Abstract
Simple SummaryCattle lameness detection as well as behaviour recognition are the two main objectives in the applications of precision livestock farming (PLF). Over the last five years, the development of smart sensors, big data, and artificial intelligence has offered more automatic tools. In this review, we discuss over 100 papers that used automated techniques to detect cattle lameness and to recognise animal behaviours. To assist researchers and policy-makers in promoting various livestock technologies for monitoring cattle welfare and productivity, we conducted a comprehensive investigation of intelligent perception for cattle lameness detection and behaviour analysis in the PLF domain. Based on the literature review, we anticipate that PLF will develop in an objective, autonomous, and real-time direction. Additionally, we suggest that further research should be dedicated to improving the data quality, modeling accuracy, and commercial availability.The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.
Highlights
Licensee MDPI, Basel, Switzerland.Livestock production is the second largest supplier of food for human consumption, after vegetable/cereal agriculture
The global livestock industry has been developing in the direction of standardisation, large scales, and intelligence
Intelligent perception for cattle monitoring is the key to the development of precision livestock farming
Summary
PLF involves the integration and interpretation of relevant sensor information to enable the management of individual animals through continuous real-time monitoring of their health, behaviour, production/reproduction, and environmental impact [6,7] Through technologies such as machine learning and Internet of Things (IoT, i.e., the interconnection between computing devices via the Internet), decision making in PLF can be better managed by fusing and analysing different sensor data streams, thereby reducing operational costs and improving animal health and welfare while increasing productivity, yield, and environmental sustainability. Sensors such as cameras, microphones, 3D accelerometers, temperature sensors, glucose sensors, and technologies such as deep learning and the IoT make it increasingly feasible to model, monitor, and control animal bio-response and to provide accurate feedback to the farmer Grounded on this basis, combined with the development of a Decision Support System (DSS) or expert systems, intelligent perception technologies can make large-scale animal husbandry more cost-effective, efficient, and sustainable [10,11]. The animal welfare measurement with relevant smart sensors is the key part, and a DSS utilises the former information to manage farming and environment protocols [15,16,17]
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