An online method for estimating grazing and rumination bouts using acoustic signals in grazing cattle

Chelotti, José O.; Vanrell, Sebastián R.; Rau, Luciano S. Martinez; Galli, Julio R.; Planisich, Alejandra M.; Utsumi, Santiago A.; Milone, Diego H.; Giovanini, Leonardo; Rufiner, Hugo Leonardo

doi:10.1016/j.compag.2020.105443

Cited by 37 publications

(17 citation statements)

References 38 publications

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“…(i) a signal conditioning stage, which attenuates the effects of environmental noises and disturbances, (ii) a set of extracted acoustical features and (iii) a machine learning model that provides the algorithm with excellent discrimination capabilities. Based on this algorithm, a foraging activity recognizer called BUFAR was proposed by Chelotti et al (2020). It analyses groups of JM to recognize grazing and rumination bouts.…”

Section: Background: Current Acoustic Methodsmentioning

confidence: 99%

“…The main disadvantage of this algorithm is the requirement of the entire sound record to achieve satisfactory performance results. More recently, Chelotti et al (2020) introduced BUFAR, an alternative algorithm to RAFAR to enable on-line processing of sound signals. Both, RAFAR and BUFAR achieved better performance compared to commercial rumination collars (Chelotti et al, 2020;Vanrell et al, 2018), yet the two also showed a noticeable misclassifications between grazing and rumination bouts.…”

Section: Introductionmentioning

confidence: 99%

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Using segment-based features of jaw movements to recognize foraging activities in grazing cattle

Chelotti¹,

Vanrell²,

Martinez-Rau³

et al. 2022

Preprint

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Precision livestock farming optimizes livestock production through the use of sensor information and communication technologies to support decision making, proactively and near real-time. Among available technologies to monitor foraging behavior, the acoustic method has been highly reliable and repeatable, but can be subject to further computational improvements to increase precision and specificity of recognition of foraging activities. In this study, an algorithm called Jaw Movement segment-based Foraging Activity Recognizer (JMFAR) is proposed. The method is based on the computation and analysis of temporal, statistical and spectral features of jaw movement sounds for detection of rumination and grazing bouts. They are called JM-

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Section: Background: Current Acoustic Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using segment-based features of jaw movements to recognize foraging activities in grazing cattle

Chelotti¹,

Vanrell²,

Martinez-Rau³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Röttgen et al [ 134 ] reported that the vocalisation rate is a suitable indicator used to confirm a cattle’s estrus status, and it was suggested that the status of the cattle can be monitored through voice analysis. Chelotti et al [ 135 ] estimated grazing and rumination bouts using acoustic signals in grazing cattle and achieved 0.75 F1-scores for both activities. However, how to effectively acquire sound and to accurately determine this information in a livestock facility is still a challenge.…”

Section: Cattle Lameness Detection and Scoringmentioning

confidence: 99%

Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review

Qiao

Kong

Clark

et al. 2021

Animals

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The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.

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“…Clapham et al [26] used manual identification and sound metrics to identify the jaw movement that detected 95% of behavior, however this system requires manual calibration periodically which is not recommended for automated learning systems. Furthermore, some systems use sound sensors to recognize the rumination and grazing behavior after analysing jaw movement [27,28]. The monitoring methods with sound sensor gave a good performance.…”

Section: Sound Sensor For Rumination Detectionmentioning

confidence: 99%

Dairy Cow rumination detection: A deep learning approach

Ayadi,

said,

Jabbar

et al. 2021

Preprint

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Cattle activity is an essential index for monitoring health and welfare of the ruminants. Thus, changes in the livestock behavior are a critical indicator for early detection and prevention of several diseases. Rumination behavior is a significant variable for tracking the development and yield of animal husbandry. Therefore, various monitoring methods and measurement equipment have been used to assess cattle behavior. However, these modern attached devices are invasive, stressful and uncomfortable for the cattle and can influence negatively the welfare and diurnal behavior of the animal. Multiple research efforts addressed the problem of rumination detection by adopting new methods by relying on visual features. However, they only use few postures of the dairy cow to recognize the rumination or feeding behavior. In this study, we introduce an innovative monitoring method using Convolution Neural Network (CNN)-based deep learning models. The classification process is conducted under two main labels: ruminating and other, using all cow postures captured by the monitoring camera. Our proposed system is simple and easy-to-use which is able to capture long-term dynamics using a compacted representation of a video in a single 2D image. This method proved efficiency in recognizing the rumination behavior with 95%, 98% and 98% of average accuracy, recall and precision, respectively.

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An online method for estimating grazing and rumination bouts using acoustic signals in grazing cattle

Cited by 37 publications

References 38 publications

Using segment-based features of jaw movements to recognize foraging activities in grazing cattle

Using segment-based features of jaw movements to recognize foraging activities in grazing cattle

Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review

Dairy Cow rumination detection: A deep learning approach

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