Classification of canola seed varieties based on multi-feature analysis using computer vision approach
Salman Qadri, Syed Furqan Qadri, Abdul Razzaq, Muzammil Ul Rehman, Naz̲īr Aḥmad, Syed Ali Nawaz, Najia Saher, Nadeem Akhtar, Dost Muhammad Khan
Abstract
This study aims to analyze the potential of the computer vision (CV) approach to classify eight canola varieties. The input images of eight canola varieties were CON-I, CON-II, CON-III, Pakola, Canola Raya, Rainbow, PARC Canola Hybrid, and Tarnab-III. A digital camera acquired these images on an open sunny day without any complex laboratory setup. First-order histogram features, second-order statistical texture features, binary features, spectral features of three bands were, blue (B), green (G), and red (R), were employed in the artificial neural network (ANN). A 10-fold stratified cross-validation method was used for classification. The best results with accuracy ranging from 95% to 98% observed when the data of regions of interest (512 × 512) deployed to the classifier.