MU Yuanjie, SHANG Minghua, ZHENG Jiye, et al. Review of automatic temperature monitoring technologies and their applications in livestock and poultry[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2025, 41(1): 40-54. DOI: 10.11975/j.issn.1002-6819.202409090
    Citation: MU Yuanjie, SHANG Minghua, ZHENG Jiye, et al. Review of automatic temperature monitoring technologies and their applications in livestock and poultry[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2025, 41(1): 40-54. DOI: 10.11975/j.issn.1002-6819.202409090

    Review of automatic temperature monitoring technologies and their applications in livestock and poultry

    • Core body temperature is one of the most critical physiological indicators of vital signs in livestock and poultry. Effective monitoring and accurate regulation of body temperature can greatly contribute to the health and physiological status of cattle and poultry. The article aims to outline the present research on the various temperature monitoring systems in the sustainable production of livestock and poultry over the last five years. The invasive, wearable and non-contact techniques were also applied to monitor the body temperature of cattle and poultry. Three monitoring systems were evaluated, in terms of data accuracy, easy operation, real-time data, equipment stability, and animal welfare. Three criteria were also set as the technology, cost and application issues. Among them, the invasive monitoring temperature was highly accurate and stable over the long periods, particularly for the scientific research with the high-precision requirements. Nevertheless, this monitoring approach can require the well-trained workers to operate, leading to some pressure on the target animals. Data exchange is also associated with the sensor loss and the displacement issues. Wearable monitoring is easily to operate for the data collection, highly suitable for the long-term continuous temperature monitoring in the large-scale livestock breeding, such as pigs and cattle. However, the monitoring location and usage have the significant impact on the accuracy of temperature monitoring and the comfort of target animals. Non-contact monitoring is extremely sensitive, stress-free and ideal for the grouped monitoring of cattle and poultry animals. The accuracy of temperature measurement is heavily influenced by some factors, such as the external environment, equipment accuracy, testing distance, and testing area. Some recommendations were given for the automatic temperature monitoring in livestock and poultry. In terms of invasive temperature monitoring, some efforts should be focused on the new biocompatible materials, non-invasive/minimally invasive and low-power data communication, in order to reduce the stress response of the target animal for the practicality of overall systems. In terms of wearable temperature monitoring, some emphasis should be put on the optimization of wearing mode, anti-interference algorithms, multi-sensor fusion, and new energy conversion, in order to improve the animal comfort and temperature measurement accuracy. Non-contact temperature monitoring can be required to improve the performance of thermal infrared sensors, with emphasis on the adaptability of temperature measurement for the different species. Deep learning can also be incorporated to construct the more accurate model of temperature monitoring for the high accuracy. The convenient, efficient, low-cost and high-precision devices or application modes can be preferred to fully meet the large-scale needs of temperature measurement in the livestock and poultry breeding. Non-contact temperature monitoring (also known as thermal infrared technology) can be expected to accurately monitor the large-scale livestock and poultry, due to its high sensitivity, absence of stress reaction, and adaptability to group monitoring. The non-contact monitoring can also be standardized to integrate the data collection with the robust algorithm using deep learning. The finding can also provide a strong reference for the more intelligent and efficient temperature monitoring in the livestock and poultry farming.
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