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.
As a result of the current imperfection of the meat traceability system, there have been numerous food safety events with serious consequences. In this paper, a meat product information traceability system is designed to efficiently prevent such problems. This system develops an identification tag information reader based on ultra-high frequency (UHF) Radio Frequency Identification (RFID). It is compatible with LoRa wireless, USB serial port, RS485, and RJ45 Ethernet connection. Among them, the efficiency analysis of the Q-value algorithm finds that the recognition rate of the system reaches a maximum of about 0.367 when the number of tags n is about the frame length. The multi-tag anti-collision algorithm design based on the algorithm improves the efficiency of information collection in production and distribution links. The traceability code identification scheme is designed to effectively match various links, and the platform of system is built using LabVIEW2014 software, which has five sub-modules including user management, farm management, slaughter management, logistics management, and sales management. The system uses MySQL databases to store traceability information so that users can complete their queries by entering the traceability code on the system platform. The system not only has a low cost and a broad range of applications, but it also realizes the tracking record of meat product traceability information from breeding to selling, completes the function from information collection to information inquiry, and solves the problem of the incomplete traceability information chain. In addition, the system not only enhances the informational transparency of meat products in the product supply chain but also provides information for the regulatory authorities to effectively monitor safety.
In the post-harvest supply chain of fruits and vegetables, the spoilage rate during storage is as high as 10% due to improper management measures, which has seriously restricted the development of related industries. In response to this problem, this paper proposes a method for predicting the storage shelf life of fruits and vegetables. In this paper, we develops a micro-environment monitoring system based on the Internet of Things (LOT) technology, integrating the sensors of temperature and humidity, oxygen and carbon dioxide, using the LoRa wireless communication module to realize real-time data transmission; With the help of MATLAB platform, we build a multi-sensor data fusion model based on artificial neural network (ANN), and we convert the environmental parameters with multi-source coupling relationship into shelf life by this model. The results show that the model has good prediction performance and meets the requirements of shelf life prediction. The method can effectively reduce the economic losses caused by fruits and vegetables spoilage, when being applied to the post-harvest supply chain management of fruits and vegetables.
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