Pecan Weevil Recognition Using Support Vector Machine Method

Abstract

Problem statement: The Pecan weevil was considered as the most dangerous pest of Pecan fruits. The aim of this research is to evaluate Support Vector Machine method (SVM) for identifying Pecan Weevil among other insects. Eventually, this recognition system will serve in a wireless imaging network for monitoring Pecan Weevils. Approach: SVM has been evaluated using two different kernel functions i.e., Polynomial Function and Radial Basis Function. Database of 205 Pecan Weevils and 75 other insects which typically exist in pecan habitat has been used. Three sets of input data for SVM have been generated by two standard region-based recognition methods. These sets are comprised of output obtained by Zernike Moments, Regional Properties and combination of these two methods. For each kernel function, the system had been trained by 25, 50 and 75% of data and remaining ratio in each case has been used for testing. Each experiment is repeated ten times and average results are considered for comparisons and analysis. Results: The optimum recognition rate had been found when system is trained by 75% of data. The results are approximately similar when the input data is obtained by Regional Properties and combination of Regional Properties and Zernike Moments methods. The optimum results are obtained when input data has been obtained by Zernike Moments alone for lower values of sigma ‘σ’. The proposed system is able to successfully recognize 99% of Pecan Weevil and 97% of the other insects using the radial basis function. The proposed system took approximately 31 sec for processing 75% of the data which include the time for training. The testing time is found to be 0.15 sec. Conclusion: Promising results can be obtained when input data is obtained by Zernike Moments and SVM is trained by RBF and 75% of data.