Real-Time Detection of Apple Leaf Diseases in Natural Scenes Based on YOLOv5

Abstract

Aiming at the problem of accurately locating and identifying multi-scale and differently shaped apple leaf diseases from a complex background in natural scenes, this study proposed an apple leaf disease detection method based on an improved YOLOv5s model. Firstly, the model utilized the bidirectional feature pyramid network (BiFPN) to achieve multi-scale feature fusion efficiently. Then, the transformer and convolutional block attention module (CBAM) attention mechanisms were added to reduce the interference from invalid background information, improving disease characteristics’ expression ability and increasing the accuracy and recall of the model. Experimental results showed that the proposed BTC-YOLOv5s model (with a model size of 15.8M) can effectively detect four types of apple leaf diseases in natural scenes, with 84.3% mean average precision (mAP). With an octa-core CPU, the model could process 8.7 leaf images per second on average. Compared with classic detection models of SSD, Faster R-CNN, YOLOv4-tiny, and YOLOx, the mAP of the proposed model was increased by 12.74%, 48.84%, 24.44%, and 4.2%, respectively, and offered higher detection accuracy and faster detection speed. Furthermore, the proposed model demonstrated strong robustness and mAP exceeding 80% under strong noise conditions, such as exposure to bright lights, dim lights, and fuzzy images. In conclusion, the new BTC-YOLOv5s was found to be lightweight, accurate, and efficient, making it suitable for application on mobile devices. The proposed method could provide technical support for early intervention and treatment of apple leaf diseases.

Highly Accurate and Lightweight Detection Model of Apple Leaf Diseases Based on YOLO

“…Compared with the study of Li [48], although all our studies are based on the YOLO series, our model is more advantageous for lesion detection in complex environments in orchards, and its design fully takes into account the diversity of actual orchard scenarios, which improves the adaptability and robustness of the module. Compared to Gao's [49] study, we used a more challenging multidimensional dataset containing images of apple leaves in complex orchard backgrounds with different weather conditions and different shooting angles, which enhanced the utility of our model.…”

Phenotypic detection of flax plants based on improved Flax-YOLOv5

“…(2022) proposed a maize field weed detection framework based on crop row pretreatment and improved YOLOv4 in UAV images. Li et al. (2023) proposed an apple leaf disease detection method based on the improved YOLOv5s model.…”

Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring

“…Li et al [6] developed BTC-YOLOv5s based on YOLOv5s for the detection of apple leaf disease. In particular, the inclusion of the transformer and convolutional block attention modules decreased the background noise.…”