  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.
