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 Roselin, J. Vimala; Sumanth, S.; Priscila, S. Silvia; Sakthivanitha, M.; Jenifer, Anciline; Lal, G. Sugin; Sheela, K.; Manikandan, N. Title A name given to the resource Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Communications in Computer and Information Science;Volume;2866 CCIS;pp.111-123 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-17300-3_9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-17300-3_9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032630115?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032630115?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 Roselin J.V., Department of Computer Science, Christ (Deemed to Be University), Bangalore, India; Sumanth S., Department of Computer Science, New Horizon College, Kasturinagar, Bangalore, 560043, India; Priscila S.S., Department of Computer Science, Bharath Institute of Higher Education and Research, Tamil Nadu, Selaiyur, 600126, India; Sakthivanitha M., Department of Computer Application, Vels Institute of Science, Technology &amp; Advanced Studies, Tamil Nadu, Chennai, India; Jenifer A., Department of MCA, Francis Xavier Engineering College, Tami Nadu, Tirunelveli, India; Lal G.S., Department of Computer Science, The New College, Tamil Nadu, Chennai, India; Sheela K., Department of Computer Science and Information Technology, School of Computing Sciences, VISTAS, Pallavaram, Tamil Nadu, Chennai, India; Manikandan N., Department of Computer Science, The New College, Chennai, India Description An account of the resource Due to biological diversity and unstructured surroundings, agricultural image analysis strives for optimal model performance to better accomplish visual identification objectives. Large-scale, balanced, and ground-truthed image datasets are very helpful, but they are frequently hard to come by, which restricts the creation of very effective models. The identification of plant diseases has benefited enormously from the continuous advancement of deep learning (DL) techniques, which provide a robust tool with incredibly accurate results. However, the efficiency of deep learning models is dependent on the quantity and caliber of labeled data used for training. Precise classification of crop diseases is important for precision agriculture. These models suffer from limited and imbalance datasets especially for rare diseases. The study suggests a framework using Generative Adversarial Network (GAN) for image generation to enhance the classification of diseases. The study employs conditional GAN trained on a PlantVillage and New plant diseases datasets to generate synthetic images of diseased leaves. The images are evaluated using Structural similarity index (SSIM). Then the augmented images are integrated with the CNN classifier to measure the accuracy of disease prediction using synthetic dataset to validate the efficiency of image generation. The Author(s) 2026. Subject The topic of the resource Conditional Generative Adversarial networks; Crop disease classification; Synthetic image generation Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303217299-0; 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 All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online