# Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model
## 1 Introduction
[11] use a method that detects diseases from plant leaf images by Tensorflow, which is an object detection API, and further improve the recognition accuracy of leaves by training the model using the Faster R-CNN method. [PERSON] et al. [12] add the Densenet module and Adaptive Attention Module (AAM) to the feature extraction part based on YOLOv3. This innovative approach, known as MD-YOLO network, improves the detection accuracy for small-sized target pests. [PERSON], et al. [13] propose a Stolon-YOLO for visual recognition of stolon of strawberry seedlings in glass greenhouses combining two modules: HorBlock-decoupled head and Stem Block feature enhancement module. Compared to YOLOX, it increases the recall rate by 8.3% and increases the accuracy by 12.2%. [PERSON] et al. [14] optimize the feature layer of the YOLOv3 model by using an image pyramid to achieve multi-scale feature detection, thereby improving the detection accuracy and speed of the Yolov3 model. [PERSON] et al. [15] present a multi-scale YOLOv5 detection network for the early detection and classification of rice crop diseases. The proposed Bidirectional Feature Attention Pyramid Network (Bi-FAPN) is used to extract the features from the segmented image and enhance the detection accuracy for diseases with different scales. [PERSON] et al. [16] put forward the Lightweight Self-Attention YOLOv8 model. They introduce an innovative feature fusion technique known as the asymptotic characteristic pyramid network (AFPN) at the Neck, thereby exhibiting an average increase in detection precision of 2.8% compared to YOLOv8. [PERSON] et al. [17] construct the self-made RiPest rice pest dataset and propose an improved YOLOv8 model, named Gi-YOLOv8, which has a 1.3% improvement of accuracy compared to the original YOLOv8 model. [PERSON] et al. [18] present a lightweight attention-based network known as TP-YOLO. It introduces Contextual Transformer and Omni-Dimensional Dynamic Convolution modules. These two attention-based components can enhance feature extraction. [PERSON] et al. [19] optimize YOLOv8s configuration by adding three extra Convolution blocks and using the Swish as the activation function, demonstrating performance in cauliflower disease detection. The methods mentioned above show good application effects in the identification of plant pests and diseases, but there is still room for improvement in the detection accuracy and detection speed. Real-time detection tasks generally require high detection speed. Therefore, this paper conducts research based on a One-stage target detection algorithm known for fast detection speed. Since its inception, the One-stage target detection algorithm YOLO has received considerable attention and has been widely applied. In recent years, the YOLO series of algorithms have been optimized, with the Ultralytics team introducing the YOLOv8 version in January 2023 [20], which achieves a good balance between accuracy and speed and is suitable for plant pest and disease recognition detection. However, there are certain issues when applying the YOLOv8 model to plant pest and disease recognition detection. Firstly, there is a background characteristic, except for in pest defects in plant leaf images, which will cause interference to detection and reduce both accuracy and speed. Secondly, pest and disease defects on plant leaves often have irregular shapes. The YOLOv8 predicts targets using the centerline, width, and height of predicted bounding boxes. In cases where defects are irregular or closely spaced, the accuracy of predictions will decrease. Thirdly, there are many plant species as well as pest and disease categories, leading to an uneven distribution of sample information and lower accuracy for the samples with small size. Therefore, to address the above issues, this paper optimizes the YOLOv8 model.