Tomato leaf disease identification via two–stage transfer learning approach

Abstract

In the last ten years, there has been an upsurge in focus on sustainable agribusiness as a response to the bio-hazards posed by the effects of climate change, severe weather events, population growth, increased demands for food security, and a lack of natural resources. Generally, tomato plants are very susceptible to a wide variety of diseases. So, when it comes to maintaining the quality of tomato crops, having a quick diagnosis that is also correct plays a significant role. Nowadays, deep learning (DL), notably convolutional neural networks (CNNs), have achieved exceptional results in numerous applications, including categorizing tomato plant diseases. This study aims to enhance the disease identification algorithm for tomatoes by using transfer learning to reduce the time needed for the model to be trained and improve its identification accuracy. The model is based on the VGGNet that has been pre-trained using ImageNet and two inception blocks. In addition, the improved categorical cross-entropy loss function for the multi-attribute identification task and two-stage transfer learning were included in the model training process. Our model obtains a better identification accuracy for tomato diseases in the test set than other state-of-the-art techniques, as shown by the experiments' outcomes. The findings indicate substantial values produced by the suggested approach, with 99.23% of accuracy. The suggested model is a beneficial tool for farmers in helping to detect and protect tomatoes from disease due to the considerably high success rate achieved by the model.