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Stress response caused by transportation is an important reason that affects the health and welfare of livestock, and the intelligent and precise monitoring of the stress state during transportation is a blank in research. On-site tracking experiments, literature review, and expert consultation indicate that environmental temperature, relative humidity and vibration intensity of vehicle are important stressors in the transportation process, and cortisol (COR) and adrenocorticotropic hormone (ACTH) are the key biomarkers to characterize the stress level. In this paper, stressors causing stress response were collected through environmental sensor network, and key biomarkers representing stress response were identified through biochemical analysis, and their change rules were analyzed and compared. Furthermore, the predictive models coupled with environmental parameters and stress markers were constructed through supervised learning networks. The results showed that: (1) After testing, the data collection performance of the multi-sensor network is reliable (P PPchip > 0.90). (2) The validation results of models showed that: for COR, the absolute error and relative error based on general regression neural network (GRNN) are 2.59 ± 0.16 ng/mL and 1.30 ± 0.07%, respectively, which is better than the accuracy based on back propagation neural network (BPNN) and Elman neural network (Elman); for ACTH, the absolute error and relative error based on GRNN models were 1.75 ± 0.13 pg/mL and 2.14 ± 0.17%, respectively, which were better than the accuracy based on BPNN and Elman models. (3) For the prediction performance, the prediction accuracy of GRNN model for COR concentration is basically equal to that of Elman model, which is 0.17% higher than that of BPNN model for COR concentration, but the maximum relative error of BPNN model for COR concentration is the smallest. The prediction accuracy of Elman model for ACTH concentration is 0.09% higher than that of GRNN model, and 0.29% higher than that of BPNN model. But the maximum relative error of Elman model for ACTH concentration is the largest, and the running time is significantly longer. Although the performance of the three modeling methods is different, their overall accuracy has reached more than 97%. Therefore, this study can effectively identify the stress state of mutton sheep in the actual transportation process, and provide technical support for the health traceability and control of mutton sheep.

North American holly (Ilex Verticillata), deciduous shrub of the Aquifoliaceae genus, is commonly known as winterberry, having a high ornamental and medicinal value. The fresh-cut branches of North American holly are prone to rapid deterioration during the cold chain. The paper presents a wireless multi-sensors system to improve postharvest quality of the fresh-cut branches of North American holly in the cold chain. The system was evaluated and validated at a sampled chain from Weihai city to Beijing. The critical ambient parameters including temperature, relative humidity, CO2 and ethylene are real-time monitored and analyzed in fresh-cut branches of the North American holly cold chain. Results show that the system can monitor accurately and transfer steadily data on critical ambient parameters through the logistics process. The traceability system can improve the quality control by tracing the critical ambient parameters and provides an early warning. The quality loss of fresh-cut branches of North American holly decreased from 25% ∼ 30% to below 15%, and the market price increased from below 20yuan/bundle to above 50yuan/bundle, which heighten benefit of cold chain.

This paper puts forward the reasonable choice of live transportation strategy to guarantee survival rate and quality of Urechis unicinctus by designing its packaging mode and carrying out real-time monitoring technologies. Ambient data sensing devices are designed and deployed in water and waterless transportation facilities by which the simulation of real live transportation is tested. During the delivery process of live Urechis unicinctus, the key ambient factors – temperature, dissolved oxygen/oxygen, carbon dioxide, PH, salinity–are dynamically sampling by on-line electronic monitoring equipment. Urechis unicinctus transport ambient data and their correlations are statistically calculated and analyzed by transportation monitoring and analysis system for the two transportation strategies. Through five control groups test for water and waterless shipment, it is found that waterless transportation is more suitable for over 30 h live transportation by studying transportation facilities management, surface characteristics and survival rate, which can provide a sound statistical basis of reasonable transportation mode to guarantee consumers to eat healthier and cheaper seafood with more convenient and economical way.

