Using automated image analysis in pig behavioural research: Assessment of the influence of enrichment substrate provision on lying behaviour

Nasirahmadi, Abozar; Edwards, Sandra A.; Matheson, Stephanie M.; Sturm, Barbara

doi:10.1016/j.applanim.2017.06.015

Cited by 35 publications

(26 citation statements)

References 33 publications

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“…For previous research on identification, References [11,16-18, 20-22,24-26,29,31,38,44-48] for detection and References [10,19,37,39,45] for tracking exist. In addition, previous studies for early detection of abnormalities exist as various topics, including research on the movement of pigs [17,62], research on aggressive behavior of pigs [63,64], research on attitude change [16,22,23,31,32,34,35,40,46], research on mounting behavior [21], research on low-growth pig's behavior [49], research on pig weight [29,33,38] and research on the density of pigs [9,11].…”

Section: Introductionmentioning

confidence: 99%

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

et al. 2020

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Automated pig monitoring is an important issue in the surveillance environment of a pig farm. For a large-scale pig farm in particular, practical issues such as monitoring cost should be considered but such consideration based on low-cost embedded boards has not yet been reported. Since low-cost embedded boards have more limited computing power than typical PCs and have tradeoffs between execution speed and accuracy, achieving fast and accurate detection of individual pigs for “on-device” pig monitoring applications is very challenging. Therefore, in this paper, we propose a method for the fast detection of individual pigs by reducing the computational workload of 3 × 3 convolution in widely-used, deep learning-based object detectors. Then, in order to recover the accuracy of the “light-weight” deep learning-based object detector, we generate a three-channel composite image as its input image, through “simple” image preprocessing techniques. Our experimental results on an NVIDIA Jetson Nano embedded board show that the proposed method can improve the integrated performance of both execution speed and accuracy of widely-used, deep learning-based object detectors, by a factor of up to 8.7.

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Section: Introductionmentioning

confidence: 99%

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

et al. 2020

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“…The spread of infectious diseases is a huge threat to livestock farming. In order to detect the early symptoms of swine disease in time, people need to conduct effective monitoring of the group-housed pigs suffering from disease [1,2] through methods such as temperature measurement by infrared ray, detection of cough [3,4], calculation of the amount of drinking water [5], eating and drinking behavior recognition [6,7], and behavioral change measurement [8]. In this process, one of the most important basic tasks is to distinguish different pigs and identify pigs with abnormal behavior.…”

Section: Introductionmentioning

confidence: 99%

“…Sensors 2020, 20, x FOR PEER REVIEW 6 of 20 2. In order to extract the local multi-directional information, the multi-directional masks are used.…”

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confidence: 99%

Weber Texture Local Descriptor for Identification of Group-Housed Pigs

Huang

Zhu

et al. 2020

Sensors

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The individual identification of group-housed pigs plays an important role in breeding process management and individual behavior analysis. Recently, livestock identification methods based on the side view or face image have strict requirements on the position and posture of livestock, which poses a challenge for the application of the monitoring scene of group-housed pigs. To address the issue above, a Weber texture local descriptor (WTLD) is proposed for the identification of group-housed pigs by extracting the local features of back hair, skin texture, spots, and so on. By calculating the differential excitation and multi-directional information of pixels, the local structure features of the main direction are fused to enhance the description ability of features. The experimental results show that the proposed WTLD achieves higher recognition rates with a lower feature dimension. This method can identify pig individuals with different positions and postures in the pig house. Without limitations on pig movement, this method can facilitate the identification of individual pigs with greater convenience and universality.

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“…presented by commercial farms. Table 1 summarizes the topview-based pig monitoring results introduced recently [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. Two-dimensional gray-scale or color information has been used to detect a single pig in a pen or a specially built facility (i.e., in “constrained” environments) [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Two-dimensional gray-scale or color information has been used to detect a single pig in a pen or a specially built facility (i.e., in “constrained” environments) [ 9 , 10 , 11 ]. However, even with advanced techniques applied to 2D gray-scale or color information, it remains challenging to detect multiple pigs accurately in a “commercial” farm environment [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. For example, images from a gray-scale or RGB camera are affected by various illuminations in a pig pen.…”

Section: Introductionmentioning

confidence: 99%

Depth-Based Detection of Standing-Pigs in Moving Noise Environments

Kim

Chung

Choi

et al. 2017

Sensors

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In a surveillance camera environment, the detection of standing-pigs in real-time is an important issue towards the final goal of 24-h tracking of individual pigs. In this study, we focus on depth-based detection of standing-pigs with “moving noises”, which appear every night in a commercial pig farm, but have not been reported yet. We first apply a spatiotemporal interpolation technique to remove the moving noises occurring in the depth images. Then, we detect the standing-pigs by utilizing the undefined depth values around them. Our experimental results show that this method is effective for detecting standing-pigs at night, in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (i.e., 94.47%), even with severe moving noises occluding up to half of an input depth image. Furthermore, without any time-consuming technique, the proposed method can be executed in real-time.

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Using automated image analysis in pig behavioural research: Assessment of the influence of enrichment substrate provision on lying behaviour

Cited by 35 publications

References 33 publications

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

Weber Texture Local Descriptor for Identification of Group-Housed Pigs

Depth-Based Detection of Standing-Pigs in Moving Noise Environments

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