Abstract
The oil palm plant is one of the major important cash crops of the Nigerian economy and a significant contributor to the world market for vegetable oils. Unfortunately, infection with fungi has caused a decline in the productivity of oil palms and subsequently the palm oil industry. Hence the need to detect oil palm plant disease earlier before it affects it informed this research to develop a fuzzy inference model to predict the influence of fungal disease on the oil plant plant. Following extensive review of related works, the factors associated with the severity of fungal diseases in the oil palm plant were identified following validation by Botanist. Fuzzy triangular membership functions were used to formulate the input factors identified alongside the target variables for identifying the severity of fungal diseases affecting the oil palm plant. The rule base was formulated using IF-THEN statements to combine the values of the input factors with the respective values of the target severity of oil palm plant disease. The classification model for oil palm plant disease severity was simulated using the Fuzzy Logic Toolbox available in the MATLAB R2015b Software. The results showed that the developed inference system for oil palm plant was capable of classifying and predicting the degree of the fungal disease infection into four groups; no severity, low severity, moderate severity and high severity.
Keywords
References
- Pacheco P, Gnych S, Dermawan A, Komarudin H and Okarda B. (2017): The palm oil global value chain: Implications for economic growth and social and environmental sustainability. Working Paper 220. Bogor, Indonesia: CIFORGoogle Scholar ↗
- Onoh, P .A .and Peter-Onoh .C .A. (2012). Adoption of improved oil palm production technology among farmers in Aboh Mbaise local government area of Imo State. International Journal of Agriculture and Rural Development.15 (2), Pp. 966 –971Google Scholar ↗
- Ekenta, C. M., Ajala, M. K., Akinola, M.O. and Oseni, Y (2017): “Abandoned Nigerian Economic Resources: A Case of Oil Palm”, International Journal of Agricultural Extension and Rural Development Studies, 4(2): 1-16.Google Scholar ↗
- FAO (2010): “Global forest resources assessment 2010”, Food and Agriculture Organization of the United Nations. Rome, Italy.Google Scholar ↗
- Ayodele T. and Eshalomi M. O. (2010). African case study: Palm oil and economic development in Nigeria and Ghana. recommendations for the world bank’s 2010 palm oil strategy. Initiative for Public Policy Analysis.Google Scholar ↗
- David L., Marco P. and Ottmar H. (2008): “Wood Decaying Fungi in the Forest: Conservation Needs and Management Options”, European Journal of Forest Resources, 1-22.Google Scholar ↗
- Owomugisha G., Friedrich M., Ernest M., John A. And Michael B. (2018): “Machine Learning for Diagnosis of Disease in Plants Using Spectral Data”, proceedings of International Conference of Artificial Intelligence, 9-15.Google Scholar ↗
- Olajide B. O., Oke A. O., Odeniyi O. A., Olabiyisi S. O. and Adeosun O. O. (2021): “An Approach to Improve the Availability of a Traffic Light System”, International Journal of Intelligent Information System, DOI: 10. 11648/j.ijiis.20211004.11, Volume 10, Issue 4, pp37-43.Google Scholar ↗
- Rizvi S., John M., Abdul R., Mohammad R. R. and Iyonna W. (2020): “ A Fuzzy Inference System to Evaluate the Security Readiness of Cloud Service Providers”, Journal of Cloud Computing, Systems and Applications, https://doi.org/10.1186/s13677-020-00192-9, 1-18.DOI ↗Google Scholar ↗
- Bejo S., Husin N., Ahmad F., Muhamad S., Mohd K., and Desa A. (2019): “Effect of Basal Stem Rot on Oil Palm Inter-frond Angles for Different Severity Levels”, Journals of Advanced Agricultural Technologies 6(2): 113-117.Google Scholar ↗
- Ishaq I., Alias M., Kadir J., and Kasawani I. (2016): “Detection of basal stem rot disease at oil palm plantations using sonic tomography”, Journal of Sustainability Science and Management, 9(2): 52-57.Google Scholar ↗
- Suharto D. Wahhyu A. Miranty L and Muhammad Y. (2013): “Expert System in Detecting Coffee Plant Diseases”, International Journal of Electrical Energy 1(3) 156-162.Google Scholar ↗
- Olanloye D. and Yerokun O. (2018): “Design and Implementation of an Expert System for Diagnosing, Treatment and Management of Poultry Disease”, FULafia Journal of Science and Technology 4(2): 87-93.Google Scholar ↗
- Kaur R. And Din S. (2016): “Web Based Expert System to Detect and Diagnose the Leaf Disease of Cereals in Punjabi Language”, International Journal of Computer Science and Information Technology 7(4) 1771-1773.Google Scholar ↗
- Awoyelu I. And Adebisi R. O. (2015): “A Predictive Fuzzy Expert System for Diagnosis of Cassava Plant Diseases”, Global Journal of Science Frontier Research, 15(5): 20-28.Google Scholar ↗