Enhancing Tomato Leaf Disease Detection through Generative Adversarial Networks and Genetic Algorithm based Convolutional Neural Network
Authors: Prashant Prashant ,   Computer Science  Engineering with Artificial Intelligence and Data Science, Sagar Institute of Science and Technology, Gandhi Nagar Campus, Opposite International Airport, Bhopal (M.P.), 462036, Madhya Pradesh, India,   Associate Professor; Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India,   Adjunct Professor, Department of CSE, Graphic Era Hill University, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India,   Professor and Head Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru, Karnataka, India,   Associate Professor (Research) Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, K L Deamed to be University, Vaddeswaram, Guntur - 522302, Andhra Pradesh, India,   Professor, Department of Artificial Intelligence and Data Science, PES Modern College of Engineering, Shivajinagar, Pune-411005, Maharashtra-0000-0002-3679-1807, India, Sharma Seema , Ramesh Janjhyam Venkata Naga , Pareek Piyush Kumar , Shukla Prashant Kumar , Pandit Shraddha V. 
Published: July 2024

In the agricultural sector, tomato leaf diseases signify a lot because they result in a lower crop yield and quality. Timely detection and classification of diseases help to ensure early interventions and effective treatment solutions. Nonetheless, the existing methods are confined by the dataset imbalance which affects class distribution negatively and thus results in poor models, especially for rare diseases. The research is designed to improve the capability of tomato leaf disease identification by investing a new deep-learning method beyond the challenge of imbalanced class distribution. By balancing the dataset, we aim to improve classification accuracy as we pay more attention to the under-represented classes. The proposed GAN-based method that combines the Weighted Loss Function to produce tomato leaf disease synthetic images is underrepresented. They improve the quality of the entire dataset, and the images from every class are now in a more balanced proportion. A CNN, which is the convolutional neural network, is trained for the classifier, with the weighted loss function as a part of the model. We used Genetic Algorithm (GA) for hyperparameter optimization of the CNN. It helps in emphasizing the learning process from the under-represented class. The suggested one will not only decrease the accuracy of tomato leaf disease detection but also increase it. Therefore, the synthetic images created by GAN enhance the dataset since the class distribution is brought to equilibrium. The incorporation of the weighted loss function into the model’s training process makes it very effective in handling with the class instability problem and consequently, the model can identify both common and rare diseases. From the outcomes of this study, it can be concluded that it is feasible to employ GAN and one loser weights function to solve the problem of class imbalance in tomato leaf disease recognition. A suggested approach that increases the model’s accuracy and reliability could be a good move to enhancing a reliable method of disease detection in the agricultural sector.