Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Prasad, Kokkula Shiva; Balaram, Ambily; Rajender, Nagunuri; Sudheer, P.; Yerram, Sneha; Begum, Sumayyam Title A name given to the resource MCCLDP: Multi Class Cotton Leaf Diseases Prediction and Classification using Deep Learning Model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived APCI 2025 - 2025 International Conference on Advancements in Power, Communication and Intelligent Systems; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/APCI65531.2025.11137216" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/APCI65531.2025.11137216</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016788187?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016788187?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Prasad K.S., Nalla Malla Reddy Engineering College, Department of ECE, Hyderabad, India; Balaram A., Christ University (Deemed to be University), Kengeri Campus, Department of CSE, Banglore, India; Rajender N., Kakatiya Institute of Tech and Science, Department of IT, Waranagal, India; Sudheer P., CVR College of Engineering, Department of CSE (AIML), Hyderabad, India; Yerram S., Nalla Malla Reddy Engineering College, Department of CSE, Hyderabad, India; Begum S., Lords Institute of Engg and Tech, Department of IT, Hyderabad, India Description An account of the resource Cotton plant disease detection is critical for sustainable agriculture and reducing crop losses. This paper proposes a novel Multi-Stream Attention-Guided Hybrid CNN (MAH-CNN) for accurate classification of cotton leaf diseases. The model leverages pre-trained ResNet152v2 and DenseNet-121 backbones for hierarchical feature extraction, complemented by a shallow CNN for localized texture analysis. A spatial attention mechanism enhances focus on disease-relevant regions, mitigating background noise. Features from the global and local streams are fused and passed through a lightweight classification head. The model achieves superior performance in terms of accuracy 97.32%, F1 score 98%, and specificity 100% on benchmark datasets which are available in open access, outperforming existing state-of-the-art methods. The integration of Grad-CAM provides interpretability, fostering trust in automated disease detection systems. 2025 IEEE. Subject The topic of the resource Attention Mechanism; Classification Accuracy, Explainable AI; CNN; Cotton Leaf Disease Detection; DenseNet-121; Grad-CAM; Hybrid Feature Fusion; ResNet152v2; Transfer Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152387-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online