A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array
In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people’s diet worldwide. Therefore, its different aspects are worth s...
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doaj-486454f8c8d944c8812a7e6f6c0fc1372021-09-09T13:56:26ZengMDPI AGSensors1424-82202021-08-01215836583610.3390/s21175836A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-ArrayAli Khorramifar0Mansour Rasekh1Hamed Karami2Urszula Malaga-Toboła3Marek Gancarz4Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranDepartment of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranDepartment of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranFaculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Kraków, PolandFaculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Kraków, PolandIn response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people’s diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples’ dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively.https://www.mdpi.com/1424-8220/21/17/5836potatoVOCsolfactory machineANNLDAPCA |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ali Khorramifar Mansour Rasekh Hamed Karami Urszula Malaga-Toboła Marek Gancarz |
spellingShingle |
Ali Khorramifar Mansour Rasekh Hamed Karami Urszula Malaga-Toboła Marek Gancarz A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array Sensors potato VOCs olfactory machine ANN LDA PCA |
author_facet |
Ali Khorramifar Mansour Rasekh Hamed Karami Urszula Malaga-Toboła Marek Gancarz |
author_sort |
Ali Khorramifar |
title |
A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array |
title_short |
A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array |
title_full |
A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array |
title_fullStr |
A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array |
title_full_unstemmed |
A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array |
title_sort |
machine learning method for classification and identification of potato cultivars based on the reaction of mos type sensor-array |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-08-01 |
description |
In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people’s diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples’ dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively. |
topic |
potato VOCs olfactory machine ANN LDA PCA |
url |
https://www.mdpi.com/1424-8220/21/17/5836 |
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