An Optimized Hybrid Model for Perishable Product Quality Inference in the Food Supply Chain

Abstract

The supply chain for perishable products faces significant challenges in monitoring and maintaining product quality. These products are particularly vulnerable to environmental dynamic conditions and variations in distribution and transportation. To address these challenges, leveraging the Internet of Things (IoT) and quality inference techniques during transportation can provide valuable insights for both consumers and producers. The objective of the research is to develop a model for inferring the quality of perishable products using an IoT sensor dataset to monitor perishable product quality continuously. This research applied a hybrid approach combining a Fuzzy Inference System (FIS), clustering models, and genetic algorithms to infer the product quality during supply chain distribution with IoT sensors. The result shows that the hybrid FIS model, which employs Gaussian membership functions and fuzzy c-means clustering for rule generation, achieves a high accuracy with an R²: 0.873. This research contributes to improving the model by employing genetic algorithms in optimizing the inference model by activating only five out of seven rules. The model optimization achieves optimal computation time while aiming to preserve model accuracy. However, test results indicate that the combination of rules has not yet significantly enhanced the model's accuracy, though it holds potential for future development.