Inception-based global context attention network for the classification of coffee leaf diseases

Abstract

Coffee is a significant global commodity that is consumed in large quantities on a daily basis, and it ranks as the second-most important product in global trade. The leaves of coffee plants are vulnerable to fungal and pest attacks that can harm their photosynthetic regions, leading to the production of low-quality, diseased beans. Accurate diagnosis of coffee disease is crucial in determining suitable remedial action. Different pathogens require different pesticides or other forms of treatment, and a precise diagnosis can guarantee that the necessary steps are taken to prevent further damage to the plant. The major objective of this research is to develop a disease detection approach for coffee leaves based on deep learning techniques. The proposed network is developed with inception modules, a global context module, and a multi-head attention module to achieve precise classification. Through the simultaneous application of different filter sizes and pooling operations on the input, inception modules facilitate the proposed network in extracting features at multiple scales and acquiring significant feature maps at different levels of abstraction. The Global Context Block employs a channel attention mechanism to generate a single feature vector that modulates the input feature maps to obtain high-level contextual information. Finally, the multi-head attention module captures complex relationships between features from different subsets and aggregates them to form a more powerful representation of the input. The experimental results indicated that the proposed network, which was trained on the BRACOL dataset, outperformed existing networks in detecting coffee leaf disease. It achieved an accuracy of 98.57% and a F1 score of 98.55%.