CoLeafNet: a dual-track deep learning network for classification of nutrient deficiencies in coffee leaves using densenet and efficient multi-scale feature attention network

Abstract Nutrient health is essential for coffee plants as it directly affects their growth, resilience and bean quality. Essential nutrients support key physiological processes in coffee plants and deficiencies can reduce crop yield, compromise bean quality, and lead to economic losses. Given the widespread importance of coffee, especially in developing economies, early detection of nutrient deficiencies in coffee leaves is crucial. However, traditional diagnostic methods like manual inspections and chemical testing are time-intensive, costly and prone to human error. This research work introduces CoLeafNet, a novel dual-track Deep Learning (DL) framework designed specifically for nutrient deficiency classification in coffee leaves. The CoLeafNet architecture integrates DenseNet to extract global, hierarchical features with an Efficient Multi-Scale Feature Attention Network (EMFANet) for detailed local feature capture. EMFANet incorporates an Efficient Channel Attention (ECA) mechanism to enhance cross-channel feature relevance and Ghost modules to minimize computational load while preserving critical information. This combination allows CoLeafNet to dynamically emphasize essential features at various scales, enabling precise detection of nutrient-specific visual patterns. To the best of our knowledge, this is the first dual-track network developed for nutrient deficiency classification in coffee leaves, offering a novel and scalable approach for precision agriculture. It was tested on the CoLeaf dataset, which contains 1,006 labeled images depicting deficiencies in key nutrients like Boron, Iron, Potassium, Calcium, Magnesium, etc CoLeafNet achieved a classification accuracy of 95.64%, outperforming existing DL models.