# A Plant Disease Classification Algorithm Based on Attention MobileNet V2
###### Abstract  
Plant growth is inevitably affected by diseases, and one effective method of disease detection is through the observation of leaf changes. To solve the problem of disease detection in complex backgrounds, where the distinction between plant diseases is hindered by large intra-class differences and small inter-class differences, a complete plant-disease recognition process is proposed. The process was tested through experiments and research into traditional and deep features. In the face of difficulties related to plant-disease classification in complex backgrounds, the advantages of strong interpretability of traditional features and great robustness of deep features are fully utilized, and include the following components: (1) The OSTU  
extracted plant image features based on SVM and ANN; [PERSON] et al. [9] focused on the salient area of plants, established a saliency map, and achieved plant-disease classification; [PERSON] et al. [10] established a disease image representation using inverse coding technology; and [PERSON] et al. [11] constructed an Adaboost-based model for disease prediction from the perspective of color. Representative algorithms based on deep feature extraction mainly include: [PERSON] et al. [12] proposed a CNN-based method for plant-disease analysis; [PERSON] et al. [13] devised an EfficientNet deep learning model to mine image depth features; [PERSON] et al. [14] conducted research based on CNN with LVQ algorithm; [PERSON] et al. [15] enhanced images and established an interactive model to facilitate disease classification; [PERSON] et al. [16] constructed capsule networks to measure the efficacy in plant-disease classification; [PERSON] et al. [17] established the semantic relationship between images and diseases in AgriDet; and [PERSON] et al. [18] introduced a deep-learning network for plant-disease classification and severity assessment. Clustering perspective: Models are built by an analysis of intra-class differences. Representative algorithms based on traditional features include: [PERSON] et al. [19] constructed a K-means model to analyze intra-class differences and achieve clustering; [PERSON] et al. [20] established an SVM to cluster different disease images; [PERSON] et al. [21] enhanced clustering accuracy by adding SVM on top of the K-means algorithm; [PERSON] et al. [22] established a model from the perspective of a color histogram for image analysis; [PERSON] et al. [23] established a K-means model for disease classification from a clustering perspective; [PERSON] et al. [24] used unsupervised machine-learning algorithms to cluster based on color features; and [PERSON] et al. [25] proposed an extended kernel-density-estimation approach to analyze disease morphology. On the other hand, representative algorithms based on deep feature extraction mainly include: [PERSON] et al. [26] obtained clustering features based on the CNN model with optimized activation functions; [PERSON] et al. [27] fused PSO and CNN algorithms to extract multi-dimensional features; [PERSON] et al. [28] experimentally demonstrated the capabilities of random forest, KNN, SVM, and CNN for clustering; [PERSON] et al. [29] established a 1D-CNN model for clustering based on image segmentation; [PERSON] et al. [30] achieved plant-disease detection based on multiple iterations of ResNet; [PERSON] et al. [31] analyzed plant diseases through the construction of the model ensemble with inception module and cluster algorithm; [PERSON] et al. [32] used deep networks to extract disease image features and analyze differences between classes; and [PERSON] et al. [33] constructed a model from the perspective of image and machine learning to achieve disease classification. In summary, plant-disease classification algorithms based on images face the following issues: (1) Traditional algorithms are based on visual features that can be easily affected by natural factors, such as lighting and angles, with limited performance improvement.