Detecting Dairy Cow Behavior Using Vision Technology

McDonagh, John; Tzimiropoulos, Georgios; Slinger, Kimberley R.; Huggett, Zoë J.; Down, Peter; Bell, Matthew

doi:10.3390/agriculture11070675

Cited by 22 publications

(14 citation statements)

References 13 publications

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“…The method was validated using datasets captured in both day and night environments, showing effective recognition of 15 types of hierarchical activities. McDonagh et al 34 . continuously monitored 46 cattle and used image recognition technology to predict their behaviors.…”

Section: Research Statusmentioning

confidence: 99%

UD-YOLOv5s: Recognition of cattle regurgitation behavior based on upper and lower jaw skeleton feature extraction

Gao,

Wang,

Wang

et al. 2023

J. Electron. Imag.

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Rumination plays a pivotal role in assessing the health status of ruminants. However, conventional contact devices such as ear tags and pressure sensors raise animal welfare concerns during rumination behavior detection. Deep learning offers a promising solution for non-contact rumination recognition by training neural networks on datasets. We introduce UD-YOLOv5s, an approach for bovine rumination recognition that incorporates jaw skeleton feature extraction techniques. Initially, a skeleton feature extraction method is proposed for the upper and lower jaws, employing skeleton heatmap descriptors and the Kalman filter algorithm. Subsequently, the UD-YOLOv5s method is developed for rumination recognition. To optimize the UD-YOLOv5s model, the traditional intersection over the union loss function is replaced with the generalized one. A self-built bovine rumination dataset is used to compare the performance of three deep learning techniques: mean shift algorithm, mask region-based convolutional neural network, and you only look once version 3 (YOLOv3). The results of the ablation experiment demonstrate that UD-YOLOv5s achieves impressive precision (98.25%), recall (97.75%), and a mean average precision of 93.43%. We conducted a generalization performance evaluation in a controlled experimental environment to ensure fairness, indicating that UD-YOLOv5s converges faster than other models while maintaining comparable recognition accuracy. Moreover, our work reveals that when convergence speed is equal, UD-YOLOv5s outperforms other models regarding recognition accuracy. These findings provide robust support for accurately identifying cattle rumination behavior, showcasing the potential of the UD-YOLOv5s method in advancing ruminant health assessment.

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Section: Research Statusmentioning

confidence: 99%

UD-YOLOv5s: Recognition of cattle regurgitation behavior based on upper and lower jaw skeleton feature extraction

Gao,

Wang,

Wang

et al. 2023

J. Electron. Imag.

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“…Posture recognition is closely related to calf behaviours, there have been many studies that have worked on calf behaviour recognition for various behaviours such as standing, lying, walking, drinking, and ruminating [9][10][11][12][13][14][15][16]. Some of these studies, especially the newer ones, used machine-vision based deep learning algorithms for calf behaviour recognition, these studies are more relevant to the algorithms being applied in the present work.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these studies, especially the newer ones, used machine-vision based deep learning algorithms for calf behaviour recognition, these studies are more relevant to the algorithms being applied in the present work. There was only one study that applied deep learning algorithms based on conventional Convolutional Neural Network (CNN) for image classification of the feeding and standing behaviour [15], while the others applied algorithms designed to accept video inputs rather than image inputs, which made the algorithms much more complicated than conventional CNN-based algorithms [10,13,14,16].…”

Section: Introductionmentioning

confidence: 99%

Calf Posture Recognition Using Convolutional Neural Network

Tung¹,

Khairuddin²,

Shapiai³

et al. 2023

Computers, Materials &Amp; Continua

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Dairy farm management is crucial to maintain the longevity of the farm, and poor dairy youngstock or calf management could lead to gradually deteriorating calf health, which often causes premature death. This was found to be the most neglected part among the management workflows in Malaysia and has caused continuous loss over the recent years. Calf posture recognition is one of the effective methods to monitor calf behaviour and health state, which can be achieved by monitoring the calf behaviours of standing and lying where the former depicts active calf, and the latter, passive calf. Calf posture recognition module is an important component of some automated calf monitoring systems, as the system requires the calf to be in a standing posture before proceeding to the next stage of monitoring, or at the very least, to monitor the activeness of the calves. Calf posture such as standing or resting can easily be distinguished by human eye, however, to be recognized by a machine, it will require more complicated frameworks, particularly one that involves a deep learning neural networks model. Large number of highquality images are required to train a deep learning model for such tasks. In this paper, multiple Convolutional Neural Network (CNN) architectures were compared, and the residual network (ResNet) model (specifically, ResNet-50) was ultimately chosen due to its simplicity, great performance, and decent inference time. Two ResNet-50 models having the exact same architecture and configuration have been trained on two different image datasets respectively sourced by separate cameras placed at different angle. There were two camera placements to use for comparison because camera placements can significantly impact the quality of the images, which is highly correlated to the deep learning model performance. After model training, the performance for both CNN models were 99.7% and 99.99% accuracies, respectively, and is adequate for a real-time calf monitoring system.

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“…Similarly, video-based systems (e.g. McDonagh et al, 2021) have the challenge of cow-identification, sufficient spatial covering, and high computation power requirements. Despite its continuous development and high potential for animal monitoring, uwb-based positioning is yet sparingly adopted for livestock applications.…”

Section: Introductionmentioning

confidence: 99%

Detecting dairy cows' lying behaviour using noisy 3D ultra-wide band positioning data

Adriaens¹,

Ouweltjes²,

Pastell³

et al. 2022

Peer Community Journal

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In precision livestock farming, technology-based solutions are used to monitor and manage livestock and support decisions based on on-farm available data. In this study, we developed a methodology to monitor the lying behaviour of dairy cows using noisy spatial positioning data, thereby combining time-series segmentation based on statistical changepoints and a machine learning classification algorithm using bagged decision trees. Position data (x, y, z -coordinates) collected with an ultra-wide band positioning system from 30 dairy cows housed in a freestall barn were used. After the data preprocessing and selection, statistical changepoints were detected per cow-day (no. included = 331) in normalized 'distance from the centre of the barn' and (z) time series. Accelerometer-based lying bout data were used as a practical ground truth. For the segmentation, changepoint detection was compared with getting-up or lying-down events as indicated by the accelerometers. For the classification of segments into lying or nonlying behaviour, two data splitting techniques resulting in 2 different training and test sets were implemented to train and evaluate performance: one based on the data collection day and one based on cow identity. In 85.5% of the lying-down or getting-up events a changepoint was detected in a window of 5 minutes. Of the events where no detection had taken place, 86.2% could be associated with either missing data (large gaps) or a very short lying or non-lying bout. Overall classification and lying behaviour prediction performance was above 91% in both independent test sets, with a very high consistency across cow-days. Per cow-day, the average error in the estimation of the lying durations were 7.1% and 7.8% for the cow-identity and time-based data splits respectively. This resulted in sufficient accuracy for automated quantification of lying behaviour in dairy cows, for example for health or welfare monitoring purposes.

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Detecting Dairy Cow Behavior Using Vision Technology

Cited by 22 publications

References 13 publications

UD-YOLOv5s: Recognition of cattle regurgitation behavior based on upper and lower jaw skeleton feature extraction

UD-YOLOv5s: Recognition of cattle regurgitation behavior based on upper and lower jaw skeleton feature extraction

Calf Posture Recognition Using Convolutional Neural Network

Detecting dairy cows' lying behaviour using noisy 3D ultra-wide band positioning data

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