Fuzzy Logic-Based Model for Predicting Quality of Fresh Tomatoes
DOI:
https://doi.org/10.62535/00zv5x32Keywords:
fuzzy logic, tomato, predicting, qualityAbstract
The quality of tomatoes has become very important since their widespread use and diverse functions, both for fresh consumption and as raw materials for processed products. Conventional assessment of tomato freshness is still based on visual inspection and laboratory tests, which are inefficient. Meanwhile, the use of technology and artificial intelligence (AI), such as fuzzy logic, has been applied to evaluate food quality efficiently, accurately, and non-destructively. Thus, this study aims to design a fuzzy logic-based tomato freshness assessment system with quality-determining variables, which are hardness and color. The fuzzy logic-based tomato quality assessment system involves four main stages, namely fuzzification, rule base formulation, fuzzy inference, and defuzzification. Fuzzy rules are also developed to describe the logical relationship between variables in the form of “if-then”. The application of the fuzzy method can improve the efficiency of tomato quality assessment activities by linking input variables (color and hardness) with outputs in the form of tomato quality levels. This system is similar to the human reasoning process, making it ideal for evaluating agricultural commodities. Therefore, the fuzzy model is an ideal method for determining the freshness quality of tomatoes, which requires a precision control system in diverse and uncertain conditions.
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