Understanding anomalies in animal behaviour: data on cow activity in relation to health and welfare

Lardy, Romain; Mialon, Marie-Madeleine; Wagner, Nicolas; Gaudron, Y.; Meunier, Bruno; Sloth, Karen Helle; Ledoux, Dorothée; Silberberg, Mathieu; Roches, Alice de Boyer Des; Ruin, Q.; Bouchon, M.; Cirié, C.; Antoine, Violaine; Koko, Jonas; Veissier, Isabelle

doi:10.1016/j.anopes.2022.100004

Cited by 5 publications

(8 citation statements)

References 4 publications

(4 reference statements)

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“…Zhou et al, 2022). By utilizing various machine learning models to anticipate bovine events and diseases in the specified data-sets (Lardy, et al, 2022), the proposed work appears to be the first of its kind when compared to the aforementioned studies.…”

Section: Related Workmentioning

confidence: 99%

“…The data used in the study was gathered by GEA Farm Technology's CowView system in Bönen, Germany (Lardy, et al, 2022). Data is gathered using a tag attached to each cow's neck collar emits radio waves, which are picked up by antennae throughout the barn and used to triangulate the cow's position.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…These sets contain information on 11 different types of events and 386 individual Holstein breed cows. These eleven different event types were categorized into three states: Six of the events included health, two involved reproductions, and three were stress-related events (Lardy, et al, 2022). Binary classification is selected, targeting events or diseases that will either occur or not.…”

Section: Introductionmentioning

confidence: 99%

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Decoding Bovine Behavior: A Machine Learning Analysis of Disease and Event Detection

Nadeem,

Zubair,

Ali

et al. 2024

ABBDM

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The dairy industry globally serves millions, creates employment opportunities, and contributes to GDP and livelihoods, supporting numerous farmers and milk processors, and contributing to the GDP of many countries. Even with the high number of cows, a variety of illnesses such lameness, mastitis, metritis, foot and mouth disorders have a significant impact on the milk output. This demands the growth of smart methods to assist dairy producers in keeping an eye on the health, nutrition, and general well-being of their cows. To identify noteworthy bovine occurrences and illnesses, this research uses Machine Learning (ML) methods such as Extreme Gradient Boost (XGB), Naive Bayes (NB), and Perceptron. In order to uncover complex relationships and unearth undiscovered information about the characteristics and occurrences of bovine’s disease like Acidosis, Calving, Estrus, Lameness, and Mastitis. We closely scrutinize four distinct datasets. Metrics like as Area under the Curve (AUC), F1 score, accuracy, precision, recall, and Receiver Operating Characteristic (ROC) curve are used to evaluate performance. When it comes to identifying estrus events, XGB has the best detection accuracy score 92.59%, whereas XGB can detect a variety of events and disease with the highest recall 100% and the highest precision 95% and AUC of 0.962 when it comes to identifying calving, mastitis, and lameness

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Section: Related Workmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decoding Bovine Behavior: A Machine Learning Analysis of Disease and Event Detection

Nadeem,

Zubair,

Ali

et al. 2024

ABBDM

View full text Add to dashboard Cite

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“…Animal behaviour research could be applied to manage both domesticated animals (e.g. Venter et al 2019;Orihuela 2021;Herrera et al 2022;Lardy et al 2022) and wild animals (e.g. Koirala 2021;Laguna et al 2021;Miglioli & Vasconcellos 2021;Lardy et al 2022;Rose et al 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Venter et al 2019;Orihuela 2021;Herrera et al 2022;Lardy et al 2022) and wild animals (e.g. Koirala 2021;Laguna et al 2021;Miglioli & Vasconcellos 2021;Lardy et al 2022;Rose et al 2022). Behavioural research was also performed to reduce human-animal conflict (Silk 2007).…”

Section: Introductionmentioning

confidence: 99%

Alternative Strategy to Improve the Conservation of Javan Deer in Pangandaran Nature Reserve, West Java, Indonesia

Firdaus

Irwanto²,

Sumarga³

2023

J.Tropical Biodiversity Biotechnology

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The habitat of Javan deer in Pangandaran Nature Reserve (PNR) faced natural changes, particularly due to the succession process of vegetation community in grazing areas, and inadequate infrastructures that affected the deer to roam outside PNR. This study aimed to formulate strategies for the conservation of Javan deer in PNR, focusing on ecological aspects and conservation management. The methods were encountering Javan deer individuals; scan sampling and continuous recording to observe the behaviour of Javan deer; calculating the productivity of grazing area by defoliation experiment and vegetation analysis; reviewing documents, reports and interviews; and analysing strategy using SWOT-QSPM. Results showed there were 43 Javan deer encountered roaming in PNR and outside the conservation area, and nine individuals gathered in Cikamal grassland. The productivity of the grazing areas (5.61 ha) was 93,826 kg of feed annually and was only sufficient for 23 individuals. The grazing areas were dominated by Cynodon dactylon. Javan deer spent their time feeding. Javan deer herd in Cikamal is more intolerant to humans compared to the herd in Pangandaran Nature Tourist Park (PNTP). This study recommends: considering the management status of Javan deer in the conservation management of PNR and PNTP; improving the conservation management of Javan deer and its habitat; improving facilities and the management system of those facilities and conservation-supporting infrastructures; collaboration with researchers to perform some research and innovations for Javan deer conservation; improving the capability of PNR staff theoretically and practically; and educating and empowering the local people in terms of Javan deer conservation.

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Discriminating pathological, reproductive or stress conditions in cows using machine learning on sensor-based activity data

Lardy

Ruin

Veissier

2023

Computers and Electronics in Agriculture

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Understanding anomalies in animal behaviour: data on cow activity in relation to health and welfare

Cited by 5 publications

References 4 publications

Decoding Bovine Behavior: A Machine Learning Analysis of Disease and Event Detection

Decoding Bovine Behavior: A Machine Learning Analysis of Disease and Event Detection

Alternative Strategy to Improve the Conservation of Javan Deer in Pangandaran Nature Reserve, West Java, Indonesia

Discriminating pathological, reproductive or stress conditions in cows using machine learning on sensor-based activity data

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