# Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model
## 1 Introduction  
Agriculture is a primary industry that supports the national economy and livelihoods of people. The yield and quantity of crops are closely linked to the high-quality development of agriculture. However, recent studies reveal that crop diseases and pests are on the rise due to factors such as global climate change [1], changes in farming practices [2], and excessive use of pesticides. These factors have a significant impact on agricultural production and development [3]. Early identification of crop pests and diseases can help determine their categories and range of spread, facilitating early monitoring and control. In addition, improving crop pest and disease monitoring and control systems helps to effectively control the spread [4]. Therefore, the identification of crop pests and diseases becomes a key link in the prevention and control system, and improving the efficiency and accuracy of identifying crop pests and diseases is of great significance in reducing agricultural production losses and promoting high-quality agricultural development. The conventional method for the identification of crop pests and diseases relies on manual recognition that is based on the judgment and experience of professional researchers and farmers, which is slow and has low accuracy [5]. With the development of imageprocessing technology, the recognition method of traditional machine learning, such as Image Processing Techniques (IPT) and Machine Learning Algorithms (MLA), is used [6], which greatly improves efficiency compared to manual recognition. Nevertheless, this method is limited by factors such as the target position and the lighting background. Furthermore, when trying to use human-made feature extraction and identifying plant diseases, it is difficult to predict which combination of pre-processing, feature extraction, and classification algorithm will produce the best results, leading to tedious trial and error. Therefore, this method cannot meet the demands of complex real-world environments in terms of accuracy and speed, giving it relatively low applicability. With the rapid development of deep learning and the sharp improvement of computing power, the target detection algorithm has shifted from traditional algorithms based on manual characteristics to deep learning-based target detection techniques. The target detection algorithm based on deep learning uses the Convolutional Neural Network (CNN) to extract features. Through model training and parameter optimization of a large amount of image data, the target algorithm enables the automatic discovery of the necessary feature information for detecting and classifying targets, meeting the requirements for speed and accuracy in target detection. Target detection algorithms based on deep learning can be divided into two categories according to the algorithmic process: Two-stage target detection algorithms and One-stage target detection algorithms. The Two-stage detection algorithms represented by the R-CNN series use the Selective Search method to generate several candidate areas for an image, utilize the CNN to achieve extraction features for each candidate area, and finally make category judgments and adjust the selection frame positions [7]. The One-stage target detection algorithms represented by the YOLO series [8] and SSD series [9] use neural networks to process images and achieve direct prediction of target categories and positions through end-to-end detection methods, thereby improving computation speed. The above two target detection algorithms are applied in the field of recognition of plant pests and diseases. [PERSON] et al. [10] put forward an Anchor-Free Region Convolutional Neural Network (AF-RCNN) based on fused feature maps, integrating it with the Fast R-CNN into a single network to detect 24 classes of pests via an end-to-end method. The proposed method exhibits certain real-time performance and accuracy. [PERSON] et al.