Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders
This work addresses the need for better explainability in deep learning models for agricultural image recognition, which is crucial for result verification, algorithm improvement, and knowledge extraction for farmers and researchers.
This paper developed a classification and explanation method using a variational autoencoder (VAE) architecture to visualize important feature variations in plant disease classification. It achieved an acceptable level of explainability on the PlantVillage dataset without sacrificing classification accuracy.
Agricultural image recognition tasks are becoming increasingly dependent on deep learning (DL); however, despite the excellent performance of DL, it is difficult to comprehend the type of logic or features of the input image it uses during decision making. Knowing the logic or features is highly crucial for result verification, algorithm improvement, training data improvement, and knowledge extraction. However, the explanations from the current heatmap-based algorithms are insufficient for the abovementioned requirements. To address this, this paper details the development of a classification and explanation method based on a variational autoencoder (VAE) architecture, which can visualize the variations of the most important features by visualizing the generated images that correspond to the variations of those features. Using the PlantVillage dataset, an acceptable level of explainability was achieved without sacrificing the classification accuracy. The proposed method can also be extended to other crops as well as other image classification tasks. Further, application systems using this method for disease identification tasks, such as the identification of potato blackleg disease, potato virus Y, and other image classification tasks, are currently being developed.