# A Hybrid Approach for the Detection and Classification of Tomato Leaf Diseases
## 2 Related Work
In [11], the authors investigated grape plant leaf diseases by designing an efficient deep transfer learning-based model. The authors introduced a fully connected layer to extract the most significant features. Then, they employed the variance technique to remove extraneous features from the feature extractor vector. The PlantVillage dataset was used in this work, where the photos were fed to retrain the EfficientNet B7 deep architecture. Then, the collected features were minimized using the logistic regression technique. The obtained classification accuracy was equal to 98.7%. Authors of [12] designed a CNN model to effectively identify and classify the tomato diseases using the google collab framework to conduct the complete experiment by adopting a dataset containing 3000 different tomato leaf images that are affected by nine various diseases and a single healthy leaf class. The developed system consists of three main phases: the images are preprocessed, and the targeted area in the pictures are segmented. Then, the images are further processed through varying hyper-parameters of the CNN model. Finally, the CNN extracts the characteristics from the image, including colors, edges, texture, etc. The developed classification model achieves a prediction accuracy of 98.49%. The work presented in [13] developed a CNN model consisting of three convolutional and max-pooling layers with a different number of filters in each layer. The authors' first time revealed that the PlantVillage dataset is unbalanced, and augmentation techniques are used to balance the images inside each class. The obtained average accuracy for this model is 91.2%, whereas the needed storage space is almost 1.5 MB, and the pretrained model needs a space of 100 MB. In [14], the authors employed the transfer learning model to minimize the training data size, computational cost, and training time when building a deep learning model. The authors used five deep network structures, including MobileNet, Resnet50, Xception, Densenet121 Xception, and ShuffleNet, to perform the feature extraction. The authors developed several experiments using different learning rates and different training parameters. The best classification accuracy was obtained with the Densenet Xception, with 97.10% classification accuracy. The work presented in [15] involves designing a dense residual network for classifying tomato leaf diseases. The authors adjusted the residual dense network architecture. The model has been transformed into a classification model that offers higher accuracy of 95%. In [16], the authors presented an automatic leaf disease identification and classification model. In the first stage, the input images are resized to minimize the computational complexity, and then segmented by blacking out the background region. In this project, the authors employed a feature extraction method using two deep learning models, VGG19 and AlexNet, that are then classified using the ECOC-based support vector machine classifier. The results show that VGG19 and AlexNet achieved 98.9% and 98.8% classification accuracy, respectively. Authors of [17] focus on the soybean plant disease classification using the multilayer perceptron deep learning and support vector machine algorithms. The support vector machine was able to classify 19 classes of diseases. The employed dataset consists of 683 instances, where 643 instances were correctly classified, and 40 instances were incorrectly classified, with a classification accuracy of 94.14%. As presented above, several deep learning and machine learning approaches for classifying tomato leaf diseases have been proposed and discussed with various classification accuracies. Table 1 further discusses the aforementioned classification systems and analyzes them in terms of the employed algorithm, disease classification capability, and the obtained accuracy.