Automated tomato leaf disease classification using transfer learning-based deep convolution neural network

Early and accurate detection of plant diseases is necessary to maximize crop yield. The artificial intelligence based deep learning method plays a vital role in the detection of the diseases using a huge volume of plant leaves images. However, to detect disease with small datasets is a challenging task using deep learning methods. Transfer learning is one of the popular deep learning methods used to accurately detect plant disease with minimal plant image data. In this paper, the transfer learning-based deep convolution neural network model to identify tomato leaf disease has proposed. The model performs detection of disease using real-time images and stored tomato plant images. Furthermore, the performance of the proposed model is evaluated using adaptive moment estimation (Adam), stochastic gradient descent (SGD), and RMSprop optimizers. The experimental result demonstrates that the proposed model using the transfer learning approach is effective in automated tomato leaf disease classification. The Adam optimizer achieves better accuracy compared with SGD and RMSprop optimizers.

Automated summary

A deep convolutional neural network model based on transfer learning is proposed for accurate identification of tomato leaf disease. The model detects disease using real-time and stored plant images, and performs well in automated disease classification.