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@article{177581,
        author = {MADHUSRI M and TAMIL SELVAN T and SIVAASH R and SANTHOSH S and CHITRA DEVI B},
        title = {FRUIT SORTING AND GRADING USING CNN AND LOCAL BINARY PATTERNS (LBP)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {986-995},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177581},
        abstract = {On This project focuses on automating fruit classification and grading using deep learning techniques, particularly leveraging the ResNet (Residual Network) architecture. Traditional methods for classifying and grading fruits are often labor-intensive, time-consuming, and susceptible to human error. As the demand for high-quality, consistent produce increases, the need for efficient and accurate fruit grading systems becomes crucial. The ResNet model, known for its deep architecture and ability to handle complex visual data, is employed to identify and categorize different fruit types based on various characteristics such as color, shape, and texture. By training the network on a large dataset of fruit images, the modelcan not only classify different fruit types but also grade them according to predefined quality standards. This approach significantly reduces human involvement and enhances the consistency and speed of the grading process. The project aims to demonstrate how deep learning can revolutionize agricultural practices, improving market efficiency, reducing waste, and ensuring higher-quality produce for consumers. Furthermore, the proposed solution can be integrated into smart farming systems to provide real-time, automated grading solutions.},
        keywords = {},
        month = {May},
        }