Low-Level Feature Aggregation Networks for Disease Severity Estimation of Coffee Leaves

This paper presents a deep learning-based approach for the severity classification of coffee leaf diseases. Coffee leaf diseases are one of the significant problems in the coffee industry, where estimating the health status of coffee leaves based on their appearance is crucial in the production process. However, there have been few studies on this task, and cases of misclassification have been reported due to the inability to detect slight color differences when classifying the disease severity. In this work, we propose a low-level feature aggregation technique for neural network-based classifiers to capture the discolored distribution of the entire coffee leaf, which effectively supports discrimination of the severity. This feature aggregation is achieved by incorporating attention mechanisms in the shallow layers of the network that extract low-level features such as color. The attention mechanism in the shallow layers provides the network with information on global dependencies of the color features of the leaves, allowing the network to more easily identify the disease severity. We use an efficient computational technique for the attention modules to reduce memory and computational cost, which enables us to introduce the attention mechanisms in large-sized feature maps in the shallow layers. We conduct in-depth validation experiments on the coffee leaf disease datasets and demonstrate the effectiveness of our proposed model compared to state-of-the-art image classification models in accurately classifying the severity of coffee leaf diseases.