Transfer learning-based Plant Disease Detection

Abstract

Deep Neural Networks in the field of Machine Learning (ML) are broadly used for deep learning. Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification problems. Deep neural networks have been highly successful in image classification problems. In this paper, we have shown the use of deep neural networks for plant disease detection, through image classification. This study provides a transfer learning-based solution for detecting multiple diseases in several plant varieties using simple leaf images of healthy and diseased plants taken from PlantVillage dataset. We have addressed a multi-class classification problem in which the models were trained, validated and tested using 11,333 images from 10 different classes containing 2 crop species and 8 diseases. Six different CNN architectures VGG16, InceptionV3, Xception, Resnet50, MobileNet, and DenseNet121 are compared. We found that DenseNet121 achieves best accuracy of 95.48 on test data.