Authors: R.Kanimozhi, Dr.V.Maniraj
Abstract: Coconut is an economically important plantation crop whose productivity is significantly affected by leaf diseases such as Leaf Rot, Grey Leaf Spot, and Bud Rot. Early detection of these diseases is essential to reduce crop losses and improve yield. Conventional disease diagnosis through manual inspection is time-consuming, subjective, and unsuitable for large-scale plantations. This paper proposes an Attention-Based Deep Learning Framework for the early detection and classification of coconut leaf diseases. The proposed framework integrates image preprocessing, data augmentation, transfer learning, and a Convolutional Block Attention Module (CBAM) to enhance feature extraction. The attention mechanism enables the model to focus on disease-affected regions while suppressing irrelevant background information. A convolutional neural network is used to classify healthy and diseased leaf images with improved accuracy. The model is evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, and confusion matrix. Experimental results demonstrate that the attention-based framework outperforms conventional CNN models in detecting early-stage disease symptoms. The proposed approach is computationally efficient and suitable for real-time deployment on mobile and edge devices. It provides an effective decision-support tool for farmers and agricultural experts, contributing to improved disease management and sustainable coconut cultivation.
DOI: http://doi.org/10.5281/zenodo.21468251
