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• Respiratory rhythm as a parameter to measure the health level of animals. • Development a flexible sensors for measuring respiratory rhythms in animals. • Exploring the pattern of change in respiratory rhythms as a function of temperature. • Anticipate the appearance of disease in animals in advance. The health status of livestock will directly or indirectly affect meat quality and farmers' income. Therefore, it is necessary to monitor the physiological indicators of livestock to timely reflect their health status. The respiratory rhythm of animals can provide early warning for the occurrence of diseases, but there is currently limited research on the characteristics of the rhythm. Therefore, this paper conducted the following research. PVDF flexible piezoelectric sensors were prepared by spin-coating, laser-induced graphene, and transfer printing processes, in which three common flexible substrate materials were compared, three key parameters of laser scanning were explored, while practical application tests were carried out on model animals, and the collected respiratory rhythm signals were analyzed in the time and frequency domains. The results showed that (1) posilicone of 25 durometer was identified as the flexible substrate material, and the LIG process with a scanning speed of 120 mm/min, a scanning power of 12 %, and a scanning interval of 0.02 mm as the optimal parameters was determined; (2) the sensor exhibited a response speed of 50 ms, an output voltage of 3.7 v, and still had a stable output; (3) in the monitoring of the respiratory rhythm of rabbits, the monitoring accuracy reached 96.7 %, while the respiratory signal collected by the sensor was randomly intercepted for 10 s, and its respiratory rhythm prediction accuracy reached 95.85 %. At the same time, the abnormal respiratory rhythm change of the animal was found 10 min in advance, giving the farmer more time to deal with the problem. (4) The monitoring of the respiration of farm animals such as Hu sheep is being realised. At the same time, the time domain as well as frequency domain waveforms of the Hu sheep were analysed and compared with the time domain frequency domain plots of the rabbit, which showed that the sensor could achieve good results for both higher frequency breathing and low-frequency breathing. It provides potential technical support for future health monitoring and early prediction of diseases in large farm animals. [ABSTRACT FROM AUTHOR]

• Microwave detection and image processing have great potential for livestock farming. • Continuous detection and early disease and aberrant behavior identification are made possible by non-contact sensing technology. • Integration of non-contact sensing technologies in smart farms to enhance precision livestock farming practices and improve efficiency. • This paper also summarizes the advantages, challenges and prospects of non-contact sensing technology. More efficient, sustainable, and precise agri-sensing technologies will be adopted in the future to promote precision livestock farming (PLF). Through a detailed survey of research related to sensing technology, we believe that non-contact sensing technology (infrared detection technology, Microwave radiometry technology, Image processing and machine vision, and acoustic detection technology) hold promise for application in intensive livestock production system. Animal welfare has always been a concern of the scientific community, as well as improving production efficiency has been the goal of the intensive livestock industry. With the help of the Internet of Things, big data, artificial intelligence, etc., non-contact sensing technology can better play its role. In this paper, we analyzed the background and technical characteristics of non-contact sensing technology and proposed a new application for applying non-contact sensing technology to intensive livestock farming. These technologies are now gradually being applied and practiced in the livestock industry and will facilitate physiological detection in the intensive livestock industry, potentially protecting animal welfare. The precise adoption of management measures based on better knowledge of animal status can also improve production efficiency. In addition, this paper discusses the advantages, challenges, and prospects of non-contact sensing technology in livestock farming. [ABSTRACT FROM AUTHOR]

