## 2 _Brief Introduction of Rice Pests and Diseases_
### Control and Detection of Rice Pests and Diseases  
With the increasingly vigorous development of image recognition and deep learning, the research in the field of pest identification and detection has been further explored more effectively and accurately. At present, image recognition and deep learning methods are widely used in the field of pest identification and detection, and both have good detection effects [4]. The application of deep learning target detection network to the identification and detection of rice pests and diseases can effectively strengthen the prevention and control of rice pests and diseases and make the detection of diseases and pests more effective, fast, and accurate. In order to accurately detect lychee pests and diseases in complex environments in real time, [PERSON] et al. proposed a YOLO v4-GCF lychee pest and disease detection model based on yolov4, which increased the detection speed by 38% and the average accuracy by 4.13 percentage points [5]. [PERSON] et al. proposed a detection model of coffee leaf pests and diseases based on yolov5 and the integrated ConvNext network and ECA attention mechanism, and the detection accuracy of this reached 94.13%, meeting the real-time requirement in actual detection [6]. [PERSON] et al. proposed the SSD-RES50-3C detection method for diseases and insect pests based on the improved SSD algorithm. ResNet50 was used to increase the feature extraction ability of diseases and insect pests, and the feature fusion module was added to improve the anti-interference ability of the algorithm. The average accuracy of the final algorithm was as high as 92.86%, which was 6.61% higher than that of the original algorithm [7]. [PERSON] et al. proposed a plant disease and insect pest monitoring model based on visual enhancement attention improvement, namely the yolov5-VE model. The feature enhancement module CBAM was designed and the DIou loss function was introduced. The detection accuracy and average accuracy of the experimental results reached 65.87% and 73.49%, which reduced the interference of complex field scenes. This model can also be widely used in large-scale plant pest detection [8]. Aiming at the problems of poor real-time detection and robustness and the high missed-detection rate of yolov3 in the identification of crop leaf pests and diseases, [PERSON] et al. adopted Darknet 53 as the feature extraction network, established a 104*104 scale detection layer, and proposed a YOLOv3-corn leaf disease and insect detection model. mAP and Recall reached 93.31% and 93.08% [9]. [PERSON] et al. proposed an improved crop disease and pest detection algorithm based on the Res2 NeXt50 model. The Res2 NeXt50 model was obtained by grouping convolution in the Res2 Net50 model, and the GELU function was used to replace the ReLU function. In training, label smoothing and the exponential moving average were used to improve the model generalization ability. Finally, the accuracy of the improved model reached 98.79% [10]. A large number of experimental results show that the crop disease and pest detection algorithm model based on the deep learning framework has great practicability and superiority in the field of disease and pest detection.