# Application of Machine Vision Technology in Citrus Production
## 2 Pest and Disease Detection
### Pest and Disease Identification Based on RGB Image
#### 2.1.2 Deep Learning for Pest and Disease Recognition  
As a branch of machine learning, deep learning has better effect on feature extraction. The current common deep learning networks are shown in Figure 2.  
On the basis of traditional methods, deep-learning-based citrus disease recognition has been studied. Figure 3 shows the image recognition process of citrus pests and diseases under the deep learning method. In order to improve the recognition accuracy, [PERSON] [11] proposed a method based on the ResNet34 model for citrus multi category disease recognition in complex natural environments to classify disease, and at the same time, it combined unsupervised clustering image segmentation, depth separable convolution, and transfer learning to identify the degree of disease. Based on convolutional neural network and hyperspectral technology, [PERSON] et al. [12] collected 81 bands of citrus disease leaves at 480-900 nm and input them into the VGG-16 model for classification. The average accuracy of the model was 98.75%. [PERSON] et al. [13] added the SE module of the attention mechanism based on the Res2 Net algorithm, resulting in an E-Res2 Net algorithm that enhanced network feature extraction and classified citrus leaf diseases into eight categories. [PERSON] [14] used the data augmentation strategy ORGAA and constructed an ORGNet classification model based on PC-DARTS neural network structure to improve the recognition accuracy of the model in order to solve the problem of small sample size in the pre-harvest lesion citrus dataset. When improving the classification and detection accuracy of post-harvest defective citrus, ResNet18 deep residual network was used as the feature extraction network for SSD, retaining the C4 and C5 feature maps. Comparative experi  
Figure 1: Traditional method identification process. ments were conducted on the input image resolution, and the final result was determined to be 768 \(\times\) 768 pixels. [PERSON] et al. [15] proposed a faster R-CNN regional detection neural network based on binarization, transforming the full connection layer in the original model into a binary system full convolutional neural network. The improved model is lighter in size and the recognition time is reduced by 0.53 s compared to the original model. [PERSON] et al. [16] improved the neck network of the YOLOv4-Tiny model, using the detailed information of the shallow network to improve the model's ability to recognize small targets of the citrus psyllids. Mosaic was used for data augmentation, and the average recognition precision of the new model for citrus psyllids reached 96.16%. [PERSON] et al. [17] used an R-CNN model based on 33-layer ResNet main trunk network to identify citrus huanglongbing, red spider infection, and canker disease. Although there were few convolutional layer networks selected, they could still achieve a high recognition accuracy. [PERSON], [PERSON] et al. [18] proposed a deep learning algorithm composed of detection network and classification network to automatically identify orchard citrus diseases. The optimized YOLO-V4 network was used for detection and EfficientNet network was used for classification. Generally, the network model is a specific single model and sometimes it cannot accurately extract and identify the disease characteristics, so the concept of fusion is proposed. [PERSON] et al. [19] designed an improved VGG19-INC model based on VGG19 for the characteristics of citrus diseases with high similarity that could not be well identified by a single model and added the Inception high-dimensional feature fusion module to make the new model have higher recognition precision and strong generalization ability. The S-ResNet model is obtained by discarding the identity mapping in partial residual structure and the M-ResNet model is obtained by using 7 \(\times\) 7 convolution kernel to express deep features. The new F-ResNet model obtained by the fusion of S-ResNet and M-ResNet by [PERSON] et al.