Abstract
References
- [1] Ahmed H., 2020. Red Palm Weevil Detection Methods: A Survey. Journal of Cybersecurity and Information Management (JCIM), 1(1), pp. 17-20.
- [2] Al-Saqer S. M., 2012. A reliable identification system for red palm weevil. American Journal of Applied Sciences, 9(8), p. 1150.
- [3] Hassan G. M., Al-Saqer S. M., 2012. Support vector machine based red palm weevil (Rynchophorus ferrugineous, Olivier) recognition system. American Journal of Agricultural and Biological Sciences, 7(1), pp. 36-42.
- [4] Alaa H., Waleed K., Samir M., Tarek M., Sobeah H., Salam M. A., 2020. An intelligent approach for detecting palm trees diseases using image processing and machine learning. Int. J. Adv. Comput. Sci. Appl., 11(7), pp. 434-441.
- [5] Eldin H. A., Waleed K., Samir M., Tarek M., Sobeah H., Salam M. A., 2020. A Survey on Detection of Red Palm Weevil Inside Palm Trees: Challenges and Applications. In Proceedings of the 2020 9th International Conference on Software and Information Engineering (ICSIE), Cairo, Egypt, 11-13 November, pp. 119-125.
- [6] Haralick R. M., Shapiro L. G., 1992. Computer and robot vision. Vol. 1. Reading: Addison-wesley.
- [7] Otsu N., 1979. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), pp. 62-66.
- [8] Saleh R. A. A., Rüştü A. K. A. Y., 2019. Classification of melanoma images using modified teaching learning based artificial bee colony. Avrupa Bilim ve Teknoloji Dergisi, pp. 225-232.
- [9] Haralick R. M., Shanmugam K., Dinstein I. H., 1973. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), pp. 610-621.
- [10] Saleh N. A., Ertunç H. M., Saleh R. A., Rassam M. A., 2021. A Simple Mask Detection Model Based On A Multi-Layer Perception Neural Network. In 2021 International Conference of Technology, Science and Administration (ICTSA), 22-24 March, pp. 1-5. IEEE.
- [11] Moré J. J., 1978. The Levenberg-Marquardt algorithm: implementation and theory. In Numerical analysis, pp. 105-116. Springer, Berlin, Heidelberg.
Development of a Neural Network Model for Recognizing Red Palm Weevil Insects Based on Image Processing
Abstract
The red palm weevil (RPW) is an invasive pest insect that attacks palm trees and threatens their existence. Accordingly, RPW causes significant and massive economic losses during the last two decades. This issue makes the early detection of RPW is a hot research topic. This study proposes a machine learning technique based on image processing to easily recognize these insect species for people who do not know them. The basic idea of the proposed research is to develop a neural network model that can use image processing to identify RPW and distinguish it from other insects found in palm tree habitats. This model consists of three stages: image pre-processing (image enhancement and segmentation using Otsu's thresholding), feature extraction (texture and color moments features), and classification (artificial neural network). The dataset used in this study includes 913 images, where 448 are RPWs' images, and the rest are ants' images. The experimental results of the ten-fold cross-validation show that the accuracy of the proposed model is 92.22%.
Keywords
ANN Color Moments Features Image Classification Image Enhancement Image Segmentation Red Palm Weevil Recognition Texture
References
- [1] Ahmed H., 2020. Red Palm Weevil Detection Methods: A Survey. Journal of Cybersecurity and Information Management (JCIM), 1(1), pp. 17-20.
- [2] Al-Saqer S. M., 2012. A reliable identification system for red palm weevil. American Journal of Applied Sciences, 9(8), p. 1150.
- [3] Hassan G. M., Al-Saqer S. M., 2012. Support vector machine based red palm weevil (Rynchophorus ferrugineous, Olivier) recognition system. American Journal of Agricultural and Biological Sciences, 7(1), pp. 36-42.
- [4] Alaa H., Waleed K., Samir M., Tarek M., Sobeah H., Salam M. A., 2020. An intelligent approach for detecting palm trees diseases using image processing and machine learning. Int. J. Adv. Comput. Sci. Appl., 11(7), pp. 434-441.
- [5] Eldin H. A., Waleed K., Samir M., Tarek M., Sobeah H., Salam M. A., 2020. A Survey on Detection of Red Palm Weevil Inside Palm Trees: Challenges and Applications. In Proceedings of the 2020 9th International Conference on Software and Information Engineering (ICSIE), Cairo, Egypt, 11-13 November, pp. 119-125.
- [6] Haralick R. M., Shapiro L. G., 1992. Computer and robot vision. Vol. 1. Reading: Addison-wesley.
- [7] Otsu N., 1979. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), pp. 62-66.
- [8] Saleh R. A. A., Rüştü A. K. A. Y., 2019. Classification of melanoma images using modified teaching learning based artificial bee colony. Avrupa Bilim ve Teknoloji Dergisi, pp. 225-232.
- [9] Haralick R. M., Shanmugam K., Dinstein I. H., 1973. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), pp. 610-621.
- [10] Saleh N. A., Ertunç H. M., Saleh R. A., Rassam M. A., 2021. A Simple Mask Detection Model Based On A Multi-Layer Perception Neural Network. In 2021 International Conference of Technology, Science and Administration (ICTSA), 22-24 March, pp. 1-5. IEEE.
- [11] Moré J. J., 1978. The Levenberg-Marquardt algorithm: implementation and theory. In Numerical analysis, pp. 105-116. Springer, Berlin, Heidelberg.
Details
| Primary Language | English |
|---|---|
| Subjects | Artificial Intelligence |
| Journal Section | Articles |
| Authors | |
| Publication Date | May 31, 2022 |
| Acceptance Date | July 7, 2021 |
| Published in Issue | Year 2022 Volume: 5 Issue: 1 |