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Browsing by Subject "Addresses limitations of manual ripeness detection (subjectivity, labor intensity, error-prone)"

Browsing by Subject "Addresses limitations of manual ripeness detection (subjectivity, labor intensity, error-prone)"

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  • Roy , Suprama (Comilla University, 2025-02-01)
    The Fruit Ripeness Detection project aims to develop an automated solution for detecting the ripeness of fruits using deep learning techniques, specifically leveraging the VGG16 model. The project focuses on classifying ...

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