Coffee Disease Visualization and Classification

• Main Authors: Milkisa Yebasse, Birhanu Shimelis, Henok Warku, Jaepil Ko, Kyung Joo Cheoi
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Plants
• Subjects: coffee disease classification; coffee disease visualization; deep learning; Grad-CAM; Score-CAM
• Online Access: https://www.mdpi.com/2223-7747/10/6/1257

Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to trust the model that it truly identifies the region of the disease in the image; it may simply use unrelated surroundings for classification. Visualization techniques can help determine important areas for the model by highlighting the region responsible for the classification. In this study, we present a methodology for visualizing coffee diseases using different visualization approaches. Our goal is to visualize aspects of a coffee disease to obtain insight into what the model “sees” as it learns to classify healthy and non-healthy images. In addition, visualization helped us identify misclassifications and led us to propose a guided approach for coffee disease classification. The guided approach achieved a classification accuracy of 98% compared to the 77% of naïve approach on the Robusta coffee leaf image dataset. The visualization methods considered in this study were Grad-CAM, Grad-CAM++, and Score-CAM. We also provided a visual comparison of the visualization methods.