# A Hybrid Approach for the Detection and Classification of Tomato Leaf Diseases
## 2 Related Work  
The most vital issue for farmers is to manage plant-related diseases. The plant leaves are the part that is affected first of all and show a noticeable change in case any attack is approaching. However, the segmentation of a sick leaf image is prone to produce uneven illumination and a cluttered composite environment. Therefore, this section discusses the recently developed classification models capable of identifying tomato leaf diseases and analyzing their efficiency. In a study [2], the authors developed a deep CNN model named efficient net CNN. The developed model was fine-tuned and trained to detect healthy and unhealthy tomato leaf images. The authors tested the performance of two segmentation models, the U-net and the modified U-net model, for segmenting the leaves. They also conducted experiments for binary classification models, six-class classification, and 10-class classification. They found that only the U-net model revealed an accuracy of 98.66% for segmenting the leaf images. On the other hand, the EfficientNet-B7 showed a reliable performance when employed with binary and six-class classification. The average accuracy obtained using segmented pictures is 99.95% for binary classification and 99.12% for six-class classification. Authors of [3] proposed an approach that can automatically detect and classify tomato disease by employing super-pixel optimized segmentation for natural images. The preprocessing phase used a color-balancing algorithm that can aid in dropping the unwarranted influence of illumination and assist in finding the local optimum threshold that different types of image datasets can adapt. In addition, a novel method based on a histogram of gradients and color variations has been employed to separate the leaves from the background in an effective manner. The feature extraction that is based on the pyramid of the histogram of gradients, shape descriptor, and gray level co-occurrence matrix (GLCM) is proved to be reliable in providing the uniqueness of specific disease patterns. In this work, several classifiers have been implemented. Still, the random forest classifier, which used a hundred trees dataset, showed the best efficiency in obtaining the performance of the proposed framework. The results obtained in this research are compared with several similar works in studies [4; 5; 6; 7] where the comparative analysis using the estimation parameters proved the best. Authors of [8] proposed a multi-class feature extraction system for classifying the tomato leaf disease. The system is based on a deep CNN model incorporating the attention method and the residual block. The outcomes demonstrated that the model could successfully extract the intricate characteristics of various diseases. Moreover, it outperforms several recently developed deep learning studies by adopting the most popular PlantVillage dataset. This work obtained an overall identification accuracy of 99.24%. The work presented in [9] involves analyzing the performance of classifying different types of tomato disease. A lightweight CNN approach has been designed and illustrated by incorporating several attention methods to enhance the performance of the models. The network architecture, performance, and computational complexity were studied in this work. The employed dataset consists of nine diseases and a single healthy case. The obtained results showed an improvement in the classification accuracy through designing a tiny and computationally effective model. The work presented in [10] involves creating different types of CNN architecture (LeNet, VGGNet, ResNet50, and Xception) for detecting tomato leaf disease. Authors trained a deep CNN using the PlantVillage dataset with 14,903 images of healthy and diseased tomato plant leaves. The obtained results showed that the fine-tuned VGGNet architecture offers the best classification, with an accuracy of 99.25%, and achieves the minimum loss among all the evaluated architectures at the cost of a long training time and needs high hardware configurations.