The Novel Combination of Nano Vector Network Analyzer and Machine Learning for Fruit Identification and Ripeness Grading
Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared...
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| Published in | Sensors (Basel, Switzerland) Vol. 23; no. 2; p. 952 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
13.01.2023
MDPI |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s23020952 |
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| Abstract | Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared spectroscopy) are already used to classify fruit. In this paper, we propose a fast and cost-effective method based on a low-cost Vector Network Analyzer (VNA) device augmented by K-nearest neighbor (KNN) and Neural Network model. S-parameters features are selected, which take into account the information on signal amplitude or phase in the frequency domain, including reflection coefficient S11 and transmission coefficient S21. This approach was experimentally tested for two separate datasets of five types of fruits, including Apple, Avocado, Dragon Fruit, Guava, and Mango, for fruit recognition as well as their level of ripeness. The classification accuracy of the Neural Network model was higher than KNN with 98.75% and 99.75% on the first dataset, whereas the KNN was seen to be more effective in classifying ripeness with 98.4% as compared to 96.6% for neural network. |
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| AbstractList | Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared spectroscopy) are already used to classify fruit. In this paper, we propose a fast and cost-effective method based on a low-cost Vector Network Analyzer (VNA) device augmented by K-nearest neighbor (KNN) and Neural Network model. S-parameters features are selected, which take into account the information on signal amplitude or phase in the frequency domain, including reflection coefficient S11 and transmission coefficient S21. This approach was experimentally tested for two separate datasets of five types of fruits, including Apple, Avocado, Dragon Fruit, Guava, and Mango, for fruit recognition as well as their level of ripeness. The classification accuracy of the Neural Network model was higher than KNN with 98.75% and 99.75% on the first dataset, whereas the KNN was seen to be more effective in classifying ripeness with 98.4% as compared to 96.6% for neural network.Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared spectroscopy) are already used to classify fruit. In this paper, we propose a fast and cost-effective method based on a low-cost Vector Network Analyzer (VNA) device augmented by K-nearest neighbor (KNN) and Neural Network model. S-parameters features are selected, which take into account the information on signal amplitude or phase in the frequency domain, including reflection coefficient S11 and transmission coefficient S21. This approach was experimentally tested for two separate datasets of five types of fruits, including Apple, Avocado, Dragon Fruit, Guava, and Mango, for fruit recognition as well as their level of ripeness. The classification accuracy of the Neural Network model was higher than KNN with 98.75% and 99.75% on the first dataset, whereas the KNN was seen to be more effective in classifying ripeness with 98.4% as compared to 96.6% for neural network. Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared spectroscopy) are already used to classify fruit. In this paper, we propose a fast and cost-effective method based on a low-cost Vector Network Analyzer (VNA) device augmented by K-nearest neighbor (KNN) and Neural Network model. S-parameters features are selected, which take into account the information on signal amplitude or phase in the frequency domain, including reflection coefficient S11 and transmission coefficient S21. This approach was experimentally tested for two separate datasets of five types of fruits, including Apple, Avocado, Dragon Fruit, Guava, and Mango, for fruit recognition as well as their level of ripeness. The classification accuracy of the Neural Network model was higher than KNN with 98.75% and 99.75% on the first dataset, whereas the KNN was seen to be more effective in classifying ripeness with 98.4% as compared to 96.6% for neural network. Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared spectroscopy) are already used to classify fruit. In this paper, we propose a fast and cost-effective method based on a low-cost Vector Network Analyzer (VNA) device augmented by K-nearest neighbor (KNN) and Neural Network model. S-parameters features are selected, which take into account the information on signal amplitude or phase in the frequency domain, including reflection coefficient and transmission coefficient . This approach was experimentally tested for two separate datasets of five types of fruits, including Apple, Avocado, Dragon Fruit, Guava, and Mango, for fruit recognition as well as their level of ripeness. The classification accuracy of the Neural Network model was higher than KNN with 98.75% and 99.75% on the first dataset, whereas the KNN was seen to be more effective in classifying ripeness with 98.4% as compared to 96.6% for neural network. |
| Author | Ferrero, Fabien Huy, Trinh Le Le-Thanh, Nhan Tran, Van Lic Doan, Thi Ngoc Canh |
| AuthorAffiliation | 1 Universite Cote d’Azur, LEAT, CNRS, 06903 Sophia Antipolis, France 5 Faculty of Computer Engineering, University of Information Technology, Ho Chi Minh City 721400, Vietnam 3 The University of Danang—University of Economics, Danang 55000, Vietnam 4 The Univeristy of Danang, Danang International Institute of Technology—DNIIT, Danang 550000, Vietnam 2 The University of Danang—University of Science and Technology, Danang 550000, Vietnam |
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| Author_xml | – sequence: 1 givenname: Van Lic surname: Tran fullname: Tran, Van Lic – sequence: 2 givenname: Thi Ngoc Canh surname: Doan fullname: Doan, Thi Ngoc Canh – sequence: 3 givenname: Fabien surname: Ferrero fullname: Ferrero, Fabien – sequence: 4 givenname: Trinh Le orcidid: 0000-0002-8340-6270 surname: Huy fullname: Huy, Trinh Le – sequence: 5 givenname: Nhan orcidid: 0000-0003-4207-2128 surname: Le-Thanh fullname: Le-Thanh, Nhan |
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| Cites_doi | 10.1109/TII.2018.2875149 10.3390/electronics9101564 10.1016/j.compag.2020.105326 10.1007/s11947-010-0411-8 10.1145/3453800.3453817 10.1109/JSEN.2017.2715222 10.1016/j.scienta.2019.108718 10.1109/JSEN.2019.2949528 10.1016/j.compag.2019.04.019 10.1016/j.dib.2019.104340 10.3390/s21113830 10.1016/j.compag.2009.09.002 10.1016/j.crfs.2022.100412 |
| ContentType | Journal Article |
| Copyright | 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023 by the authors. 2023 |
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| SubjectTerms | Accuracy Algorithms Antennas Automation Classification Cluster Analysis Data collection Datasets Fruit - chemistry fruit classification Fruits Harvest KNN Machine Learning Methods neural network Neural networks Neural Networks, Computer Performance evaluation Software Spectroscopy, Near-Infrared - methods Spectrum analysis Support Vector Machine VNA |
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| Title | The Novel Combination of Nano Vector Network Analyzer and Machine Learning for Fruit Identification and Ripeness Grading |
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