## 2 Related Works  
Early disease and insect image recognition is mainly based on traditional machine learning, which uses image preprocessing (grayscale, histogram equalization, color space conversion, median filtering, etc. ), image segmentation, and feature extraction techniques (local binarization, grayscale co-occurrence matrix, color co-occurrence matrix, inter grayscale correlation matrix, etc.) to manually select and design features. Afterwards, specific machine learning algorithms such as support vector machines, random forests, linear regression analysis, principal component analysis, etc., are used to train their classification ability for specific feature vectors in order to achieve the goal of identifying pest and disease images [2]. [PERSON], [PERSON] et al. [3] trained and optimized the SVM model, by extracting the color, shape, and texture features of grape disease spots, to identify grape diseases. They found that the comprehensive features of color and texture have stronger expression ability than single features. [PERSON], [PERSON] et al. [4] used the Back Propagation (BP) neural network to recognize and classify the shape, color, and texture features of extracted grape disease spots; their results showed that the recognition accuracy of grape disease spot samples reached 92.6%. In 2015, [PERSON], [PERSON] [5] conducted a study on the use of SVM to identify crop diseases. In order to obtain clearer features, this method uses image segmentation algorithms to separate leaves from the background. After adjusting the color tone, SVM can be used to classify diseases. [PERSON], [PERSON] et al. [6] constructed a bag of words model for rice diseases using multispectral images and identified the detected rice diseases using a bag of words dictionary. In the end, they achieved a recognition accuracy of 94.72%, far higher than traditional spectral image analysis methods. [PERSON] et al. [7] utilized the K-Means clustering algorithm to preprocess the identified samples and separate diseased grape leaves from the background. SVM was used to extract disease classification from leaves, achieving a recognition rate of 88.89% for grape diseases. [PERSON], [PERSON] et al. [8] used object detection methods to determine the position of grape leaves, extracted features of grape diseases using HOG (directional gradient histogram), and then used SVM for classification and recognition. [PERSON] [9] identified potato diseases based on multi-classification SVM. After separating the background and lesions from the diseased leaves, they extracted the color and texture features of potato lesions. Finally, using multi-class support vector machines to classify the extracted features, they achieved a recognition rate of 95% for potato diseases. In 2018, [PERSON], [PERSON] et al. [10] proposed a method for identifying rice blast spores using additive cross kernel support vector machines based on HOG features. This solved the shortcomings of traditional recognition methods such as a cumbersome process, strong subjective dependence, and low recognition rate, and achieved automatic recognition of rice blast spores. In 2019, [PERSON] et al. [11] used a simple linear iterative clustering algorithm as a preprocessing step for segmentation to extract the significance map of tea plant diseases. They extracted superpixel feature blocks from multiple directions using a grayscale co-occurrence matrix, and used a support vector machine to classify and detect tea diseases. [PERSON], [PERSON] et al. [12] used denoising filtering algorithms to denoise the original image of barley leaves, and separated the disease spots in the barley leaves from the background based on the color and texture differences of different diseases. Support vector machines were then used to identify the texture and color characteristics of barley disease spots. Deep learning is a popular research area in artificial intelligence, particularly prevalent in the field of computer vision.