Predictions on wheat crop yielding through fuzzy set theory and optimization techniques

Abstract

Agricultural field’s production is commonly measured through the performance of the crops in terms of sow amount, climatology, and the type of crop, among other. Therefore, prediction on the performance of the crops can aid cultivators to make better informed decisions and help the agricultural field. This research work presents a prediction on wheat crop using the fuzzy set theory and the use of optimization techniques, in both; traditional methods and evolutionary meta-heuristics. The performance prediction in this research has its core on the following parameters: biomass, solar radiation, rainfall, and infield’s water extractions. Besides, the needed standards and the efficiency index (EFI) used come from already developed models; such standards include: the root-mean-square error (RMSE), the standard deviation, and the precision percentage. The application of a genetic algorithm on a Takagi-Sugeno system requires and highly precise prediction on wheat cropping; being, 0.005216 the error estimation, and 99.928 the performance percentage.