Adaptive neuro-fuzzy inference system for okra yield prediction

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.