Evaluation of an infrared thermography camera for measuring body temperature in dairy calves

Cantor, Melissa C.; Goetz, H.M.; Beattie, K.; Renaud, D.L.

doi:10.3168/jdsc.2022-0227

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Scoley et al [ 80 ] obtained weak correlations between the calves’ core body temperature (rectal temperature) and the infrared temperature of the eye and the rectal area (correlations ranging from 0.16 to 0.47). Cantor et al [ 94 ] also tested the infrared temperature of the orbital region against the rectal temperature in 318 male Holstein calves, but the results substantiated the previous findings. From these results, IRT is not a viable technique to diagnose pyrexia, which can be easily performed by measuring rectal temperature with a thermometer, despite the practical advantages of an automated system that could identify fever in group-housed calves.…”

Section: Technological Applications To Monitor Calves’ Health and Wel...mentioning

confidence: 63%

Literature Review on Technological Applications to Monitor and Evaluate Calves’ Health and Welfare

Silva

Conceição

Pereira

et al. 2023

Animals

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Precision livestock farming (PLF) research is rapidly increasing and has improved farmers’ quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and 3D accelerometers have been the most extensively used technologies in dairy calves. However, other technologies have been emerging in dairy calves’ research, such as infrared thermography (IRT), 3D cameras, ruminal bolus, and sound analysis systems, which have not been properly validated and reviewed in the scientific literature. Thus, with this review, we aimed to analyse the state-of-the-art of technological applications in calves, focusing on dairy calves. Most of the research is focused on technology to detect and predict calves’ health problems and monitor pain indicators. Feeding and lying behaviours have sometimes been associated with health and welfare levels. However, a consensus opinion is still unclear since other factors, such as milk allowance, can affect these behaviours differently. Research that employed a multi-technology approach showed better results than research focusing on only a single technique. Integrating and automating different technologies with machine learning algorithms can offer more scientific knowledge and potentially help the farmers improve calves’ health, performance, and welfare, if commercial applications are available, which, from the authors’ knowledge, are not at the moment.

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Section: Technological Applications To Monitor Calves’ Health and Wel...mentioning

confidence: 63%

Literature Review on Technological Applications to Monitor and Evaluate Calves’ Health and Welfare

Silva

Conceição

Pereira

et al. 2023

Animals

View full text Add to dashboard Cite

show abstract

“…Sensor-based monitoring, including wearable accelerometers, provides insights into cow movements and posture (Riaboff et al, 2022). Thermal cameras and infrared sensors have been used to measure body temperature changes, indicative of impending calving (Cantor et al, 2022). Additionally, bio-metric and physiological data analysis, such as heart rate and rumination patterns, offers valuable indicators (Alipio et al, 2022).…”

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confidence: 99%

Investigation of Bovine Disease and Events through Machine Learning Models

Nadeem,

Anis

2024

PJAR

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T he dairy industry plays a pivotal role in global food security by providing essential nutrition through milk production, relies heavily on the health and productivity of dairy cows. However, ensuring the health and well-being of dairy cows is of paramount importance for sustaining a thriving dairy industry, making bovine diseases a top concern for farmers worldwide. Bovine diseases, such as calving complications, mastitis, estrus, acidosis, and lameness, pose significant challenges. Detecting and diagnosing Abstract | Bovine disease identification utilizing multiple information sources and methods is widely applicate in the field of bovine disease prevention and health monitoring. Bovine disease detection is an emerging subject today to accomplish the farms demands of individuals across the globe. This research delves into the realm of bovine disease and event detection using advanced Machine Learning (ML) techniques. Focusing on the critical events of estrus, acidosis, mastitis, lameness, and calving, our study aims to revolutionize disease identification and timely intervention within the dairy industry. By leveraging four distinct ML models-Random Forest, XGBoost, Logistic Regression, and Single Perceptron we meticulously analyze four diverse data-sets to uncover intricate patterns and unveil hidden insights. The efficiency of random sampling in resolving the class imbalance issue is tested along with the validity and adaptability of these models utilizing GridSearchCV optimum parameter modification. The performance evaluation is based on accuracy, precision, recall, F1 score, and Area Under the Curve (AUC) metrics. With a resounding highest accuracy metric, the Random Forest model achieves a notable accuracy of up to 98.25%, while the recall score of 100, and Precision up to 97% affirming its supremacy in classifying bovine events. This achievement underscores the efficacy of employing ML algorithms for accurate and timely disease identification. This ground-breaking fusion of ML techniques with bovine disease detection holds trans-formative potential, promising to elevate animal welfare standards, optimize dairy productivity, and usher in a new era of datadriven dairy management.

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Association between a pyroelectric infrared sensor monitoring system and a 3-dimensional accelerometer to assess movement in preweaning dairy calves

Sonntag,

Borchardt,

Heuwieser

et al. 2024

JDS Communications

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Evaluation of an infrared thermography camera for measuring body temperature in dairy calves

Cited by 5 publications

References 18 publications

Literature Review on Technological Applications to Monitor and Evaluate Calves’ Health and Welfare

Literature Review on Technological Applications to Monitor and Evaluate Calves’ Health and Welfare

Investigation of Bovine Disease and Events through Machine Learning Models

Association between a pyroelectric infrared sensor monitoring system and a 3-dimensional accelerometer to assess movement in preweaning dairy calves

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