Automatic Detection of Soybean Leaf Disease Using Optimistic Firefly and Dilation CNN

In India, agriculture plays a significant role in providing food for a substantial portion of the nation's pop-ulation. Plant diseases are a common challenge. Research on soybean plant leaf disease detection plays an important role in improving the growth, production and quality of plants. In computer vision, deep learning models are more effective in detection disease. However, these approaches have some limitations. Therefore, in this study, an intelligent model based on dilation CNN (convolutional neural network) has been proposed for the effective detection of plant leaf diseases. In addition, the suggested approach makes use of firefly algorithm as the performance of the system can be enhanced by fine tuning the hyper-parameters of the dilated CNN model. Further, the performance of the suggested approach along with other deep learning has validated using evaluation metrics and the experimental outcomes indicate that dilated CNN optimized with firefly algorithm has attained better performance with accuracy of 97.62 % in comparison with other considered models.