Detection and Identification of Grape Leaf Diseases Based on Improved YOLOv5

Highlights A feature pyramid network with Scaling Feature Fusion and Enhanced Expansion Feature module is constructed to overcome the shortcomings of feature pyramids for small target detection. Noise addition, cropping, and other data augmentation methods for expanding data sets. Multi-channel Denoising Feature Fusion is constructed in the detection module to solve the inconsistencies inside the feature pyramid. Combine with YOLOv5's Backbone to complete the feature anchor box selection and detection can quickly and accurately complete the grape leaf disease detection. ABSTRACT. Grape leaf diseases all over the world cannot be ignored; it is one of the important causes of agricultural economic damage. In order to control the cost of manual detection of grape diseases and to overcome the impact on detection accuracy due to the large variation of target scales during automatic detection of grape leaves, the feature pyramid method is commonly used to solve this problem, but it may destroy the feature consistency of grape leaves at different scales. In addition, in practical applications, the commonly used methods are difficult to improve the detection accuracy of multi-scale grape leaves while ensuring detection. In this paper, we apply the improved YOLOv5 algorithm, which is used to detect the images of grapevine powdery mildew, grape mosaic virus disease, and grape downy mildew, as well as the images of healthy grape leaves. The proposed algorithm is based on the improved feature pyramid network SE-FPN, which added Scaling Feature Fusion (SFF) to enhance feature map information and Enhanced Expansion Feature (EEF) to improve multi-scale accuracy prediction. And replacing the original Path Aggregation Network (PANet) detection module with the Multi-channel Denoising Feature Fusion (MDFF) network improves the scale invariance of features and reduces the inference cost. By experimentally comparing the original YOLOv5 network and the YOLOv5 network with other improved methods applied, considering the following metrics: mean Average Precision (mAP), accuracy, recall, and mean target detection loss, mean classification loss, and runtime of the validation set, the results conclude that the improved model proposed in this paper performs the best, which increases the accuracy by 5.96% and the recall by 4.69% compared to the original YOLOv5 model. The algorithm provides good technical support for real-time detection and control of grapevine diseases. Keywords: Detection module, Disease identification, Feature pyramid network, Grape leaf disease, YOLO.