techniques to improve training data diversity. All experiments were conducted on three models: YOLO11n, YOLOv10n, and YO- LOv8n. Evaluation shows that the YOLO11n configuration using AdamW and a learning rate of 0.01 achieves mAP@50 of 0.592 and precision of 0.852. The system supports data-driven agronomic decision-making to anticipate crop failure risks, thus assisting large-scale rice field owners in monitoring seedling effectively and efficiently.
Yulianingsih et al. (Tue,) studied this question.