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 Bomble, Shivam Vijay; Nizar Banu, P.K. Title A name given to the resource Analyzing Deep Learning Architectures in Cotton Crop for Precision Disease Diagnosis 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 2025 17th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2025;pp.337-344 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CICN67655.2025.11368201" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CICN67655.2025.11368201</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034688816?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034688816?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 Bomble S.V., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Nizar Banu P.K., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India Description An account of the resource Cotton is an important cash crops worldwide, providing raw materials for the textile industry and is the basis of livelihood of millions of farmers. In India, it has an important place in the agricultural economy, which contributes significantly to both domestic consumption and export income. However, cotton production is highly sensitive to infection of various diseases and insects, such as bacterial scorching, powdery mildew and targeted spots, which can cause severe yield reduction and economic loss. Traditional disease management methods often depend on manual inspection, which is difficult to scale in time consuming, human error and large cultivated areas. Therefore, it is necessary to detect the initial and accurate detection of the disease to ensure plant health and maximize productivity. This study examines advanced intensive teaching methods for automatic cotton disease diagnosis, and compare the performance of VGG16 and ResNet18 architecture. Experimental results showed that the VGG16 model achieved verification accuracy of 99.69%, while ResNet18 achieved an accuracy of 99.58%. In addition, a real time forecasting interface was developed from the URL provided by the user to classify images of cotton leaves, making practical signs possible for use in the area. This research highlights effectiveness of deep learning in improving accurate agriculture, which helps in timely detection of diseases to reduce the loss of crops. 2025 IEEE. Subject The topic of the resource Convolutional Neural Networks (CNN); Cotton Disease; Precision Agriculture; ResNet18; VGG16 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158733-8; 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