Open AccessADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR OKRA YIELD PREDICTION
Author(s) -
Olusegun A. Ojesanmi,
A D Adekoya,
Ayomikun Abayomi AWOSEYI
Publication year - 2017
Publication title -
journal of natural sciences, engineering and technology/journal of natural science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2315-7461
pISSN - 2277-0593
DOI - 10.51406/jnset.v15i2.1690
Subject(s) - adaptive neuro fuzzy inference system , yield (engineering) , relative humidity , inference system , hectare , pan evaporation , neuro fuzzy , mathematics , environmental science , agriculture , agricultural engineering , fuzzy logic , statistics , meteorology , evaporation , computer science , fuzzy control system , engineering , geography , artificial intelligence , materials science , metallurgy , archaeology
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.