Tian, Y. 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. Li, Z. 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, Kaur, R. [
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. Huang, S. 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. Podol 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. Ma, Y. 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. Islam M [
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, Wang, Z. 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, Sun 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. Zhu, R. 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.
Sladojevic et al. [
14] proposed a plant disease recognition algorithm based on fine tuning deep convolutional neural networks capable of detecting plant leaves and distinguishing between 13 different plant diseases and healthy leaves, achieving a recognition accuracy of 96.3%. Brahimi et al. [
15] proposed a tomato disease image recognition algorithm based on deep convolutional neural networks. CNN was used to extract features from tomato disease images, and visualization was used to locate tomato disease areas, achieving high recognition accuracy. Xiao, Z. et al. [
16] used the Hough transform to locate potato leaf disease areas and used morphological segmentation to segment disease areas. They also used principal component analysis to adaptively fuse the color and texture features of the disease, achieving rapid recognition of potato diseases. Veeraballi et al. [
17] proposed a deep learning based papaya disease image recognition algorithm which classifies papaya disease images based on ResNet50. The algorithm can accurately identify papaya diseases under complex conditions such as insufficient lighting and different image sizes, and has high robustness. Zhang et al. [
18] proposed an enhanced corn disease image recognition algorithm using GoogLeNet. By adjusting parameters, improving pooling layers, and reducing the number of classifiers, they improved both recognition accuracy and model training efficiency for corn leaf diseases. Silva et al. [
19] proposed a plant disease image recognition algorithm based on multichannel convolutional neural networks. The algorithm did not introduce pretraining parameters, but instead used the PlantVillage dataset to train multichannel CNN from scratch. The model was trained on a segmented RGB grayscale RGB dataset, which accelerated the training speed and significantly improved the recognition accuracy of plant disease images. Hu, Z. et al. [
20] extracted dispersed early disease features of tomato leaves through a multi-layer attention residual module. To enhance feature reuse, they fused low-level and high-level features to achieve fine-grained recognition of tomato leaf diseases. Sharma et al. [
21] used FCN to segment tomato leaf diseases, and then used convolutional neural networks to recognize the segmented lesion images. The recognition accuracy was significantly improved, but the recognition effect was poor for diseases with dense lesions. Jiang et al. [
22] proposed a rice leaf disease recognition algorithm based on an improved convolutional neural network. The algorithm combines CNN with support vector machine to extract features from rice leaf disease images. The features extracted by CNN are classified using support vector machine. The optimal parameters of the support vector machine are obtained through tenfold cross-validation, and the accuracy on the rice leaf disease image test set is 96.8%. Hou Jinxiu et al. [
23] designed a residual attention network to extract multichannel features, recalibrated them, and fused them to solve the problem of identifying multiple types of plant diseases. Su et al. [
24] studied a DNN algorithm for real-time segmentation of weeds between rows. Compared with DNN using traditional encoders, two new subnet structures were adopted to improve segmentation accuracy. Liu, Y. et al. [
25] used U-Net to perform semantic segmentation on corn leaves and four common corn diseases, achieving better segmentation results than traditional image segmentation methods. Zhong Changyuan et al. [
26] proposed an attention module based on group activation strategy, which uses high-order features to guide the enhancement of low-level features. By calculating the intra group enhancement coefficient, the suppression effect between different groups is reduced, and the feature expression ability is enhanced. ResNet18 is used to extract features, completing the segmentation of six common disease images of cucumber and rice and improving the segmentation accuracy. Su et al. [
27] introduced a new data augmentation framework based on random cropping and RICAP. This framework is utilized to enhance image classification data and expand semantic segmentation tasks, resulting in improved segmentation accuracy. Jiang et al. [
28] proposed a multitask recognition algorithm based on improved VGG16. The algorithm uses transfer learning to fine tune the parameters of VGG16 and trains the model using alternating learning. It can simultaneously recognize wheat leaf diseases and rice leaf diseases. Compared with the single task method, the proposed multitask recognition algorithm has fewer training parameters and higher recognition accuracy. Xiangpeng, F. et al. [
29] proposed an improved CNN based corn disease recognition algorithm and designed a convolutional neural network structure with five layers of convolution, four layers of pooling, and two fully connected layers. The network was optimized using L2 regularization and Dropout strategy and verified on a corn disease image test set. The average recognition accuracy was 97.10%. Su, S. et al. [
30] transferred the knowledge learned from ImageNet to VGG16 to accelerate model training speed and achieve small sample recognition of grape leaf diseases. Li, Q. et al. [
31] transferred pretrained weights from ImageNet to ResNet, and designed an attention residual module to reduce the number of parameters, solving the problem of rapid identification of corn diseases with small amounts of data. Zhang, N. et al. [
32] designed a multi-scale convolutional module based on an attention mechanism to improve the ability of effective feature extraction, and applied it to InceptionV3. At the same time, transfer learning was used during the training process to avoid overfitting, achieving tomato leaf disease recognition. Wan, J. et al. [
33] used transfer learning and GoogLeNet to achieve fruit tree disease identification and severity grading. Bao, W. et al. [
34] introduced selective convolution in VGG16 to extract small disease features and used transfer learning to train the model, solving the problem of identifying small apple diseases.