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 Jevoor, Susmita; Girish, V.; Chandan, N.; Umamaheswari, D.; Gondkar, Raju Ramakrishna Title A name given to the resource A Comprehensive Comparison of MobileNet, ResNet50 and InceptionV3 for Efficient Plant Pathology Detection 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 Proceedings of 2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/COMP-SIF65618.2025.10969933" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/COMP-SIF65618.2025.10969933</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004984319?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004984319?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 Jevoor S., Christ (Deemed to be University), Dept. Computer Science, Bangalore, India; Girish V., Christ (Deemed to be University), Dept. Computer Science, Bangalore, India; Chandan N., Christ (Deemed to be University), Dept. Computer Science, Bangalore, India; Umamaheswari D., Christ (Deemed to be University), Dept. Computer Science, Bangalore, India; Gondkar R.R., Christ (Deemed to be University), Dept. Computer Science, Bangalore, India Description An account of the resource Plant diseases have a strong impact on agricultural productivity due to economic factors and a reduction in crop quality. This work focuses on the classification of apple leaves into four classes: healthy, rust-infected, scab-infected, and infected by both diseases, using the Plant Pathology FGVC7-2020 dataset that contains 3,642 images in total. The work involves the analysis of three sophisticated deep learning architectures: ResNet50, MobileNet and InceptionV3.It turned out that MobileN et showed the highest performance, reaching a 92% accuracy rate; it was followed by ResNet50 with 75% accuracy and InceptionV3 at 73%, hence underlining its better generalization capability and efficiency in classifying. We discuss the proposed methodology, which includes data preprocessing techniques, experimental results and final conclusions, is discussed in detail. These results underline the fundamental importance of determining an appropriate neural network architecture for the recognition of plant diseases, which is of prime importance to improve agricultural productivity. 2025 IEEE. Subject The topic of the resource Agriculture; CNN; Deep learning; Inception V3; MobileNet; Plant disease detection; ResNet50 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153853-8; 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