Support Vector Machine Based Red Palm Weevil (Rynchophorus Ferrugineous, Olivier) Recognition System
Ghulam Mubashar Hassan and Saleh Mufleh Al-Saqer
DOI : 10.3844/ajabssp.2012.36.42
American Journal of Agricultural and Biological Sciences
Volume 7, Issue 1
Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir) is an insect which threatens the existence of palm trees. The proposed research is to develop a RPW identification system using Support Vector Machine method. The problem is to extract image features from an image and using SVM to find out the existence of RPW in an image. Approach: Images are snapped and image processing techniques of Regional Properties and Zernike Moments are used to extract different features of an image. The obtained features are fed into the SVM based system individually as well as in combination. The database used to train and test the system includes 326 RPW and 93 other insect images. The input data from database is selected randomly and fed into the system in three steps i.e., 25, 50 and 75% while remaining database is used for testing purpose. In SVM, polynomial kernel function and Radial Basis Function are used for training. Each experiment is repeated 10 times and the average results are used for analysis. Results: The optimal results are obtained by using Radial Basis Function in SVM at lower values of sigma ‘σ’ while Polynomial kernel function is not successful in returning adequate results. Further detailed analysis of results for ‘σ’ value of 10 and 15 revealed that proposed system works well with large training data and with inputs obtained by Regional Properties. The optimal value of ‘σ’ for proposed system is found to be 10 when training data ratio is 50%. The training time for proposed system depends on size of database and is found to be 0.025 sec per image while time consumed by proposed system for identification of RPW in an image is found to be 15 milli sec. The proposed system’s success in identification of RPW and other insect is found to be 97 and 93% respectively. Conclusion: It is concluded that SVM based system using Radial Basis Function having ‘σ’ value of 10 is optimal in identifying RPW from an image. The optimal input data for the proposed system needs to be obtained by Regional Properties only.
© 2012 Ghulam Mubashar Hassan and Saleh Mufleh Al-Saqer. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.