Deep Learning for Fungus Classification

This research work presents a novel approach for the classification of fungal species using ConvMixer, a recently proposed architecture that combines convolutional and mixer layers. Fungal infections pose a significant threat to agriculture and public health, making accurate and efficient classification methods crucial for disease management. The proposed method leverages ConvMixer blocks to extract hierarchical features from fungal images, allowing for robust classification across multiple species. This study has then trained and evaluated the proposed model on a dataset consisting of images from five different fungal species, achieving promising results with an average accuracy of 95 percent. Additionally, this study has conducted a comprehensive analysis of the proposed model’s performance, including confusion matrix visualization and random sample predictions, to assess its generalization capabilities. Overall, the research findings demonstrate the effectiveness of ConvMixer in fungal species classification and highlight its potential for real-world applications in agriculture and healthcare.