Monitoring estrous activity in pasture-based dairy cows

Moore, S.G.; Aublet, V.; Butler, Stephanie

doi:10.1016/j.theriogenology.2020.11.002

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…This valuable information can be collected continuously, providing insight into the progression or decline of an animal's mobility as influenced by genetics or outside factors [3]. The pedometer has been shown to be a reliable tool for estrus detection through measurement of lying behavior and rumination time in dairy cows [47]. With specialized devices, activity sensors could pinpoint areas of poor mobility in specific individuals, encouraging proper treatment and balanced breeding.…”

Section: Activity Sensorsmentioning

confidence: 99%

Digital Phenotyping in Livestock Farming

Neethirajan

Kemp

2021

Animals

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Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored efficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly addressing farm animals’ individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future research is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.

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Section: Activity Sensorsmentioning

confidence: 99%

Digital Phenotyping in Livestock Farming

Neethirajan

Kemp

2021

Animals

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“…It should be noted that this procedure requires personnel’s assistance to verify that the patches are not stained and to apply them again if needed. There are also pressure-sensitive adhesive devices, such as FlashMate [ 13 ], which identifies cows in heat when the contacts’ frequency exceeds a threshold within a variable time window in the previous hours. This device does not transmit data wirelessly, but produces a blinking visual signal when activated [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

IMU Auto-Calibration Based on Quaternion Kalman Filter to Identify Movements of Dairy Cows

Muñoz-Poblete,

González-Aguirre,

Bishop

et al. 2024

Sensors

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This work is focused on developing a self-calibration algorithm for an orientation estimation of cattle movements based on a quaternion Kalman filter. The accelerometer signals in the earth’s frame provide more information to confirm that the cow is performing a jump to mount another cow. To obtain the measurements in the earth’s frame, we propose a self-calibration method based on a strapdown inertial navigation system (SINS), which does not require intervention by the user once deployed in the field. The self-calibration algorithm uses a quaternion-based Kalman filter to predict the angular orientation with bias correction, and update it based on the measurements of accelerometers and magnetometers. The paper also depicts an alternate update to adjust the inclination using only the accelerometer measurements. We conducted experiments to compare the accuracy of the orientation estimation when the body moves similarly to cow mount movements. The comparison is between the proposed self-calibration algorithm with the IvenSense MPU9250 and Bosch BNO055 and the quaternion attitude estimation provided in the BNO055. The auto-calibrating algorithm presents a mean error of 0.149 rads with a mean consumption of 308.5 mW, and the Bosch algorithm shows an average error of 0.139 rads with a mean consumption of 307.5 mW. When we executed this algorithm in an MPU9250, the average error was 0.077 rads, and the mean consumption was 277.7 mW.

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