ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR OKRA YIELD PREDICTION
DOI:
https://doi.org/10.51406/jnset.v15i2.1690Keywords:
Crop yield, Prediction, agro meteorological data, cultivation, harvest.Abstract
This paper, adaptive neuro-fuzzy inference system for okra yield prediction, describes the use of neuro-fuzzy inference system in the prediction of okra yield using environmental parameters such as minimum temperature, relative humidity, evaporation, sunshine hours, rainfall and maximum temperature as input into the neuro-fuzzy inference system, and yield as output. The agro meteorological data used were obtained from the department of agro meteorological and water management, Federal University of Agriculture, Abeokuta and the yield data were obtained from the Department of Horticulture, Federal University of Agriculture, Abeokuta. MATLAB was used for the analysis of the data. From the results, the maximum predicted yield showed that at minimum temperature of 24.4 oc, relative humidity of 78.3% and evaporation of 5.5mm, the yield predicted is 1.67 tonnes/hectare.
References
Czogala E., Leski J. 2000. “Neuro-Fuzzy Intelligent Systems, Studies in Fuzziness and Soft Computing”, Springer, Verlag, Germany.
Ehret D.L., Bernard D. Hill, David, A., Raworth, B. E. 2011. Neural Network Modelling Of Greenhouse Tomato Yield, Growth And Water Use From Automated Crop Monitoring Data. Computers and Electronics in Agriculture 79: 82–89.
Food and Agriculture Organization (FAO) of the United Nations, 2013. The State of Food And Agriculture. http://www.fao.org/docrep/018/i3300e/i3300e00.htm, accessed on 15-07-2015.
Jang, J.S.R. 1993, “ANFIS: Adaptive-Network Based Fuzzy Inference System”, IEEE Trans. Systems, Man, Cybernetics. 23(5/6):665-685.
Kefaya, Q., Evor, H., Daciana I. 2012. Adaptive Neuro-fuzzy Modelling for Crop Yield prediction. School of Engineering, the University of Warwick United Kingdom. ISBN: 978-960-474-273-4.
Kosko, B., 1992. “Neural Networks and Fuzzy Systems: A Dynamical System Approach to Machine Intelligence”. Prentice Hall, Englewood Cliffs, New Jersey.
Odjugo, P. A. O., 2010. “General Overview of Climate Change Impacts in Nigeria”. Journal Hum Ecol, 29(1): 47-55.
Pankaj, K., 2011. Crop Yield Forecasting by Adaptive Neuro Fuzzy Inference System. VCSG College of Horticulture, GBPUA&T, Pantnagar, Uttarakhand.
Smith, B.A. et al., 2009. Artificial Neural Networks for Automated Year round Temperature Prediction. Computers and Electronics in Agriculture 68: 52–6.
Snehal S. D., Sandeep, R. V. 2014. Agricultural Crop Yield Prediction Using Artificial Neural Network Approach. International Journal of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering 2 (1):.
Stathakisa D., Savina, I., Nègre T. 2006. Neuro-Fuzzy Modeling For Crop Yield Prediction. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 34, Part XXX.
Takagi, T., Sugeno, M. 1983. Derivation of fuzzy control rules from human operator’s control action. In: Proc. IFAC Symp. Fuzzy Inform., Knowledge Representation and Decision Analysis, , pp. 55–60.
