Plant Disease Detection using Deep Transfer Learning

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Sukhwinder Kaur, Saurabh Sharma

Abstract

A country’s economy largely depends on crops. Crops are the most important factor in food production. Healthy plants lead to healthy crops. If the plants are infected, this can adversely affect the food production. Plant diseases are mainly caused by viral or bacterial organisms. Disease on a plant can be identified by a change in colour of the leaf or even shape. It becomes very important to detect plant diseases as early as possible so that food production doesn’t get affected. Detecting plant diseases via technology is a significant step in this direction. Using Deep Learning methods for this task can help us to identify diseases in plants. In this study, we use the VGGNet-19 model that is pre-trained using the weights of the ‘ImageNet’ dataset. By freezing the top layers and using transfer learning, we add a few layers to the model to try and improve the performance and accuracy of the model. This results in the accuracy of 97.52per cent for apple leaves and 95.75 per cent for grape leaves after running the model for 20 epochs.

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