###### Abstract
[15] (2019) presented a Multilayer Perceptron (MLP) CNNs model for the detection of plant diseases and pests. They first used different CNN models to extract deep features and then Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers for feature classification. In this study, LSTM classifier training with a high dimensional feature vector requires a longer processing time. Aside from the processing time, finding the optimal LSTM parameters was another limitation of this work. [PERSON] and [PERSON] [16] (2020) proposed an improved You Only Look Once (Yolo) V3 algorithm for the diagnosis of tomato diseases and insect pests. The Yolo V3 network algorithm used multidimensional feature recognition based on the image pyramid, object constraint box clustering, and multidimensional training, which drastically improved the algorithm's performance. Experimental results showed that the detection accuracy of the algorithm was 92.39% and the detection time was only 20.39 ms. Therefore, for diagnosing tomato diseases and pests, the improved Yolo V3 algorithm can identify the location and category of tomato diseases and pests accurately and quickly. An improved convolution neural network model based on VGG16 was proposed by [PERSON] et al. [17] (2020). The VGG16 Classic Network Classifier was modified by adding a batch of normalization layers, a global average integration layer, and a fully connected layer to accelerate convergence and reduce training parameters. The proposed model used 2141 apple leaves to identify apple leaf diseases in the training set. In this study, although the training time was longer than AlexNet and ResNet, the proposed model had fewer parameters and provided higher accuracy. [PERSON] et al. [18] (2020) investigated the recent advances in machine learning to diagnose plant diseases. In the paper, studies are categorized based on machine learning architecture, in which shallow architectures are used in machine learning. Deep Learning is used to find good features of suitable classifiers for this purpose. With the promising performance of deep learning, it is expected to be the dominant technology in this field. [PERSON] et al. [19] (2021) collected data from healthy and infected apple leaves from various orchards in the Kashmir Valley. They developed a deep learning model to identify and classify apple disease using transitional learning automatically. The results obtained with the proposed approach were promising and reported accuracy of about 97%. Because image processing algorithms (operations such as feature detection, image tagging, etc.) use pixels' color or gray values, each pixel represents an element of a two-dimensional array. The image processing operation is always associated with working on matrices. In many image tagging methods, only a portion of the image is used, representing the features considered for the image clustering or classification. Other parts of the image (such as the image foreground) are not used, and the tagging operation used is prone to errors. The sparse matrix is a matrix with very high zero elements. Since the storage and use of zero elements are not cost-effective, and it is always used to reduce the storage of these elements to make the matrix operations faster, the images can be expressed by the sparse matrices [20]. [PERSON] and [PERSON] [21] presented the idea of fast image processing using sparse coding. Sparse coding is an interesting technique for computer vision that has not been utilized for detection of pests and diseases of fruits and vegetables. In the literature, there are several studies on the diagnosis of apple diseases. Although the latest models developed, including the deep learning models, are more accurate than traditional machine learning methods, there are still shortcomings, such as high complexity and long training times, which prevent their practical application in real environments.