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@article{164209,
        author = {Aswin Jeba mahir.A and Krishna R.V and Tamilselvan M and Surendar R},
        title = {AUDIO-ENHANCED ADVANCED CNN FRAMEWORK FOR FRUITS & VEGETABLES RECOGNITION},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1433-1439},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164209},
        abstract = {The recognition and classification of fruits and vegetables are essential tasks in various domains such as agriculture, food processing, and retail. Convolutional Neural Networks (CNNs) have demonstrated remarkable success in image recognition tasks, including those involving fruits and vegetables. The framework employs a combination of techniques aimed at optimizing both accuracy and efficiency. Firstly, a lightweight CNN architecture is designed to ensure rapid inference without compromising recognition performance. Secondly, data augmentation techniques are employed to enrich the training dataset, thereby enhancing the model's generalization capabilities. Thirdly, transfer learning is utilized to leverage pre-trained models and adapt them to the specific task of fruit and vegetable recognition, reducing the need for extensive training on limited datasets.That datasets comprising various types of fruits and vegetables. Results demonstrate that the proposed framework achieves competitive accuracy levels while significantly reducing computational requirements and inference time compared to existing approaches. Overall, the proposed CNN-based framework offers a promising solution for efficient and accurate fruits and vegetables recognition, with potential applications in agriculture automation, food quality assessment, and retail inventory management.},
        keywords = {Machine learning; Convolutional Neural Network (CNN); Recognition; Object detection; Fruits and Vegetable detection; Voice generation.},
        month = {},
        }