Machine Vision Based Potato Species Recognition

Abstract

Potato is one of the tasteful vegetable in the list of our daily delicious food. At present, there are 42000 kind of potatoes available in the world. In Bangladesh, we cultivate 82 species every year. Potatoes are used for other purposes besides eating. So it is produced by thinking of other purposes besides eating. Different varieties of potatoes are used for different purposes. But People usually do not know which variety of potato is suitable for which work. We took this research step to solve this problem. There are currently some conventional common detection methods that are not very convenient. Therefore, we have introduced Machine Vision Recognition (MVR) procedure to discover a suitable technique. As if, through this method people can easily identify the potato species. In this research paper, we want to show how to identify different varieties of potatoes in Bangladesh using machine vision approach. We have been collected total 1200 potato imagers from four fact for our experience. To reach our aim, several machine learning algorithms have been applied to the datasets, like Random Forest Classifier (RF), Linear Discriminant Analysis (LDA), Logistic Regression, Support Vector Machine (SVM), CART, NB, and KNN. Once we have been applied all the algorithms, different results have been shown by each algorithm. Logistic Regression shows the best result, which has an accuracy rate of 98%. In contrast, the lowest rate has been shown by the SVM. The accuracy rate of SVM is 33% which is not only a good fit for future research but also promising.