Development and Evaluation of a Fuzzy Inference System and a Neuro-Fuzzy Inference System for Grading Apple Quality

Abstract

In this research work, a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) were developed to classify apple total quality based on some fruit quality properties, i.e., fruit mass, flesh firmness, soluble solids content and skin color. The knowledge from experts was used to construct the FIS in order to be able to efficiently categorize the total quality. The historical data was used to construct an ANFIS model, which uses rules extracted from data to classify the apple total quality. The innovative points of this work are (i) a clear presentation of fruit quality after aggregating four quality parameters by developing a FIS, which is based on experts’ knowledge and next an ANFIS based on data, and (ii) the classification of apples based on the above quality parameters. The quality of apples was graded in five categories: excellent, good, medium, poor and very poor. The apples were also graded by agricultural experts. The FIS model was evaluated at the same orchard for data of three subsequent years (2005, 2006 and 2007) and it showed 83.54%, 92.73% and 96.36% respective average agreements with the results from the human expert, whereas the ANFIS provided a lower accuracy on prediction. The evaluation showed the superiority of the proposed expert-based approach using fuzzy sets and fuzzy logic. © 2018 Taylor & Francis.