Drones for Sheep Livestock Monitoring

Al-Thani, Najla N. J.; Albuainain, Alreem; Al-Naimi, Fatima

doi:10.1109/melecon48756.2020.9140588

Cited by 39 publications

(21 citation statements)

References 10 publications

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“…Along with identification, the opportunity for continuously monitoring of herds, especially on grazing farms, is of particular interest. As a result, several recent studies have investigated the use of GPS sensors [107,108] and drones [109,110] to monitor animals in real time, revealing an accuracy of 96-97%.…”

Section: Animal Identification and Automatic Trackingmentioning

confidence: 99%

Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions

Morrone

Dimauro

Gambella

et al. 2022

Sensors

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Precision livestock farming (PLF) has spread to various countries worldwide since its inception in 2003, though it has yet to be widely adopted. Additionally, the advent of Industry 4.0 and the Internet of Things (IoT) have enabled a continued advancement and development of PLF. This modern technological approach to animal farming and production encompasses ethical, economic and logistical aspects. The aim of this review is to provide an overview of PLF and Industry 4.0, to identify current applications of this rather novel approach in different farming systems for food producing animals, and to present up to date knowledge on the subject. Current scientific literature regarding the spread and application of PLF and IoT shows how efficient farm animal management systems are destined to become. Everyday farming practices (feeding and production performance) coupled with continuous and real-time monitoring of animal parameters can have significant impacts on welfare and health assessment, which are current themes of public interest. In the context of feeding a rising global population, the agri-food industry and industry 4.0 technologies may represent key features for successful and sustainable development.

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Section: Animal Identification and Automatic Trackingmentioning

confidence: 99%

Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions

Morrone

Dimauro

Gambella

et al. 2022

Sensors

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“…The results showed that by equipping drones with the aerial VSLAM algorithm, indoor livestock monitoring can be feasible. The authors in [ 31 ] designed a system for sheep monitoring and tracking based on image processing techniques. The study demonstrated the hardware and software configuration for developing a drone and the measurement results showed that the system can reliably (with an accuracy of 89–97%) detect sheep on a farm.…”

Section: Related Workmentioning

confidence: 99%

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

Behjati

Noh

Alobaidy

et al. 2021

Sensors

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Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).

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“…Injectable transponders [177,178] RFID sensors [156,179,180] Endoruminal bolus [180,181] Drones-image analysis [182] Age identification Sound recorders analysis [183] Flock monitoring Drones-image analysis [182,184] Weight monitoring Automatic weigh-crates [157] Standing/lying behaviour monitoring Camera-based analysis [185] Ultra-wide band real-time location [185] Animal mounted accelerometers [186] in goats Flock management Virtual fence (i.e., animal-mounted collars embedded with electromagnetic transmitters and ground-installed receivers and sound speakers) [187][188][189][190][191][192]; [193] in goats PLF's adoption in commercial applications is more common in intensive large-scale farms. Although several sensors, injectable devices, ear tags, rumen boluses, pedometers, collars and infrared cameras have been developed, the most common PLF application for sheep and goats is to install sensors in the milking parlour.…”

Section: Camera-based Analysis [161] Microphonesmentioning

confidence: 99%

Contribution of Precision Livestock Farming Systems to the Improvement of Welfare Status and Productivity of Dairy Animals

Simitzis

Tzanidakis

Tzamaloukas

et al. 2021

Dairy

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Although the effects of human–dairy cattle interaction have been extensively examined, data concerning small ruminants are scarce. The present review article aims at highlighting the effects of management practices on the productivity, physiology and behaviour of dairy animals. In general, aversive handling is associated with a milk yield reduction and welfare impairment. Precision livestock farming systems have therefore been applied and have rapidly changed the management process with the introduction of technological and computer innovations that contribute to the minimization of animal disturbances, the promotion of good practices and the maintenance of cattle’s welfare status and milk production and farms’ sustainability and competitiveness at high levels. However, although dairy farmers acknowledge the advantages deriving from the application of precision livestock farming advancements, a reluctance concerning their regular application to small ruminants is observed, due to economic and cultural constraints and poor technological infrastructures. As a result, targeted intervention training programmes are also necessary in order to improve the efficacy and efficiency of handling, especially of small ruminants.

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Drones for Sheep Livestock Monitoring

Cited by 39 publications

References 10 publications

Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions

Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

Contribution of Precision Livestock Farming Systems to the Improvement of Welfare Status and Productivity of Dairy Animals

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