Modelling Hierarchical Fuzzy Systems for Mango Grading via FuzzyR Toolkit

Mango is the third most crucial fruit product worldwide in terms of value and production volume, after pineapple and banana. However, assessing the quality grading of mangoes in an agricultural environment as a manual task is inefficient, labour demanding, and prone to errors. Thus, this task entails uncertainty in human decision-making, i.e. choice subjectivity due to a diverse perspective, experience, and knowledge. When dealing with ambiguity, fuzzy logic systems (FLSs) can help aid in the systematic automation of the human grading system. However, the underlying problem with FLSs is that they have difficulty dealing with large and complex real-world situations, in which in the curse of dimensionality is an issue. A possible option is to employ a hierarchical fuzzy system, a subtype of an FLS that is particularly effective in reducing the complexity and increasing the interpretability of the overall system. This study proposes an approach to model uncertainty in mango grading decision-making using a hierarchical fuzzy system. We demonstrate the HFS for mango grading application using a FuzzyR toolkit, together with an FLS for comparison. Additionally, this paper explores the importance of uncertainty arising from human knowledge, which will be critical in determining the most suitable system (FLS or HFS) closest to the experts’ opinion. Additionally, we also evaluate both systems’ interpretability.