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@article{177602,
        author = {Nishitha.Pochareddy and Mr. Suresh Tiruvalluru},
        title = {Milk Quality Assurance Using transfer learning: A Comprehensive Guide},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1019-1022},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177602},
        abstract = {Ensuring milk quality is a critical challenge in the dairy industry, directly affecting public health, consumer trust, and economic sustainability. Traditional quality assurance methods are often labor-intensive, time-consuming, and prone to human error. This paper presents a modern approach to milk quality assessment using transfer learning, a powerful deep learning technique that leverages pre-trained models for image and sensor-based classification tasks. The proposed method uses convolutional neural networks (CNNs) adapted from models like ResNet, VGG, or MobileNet, retrained on datasets containing visual and chemical characteristics of milk. The system enables fast, scalable, and accurate classification of milk quality, providing a real-time and cost-effective alternative to conventional testing. Experimental results show high accuracy in detecting contaminated or substandard milk samples, demonstrating the potential of AI in food safety and quality control. Furthermore, it was more accurate and stable than the other methods.},
        keywords = {ANN, KNN, RF, and SVM.},
        month = {May},
        }