Abstract
An accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the economic losses substantially. Recent developments in deep learning based convolutional neural networks (CNN) have greatly improved image classification accuracy. In this paper, we present deep learning based approaches to detect diseases and pests in rice plants using images captured in real life scenario. We have experimented with various state-of-the-art CNN architectures on our large dataset of rice diseases and pests collected manually from the field, which contain both inter-class and intra-class variations and have nine classes in total. The results show that we can effectively detect and recognize rice diseases and pests using CNN with the best accuracy of 99.53% on test set using CNN architecture, VGG16. Though the accuracy of CNN models built on VGG16 or other similar architectures is impressive, these models are not suitable for mobile devices due to their large size having a huge number of parameters. To solve this problem, we propose a new CNN architecture, namely stacked CNN, that exploits two stage training to reduce the size of the model significantly while at the same time maintaining high classification accuracy. Our experimental results show that we achieve 95% test accuracy with stacked CNN, while reducing the model size by 98% compared to VGG16. This kind of memory efficient CNN architectures can contribute in rice disease detection and identification based mobile application development.
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URL
http://arxiv.org/abs/1812.01043