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@article{177994,
        author = {B.GAYATHRI and D. BHAVITH SAI and A. PRUDHVI RAJ and Dr. A. THEJA},
        title = {Fruit Quality And Defect Image Classification With Conditional Generative Adversarial Network (GANs) Data Augmentation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2548-2552},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177994},
        abstract = {The quality and safety of fruits play a vital role in the agricultural industry, influencing both consumer satisfaction and market value. However, traditional image classification techniques face challenges such as limited and imbalanced datasets, as well as high variability in fruit appearance due to differences in size, shape, color, and surface defects. This project proposes an innovative fruit quality classification and defect detection system using Conditional Generative Adversarial Networks (GANs) for data augmentation. By generating realistic synthetic images representing various fruit conditions, the system enhances the training process of classification models, improving their accuracy and robustness. The integration of both real and augmented data helps the model effectively recognize subtle quality differences, making it more resilient to diverse real-world conditions. This automated approach supports large-scale fruit sorting, reduces reliance on manual inspection, and promotes more sustainable agricultural practices by minimizing food waste and ensuring consistent quality.},
        keywords = {Fruit Quality Classification, Defect Detection, Conditional GANs, Data Augmentation, Automated Sorting, Agricultural Technology, Synthetic Image Generation, Computer Vision, Deep Learning, Smart Farming},
        month = {May},
        }