Apple-YOLO: A Novel Mobile Terminal Detector Based on YOLOv5 for Early Apple Leaf Diseases

Abstract

Early detection of apple leaf diseases is the basis for timely precautions, which can inhibit the spread of the diseases and minimize the severe economic loss. Nowadays, CNN-based models are used for apple leaf diseases detection. However, due to the large model size and inference delay, the model is challenging to be transplanted to mobile terminals with good detection performance. This paper proposes a lightweight detection model Apple-YOLO on mobile terminals for real-time apple leaf diseases detection. First, a dataset named AppleSet8 is constructed using digital image processing and Mosaic data augmentation to improve the robustness and generalization ability of the model. Then the double-branch Apple-CSP module is presented to reduce the model parameters and guarantee feature extraction capability. Fur-thermore, the improved FDSA (Focus layer with depthwise separable convolution and attention mechanism) module effectively decreases the model's FLOPs and enhances the network's attention to the disease spots. Finally, the Skip-Spp (Skip-connection and Spatial pyramid pooling) module is built to strengthen the detection performance for multi-scale disease spots. The experiment results show that mobile-based Apple-YOLO has achieved 96.04% mAP, the inference speed of 34 FPS, and the size is only 5.33 ME, indicating that Apple-YOLO is suitable for the real-time detection of early apple leaf diseases in the real scenario.