Denoising Convolutional Variational Autoencoders-Based Feature Learning for Automatic Detection of Plant Diseases

Early detection is critical for maintaining quantity and quality of farming commodity. Currently, detection of plant diseases still requires human expertise and/or need microscopic identification such as spectroscopic technique and molecular biological. So, it would be very costly and time consuming, and hence unattainable for small-holder farmers. The rapid development of intelligent agriculture using machine learning has led the widespread use of computer or smart-phones to solve this problem. So, early detection of plant disease can be performed with minimal support from human experts and microscopic identification is no longer needed. However, conventional machine-learning techniques are limited in their ability to process raw data directly. So it require some efforts and domain expertise to design feature extractor to support it. Moreover, impulse noise such as salt-pepper noise may present on the images and it arises another challenge to provide a robust system. In this paper, we present denoising convolutional variational autoencoders as automatic unsupervised feature extractor and automatic denoiser to learn and to extract good features directly from the raw data. Here, we use the output of denoising convolutional variational auto encoders as inputs to fully connected networks classifiers for automatic detection of plant diseases. Our experiments show the average accuracies of our method is better than denoising variational autoencoders which is built using fully deep connected networks architectures. We also found that our proposed method is more robust against noisy test data.