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@article{181515,
        author = {Bency S and Aswani V A and Christo Sabu and Gopika S and Vidya C A},
        title = {Tomato Quality Classification based on Transfer Learning Feature Extraction and Machine Learning Algorithm Classifier},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4312-4318},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181515},
        abstract = {The System propose an automated tomato quality classification system using Support Vector Machine (SVM) for classification and Inception V3 as a feature extractor. Traditional methods for assessing tomato quality rely on manual inspection, which is time-consuming, inconsistent, and prone to human error. To address these challenges, we leverage deep learning-based feature extraction and machine learning classification for accurate and efficient quality assessment. Inception V3, a pre-trained Convolutional Neural Network (CNN), is utilized to extract high-level features from tomato images, capturing essential texture, color, and shape attributes. These extracted features are then passed to an SVM classifier, which categorizes tomatoes based on predefined quality classes such as ripe, unripe, and defective. The model is implemented using Python, ensuring scalability and ease of deployment. The proposed system enhances accuracy, consistency, and automation in tomato quality classification, making it suitable for agricultural and food processing industries. Experimental results demonstrate that the Inception V3 + SVM combination improves classification performance compared to traditional feature extraction and machine learning techniques.},
        keywords = {Convolution Neural Network, Support Vector Machine, InceptionV3.},
        month = {June},
        }