• We designed and developed a wearable meat sheep physiological dynamic sensing and transport environment continuous monitoring system, and constructed a set of systematic biological signal detection, processing and modeling methods from biological signal sensing, core waveform extraction to characteristic parameter estimation, which realized the dynamic and continuous acquisition of physiological medical signals and transport environment signal change patterns of sheep during the transportation process of meat sheep. • A qualitative meat sheep transportation stress quantitative assessment model based on expert experience was constructed to realize the meat sheep transportation stress assessment through biological epigenetic information. • Sportation stress with the fusion of biological perception signals and environmental monitoring signals was constructed to realize the prediction analysis and estimation management mechanism of stress degree based on the reverse artificial neural network optimized by genetic algorithm. Transportation of live sheep is a relatively important part of the meat sheep industry, and the generation of transportation stress seriously threatens the organism health of live sheep as well as the quality of lamb meat. In this study, we selected Lake sheep, one of the edible sheep breeds, as the meat sheep research object, analyzed the actual needs of meat sheep transportation bio-sensing and modeling evaluation, and built a systematic bio-signal detection, processing and modeling method from bio-signal sensing, core waveform extraction to feature parameter estimation using wearable meat sheep physiological dynamic sensing and continuous monitoring of transportation environment sensors, and used modern spectrum estimation methods to judge and strip the target signal waveform from it to achieve accurate sensing and acquisition of transport key parameters. We also use modern spectral estimation methods to determine and extract the target signal waveforms from them, and achieve accurate sensing and acquisition of transport key parameters. Based on this, a qualitative stress assessment method based on external performance and a graded prediction model for transportation stress of meat sheep are constructed, and a predictive analysis and estimation management mechanism of stress level based on genetic algorithm optimized reverse artificial neural network is realized. Using a qualitative stress assessment method based on external manifestations, we established stage-specific classification rules for monitoring data in the transportation environment of meat sheep.The artificial neural network with optimized parameters based on the classification rules applying genetic algorithm is designed and implemented for real-time and efficient stress level prediction management mechanism for meat sheep transportation. The graded prediction model of meat sheep transportation stress was established by GA-BPNN and compared with the BP neural network optimized by particle swarm algorithm, simulated annealing algorithm and ant colony algorithm. The best optimization effect of GA was obtained, and the average prediction accuracy of the model could reach 89.81%. Finally, it achieves to improve the transportation reliability, reduce the transportation risk, and solve the problems of inefficient meat sheep transportation supervision and quality control. [ABSTRACT FROM AUTHOR]

• Transport stress seriously affects the health and welfare of livestock. • Ambient temperature, humidity and vibration intensity are main stressors. • Multiple environmental sensors can continuously monitor transport stressors. • COR and ACTH are the key biomarkers for characterizing stress status. • Supervised learning networks can effectively predict the transport stress. 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 < 0.05); transportation will cause significant changes in the concentration of COR and ACTH (P < 0.05); the simulation results of stress markers data have no significant difference compared with the sample data (P Spline > P Pchip > 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. [ABSTRACT FROM AUTHOR]

• Environmental comfort directly affects the quality and safety in the farming stage. • WMSE-DSS integrates wireless monitoring of environmental and psychological factors. • Environmental comfort evaluation model and comfort level prediction model are designed. • WMSE-DSS can provide farmers with automatic and accurate decision-making. There is a tendency that the quality and safety of mutton sheep are paid more and more attention and environmental comfort directly affects it in the farming stage. However, the traditional environmental comfort evaluation is mainly based on the subjective experience of people or environmental factors, which is inaccurate, inefficient, untimely and costly. Therefore, environmental comfort decision supports system based on monitoring data from wearable sensors and ambient multi-sensors is considered an alternative and promising strategy. This paper aims to develop an wearable multi-sensor enabled decision support system (WMSE-DSS) for mutton sheep farming that integrates wireless monitoring of environmental factors and psychological factors, evaluation and prediction of environmental comfort to ensure the quality and safety of mutton sheep. In this research, mechanism of environmental comfort formation is analyzed to obtain key impacts, and the overall structure and function of the system are analyzed and designed according to the actual needs, environmental comfort evaluation model and environmental comfort level prediction model designed for grasping environmental comfort situation. To demonstrate the automatic monitoring and evaluation in this platform, experiments have been evaluated and analyzed. The results illustrate this system can reduce the potential risks, control environmental comfort accurately and provide farmers with automatic decision-making. [ABSTRACT FROM AUTHOR]