Performance of an automatic inspection system for classification of Fusarium Moniliforme damaged corn seeds by image analysis

Abstract

This paper presents algorithms for pre-processing, feature selection and classifier design which are used for Parameter Refining in the task of development of an automatic system for recognition and grading of corn seeds with external signs of Fusarium Moniliforme disease. The abilities of several feature selection methods – FDR, Scatter matrices and Stepwise Discriminant Analysis and two classification methods - Support Vector Machine (SVM) and K-Nearest Neighbours (K-NN) are investigated. Design and implementation of the system also has been showed. The system could continually present one by one positioned corn kernels to CCD camera, perform a classification procedure of captured images and discharge seeds to assigned containers. The software was developed in LabVIEW environment including image analysis and classification procedures performed using MATLAB Script. Results for total error rate of 8.4% - 7.2% from preliminary classification related to 8480 seeds from16 Bulgarian varieties and total error rate of 6.95% - 20.4% for experimental results obtained with the system during the control measurements of the seed sample are obtained.