Proceedings of the 7th International Conference on Food, Agriculture, and Natural Resources (IC-FANRES 2022)

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The Best Combination of Gas Sensor and Machine Learning Classification Algorithm in Detecting Mango (Mangifera indica L.) Quality

Authors
Joko Sumarsono1, *, Murad1, Ida Ayu Widhiantari1, Syahroni Hidayat2, Ulfah Mediaty Arief2, Tatyantoro Andrasto2
1Department of Agricultural Engineering, Faculty of Food Technology and Agroindustries, University of Mataram, Mataram, Indonesia
2Departement of Electrical Engineering, Universitas Negeri Semarang, Office E11 Sekaran Campus, 50229, Semarang, Indonesia
*Corresponding author. Email: sumarsonoj@gmail.com
Corresponding Author
Joko Sumarsono
Available Online 27 October 2023.
DOI
10.2991/978-94-6463-274-3_11How to use a DOI?
Keywords
Mango; Non-destructive; Machine learning
Abstract

Mango is a climacteric fruit with high transpiration activity when it reaches physiological maturity due to ethylene gas production. As a result, the quality of mangoes varies from day to day. Mango quality can be determined non-destructively by using gas sensors and machine learning to detect the gas produced. However, the classification accuracy remains low. Therefore, the aim of this study was to determine the type of gas sensor, the combination of gas sensors, and the combination of gas sensors and classification algorithms in determining the quality of mangoes. The gas sensors employed are TGS 2600, MQ3, MQ2, MQ4, and MQ8. While the classification algorithms are Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The results demonstrate that when paired with the SVM and KNN algorithms, the TGS 2600 sensor provided the best mango fruit quality classification results. Meanwhile, KNN’s classification method outperforms SVM.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 7th International Conference on Food, Agriculture, and Natural Resources (IC-FANRES 2022)
Series
Advances in Biological Sciences Research
Publication Date
27 October 2023
ISBN
978-94-6463-274-3
ISSN
2468-5747
DOI
10.2991/978-94-6463-274-3_11How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Joko Sumarsono
AU  - Murad
AU  - Ida Ayu Widhiantari
AU  - Syahroni Hidayat
AU  - Ulfah Mediaty Arief
AU  - Tatyantoro Andrasto
PY  - 2023
DA  - 2023/10/27
TI  - The Best Combination of Gas Sensor and Machine Learning Classification Algorithm in Detecting Mango (Mangifera indica L.) Quality
BT  - Proceedings of the 7th International Conference on Food, Agriculture, and Natural Resources (IC-FANRES 2022)
PB  - Atlantis Press
SP  - 130
EP  - 142
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-274-3_11
DO  - 10.2991/978-94-6463-274-3_11
ID  - Sumarsono2023
ER  -