Attention-Based Recurrent Neural Network for Plant Disease Classification - Lee, Sue Han; Goëau, Hervé; Bonnet, Pierre; Joly, Alexis (Frontiers in Plant Science, 2020)
Plant diseases have a significant impact on global food security and the world agricultural economy. Their early detection and classification increases the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images, on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and have significant advantages to support human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by the plant disease to allow their classification, and sometimes can take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN based approach is more robust and has a greater ability to generalize to unseen contaminated crop species compared to classical CNN approaches. We also analyze the focus of attention learned by our RNN and show that our approach is able of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.