Hyperspectral Method Integrated with Machine Learning to Predict the Acidity and Soluble Solid Content Values of Kiwi Fruit During the Storage Period
Authors
Amir Mansourialam
University of Mohaghegh Ardabili, Faculty of Agricultural and Natural Sciences, Department of Biosystem Engineering, Ardabil, Iran
Mansour Rasekh
University of Mohaghegh Ardabili, Faculty of Agricultural and Natural Sciences, Department of Biosystem Engineering, Ardabil, Iran
Sina Ardabili
University of Mohaghegh Ardabili, Faculty of Advanced Technologies, Department of Engineering Sciences, Ardabil, Iran
Majid Dadkhah
University of Mohaghegh Ardabili, Faculty of Agricultural and Natural Sciences, Department of Biosystem Engineering, Ardabil, Iran
Amir Mosavi
Obuda University, John von Neumann Faculty of Informatics, Software Engineering Institute, Ludovika University of Public Service, Institute of Information Society, Budapest
DOI: https://doi.org/10.2478/ata-2024-0025 | Journal eISSN: 1338-5267
Language: English
Page range: 187 - 193
Published on: Nov 28, 2024
Published by: Slovak University of Agriculture in Nitra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
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© 2024 Amir Mansourialam, Mansour Rasekh, Sina Ardabili, Majid Dadkhah, Amir Mosavi, published by Slovak University of Agriculture in Nitra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.