Deep interpretable architecture for plant diseases classification

Recently, many works have been inspired by the success of deep learning in computer vision for plant diseases classification. Unfortunately, these end-to-end deep classifiers lack transparency which can limit their adoption in practice. In this paper, we propose a new trainable visualization method for plant diseases classification based on a Convolutional Neural Network (CNN) architecture composed of two deep classifiers. The first one is named Teacher and the second one Student. This architecture leverages the multitask learning to train the Teacher and the Student jointly. Then, the communicated representation between the Teacher and the Student is used as a proxy to visualize the most important image regions for classification. This new architecture produces sharper visualization than the existing methods in plant diseases context. The proposed visualization method is compared quantitatively with the state-of-the-art methods using Area Over perturbation curve (AOP C). The obtained results shows that the proposed visualization method outperforms the existing methods with AOP C = 0.907. All experiments are achieved on PlantVillage dataset that contains 54306 plant images.