Decandia M.1, Giovanetti V.1, Acciaro M.1, Mameli M.3, Molle G.1, Cabiddu A.1, Manca C.1, Cossu R.2, Serra M.G.1, Dimauro C.2 and Rassu S.P.G.2
1AGRIS Sardegna, 07040 Olmedo, Italy; 2Dipartimento di Agraria, Università di Sassari, viale Italia 39, 07100 Sassari, Italy;3Electronic systems, via Sassari 101, 07041 Alghero, Italy;
mdecandia@agrisricerca.it
Abstract
Developments in microelectromechanical system (MEMS) and information and communication technologies provide new opportunities for the automatic monitoring of animal behaviour. The BEHARUM project is aimed to realize an automatic recording device for monitoring the grazing behaviour of ruminants. The device includes a small, low power, complete three-axis accelerometer with signal-conditioned voltage outputs. It can measure the static acceleration of gravity in tilt-sensing applications, as well as dynamic acceleration resulting from motion, shock, or vibration. The sensor inserted in MEMS is attached to the lower jaw and, thanks to its miniaturization, has a minimum impact on the animal. The system is able to continuously sample accelerations and to send three values at 1-s intervals to a nearby computer, by a ZigBee wireless module connected to it, or to a remote computer via Global System for Mobile Communications (GSM). Data acquisition and analysis is realized by a software installed in the receiving computer. Calibration and validation of the system is ongoing, comparing acceleration with video-recording data to automatically classify three activities, eating, ruminating and resting in grazing dairy sheep. A data mining approach followed by multivariate analyses procedures, allows automatic classification of the three activities, saving battery charge to lengthen the recording session. The system is upgradable with other sensors such as the global positioning system and it could become a useful tool to effectively drive the management of pastoral resources.
Keywords: accelerometer, grazing, ruminating, resting, multivariate analysis, dairy sheep
Introduction
Monitoring the behaviour of ruminants in near real-time is important to check the living conditions of animals and manage them more effectively in terms of performance, welfare and environmental impact.
The growing access to information and communication technologies (ICT) is boosting precision farming that may allow individual livestock management in order to maximise individual animal potential (Moreau et al., 2009).
The miniaturization of electronic components allows their minimum impact on the animal. Moreover, through the use of wireless modules, is possible to send data upon certain distances opening new prospects for studying animal behaviour where access to visual observation is difficult (Brown et al., 2013). The BEHARUM project has contributed to this research area by developing an accelerometer-based recording device able to classify automatically the behaviour of grazing sheep.
Materials and methods
The project was funded by the regional government of Sardinia to promote research on ICT applied to ruminant management. The involved partners were: the department of agriculture of the University of Sassari, the agricultural research agency of Sardinia (AGRIS Sardegna) and a local enterprise (LE, Electronic System).
The BEHARUM device consists of a halter equipped with a three-axial accelerometer sensor positioned under the lower jaw of the sheep. The animal’s movements are detected through accelerations measured in the x (longitudinal), y (horizontal) and z (vertical) axis. The sensor is inserted in a micro-electromechanical compact system (MEMS) with lots of on-board peripherals. The acceleration sensor records both accelerations related to changes in the movements of the sheep (dynamic accelerations) and static accelerations (-9.8 m s-2). The microcontroller samples the row acceleration data at a frequency of 62.5 Hz and encode them, through an analog-to-digital converter with a resolution of 8 bits, into levels ranging from 0 to 255, then selects only three acceleration converted values per second and axis.
Acceleration converted data are sent (ZigBee wireless system) to a receiver nearby computer equipped with an antenna or to a remote computer through a local server using the GSM services.
Several calibration trials have been conducted at Bonassai experimental farm of AGRIS, located in the NW of Sardinia (40°N, 32°E, 32 m a.s.l). Three Sarda sheep were equipped with the BEHARUM device and acceleration data were recorded as animals were grazing the following different forages: Medicago sativa L., Cichorium intybus L., Hedysarum coronarium L., Trifolium alexandrinum L., and Lolium multiflorum Lam.. During each trial sheep behaviour was video-recorded by a fixed camera. Acceleration data were acquired with a software developed by the LE and installed in the receiving computer that stored data in a .csv file.
According to Watanabe et al. (2008), from the acceleration data, were calculated the mean, the variance and the inverse coefficient of variation (ICV; mean/standard deviation) per minute for each axis.
Moreover, the resultant mean, variance and ICV values of the three axes was also calculated. On the basis of the video recording, the behaviour of the animals was classified, at 1-min intervals, into three activities:
grazing, ruminating and resting. Resting activity includes sheep lying down, standing or walking in absence of jaw activity. The dataset obtained combining the three activities with the 12 variables concerning acceleration was analysed by using three multivariate statistical techniques: the stepwise discriminant analysis (SDA), to select the best variable subset, the canonical discriminant analysis (CDA), to test the effective separation among the three behaviours, and, finally, the discriminant analysis (DA), to assign minutes to the correct activity.
Results and discussion
Preliminary calibration results of the BEHARUM device are promising for classifying eating, ruminating and resting activities also in small ruminant as dairy sheep, in agreement with other studies (Moreau et al., 2009; Marais et al., 2014), even when data mining procedures has been applied to the raw acceleration dataset. The SDA selected 7 variables over 12 and the subsequent CDA, developed by using those characters, significantly discriminated the three behaviours (Hotelling’s test P-value <0.0001). The variation explained by the two extracted canonical functions, CAN1 and CAN2, was 0.68 and 0.32, respectively. CAN1 discriminates between grazing and resting whereas CAN2 differentiates the grazing from the ruminating behaviour (Figure 1). Finally, the DA correctly assigned 92.96% of minutes to behavioural activities. More trials should be run in the near future to complete the calibration process and afterward a validation test may be conducted in different environmental conditions. The system is designed to be upgradable with other sensors such as the global positioning system in order to open its use for spatio-temporal behavioural studies.
Conclusions
The BEHARUM project demonstrates that accelerometers and information and communication technologies are useful tools to discriminate behaviour activities of grazing dairy sheep. Additional types of sensor could be added to this device in order to improve the overall classification accuracy and to effectively drive the management of pastoral resources.
Acknowledgements
The Authors acknowledge the financial support of Sardinian Regional Government (Projects CRP-17287 PO Sardegna (Italy), FSE 2007-2013 LR 7/2007) and thank Mr S. Picconi and Mr S. Pintus for their technical help.
References
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Marais J., Le Roux S.P., Wolhuter R. and Niesler T. (2014) Automatic classification of sheep behaviour using 3-axis accelerometer data.
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Moreau M., Siebert S., Buerkert A. and Schlecht E. (2009) Use of a tri-axial accelerometer for automated recording and classification of goats’ grazing behaviour. Applied Animal Behaviour Science 119, 158-170.
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Figure 1. Graph of the two canonical functions (CAN1 and CAN2) obtained in the canonical discriminant analysis applied to acceleration data.