Intelligent Assessment of Sun Flower Seeds Using Machine Learning Approaches

Abstract

Pakistan is an agricultural country. Sun flower is the major crop of Pakistan which is being sowing in many areas of country. It fulfills the requirement of edible oil. In this paper we are trying to identify the best quality from different sun flowers seeds verities by using machine learning approaches. We take the images of four kinds of sunflower seeds which names Top sun(A), High Sun(B),US666(C) and Seji(D) for classification. We get eight different images of each kind of sunflower. In this paper sun flowers seeds varieties were categorized by using Computer vision image processing tool (CVIP). The experience and knowledge of inspectors are required to perfectly perform this assessment process. We use the RST-Invariant Features, Histogram Features, Texture Features, and Pattern Classification and also use the nearest neighbor and k-nearest neighbor algorithms for final classification. We achieved the final results of four kinds of sunflower using nearest neighbor on distance one and two 89% and 72% average and on k-nearest neighbor 89% and 73% average percentage. These are the best percentage results using these algorithms for classification. In this way we can easily classify the sunflower seeds and also these methods provide opportunity to farmer and other people for identify and select the different better and healthy sunflower seeds for better benefits.

Keywords: RST-Invariant Features, Histogram Features, Texture Features, Classification Algorithms