Abstract
PurposeThe purpose of the food traceability is used to retain the good quality of raw material supply, diminish the loss and reduced system complexity.Design/methodology/approachThe proposed hybrid algorithm is for food traceability to make accurate predictions and enhanced period data. The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers.FindingsIn order to survive with the existing financial circumstances and the development of global food supply chain, the authors propose efficient food traceability techniques using the internet of things and obtain a solution for data prediction.Originality/valueThe operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers. The experimental analysis depicts that proposed algorithm has high accuracy rate, less execution time and error rate.
Highlights
For the past decade, food is the primary energy resource of human civilization and its quality and safety has been a major issue throughout the world especially in China for several causes (Liu et al, 2016)
internet of things (IoT) technologies should be capable of providing possible solutions for identifying traceability, tracking and manageability concerns for food supply chain (FSC)
The C5.0BN model works by dividing data on food quality training and gives full impact
Summary
Food is the primary energy resource of human civilization and its quality and safety has been a major issue throughout the world especially in China for several causes (Liu et al, 2016). It is very important to expand technologies to ensure food safety for entire food supply chain (FSC) includes manufacture, processing, warehouse, shipping, storage and distribution. For people’s wellbeing and social security and growth, food traceability plays an extremely rare role It is an essential indicator of risk management for food safety and an efficient technology to monitor the whole supply chain. This paper suggests a hierarchical technique called the C5.0 Bayesian Network which enables determined sets of their entity to be combined with other distinct ones Instead of directly using the most specific local classifier (mostly the classifier in a leaf node) to making classification in C5.0 BN, our pruning strategy uses the measurement of local accuracy to guide the selection of local classifier for decision
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