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@article{168958,
        author = {Dhaval Thaware and Tejas Chikane and Sahil Jadhav and Sarthak Boralkar and Dr. Arati Kale},
        title = {Machine Learning-Based Solutions for Fruit Industries: Automated Mango Ripeness Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {151-154},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168958},
        abstract = {The fruit industry is a pivotal sector in many economies, particularly in tropical regions where mangoes are a significant agricultural product. How-ever, this industry faces ongoing challenges in accu-rately assessing the quality and ripeness of fruits, which traditionally relies on subjective evaluations based on appearance, shape, color, and texture. Such methods are not only time-consuming but also prone to human error, leading to inconsistencies in quality assessments and significant post-harvest losses. This project proposes a machine learning-based solution specifically designed for the mango industry, focusing on enhancing the detection and classification of ripe mangoes through image-based analysis. By employing advanced machine learning algorithms, particularly Convolutional Neural Networks (CNNs), the project aims to automate the pro-cess of ripeness detection. The system will analyze images captured from live feeds or uploaded by users, allowing for accurate predictions of ripeness levels—unripe, ripe, or overripe. The project emphasizes the importance of deep learning techniques in improving the accuracy and efficiency of fruit classification. CNNs will be utilized to extract relevant features from mango images effectively, enabling the model to learn from a diverse dataset that reflects various ripeness stages. This approach not only reduces reliance on manual inspection but also enhances the consistency and reliability of quality assessments.},
        keywords = {Machine Learning, Convolutional Neural Networks, Mango Ripeness Detection, Fruit Classification, Image Processing, Agricultural Automation},
        month = {November},
        }