Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach

Qiao, Yongliang; Clark, Cameron; Lomax, Sabrina; Kong, He; Su, Daobilige; Sukkarieh, Salah

doi:10.3389/fanim.2021.759147

Cited by 10 publications

(5 citation statements)

References 55 publications

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“…Qiao et al [40] proposed and implemented a unified deep learning method for bovine recognition based on video analysis, which is composed of convolutional neural networks and bidirectional long short-term memory (BiL STM) with self-attention mechanism. Lima Weber et al [41] purpose to identify cattle breeds using convolutional neural networks.…”

Section: Cnn Identification Methodsmentioning

confidence: 99%

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Open Pose Mask R-CNN Network for Individual Cattle Recognition

Wang,

Zhang,

Gao

et al. 2023

IEEE Access

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Cattle's individual identification plays a crucial role in effectively managing large farms. To enhance agricultural efficiency, promote the digital transformation of animal husbandry, and improve animal welfare, it is essential to employ advanced identification technologies capable of real-time monitoring of cattle individual. This paper introduces a novel network called Open Pose Mask R-CNN (OP-Mask R-CNN) for individual cattle identification, which combines Open Pose with the Mask R-CNN network. Three key strategies are presented to improve the identification of individual cattle. First, optimize the number of convolutional layers in the Mask R-CNN backbone network, i.e., ResNet101. Second, introduce an Open Pose-based bovine skeleton feature extraction method. Finally, construct a fusion mechanism that combines the attention module, the convolutional block attention module (CBAM), the open pose module, and the ResNet101. Experimental results demonstrate that our proposed method achieves a 5.6% increase in recognition accuracy and improves recognition speed compared to the original Mask R-CNN model. This work strikes a balance between accuracy and complexity, facilitating the development of a lightweight bovine individual recognition technique.

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Section: Cnn Identification Methodsmentioning

confidence: 99%

“…We evaluated the performance of four different convolutional neural network models, namely Mask R-CNN, Attention-BiLSTM [40]…”

Section: B Test Environment and Parameter Settingsmentioning

confidence: 99%

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Wang,

Zhang,

Gao

et al. 2023

IEEE Access

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“…The recent advancements in artificial intelligence have made it even easier to analyze animal behavior in videos using machine vision and machine learning (139). The development of predictive models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based on modern machine techniques are helpful in livestock research (139,140). It was shown that the development of a recurrent neural network (RNN) model with an LSTM could classify cattle behavior in a reasonable manner (141).…”

Section: Machine and Deep Learning Approaches In Livestock Researchmentioning

confidence: 99%

“…It was shown that the development of a recurrent neural network (RNN) model with an LSTM could classify cattle behavior in a reasonable manner (141). Recently, CNN and Bidirectional Long Short-Term Memory (BiLSTM) were used for video-based identification of individual cattle (140), and C3D-ConvLSTM (Convolutional 3D-Convolutional Long Short-Term Memory) based model was used for cow behavior classification over 86% accuracy (142).…”

Section: Machine and Deep Learning Approaches In Livestock Researchmentioning

confidence: 99%

Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science

Pathak

Kim

2022

Front. Vet. Sci.

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Having played important roles in human growth and development, livestock animals are regarded as integral parts of society. However, industrialization has depleted natural resources and exacerbated climate change worldwide, spurring the emergence of various diseases that reduce livestock productivity. Meanwhile, a growing human population demands sufficient food to meet their needs, necessitating innovations in veterinary sciences that increase productivity both quantitatively and qualitatively. We have been able to address various challenges facing veterinary and farm systems with new scientific and technological advances, which might open new opportunities for research. Recent breakthroughs in multi-omics platforms have produced a wealth of genetic and genomic data for livestock that must be converted into knowledge for breeding, disease prevention and management, productivity, and sustainability. Vetinformatics is regarded as a new bioinformatics research concept or approach that is revolutionizing the field of veterinary science. It employs an interdisciplinary approach to understand the complex molecular mechanisms of animal systems in order to expedite veterinary research, ensuring food and nutritional security. This review article highlights the background, recent advances, challenges, opportunities, and application of vetinformatics for quality veterinary services.

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“…One such approach involved evaluating wearable devices and cameras attached to cattle bodies for behavior monitoring [ 23 ]. Another study proposed a deep-learning-based approach for cattle identification using video analysis [ 24 ]. This approach utilized convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) with a self-attention mechanism, implemented to monitor 50 individuals.…”

Section: Introductionmentioning

confidence: 99%

Multiview Monitoring of Individual Cattle Behavior Based on Action Recognition in Closed Barns Using Deep Learning

Fuentes

Han

Nasir

et al. 2023

Animals

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Cattle behavior recognition is essential for monitoring their health and welfare. Existing techniques for behavior recognition in closed barns typically rely on direct observation to detect changes using wearable devices or surveillance cameras. While promising progress has been made in this field, monitoring individual cattle, especially those with similar visual characteristics, remains challenging due to numerous factors such as occlusion, scale variations, and pose changes. Accurate and consistent individual identification over time is therefore essential to overcome these challenges. To address this issue, this paper introduces an approach for multiview monitoring of individual cattle behavior based on action recognition using video data. The proposed system takes an image sequence as input and utilizes a detector to identify hierarchical actions categorized as part and individual actions. These regions of interest are then inputted into a tracking and identification mechanism, enabling the system to continuously track each individual in the scene and assign them a unique identification number. By implementing this approach, cattle behavior is continuously monitored, and statistical analysis is conducted to assess changes in behavior in the time domain. The effectiveness of the proposed framework is demonstrated through quantitative and qualitative experimental results obtained from our Hanwoo cattle video database. Overall, this study tackles the challenges encountered in real farm indoor scenarios, capturing spatiotemporal information and enabling automatic recognition of cattle behavior for precision livestock farming.

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Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach

Cited by 10 publications

References 55 publications

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science

Multiview Monitoring of Individual Cattle Behavior Based on Action Recognition in Closed Barns Using Deep Learning

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