Research Article of Research Journal of Mathematics and Computer Science
A Neuro-Fuzzy System For Diagnosis of Soya-Beans Diseases
Rahmon,I.A, Adebola Akinsanya and Eze, M.O
Department of Computer Science, Babcock University,Ilishan-Remo, Ogun State, Nigeria.
Soyabean is an important legume crop, extensively cultivated for food on which low-income population highly depend because of its proteineous nutrient on daily basis for food.The efforts of farmers to specifically identify the specific pests responsible for damaging of plants segment such as petioles, roots, stem, pod and leaves still remain vague and imprecise to many farmers. In this work, a neuro-fuzzy system will be built with MATLAB version 8 with 100 rules on five input parameters as linguistic variables or symptoms into the system to determine the disease type either as fungi or bacteria or virus, and to also determine intensity rate as the output in form of a crisp. The output of the system will produce results for the decision maker to provide solution regarding the treatment of the infected plant for bountiful and quality harvest.
Keywords: Neuro-fuzzy system,crisp,matlab.
How to cite this article:
Rahmon,I.A, Adebola_Akinsanya and Eze, M.O, A Neuro-Fuzzy System For Diagnosis of Soya-Beans Diseases. Research Journal of Mathematics and Computer Science, 2018; 2:13. DOI: 10.28933/rjcms-2018-04-0501
1. Abdullah, S., Bakar, A.A., Mustafa, N., Yusuf, M., and Hamdan, A.R. (2007). Fuzzy Knowledge Modelling for Image-based Paddy Disease Diagnosis Expert System: Proceedings of the International Conference on Electrical Engineering and Informatics, Institute TeknologiBandung (Indonesia), June 17-19, pp. 642-644.
2. Aderounmu, G.A., Omidiora E.O., Adegoke, B.O., and Taiwo T.A., (2013). Neuro-fuzzy System for Livestock Feed Formulation (Africa Poultry): International Journal of Engineering and Science (IJES), 2(5), pp.25 – 32.
3. Asoegwu, S.N, Asoegwu A.O. (2007). An overview of Agricultural Mechanization and Its Environmental Management in Nigeria: Agricultural Engineering International: The
4. CIGR Ejournal; 9(6): pp.6-18.
5. Babu, M.S., and Roa, N.T. (2010). Implementation of Artificial Bee Colony (ABC) Algorithm On Garlic Expert Advisory System: International Journal of Computer Science and Research, Vol. 1, No. 1, pp. 69-74.
6. Cintra, M.E., Meira, C.A., Monard, M.C., Camargo, H.A. and Rodrigues, L.H. (2011): The use of fuzzy decision trees for coffee rust warning in Brazilian crops: 11th International Conference on Intelligent Systems Design and Applications (ISDA), November 22-24, pp. 1347-1352.
7. Duan, J.S., Edwards, and Xu, M.X. (2005): Web-Based Expert Systems: Benefits and Challenges”, Information and Management, Vol. 42, No. 6, pp. 799-811.
8. Dugje, I.Y., Omoigui, L.O., Ekeleme, F.,Bandyopadhyay, R., Lava-Kuma P, and Kamara, A.Y. (2009): Farmers’ Guide to Soybean Production in Northern Nigeria. IITA, Ibadan, Nigeria
9. Le Gal, P.Y., Dugué, P., Faure, G. and Novak, S. (2011): How Does Research Address The Design of Innovative Agricultural Production Systems At The Farm Level? A review, Agriculture Systems Vol. 104 No. 9, pp. 714-728.
10. Gonzalez-Diaz, L. , Martínez-Jimenez, P., Bastida, F., and Gonzalez-Andujar, J.L. (2009): Expert System For Integrated Plant Protection In Pepper (Capsicum annuun L.), Expert Systems With Applications, Vol. 36, No. 5, pp. 8975-8979.
11. Kaloudis, S., Anastopoulos, D., Yialouris, C.P. Lorentzos, N.A. and Sideridis, A.B.(2005): Insect Identification Expert System For Forest Protection, Expert Systems with Applications, Vol. 28, No. 3, pp. 445-452.
12. Khan, S. F., Razzaq, S., Irfan, K., Maqbool, F., Farid A., Illahi, I. and Tauqeerulamin, (2008): A Web-based Expert System for Diagnosis of Diseases and Pests in Pakistani Wheat, Proceedings of the World Congress on Engineering, London, UK,(1):pp. 1-6,.
13. Kolhe, S., Kamal, R., Saini, H.S. and Gupta, G.K., (2011): A Web-Based Intelligent Disease-Diagnosis System Using A New Fuzzy-Logic Based Approach For Drawing The Inferences In Crops”, Computers and Electronics in Agriculture, Vol. 76, No. 1, pp. 16-27.
14. Koumpouros, Y., Mahaman, B.D., Maliappis, M., Passam, H.C., Sideridis, A.B. and Zorkadis, V. (2004): Image Processing For Distance Diagnosis In Pest Management”, Computers and Electronics in Agriculture, Vol. 44, No. 2, pp. 121-131.
15. Li, D., Fu, Z. and Duan, Y. (2002): Fish-Expert: A Web-Based Expert System For Fish Disease Diagnosis, Expert Systems with Applications, Vol. 23, No. 3, pp. 311-320.
16. López-Morales, V., López-Ortega, O., Ramos-Fernández, J., and Muñoz (2008): JAPIEST: An Integral Intelligent System For The Diagnosis And Control Of Tomatoes Diseases And Pests In Hydroponic Greenhouses, Expert Systems with Applications, Vol. 35, No. 4, pp. 1506-1512.
17. Mahaman, B.D., Passam, H.C., Sideridis, A.B., and Yialouris, C.P. (2003): DIARES-IPM: A Diagnostic Advisory Rule-Based Expert System For Integrated Pest Management In Solanaceous Crop Systems, Agricultural Systems, Vol. 76, No. 3, pp. 1119-1135.
18. Patterson, D.W. (2004): Introduction to Artificial Intelligence and Expert Systems. Prentice-Hall: New Delhi.
19. Quinn, J. A., Leyton-Brown, K., Mwebaze, E. (2011): Modeling and Monitoring Crop Disease in Developing Countries. Conference of the Association for the Advancement of Artificial
20. Intelligence (AAAI), .
21. Shafinah K.,Noraidah,S., Riza, S., Mohd S., Mohammad, M. (2013): A Framework Of An Expert System For Crop Pest And Disease Management: Journal of Theoretical and Applied Information Technology, 10th December, Vol. 58 No.1
22. Saini, G.K., Gupta, S., Kolhe, R. Kamal, H.S. (2011): A Web-Based Intelligent Disease-Diagnosis System Using A New Fuzzy-Logic Based Approach For Drawing The Inferences In Crops, Computers and Electronics in Agriculture, Vol. 76, No. 1, pp. 16-27.
23. Singh, A., Singh,S.K., Sarma, K.R. (2010): An Expert System For Diagnosis Of Diseases In Rice Plant, International Journal of Artificial Intelligence, Vol. 1, No. 1, pp. 26-31.
24. Yialouris, C.P. and Sideridis, A.B. (2010): An Expert System For Tomato Diseases, Computers And Electronics In Agriculture, Vol. 14, No. 1, pp. 61-76.
25. Zhang, K., Chai, Y. and Yang, S.X. (2010): Self-Organizing Feature Map For Cluster Analysis In Multi-Disease Diagnosis, Expert Systems With Applications,Vol. 37, No. 9, pp. 6359-6367.
This work and its PDF file(s) are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.