Guttation plays an important role in increasing rice yield, preventing crop diseases and improving soil fertility, and is an important index to measure the water status in the field. However, the real-time detection of droplets generated by guttation is a difficult task. Droplets are small and dense targets. In the deep learning model, the detection of small objects in high-resolution images is a key problem to be solved. A Droplet-YOLO method is proposed, which combines the YOLOv8 model and multi-instance learning. The original image is divided into multiple sub-images using a sliding window and detected by YOLOv8. The detection of each sub-image is carried out by an independent repeated test, so multi-instance learning ensures 99.99% probability of detecting droplets. By comparing various models, Droplet-YOLO performs best in terms of precision, recall, and mean precision (mAP). In addition, through the hyper-parameter adjustment experiment, the configuration with a batch size of 32 and epoch of 200 was finally selected. The accuracy and recall rate of the model reached 96.8% and 95.7%, respectively, and the mAP reached 99.0%. Experiments show that this method has significant advantages for small object detection in high-resolution images, and the proposed Droplet-YOLO model is superior to the most advanced methods.
Gong et al. (Fri,) studied this question.