A Framework for Real-time Cattle Monitoring using Multimedia Networks

Abstract

Monitoring cattle behaviour has been a perpetual challenge in animal husbandry and animal breeding. Various methods have been used in the past to monitor the behaviour of cattle and their grazing patterns. We proposed a framework for real time cattle monitoring using multimedia networks. The study observes the grazing patterns of cattle and their behaviours in the grazing field. In order to accomplish the above observation, cumulatively stored in memory that is on-boarded and big enough for the fixing of position. Each fix record is comprised of corresponding estimation of height, date and time of global positioning system, precision value dilution, status of fix, temperature, including horizontal and vertical activity sensor counts in intervals that are fixed. Units of collar were well compacted, robust, and of about 1 kg in weight. Location fixes for over a day period were showed through testing to be accurate, at roughly 95% for eight minutes of time after correction of the difference. There was no bias of direction on the part of errors that have consistency with other studies' finding. global positioning system monitoring collar for cattle is utilized; this technology monitors the activities of individual cattle during grazing through a networked system. The materials used in carrying out the work include cattle, waterproof global positioning system tracking collars (TR20) for cattle, and supporting application software by Sigfox's global Internet of Things network. The paddock size was 121 by 174 m. Forage was predominantly agricultural plants. Cattle weighing up to 580 kg were used. The collared cattle were used to initiate all data collections. Latitude and longitude information of location was

Application of Precision Technologies to Characterize Animal Behavior: A Review

“…Therefore, one of its main uses is in monitoring livestock behaviors under pasture conditions. Several authors [63][64][65] have studied different aspects of cattle behavior. Castillo-Garcia et al [25] evaluated the sheep's grazing effects on vegetation to determine whether they were beneficial or not for pastures.…”

“…The combination of accelerometers and GPS results in a synergistic relationship that exploits the strengths of both sensors to provide a good understanding of ruminants. Australia Accuracy of 88% to 98% in monitoring licking behavior [42] Australia 4-month-old calves suckled fewer times, but for longer [73] United Kingdom Classification of rumination, eating, and other behaviors with precision of 0.83 [74] Pasture-based France The accuracy of prediction of the main behaviors was 98% [40] Semi-enclosed barn United States Accuracy of rumination detection was 86.2% [41] Three dairy farms Italy Accuracy of behavior detection was 85.12% [75] Dairy farm Italy Accuracy of classifying behavior was 96% [76] GPS Extensive United States Cattle followed water more than salt [3] Hungary Weather fronts affected the herd's route [64] Pasture-based Malaysia Observation of the grazing patterns was accurate [63] England Cattle tended to favor shorter material during the day and material of higher crude fiber in the evening [66] Commercial farm Spain Sensor was able to detect hotspots of dung deposition [77] GPS-GPRS Extensive Spain Distance traveled daily was 3147 m [65] Accelerometer, GPS Pasture-based Australia Description of the animals' movement and some behaviors was successful [78] Spain Accuracy of classification of behavior was 93% [70] Accelerometer, RFID Pasture-based Australia Accelerometer correlated highly with the observed duration of drinking events [79] Accelerometer, magnetometer Intensive Tasmania Grazing, ruminating, and resting were identified accurately [80] Accelerometer, cameras Intensive China Accuracy of 94.9% in recognizing behavior [81] Table 1. Cont.…”

“…Real-time monitoring [63] Non-invasive [146] Reduces labor [147] Remote monitoring [147] Data-driven decision making [88] Battery life [21] Cost [148] Device attachment problems [35] Ethical considerations [147] Cameras Real-time monitoring [69] Non-invasive [146] Capture detailed data [62] Environmental conditions [146] Limited field of view [62] Cost [146]…”

Development of a Sustainable Internet of Things-Based System for Monitoring Cattle Health and Location with Web and Mobile Application Feedback

“…The investigation of cattle grazing patterns and behaviors in the grazing field studied the use of a water-resistant GPS monitoring collar device (TR20) for cows which tracks the activity of each cow during grazing through a networked system [17]. Following resolution of ambiguity, location fixes were accurate for almost one day with 95% accuracy for 8 minutes.…”

“…𝐿 𝐴 = 𝐿 𝑡𝑎 + 𝐿 𝑟𝑎 + 𝐴 𝑎𝐼 + 𝐿 𝑝𝑜𝑖𝑛𝑡 + 𝐴 𝑝𝑜𝑙 + 𝐴 𝑟𝑎𝑖𝑛 (17) where, LA=Additional losses Lta=Losses associated with transmitted antenna Lra=Losses associated with receiver antenna AaI=Attenuation by atmosphere and Ionosphere Lpoint=Losses causes as a result of antenna depointing Apol=Attenuation cause by polarization mismatch between transmitting antenna and receiving antenna Arain=Attenuation cause by rain (cloud) These factors in Eq. ( 17) vary from time to time due to weather condition a particular area.…”