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Hyperspectral Method Integrated with Machine Learning to Predict the Acidity and Soluble Solid Content Values of Kiwi Fruit During the Storage Period Cover

Hyperspectral Method Integrated with Machine Learning to Predict the Acidity and Soluble Solid Content Values of Kiwi Fruit During the Storage Period

Acta Technologica Agriculturae
Volume 27 (2024): Issue 4 (December 2024)
By: Amir Mansourialam,  Mansour Rasekh,  Sina Ardabili,  Majid Dadkhah and  Amir Mosavi  
Open Access
|Nov 2024

Authors

Amir Mansourialam

amir.mansouriala@uma.ac.ir

University of Mohaghegh Ardabili, Faculty of Agricultural and Natural Sciences, Department of Biosystem Engineering, Ardabil, Iran

Mansour Rasekh

m_rasekh@uma.ac.ir

University of Mohaghegh Ardabili, Faculty of Agricultural and Natural Sciences, Department of Biosystem Engineering, Ardabil, Iran

Sina Ardabili

sina.faiz@uma.ac.ir

University of Mohaghegh Ardabili, Faculty of Advanced Technologies, Department of Engineering Sciences, Ardabil, Iran

Majid Dadkhah

dadkhah.majid1379@gmail.com

University of Mohaghegh Ardabili, Faculty of Agricultural and Natural Sciences, Department of Biosystem Engineering, Ardabil, Iran

Amir Mosavi

amir.mosavi@uni-obuda.hu

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
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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:
smart agriculture,
fruit storage,
machine learning,
hyperspectral imaging,
artificial intelligence,
big data,
data science,
soft computing,
sustainable development goals,
quality control
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 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.

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