Convolutional Neural Network for Automated Kaki Ripening Classification via Image Processing
This dataset and associated code provide a Convolutional Neural Network (CNN) framework for the automated visual classification of kaki (persimmon) ripening stages. The methodology employs advanced image processing techniques, including Segmentation and Augmentation, to enhance the model's robustness and accuracy under varying lighting and background conditions. A CNN model was trained to distinguish between 6 key ripening phases, offering a non-invasive and valuable solution for fruit quality assessment and optimizing harvest and storage timing.
Resource information
- Format
- zip
- Original repository
- 17640555
- DOI
- 10.5281/zenodo.17640555
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- cc-by-4.0
- Last record update
- Nov 5, 2026
About this resource
This dataset and associated code provide a Convolutional Neural Network (CNN) framework for the automated visual classification of kaki (persimmon) ripening stages. The methodology employs advanced image processing techniques, including Segmentation and Augmentation, to enhance the model's robustness and accuracy under varying lighting and background conditions. A CNN model was trained to distinguish between 6 key ripening phases, offering a non-invasive and valuable solution for fruit quality assessment and optimizing harvest and storage timing.