Wheat seed classification using neural network pattern recognizer

Abstract

Classification of wheat after harvest is a challenging task. Several techniques are prevalent for measuring the geometrical parameters of the wheat and for scrutinizing the wheat seeds. Parameters used for the classifier includes area, perimeter, compactness, length of kernel, width of kernel, asymmetry coefficient, length of kernel groove. Neural network pattern recognizer is used for the classification of the wheat seeds in this paper. The database for the Wheat seed is downloaded from the UCI machine learning repository. Confusion matrix and ROC is computed as the performance measure. Results show that neural network pattern recognizer is an apt tool for classification of wheat seeds with an overall accuracy of 96.7%.