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.

An optimal decision for fresh products' cold chain considering freshness and carbon emission reduction

“…, 2023), the real-time cold chain egg freshness prediction method based on transfer learning (Kim et al. , 2022), and the strawberry cold chain transportation quality perception method based on BP neural network (Qiao et al. , 2022) are proposed.…”

“…Considering freshness and consumer preference, a fresh dynamic game model on temperature control and demand function of fresh agricultural products is established, which shows that the input level of temperature control, profit and overall profit of supply chain in the three-level supply chain composed of fresh agricultural Fresh products' cold chain products suppliers, retailers and third-party logistics are positively correlated with consumer preference (Luo et al, 2022;Sun et al, 2022). In terms of the algorithm models for freshness prediction, such algorithm models as the blueberry freshness prediction model based on machine learning (Huang et al, 2023), the real-time cold chain egg freshness prediction method based on transfer learning (Kim et al, 2022), and the strawberry cold chain transportation quality perception method based on BP neural network (Qiao et al, 2022) are proposed. The prediction algorithm has a good effect on improving the prediction accuracy of cold chain logistics freshness and improving the intelligence and preservation accuracy of fresh supply chain.…”

Recent advances in shelf life prediction models for monitoring food quality

“…The relative error of the model was less than 1, successfully proving its reliability. In a recent study, Qiao et al (2022) accurately predicted the shelf life of strawberries by constructing a BP model. And in high-value products, the accurate prediction of the model will reduce a great economic loss.…”