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

Abstract

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.