Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-Autoencoder

Zhang, Kai; Han, Shuqing; Wu, Jianzhai; Cheng, Guodong; Wang, Yali; Wu, Saisai; Liu, Jifang

doi:10.1016/j.compag.2023.108252

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Computers and Electronics in Agriculture

2023

DOI: 10.1016/j.compag.2023.108252

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Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-Autoencoder

Kai Zhang,

Shuqing Han,

Jianzhai Wu

et al.

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2024

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Cited by 7 publications

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“…Precision was assessed using Cohen's kappa coefficient, and precision and recall were also considered. Building upon the pioneering work of 54 represents a pioneering use of deep learning-based gait reconstruction and anomaly detection for early lameness detection, leveraging the portability and real-time capabilities of wearable gait analysis to enhance animal welfare, our proposed system prioritizes a more natural approach. We aim to minimize stress on cattle and avoid altering their environment.…”

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confidence: 99%

Development of a real-time cattle lameness detection system using a single side-view camera

Myint,

Onizuka,

Tin

et al. 2024

Sci Rep

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Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracking, and classification. This research investigates the application of deep learning for cattle lameness detection in dairy farming. Our study employs image processing techniques and deep learning methods for cattle detection, tracking, and lameness classification. We utilize two powerful object detection algorithms: Mask-RCNN from Detectron2 and the popular YOLOv8. Their performance is compared to identify the most effective approach for this application. Bounding boxes are drawn around detected cattle to assign unique local IDs, enabling individual tracking and isolation throughout the video sequence. Additionally, mask regions generated by the chosen detection algorithm provide valuable data for feature extraction, which is crucial for subsequent lameness classification. The extracted cattle mask region values serve as the basis for feature extraction, capturing relevant information indicative of lameness. These features, combined with the local IDs assigned during tracking, are used to compute a lameness score for each cattle. We explore the efficacy of various established machine learning algorithms, such as Support Vector Machines (SVM), AdaBoost and so on, in analyzing the extracted lameness features. Evaluation of the proposed system was conducted across three key domains: detection, tracking, and lameness classification. Notably, the detection module employing Detectron2 achieved an impressive accuracy of 98.98%. Similarly, the tracking module attained a high accuracy of 99.50%. In lameness classification, AdaBoost emerged as the most effective algorithm, yielding the highest overall average accuracy (77.9%). Other established machine learning algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Random Forests, also demonstrated promising performance (DT: 75.32%, SVM: 75.20%, Random Forest: 74.9%). The presented approach demonstrates the successful implementation for cattle lameness detection. The proposed system has the potential to revolutionize dairy farm management by enabling early lameness detection and facilitating effective monitoring of cattle health. Our findings contribute valuable insights into the application of advanced computer vision methods for livestock health management.

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mentioning

confidence: 99%

Development of a real-time cattle lameness detection system using a single side-view camera

Myint,

Onizuka,

Tin

et al. 2024

Sci Rep

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Sensor-type agnostic heat detection in dairy cows using multi-autoencoders with shared latent space

Lee,

Lee

et al. 2024

Applied Soft Computing

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Scientific and production substantiation of the effectiveness of the product for dry hoof baths

Tarasova,

Khuzin,

Mingaleev

et al. 2024

BIO Web Conf.

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This paper presents the results of a study of the adsorption activity of a developed complex product for the treatment and prevention of diseases of the distal extremities of cattle and its effectiveness with individual and group use using the method of dry hoof baths in a number of agricultural enterprises with tethered and freestall housing of cattle. It was shown that the developed product has a porous structure and a wide range of adsorption. The main cause of infection causing mass lesions of the fingers and hooves of cattle in the surveyed agricultural enterprises was an increase in the threshold number of the diversity of saprophytic and opportunistic microorganisms transmitted directly from one animal to another. Associations of bacteria and micromycetes determined the cause of the occurrence and spread of infectious diseases of the fingers and hooves. The test results of the developed complex product, which includes inorganic adsorbents and active substances with pronounced fungicidal, bactericidal and anti-inflammatory properties indicate the possibility of its introduction into the system of control measures with diseases of fingers and hooves in disadvantaged agricultural enterprises with both tethered and free-stall livestock keeping.

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Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-Autoencoder

Cited by 7 publications

References 48 publications

Development of a real-time cattle lameness detection system using a single side-view camera

Development of a real-time cattle lameness detection system using a single side-view camera

Sensor-type agnostic heat detection in dairy cows using multi-autoencoders with shared latent space

Scientific and production substantiation of the effectiveness of the product for dry hoof baths

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