Research on Strawberry Cold Chain Transportation Quality Perception Method Based on BP Neural Network

Abstract

Post-harvest strawberries are hard to store and can easily rot during cold chain transportation (CCT). This leads to considerable economic losses. This paper proposes a strawberry quality perception method used in CCT, based on the correlation between environmental parameters and strawberry quality parameters. The proposed method constructs a shelf-life prediction model based on a back propagation (BP) neural network, using four kinds of environmental parameters, including temperature, humidity, oxygen, and carbon dioxide, to perceive the quality of post-harvest strawberries, and builds a cold chain transportation quality perception system (CCT-QPS) with the help of LabVIEW software for monitoring the cold chain environment and commodity quality constantly. The results showed that the proposed method could precisely predict the remaining shelf-life of post-harvest strawberries. In addition, the proposed system could reflect the vehicle operation in real time, such as commodity quality and the internal environment of transport carriages. Moreover, the quality perception approach can inform decision making for managers and effectively improve the related regulatory measures in the strawberry supply chain.