## 1 Introduction  
China is one of the largest strawberry-producing countries and has the highest yield in the world. However, the commodity value of post-harvest fruit is lower because of the underdevelopment of cold chain logistics, fresh-keeping tech, and the weakness of supply chain management. This is a severe constraint to the development of the strawberry industry in this country [1]. Although strawberries have great nutritional and economic value, they are perishable, hard to store, and easily damaged in the post-harvest supply chain. It has been approved that post-harvest transportation is one of the worst impact processes in the entire supply chain. This process causes a quality decline in the strawberries and is considered a critical supply chain step [2]. It is vital to take effective measures for monitoring the quality of goods in real time during cold chain transportation (CCT).  
In traditional quality modeling research about cold chain logistics of fruits and vegetables, many scholars at home and abroad have widely used the zero-order or first-order reactions model based on chemical kinetics. The Arrhenius equation was used to perceive the state of quality by temperature [3; 4; 5], but the modeling method above only takes one single environmental factor into account. There are many kinds of environmental parameters that cannot be ignored, in addition to temperature, such as relative humidity [6], oxygen, carbon dioxide [7; 8], and so on. Each of them has an impact on the quality of fruits and vegetables. Therefore, it is critical to achieving comprehensive quality state analysis with multiple environmental parameters. With the universal utilization of artificial intelligence algorithms in the field of food shelf-life prediction in recent years, many scholars have studied quality prediction models based on neural networks [9; 10]. They implementedthe remaining shelf-life prediction of food with related physical and chemical indexes. In addition, the prediction performance of this method is more accurate compared with traditional dynamic models [11]. Regardless, the physiochemical indexes are multiple and hard to be determined in real time during CCT; thus, it is not easy to implement in practice. Therefore, in the strawberry supply chain, it is essential to improve the real-time performance of commodity quality perception in the step of CCT. At this point, it is necessary to use the quality modeling method based on an artificial intelligence algorithm and comprehensively consider a variety of environmental parameters to monitor the state of commodity quality effectively.  
This paper chose four environmental parameters (temperature, humidity, oxygen, and carbon dioxide concentration). They have the most important influence on strawberry quality. We correlativity analyzed the physicochemical indexes of strawberries, and the environmental parameters were used for quality prediction instead of using the physical and chemical index. The remaining shelf-life was quantified for post-harvest strawberry quality to make quality perception more intuitive. The environmental parameters received by the cold chain logistics data acquisition unit were used for the remaining shelf-life predictions, by constructing a shelf-life prediction model based on the back propagation (BP) neural network. This could provide the theoretical basis for cold chain management and decision-making support and reduce the economic costs of strawberry CCT.