2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 2 , PP: 147-177, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Vasima Khan 1 , Seema Sharma 2 , Janjhyam Venkata Naga Ramesh 3 , Piyush Kumar Pareek 4 , Prashant Kumar Shukla 5 * , Shraddha V. Pandit 6
Doi: https://doi.org/10.54216/FPA.160210
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.
GAN (Generative Adversarial Networks) , Weighted Loss Function , Synthetic images , Convolutional Neural Network (CNN) , Dataset diversity , Model accuracy , Robustness , Disease detection efficacy
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