Identification of Canola Seeds through Computer Vision Image Processing

Abstract

The objectives of this research are to present the automatic organization of agricultural seeds with the explosion of digital information through compute image vision processing. In this research paper CVIP (computer vision image processing) tool has been applied on different varieties and categorized of canola seeds. We had the 4 different varieties of canola seeds which were named as Gobhi Sarson (A), Barassica comp (B), Sathri (C) and Rocket Herbof (D). Each variety had 10 images and total 10*4 =40 images of canola seeds. We took the train data results of all kinds of canola seeds. After that the train data results were compared for pattern classification and apply the nearest neighbor and k-nearest neighbor algorithms for final classification. We achieved in nearest neighbor 85% and 76% average and k-nearest neighbor 90% and 73% average as a final pattern classification results. These are the best percentage for classification and provide more accuracy. The formers can select best healthy seed variety with the help of the results of this research.

Keywords:  Features, Pattern classification, nearest neighbor, k-nearest neighbor