Trees Detection from Aerial Images Using the YOLOv5 Family

Deep learning object recognition models, which are widely used in computer vision, may provide an opportunity to accurately recognize fruit trees. This is essential for fast data collection, selection and reducing human operational errors. This paper proposes a YOLOv5-based detection model for fruit tree detection in the farm plantation using UAV-collected data. This proposed model detects individual fruit trees from the agriculture field and also provide counts how many trees are detected. An image dataset was created from the publicly available UAV captured data which contains total 36 images. Among them 27 were used for training and 9 for testing the proposed model. Four different YOLOv5 scales for object recognition (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) were selected for training, validation, and testing on image datasets.