ItemConference Paper

Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks

Title
Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks
Creator
Roselin, J. Vimala; Sumanth, S.; Priscila, S. Silvia; Sakthivanitha, M.; Jenifer, Anciline; Lal, G. Sugin; Sheela, K.; Manikandan, N.
Description
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.
Source
Communications in Computer and Information Science;Volume;2866 CCIS;pp.111-123
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Conditional Generative Adversarial networks; Crop disease classification; Synthetic image generation
Coverage
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 & 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
Rights
All Open Access; Hybrid Gold Open Access
Relation
ISSN: 18650929; ISBN: 978-303217299-0;
Format
online
Language
English
Type
Conference paper

Citation

Roselin, J. Vimala; Sumanth, S.; Priscila, S. Silvia; Sakthivanitha, M.; Jenifer, Anciline; Lal, G. Sugin; Sheela, K.; Manikandan, N., “Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25395.

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