Automatic recognition and classification of cattle chewing activity by an acoustic monitoring method with a single-axis acceleration sensor

Tani, Yukinori; Yokota, Yasunari; Yayota, Masato; Ohtani, Shigeru

doi:10.1016/j.compag.2013.01.001

Cited by 42 publications

(22 citation statements)

References 18 publications

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“…C, B and CB), reaching a successful detection rate of 94%. In a similar way, Tani et al (2013) detected ingestive and ruminating chewing with approximately a 98% detection success. These quantitative results (except the results of algorithm developed by Milone et al (2012) that used the same RDb database) are not directly comparable to the present study because the studies vary in number and type of events analyzed, duration of records, type and height of pastures, recording procedures and devices, and validation methods.…”

Section: Discussionmentioning

confidence: 75%

“…The procedure eliminated the need of calibrations and allowed a detection of ingestive events with a 94% correct and 7% false identification. More recently, Tani et al (2013) applied pattern recognition techniques to iteratively measure eating and ruminating events collected by a single-axis accelerometer. The recognition patterns were defined in frequency domain and used to identify and classify likely eating and rumination events.…”

Section: Introductionmentioning

confidence: 99%

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A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle

Chelotti

Vanrell

Milone

et al. 2016

Computers and Electronics in Agriculture

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a b s t r a c tAssessment of both grazing behavior and herbage intake are two very difficult tasks that can be concurrently accomplished by means of accurate detection, classification and measurement of grazing events such as chews, bites and chew-bites. It is well known that acoustic monitoring is among the best methods to automatically quantify and classify ingestive and rumination events in grazing animals. However, most existing methods of signal analysis appear to be computationally complex and costly, and are therefore difficult to implement. In this work, we present and test a novel analysis system called Chew-Bite Real-Time Algorithm (CBRTA) that works fully automatically in real-time to detect and classify ingestive events of grazing cattle. The system employs a directional wide-frequency microphone facing inwards on the forehead of animals, and a coupled signal analysis and decision logic algorithm that measures shape, amplitude, duration and energy of sound signals to iteratively detect and classify ingestive events. Performance and validation of the CBRTA was determined using two databases of grazing signals. Signals were recorded on dairy cows offered either, natural pasture (N ¼ 25), or experimental micro-swards in indoor controlled environment (N ¼ 50). The CBRTA exhibited a simple linear complexity capable to execute 50 times faster than real-time and without undermining overall recognition rate and accuracy when signals were processed at 4 kHz sampling frequency and 8 bits quantization. Furthermore, CBRTA was capable to detect ingestive events with a 97.4% success rate, while achieving up to 84.0% success for their classification as exclusive chews, bites or composite chew-bites. The methodology proposed with CBRTA has promising application in embedded microcomputer systems that necessarily depend on fast real-time execution to minimize computational load, power source and storage memory. Such a system can readily facilitate the transmission of processed data through wireless network or the storage in an onboard device.

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Section: Discussionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle

Chelotti

Vanrell

Milone

et al. 2016

Computers and Electronics in Agriculture

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“…Therefore, an automated behavior‐monitoring system is needed. In response to this need various research studies have been conducted and published, such as: a one‐axis accelerometer mounted to the collar of cattle (Ueda, Akiyama, Asakuma, & Watanabe, ), the detection of eating and estimation of feed intake by pendulum (Uemura, Wanaka, & Ueno, ), a bitemeter incorporated microphone and a mercury switch (Delagarde, Caudal, & Peyraud, ), a pressure sensor attached to the halter (Braun, Trösch, Nydegger, & Hässig, ), the detection of eating and rumination by one‐axis accelerometer with a voice recorder attached to a horn (Tani, Yokota, Yayota, & Ohtani, ), the detection of rumination time by a logger attached to the collar (Schirmann, von Keyserlingk, Weary, Veira, & Heuwieser, ), and the monitoring of lying behavior using a pedometor (Mattachini, Antler, Riva, Arbel, & Provolo, ). However, these are limited to the detection of simple behaviors, which is unsuitable for understanding complex behaviors.…”

Section: Introductionmentioning

confidence: 99%

Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Tamura

Okubo

Deguchi

et al. 2019

Animal Science Journal

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Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three‐axis neck‐mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors (p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross‐validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three‐axis accelerometers mounted to a collar.

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“…Accelerometers can be used to measure static acceleration due to gravity, the low-frequency component of the acceleration and the dynamic acceleration due to animal movement (Herinaina et al, 2016). Several researchers have demonstrated the use of accelerometers for analysing the grazing behaviour of animals (Mattachini et al, 2016; Tani et al, 2013; Giovanetti et al, 2017). Andriamandroso et al .…”

Section: Mechanical Sensors (Pressure Sensors) Acoustic Sensors (Micmentioning

confidence: 99%

Recent Advancement in Biosensors Technology for Animal and Livestock Health Management

Neethirajan

Huang

Tuteja

et al. 2017

Preprint

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Automatic recognition and classification of cattle chewing activity by an acoustic monitoring method with a single-axis acceleration sensor

Cited by 42 publications

References 18 publications

A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle

A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle

Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Recent Advancement in Biosensors Technology for Animal and Livestock Health Management

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