Classification and Grading of Bulk Seeds Using Aritificial Neural Network

Abstract

This paper describes a different neural network model for classification and grading of bulk seeds samples using different artificial neural network models. Algorithms are developed to acquire and process color images of bulk seeds samples. Different seeds like Groundnut, Jowar, Wheat, Rice, Metagi, Red gram, Bengal gram, and Lentils etc. are considered for the study. The developed algorithms are used to extract over 11 (9 color, area and equidiameter) features, 18 (color only) features and 20 (18 color and 2 boundary) features. The area and equidiameter features are extracted from the watershed segmentation. Different types of Neural Network based classifier is used to identify the unknown seeds samples. The classification is carried out using different types of features sets, viz., color, area and equidiameter. Classification accuracies of over 85% are obtained for all the seeds types using all the three feature sets. And also different neural network gives different accuracies and time period taken for training all the three feature sets.

Identification and Evaluation of Technology for Detection of Aflatoxin Contaminated Peanut

“…A color based sorter for separating red and white wheat was developed by Tom pearson et al [16]. A comparative study was done by Anil kannur et al [17], to classify and grade bulk seed samples using artificial neural network. Three sets of features namely color, area and equidiameter are extracted for classification, and combinations of these features are tested with different artificial neural networks.…”