Prediction of Tomato Leaf Disease Plying Transfer Learning Models

The tomato has a high market value and is one of the vegetables grown in the most significant quantity globally. Tomato plants are susceptible to diseases, which can negatively impact the fruit's yield and quality. Detecting these illnesses at an early stage and their accurate identification is necessary for successfully managing diseases and reducing losses. In recent years, deep learning methods such as convolutional neural networks (CNNs) have demonstrated significant promise in identifying plant diseases from images. This research suggested a CNN-based strategy for detecting tomato leaf diseases using transfer learning. Transfer learning enables us to enhance the performance of our disease detection model using a smaller dataset by leveraging pre-trained CNN models that have been trained on large datasets. The proposed transfer learning model through Resnet50 and Inception V3 is effective by applying it to a dataset of tomato leaf images. As a result, a high level of accuracy is achieved and could be indulged for practical applications in agriculture.