A WGS2 for table grape cold chain was developed and evaluated.The key traceability indicators include temperature, humidity, CO2, O2, and SO2.The shelf life model was built to predict the quality indicators.Tested and analyzed in two sample table grapes cold chains in China.Improving the traceability and transparency of the table grape cold chain. There is increasing requirement to improve traceability and transparency of table grapes cold chain. Key traceability indicators including temperature, humidity and gas microenvironments (e.g., CO2, O2, and SO2) based on table grape cold chain management need to be monitored and controlled. This paper presents a Wireless Multi-Gas-Sensors System (WGS2) as an effective real-time cold chain monitoring system, which consists of three units: (1) the WMN which applies the 433MHz as the radio frequency to increase the transmission performance and forms a wireless sensor network; (2) the WAN which serves as the intermediary to connect the users and the sensor nodes to keep the sensor data without delay by the GPRS remote transmission module; (3) the signal processing unit which contains embedded software to drive the hardware to normal operation and shelf life prediction for table grapes. Then the study evaluates the WGS2 in a cold chain scenario and analyses the monitoring data. The results show that the WGS2 is effective in monitoring quality, and improving transparency and traceability of table grape cold chains. Its deploy ability and efficiency in implantation can enable the establishment of a more efficient, transparent and traceable table grape supply chain.

The underwater non-contact methods for fish length measurement play an important role in aquaculture for fish sorting, grading and effective management. One challenge is to obtain the fish length data automatically instead of to locate the key measurement points of fish manually during the processing. This study presents a stereo vision system with procedure that obtains the oplegnathus punctatus fish body length based on LabVIEW. The key measurement points of fish snout tip and caudal peduncle can be automatically extracted through the processing algorithm that is based on the contour and convex hull calculation. The experiment validated the success rate and accuracy of fish length estimation at different angles of the free-swimming fish body to the optical axis. Experimental results showed that the proposed system could estimate the fish length with high accuracy and success rate. The regression analysis between estimated and manually measured body length showed a good linear relationship with R2 > 0.91 and mean relative error

Cold chain is one of the most important ways to guarantee the quality and safety of table grapes. It is necessary to monitoring the temperature in the whole cold chain process to keep the constant temperature and guarantee the quality and safety of table grapes in cold chain. This paper aims to develop a battery-free wireless sensor system with compressed sensing for the table grapes cold chain. The battery-free wireless sensor system was realized by using the ultra high frequency radio frequency identification (UHF RFID) on-chip temperature sensor with no battery. The temperature fluctuation performance of the table grapes in the cold chain, the compressed sensing (CS) performance of the temperature reconstructed error with the original data and the battery-free wireless sensor system with compressed sensing evaluation were comprehensively analyzed and discuss. The developed battery-free wireless sensor system with compressed sensing could effectively monitor the actual temperature with high reconstruction accuracy to save the data storage space, transmission and operation resources, improve the temperature transparency in cold chain, and finally ensure the quality and safety of table grapes. The developed battery-free wireless sensor system with compressed sensing could be extended for other cold chain applications to improve their transparency and transmission efficiency of the wireless monitoring. It is also possible to integrate the relative humidity, gas sensors into the battery-free wireless sensor system to monitor more environment parameters in cold chain.

The uncertainty of transport environment threatens the comfort and health of live mutton sheep and even affects the quality of meat after slaughter. Using modern information technology to solve the deficiencies of live animal centralized transport is of great significance for the stable development of animal husbandry. In this paper, the wearable multi-sensor system was specially designed, and the system test experiment and transport monitoring experiment were carried out. According to the continuous and real-time data of environmental and physiological parameters obtained from the transport monitoring experiment, the optimization extraction, data collection analysis, correlation analysis and simulation analysis of the internal environment in the carriage were carried out, and the prediction model between environmental and physiological parameters based on generalized regression neural network (GRNN) and the prediction model of comfort and health evaluation based on back propagation neural network (BPNN) were established. The results show that: (1) The wearable multi-sensor system had high accuracy and stability of data collection, and the power consumption and communication performance can meet the monitoring requirements. (2) The observation values of internal environmental parameters increased gradually with the accumulation of transport time, and finally reached the state of dynamic balance. The heart rate fluctuated greatly and was higher than the normal range. The blood oxygen saturation showed a gradual decrease trend, but the overall was in the normal range. The body temperature gradually increased, and was affected by discomfort. (3) The correlation analysis showed that there was a significant correlation between environmental and physiological parameters. (4) The area with larger value of environmental parameters was close to the middle and front of the carriage, which was mainly caused by the high density and poor air circulation. (5) The prediction model between environmental and physiological parameters based on GRNN and the prediction model of comfort and health evaluation based on BPNN had high prediction accuracy, and the combination of the two models can map the impact of environmental factors on the change of vital signs to the relationship between comfort and health. Therefore, in the case of unknown vital signs information, only obtaining environmental information can also predict the health levels, which can provide decision-making basis for relevant practitioners.