International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Image Based Classification of Fruit Ripening Stages

Author(s) Prof. Vanitha Mani K, Ms. Abinaya J, Ms. Deepika A
Country India
Abstract An automated image-based system using CNN and YOLO has been developed to assess fruit ripeness and reduce post-harvest losses. Unlike manual inspection, the system provides objective, consistent, and real-time classification into six categories: Fresh Ripe, Fresh Unripe, Ripe, Unripe, Overripe, and Rotten. Experimental results show high accuracy and efficiency, making it suitable for smart farming, automated sorting, and retail quality control, offering a non-destructive and scalable solution for monitoring fruit quality.
Keywords Fruit Ripeness Classification, Convolutional Neural Networks (CNN), YOLO Object Detection
Field Engineering
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-24
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.58685
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E-ISSN 2582-2160
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