Integrating Few-Shot Learning and Multimodal Image Enhancement in GNut: A Novel Approach to Groundnut Leaf Disease Detection

Groundnut is a vital crop worldwide, but its production is significantly threatened by various leaf diseases. Early identification of such diseases is vital for maintaining agricultural productivity. Deep learning techniques have been employed to address this challenge and enhance the detection, recognition, and classification of groundnut leaf diseases, ensuring better management and protection of this important crop. This paper presents a new approach to the detection and classification of groundnut leaf diseases by the use of an advanced deep learning model, GNut, which integrates ResNet50 and DenseNet121 architectures for feature extraction and Few-Shot Learning (FSL) for classification. The proposed model overcomes groundnut crop diseases by addressing an efficient and highly accurate method of managing diseases in agriculture. Evaluated on a novel Pak-Nuts dataset collected from groundnut fields in Pakistan, the GNut model achieves promising accuracy rates of 99% with FSL and 95% without it. Advanced image preprocessing techniques, such as Multi-Scale Retinex with Color Restoration and Adaptive Histogram Equalization and Multimodal Image Enhancement for Vegetative Feature Isolation were employed to enhance the quality of input data, further improving classification accuracy. These results illustrate the robustness of the proposed model in real agricultural applications, establishing a new benchmark for groundnut leaf disease detection and highlighting the potential of AI-powered solutions to play a role in encouraging sustainable agricultural practices.