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@article{189556,
        author = {Likhith S Gowda and C Vaishnavi and Yasmina B and sidharth A and Vishwanath rajaput},
        title = {Potato Breed Recognition Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {7033-7039},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189556},
        abstract = {Potatoes are among the most widely cultivated and economically significant crops worldwide, and accurate identification of potato varieties plays a vital role in agricultural production, breeding programs, and market management. Traditional methods of potato breed identification rely heavily on expert visual inspection, which is time-consuming, subjective, and unsuitable for large-scale applications. To address these limitations, this paper presents a deep learning–based approach for automated potato breed recognition using image analysis techniques. The study evaluates the performance of deep learning models implemented using Keras and stored in .h5 format, along with a custom-designed Convolutional Neural Network (CNN). The proposed models are trained to classify potato breeds by learning discriminative visual features such as shape, colour, texture, and surface patterns from image datasets. Experimental evaluation is conducted using accuracy as the primary performance metric. The results demonstrate that the custom CNN model achieves superior performance, attaining an accuracy of 99%, outperforming the other evaluated approaches. The proposed system provides an efficient and reliable solution for automated potato breed identification and has practical applications in precision agriculture, crop management, and agricultural decision-support systems.},
        keywords = {Convolutional neural network, deep learning, image classification, potato breed recognition.},
        month = {December},
        }