Classification of potatoes according to their cultivated field by SVM and KNN approaches using an electronic nose

Ali Amkor, Noureddine El Barbri


In this article, we propose a homemade electronic nose to distinguish between two types of potatoes: the first type is traditionally treated with donkey and sheep manure, and the other type is treated with chicken manure. The proposed tool consists of a network of commercial metal oxide sensors, a data acquisition card, and a personal computer for data pre-processing and processing. Two methods were used, namely, support vector machines (SVM) and k-nearest neighbors (KNN) with 5-fold cross-validation and which achieved the same success rate of 97.5%. These results demonstrate that our concept, which is quick, simple, and inexpensive, can discriminate between potatoes based on the method of fertilization used in the field.


Electronic nose; Gas sensor; KNN algorithm; Potato; SVM algorithm

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).