A deep learning-based cow behavior recognition scheme for improving cattle behavior modeling in smart farming

Shakeel, P. Mohamed; Aboobaider, Burhanuddin bin Mohd; Salahuddin, Lizawati

doi:10.1016/j.iot.2022.100539

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…However, these approaches exhibit two primary shortcomings. Firstly, they tend to possess inherent limitations in terms of scope [ 41 , 42 , 43 ]. Secondly, they might overlook latent nonlinear and sophisticated features present within the data.…”

Section: Discussionmentioning

confidence: 99%

CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM

Gao,

Wang,

Wang

et al. 2023

Sensors

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Cattle behavior classification technology holds a crucial position within the realm of smart cattle farming. Addressing the requisites of cattle behavior classification in the agricultural sector, this paper presents a novel cattle behavior classification network tailored for intricate environments. This network amalgamates the capabilities of CNN and Bi-LSTM. Initially, a data collection method is devised within an authentic farm setting, followed by the delineation of eight fundamental cattle behaviors. The foundational step involves utilizing VGG16 as the cornerstone of the CNN network, thereby extracting spatial feature vectors from each video data sequence. Subsequently, these features are channeled into a Bi-LSTM classification model, adept at unearthing semantic insights from temporal data in both directions. This process ensures precise recognition and categorization of cattle behaviors. To validate the model’s efficacy, ablation experiments, generalization effect assessments, and comparative analyses under consistent experimental conditions are performed. These investigations, involving module replacements within the classification model and comprehensive analysis of ablation experiments, affirm the model’s effectiveness. The self-constructed dataset about cattle is subjected to evaluation using cross-entropy loss, assessing the model’s generalization efficacy across diverse subjects and viewing perspectives. Classification performance accuracy is quantified through the application of a confusion matrix. Furthermore, a set of comparison experiments is conducted, involving three pertinent deep learning models: MASK-RCNN, CNN-LSTM, and EfficientNet-LSTM. The outcomes of these experiments unequivocally substantiate the superiority of the proposed model. Empirical results underscore the CNN-Bi-LSTM model’s commendable performance metrics: achieving 94.3% accuracy, 94.2% precision, and 93.4% recall while navigating challenges such as varying light conditions, occlusions, and environmental influences. The objective of this study is to employ a fusion of CNN and Bi-LSTM to autonomously extract features from multimodal data, thereby addressing the challenge of classifying cattle behaviors within intricate scenes. By surpassing the constraints imposed by conventional methodologies and the analysis of single-sensor data, this approach seeks to enhance the precision and generalizability of cattle behavior classification. The consequential practical, economic, and societal implications for the agricultural sector are of considerable significance.

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Section: Discussionmentioning

confidence: 99%

CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM

Gao,

Wang,

Wang

et al. 2023

Sensors

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“…At present, the animal behavior analysis system on the farm has applied a variety of intelligent technologies, and an innovative behavior recognition and computing scheme (BRCS) has been adopted in Europe and the United States, which can collect and analyze the behavior data of cattle by obtaining information through microchips swallowed by cattle [2]. In addition, image processing and deep learning were used to accomplish target localization and behavior recognition, which showed an accuracy rate of more than 80% in the reproductive health of dairy cows, especially in the recognition rate of hoof disease and heat, while the false negative rates of heat and hoof disease were 3.28% and 5.32%, respectively [3].…”

Section: Research Status At Home and Abroadmentioning

confidence: 99%

Research on Abnormal Behavior Detection Technology for Simmental Cattle

Jia,

Qin,

et al. 2024

FCIS

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This paper mainly studies the abnormal behavior detection technology of Simmental cattle, aiming to establish an efficient and reliable abnormal behavior detection system, so as to detect abnormal situations in time and take corresponding measures to deal with potential problems. At the same time, by establishing a dataset for the abnormal behavior of Simmental cattle, the study uses deep learning algorithms to accurately capture the existence, location and key body parts of Simmental cattle, and accurately identify abnormal behaviors such as convulsions and falls. The application of abnormal behavior detection technology to animal husbandry to achieve contactless, automated, and efficient monitoring of Simmental cattle behavior can provide advanced and comprehensive intelligent health management solutions for animal husbandry and promote the wide application of intelligent management in animal husbandry. Real-time monitoring, early warning and fine management of Simmental cattle behavior can effectively reduce the risk of disease transmission and improve the production safety of farms.

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“…They integrated a customized filter layer with an average filtering algorithm and leaky rectified linear unit (leaky ReLU) function to reduce training interference. Shakeel et al 29 introduced an innovative behavior recognition and computing scheme to predict cattle behavior. The proposed method used a deep recurrent learning paradigm to cycle the recognition pattern and classify abnormal situations based on differentiated data patterns.…”

Section: Advances In Deep Learning-based Cattle Regurgitation Recogni...mentioning

confidence: 99%

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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A deep learning-based cow behavior recognition scheme for improving cattle behavior modeling in smart farming

Cited by 9 publications

References 27 publications

CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM

CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM

Research on Abnormal Behavior Detection Technology for Simmental Cattle

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

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