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Development of a Plant Disease Classification System using an Improved Counter Propagation Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 20 |
| Year of Publication: 2020 |
| Authors: B.O. Ola, J.P. Oguntoye, O.O. Awodoye, M.O. Oyewole |
10.5120/ijca2020920729
|
B.O. Ola, J.P. Oguntoye, O.O. Awodoye, M.O. Oyewole . Development of a Plant Disease Classification System using an Improved Counter Propagation Neural Network. International Journal of Computer Applications. 175, 20 ( Sep 2020), 19-26. DOI=10.5120/ijca2020920729
Abstract
Plant diseases are a major threat to food security and can be precisely and accurately recognized through the images of plant leaves. The recent advances in computer vision made possible by the various computational method have paved the way for computer-assisted disease diagnosis. Thus, automated recognition of diseases on leaves plays a crucial role in the agriculture sector. Counter Propagation Neural Network (CPN) is highly desirable because it comprises the advantages of supervised and unsupervised training approaches. CPN in most image processing application guarantee high accuracy but consume more time for convergence. In this study, the development of a plant disease classification system using an improved Counter Propagation Neural Network (CPN) technique was carried out. Gravitational Search Algorithm (GSA) was applied to optimize the network of CPN for improved performance. The approach adopted in this study enhances CPN by making it free from the iterative adjustment of weights which increases the computational speed to a higher extent. The experimental results reveal that the proposed technique achieved improved performance in terms of recognition accuracy and prediction time.
References
- Aditi G., Nirmala S., & Harish S. (2017). Exploitative Gravitational Search Algorithm. Proceedings of Sixth International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 546, DOI 10.1007/978-981-10-3322-315.
- Akbarzadeh, S., Paap, A., Ahderom, S., Apopei, B., & Alameh, K. (2018). Plant discrimination by Support Vector Machine classifier based on spectral reflectance. Computers and electronics in agriculture, 148, 250-258.
- Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., & Alrahamneh, Z. (2011). Fast and accurate detection and classification of plant diseases. International Journal of Computer Applications, 17(1), 31-38.
- Ballabio, D., Vasighi, M., Consonni, V., & Kompany-Zareh, M. (2011). Genetic algorithms for architecture optimisation of counter-propagation artificial neural networks. Chemometrics and intelligent laboratory systems, 105(1), 56-64.
- Caselles, V., Catté, F., Coll, T., & Dibos, F. (1993). A geometric model for active contours in image processing. Numerische mathematik, 66(1), 1-31.
- Cohen, F. S., & Fan, Z. (1992). Maximum likelihood unsupervised textured image segmentation. CVGIP: Graphical models and image processing, 54(3), 239-251.
- Du, K. L. (2010). Clustering: A neural network approach. Neural networks, 23(1), 89-107.
- Guang, C., Li, Q., & Yang, L. (2008). Research on the application of CPN neural network to fault diagnosis of analog circuits. Ship Electronic Engineering, 28(5), 5-7.
- Hamuda, E., Mc Ginley, B., Glavin, M., & Jones, E. (2017). Automatic crop detection under field conditions using the HSV colour space and morphological operations. Computers and electronics in agriculture, 133, 97-107.
- Hemanth, D. J., Vijila, C. K. S., & Anitha, J. (2010). Performance improved PSO based modified counter propagation neural network for abnormal MR brain image classification. Int. J. Advance. Soft Comput. Appl, 2(1), 65-84.
- Jiang, S. F., Fu, C., Zhang, C. M., & Wu, Z. Q. (2013). A revised counter-propagation network model integrating rough set for structural damage detection. International Journal of Distributed Sensor Networks, 9(11):1-9.
- Kho, S. J., Manickam, S., Malek, S., Mosleh, M., & Dhillon, S. K. (2017). Automated plant identification using artificial neural network and support vector machine. Frontiers in Life Science, 10(1), 98-107.
- Kuzmanovski, I., & Novič, M. (2008). Counter-propagation neural networks in Matlab. Chemometrics and Intelligent Laboratory Systems, 90(1), 84-91.
- Kuzmanovski, I., Novič, M., & Trpkovska, M. (2009). Automatic adjustment of the relative importance of different input variables for optimization of counter-propagation artificial neural networks. Analytica chimica acta, 642(1-2), 142-147.
- Liu, B., Ding, Z., Tian, L., He, D., Li, S., & Wang, H. (2020). Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks. Frontiers in Plant Science, 11, 1082.
- Liu, P. (2015). The future of food and agriculture: Trends and challenges. Food and Agriculture Organization of the United Nations.
- Lu, P., Ye, L., Sun, B., Zhang, C., Zhao, Y., & Teng, J. (2018). A new hybrid prediction method of ultra-short-term wind power forecasting based on EEMD-PE and LSSVM optimized by the GSA. Energies, 11(4), 697.
- Mainkar, P. M., Ghorpade, S., & Adawadkar, M. (2015). Plant leaf disease detection and classification using image processing techniques. International Journal of Innovative and Emerging Research in Engineering, 2(4), 139-144.
- Malladi, R., & Sethian, J. A. (1995). Image processing via level set curvature flow. proceedings of the National Academy of Sciences, 92(15), 7046-7050.
- Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995). Shape modelling with front propagation: A level set approach. IEEE transactions on pattern analysis and machine intelligence, 17(2), 158-175.
- Ngugi, L. C., Abelwahab, M., & Abo-Zahhad, M. (2020). Recent Advances in Image Processing Techniques for Automated Leaf Pest and Disease Recognition-A Review. Information Processing in Agriculture.
- Ola B. O., Awodoye O. O. and Oguntoye J. P. (2019). A comparative study of particle swarm optimization and gravitational search algorithm in a poultry house temperature control system. World Journal of Engineering Research and Technology (WJERT). 5(6): 272-289.
- Pujari, D., Yakkundimath, R., & Byadgi, A. S. (2016). SVM and ANN-based classification of plant diseases using feature reduction technique. IJIMAI, 3(7), 6-14.
- Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm.” Information sciences, 179(13): 2232-2248.
- Rath, A. K., & Meher, J. K. (2019). Disease detection in infected plant leaf by computational method. Archives of Phytopathology and Plant Protection, 52(19-20), 1348-1358.
- Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., & Batra, N. (2020). PlantDoc: a dataset for visual plant disease detection. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (pp. 249-253).
- Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks-based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience, 2016.
- Taisir E. & Al Qasim R. (2013). On the Performance of the Gravitational Search Algorithm. International Journal of Advanced Computer Science and Applications (IJACSA). Vol. 4, No. 8, pp. 74-78.
- Waghmare, H., Kokare, R., & Dandawate, Y. (2016). Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for an automated decision support system. In 2016 3rd international conference on signal processing and integrated networks (SPIN) (pp. 513-518). IEEE.
- Wang, X., Zhu, C., Fu, Z., Zhang, L., and Li, X. (2019). Research on Cucumber Powdery Mildew Recognition Based on Visual Spectra. Spectroscopy And Spectral Analysis, 39(6), 1864-1869.
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