Food safety forecasting using internet of things and machine learning

Abstract

Food sector is a significant part of the economy but it faces challenges with food spoilage, especially in meat, fruits, and vegetables. This issue involves food items, especially meat, fruits, and vegetables, going stale and often reaching consumers unnoticed. Additionally, during the food chain there may be instances where the food may still be within the proposed shelf life but may be spoilt before it gets to the consumer, therefore, it is important to test them and envisage when it will be inedible. This paper presents a predictive model which is used to forecast when the fruits will he inedible via the use of time series data generated from internet of things (loT) based device. The loT device developed in this research is used to monitor the decline of the freshness of the fruit to the state of inedibility. This device measures parameters such as alcohol, and ammonia around the fruit, as such large amounts of real-time data are generated. A web server is used for the storage of data values sensed in real time and also for the analysis of results. Long Short-Term Memory (LSTM) predictive model is used to forecast the time the fruit will be inedible via the use of time series data harvested from the cloud. The implementation of this technology enhances traceability. minimizes food wastage, and, most importantly, protects consumers from foodborne illnesses. Keywords: Food safety, loT, Machine learning