A fuzzy regression approach to a hierarchical evaluation model for oil palm fruit grading

• Published in: Fuzzy optimization and decision making Vol. 9; no. 1; pp. 105 - 122
• Main Authors: Nureize, A., Watada, J.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.03.2010; Springer Science + Business Media B.V; Springer Nature B.V
• Subjects: Agricultural production; Agriculture; Artificial Intelligence; Calculus of Variations and Optimal Control; Optimization; Decision making; Discriminant analysis; Fruits; Fuzzy logic; Mathematical Logic and Foundations; Mathematics; Mathematics and Statistics; Oils & fats; Operations Research/Decision Theory; Optimization; Petroleum industry; Probability Theory and Stochastic Processes; Regression analysis; Studies; Vegetable oils; Oil palm fruit grading; Fuzzy regression analysis; Fuzzy hierarchical model; Multicriterion
• ISSN: 1568-4539; 1573-2908
• DOI: 10.1007/s10700-010-9072-3

Measurement of quality is an important task in the evaluation of agricultural products and plays a pivotal role in agricultural production. The inspection process normally involves a visual examination according to the ripeness standards of crops, and this grading is subject to expert knowledge and interpretation. Therefore, the quality inspection process of fruits needs to be conducted properly to ensure that high-quality fruit bunches are selected for production. However, human subjective judgments during the evaluation make the fruit grading inexact. The objectives of this paper are to build a fuzzy hierarchical evaluation model that characterises the criteria of oil palm fruits to decide the fuzzy weights of these criteria based on a fuzzy regression model, and to help inspectors conduct a proper total evaluation. A numerical example is included to illustrate the computational process of the proposed model.