A comparative study of fuzzy linear regression and multiple linear regression in agricultural studies: a case study of lentil yield management
This study investigates the advantages of two fuzzy linear regression (FLR) models, namely the Tanaka and the Savic and
Pedrycz models, over multiple linear regression (MLR) for lentil yield management. We used a fuzzy approach to model the yield
of lentil genotypes in which the input is crisp and the output fuzzy. Moreover, after finding FLR equations, we estimated the output
corresponding to the collection of fuzzy inputs by using fuzzy algebraic operations and an appropriate defuzzification method known
as the center of area method. Results showed the superiority of the Tanaka model over MLR because of reducing the included variables,
especially variables available after harvest. The study also emphasizes the advantage of the Savic and Pedrycz model in comparison to
the other two models with a smaller error rate.
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