Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction
- DOI
- 10.5220/0013110800003890
- Published
- 2025
- Container
- Proceedings of the 17th International Conference on Agents and Artificial Intelligence
- Publisher
- SCITEPRESS - Science and Technology Publications
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.5220/0013110800003890,
title = {Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction},
author = {Ahmed Baha Ben Jmaa and Faten Chaieb and Anna Fabijańska},
year = {2025},
journal = {Proceedings of the 17th International Conference on Agents and Artificial Intelligence},
doi = {10.5220/0013110800003890},
url = {https://doi.org/10.5220/0013110800003890}
}RIS
TY - JOUR TI - Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction AU - Ahmed Baha Ben Jmaa AU - Faten Chaieb AU - Anna Fabijańska PY - 2025 JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence DO - 10.5220/0013110800003890 UR - https://doi.org/10.5220/0013110800003890 ER -
APA
Jmaa, A. B. B., Chaieb, F., & Fabijańska, A. (2025). Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction. Proceedings of the 17th International Conference on Agents and Artificial Intelligence. https://doi.org/10.5220/0013110800003890
Source records
- crossref · retrieved 2026-09-25T12:23:42.210Z