ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases - Xu, Weishi; Wang, Runjie (Frontiers in Plant Science, 2023)
Suffering from various apple leaf diseases, timely preventive measures are necessary to take. Currently, manual disease discrimination has high workloads, while automated disease detection algorithms face the trade-off between detection accuracy and speed. Therefore, an accurate and lightweight model for apple leaf disease detection based on YOLO-V5s (ALAD-YOLO) is proposed in this paper. An apple leaf disease detection dataset is collected, containing 2,748 images of diseased apple leaves under complex environment, such as from different shooting angles, during different spans of the day, and under different weather conditions. Moreover, various data augmentation algorithms are applied to improve the model generalization. The model size is compressed by introducing Mobilenet-V3s basic block which integrates Coordinate attention (CA) mechanism in the backbone network and replacing the ordinary convolution with group convolution in the Spatial Pyramid Pooling Cross Stage Partial Conv (SPPCSPC) module, depthwise convolution and Ghost module in the C3 module in the neck network, while maintaining a high detection accuracy. Experimental results show that ALAD-YOLO balances detection speed and accuracy well, achieving an accuracy of 90.2% (an improvement of 7.9% compared to yolov5s) on the test set and reducing floating point of operations (FLOPs) to 6.1G (a decrease of 9.7G compared to yolov5s). In Summary, this paper provides an accurate and efficient detection method for apple leaf disease detection and other related fields.