Automatic recognition of sow nursing behaviour using deep learning-based segmentation and spatial and temporal features

Yang, Aqing; Huang, Huasheng; Zhu, Xunmu; Yang, Xin‐She; Chen, Pengfei; Li, Shimei; Xue, Yong

doi:10.1016/j.biosystemseng.2018.09.011

Cited by 53 publications

(31 citation statements)

References 35 publications

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“…Big data and machine learning have been used for the analysis of animal behavior [ 133 ]. Computer vision-based methods have been employed for automatic recognition in commercial farms using spatial and temporal information of the nursing behavior of animals and individual pig recognition, with accuracy rates of 96.7% [ 134 , 135 ].…”

Section: Limitations Of Sensing Technologiesmentioning

confidence: 99%

Transforming the Adaptation Physiology of Farm Animals through Sensors

Neethirajan¹

2020

Animals

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Despite recent scientific advancements, there is a gap in the use of technology to measure signals, behaviors, and processes of adaptation physiology of farm animals. Sensors present exciting opportunities for sustained, real-time, non-intrusive measurement of farm animal behavioral, mental, and physiological parameters with the integration of nanotechnology and instrumentation. This paper critically reviews the sensing technology and sensor data-based models used to explore biological systems such as animal behavior, energy metabolism, epidemiology, immunity, health, and animal reproduction. The use of sensor technology to assess physiological parameters can provide tremendous benefits and tools to overcome and minimize production losses while making positive contributions to animal welfare. Of course, sensor technology is not free from challenges; these devices are at times highly sensitive and prone to damage from dirt, dust, sunlight, color, fur, feathers, and environmental forces. Rural farmers unfamiliar with the technologies must be convinced and taught to use sensor-based technologies in farming and livestock management. While there is no doubt that demand will grow for non-invasive sensor-based technologies that require minimum contact with animals and can provide remote access to data, their true success lies in the acceptance of these technologies by the livestock industry.

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Section: Limitations Of Sensing Technologiesmentioning

confidence: 99%

Transforming the Adaptation Physiology of Farm Animals through Sensors

Neethirajan¹

2020

Animals

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“…From this, we can see the importance of temporal features for recognition. Sows were segmented from all frames through the FCN model to extract spatial features; then, the temporal features were designed and extracted, and the classifier was used to classify nursing behavior finally [23]. The method of this paper can extract spatial and temporal features directly through training and is end-to-end.…”

Section: Feeding Scratching Mounting Lying Walkingmentioning

confidence: 99%

“…Zheng et al [2] and Yang et al [22] used Faster Region-Convolutional Neural Networks (Faster-RCNN), which can detect pigs effectively to recognize pig postures and feeding behaviors. Sows were segmented from all frames through the Fully Convolutional Network (FCN) model, which could help recognize sows' nursing behavior with an accuracy of 97.6% [23]. Nasirahmadi et al [16] proposed three detector methods including Faster R-CNN, single shot multibox detector (SSD), and region-based fully convolutional network (R-FCN) to recognize the standing, lying on side, and lying on belly postures of pigs with a mean average precision (mAP) of over 0.93.…”

Section: Introductionmentioning

confidence: 99%

Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks

Zhang¹,

Li²,

Huang³

et al. 2020

Sensors

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The detection of pig behavior helps detect abnormal conditions such as diseases and dangerous movements in a timely and effective manner, which plays an important role in ensuring the health and well-being of pigs. Monitoring pig behavior by staff is time consuming, subjective, and impractical. Therefore, there is an urgent need to implement methods for identifying pig behavior automatically. In recent years, deep learning has been gradually applied to the study of pig behavior recognition. Existing studies judge the behavior of the pig only based on the posture of the pig in a still image frame, without considering the motion information of the behavior. However, optical flow can well reflect the motion information. Thus, this study took image frames and optical flow from videos as two-stream input objects to fully extract the temporal and spatial behavioral characteristics. Two-stream convolutional network models based on deep learning were proposed, including inflated 3D convnet (I3D) and temporal segment networks (TSN) whose feature extraction network is Residual Network (ResNet) or the Inception architecture (e.g., Inception with Batch Normalization (BN-Inception), InceptionV3, InceptionV4, or InceptionResNetV2) to achieve pig behavior recognition. A standard pig video behavior dataset that included 1000 videos of feeding, lying, walking, scratching and mounting from five kinds of different behavioral actions of pigs under natural conditions was created. The dataset was used to train and test the proposed models, and a series of comparative experiments were conducted. The experimental results showed that the TSN model whose feature extraction network was ResNet101 was able to recognize pig feeding, lying, walking, scratching, and mounting behaviors with a higher average of 98.99%, and the average recognition time of each video was 0.3163 s. The TSN model (ResNet101) is superior to the other models in solving the task of pig behavior recognition.

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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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Automatic recognition of sow nursing behaviour using deep learning-based segmentation and spatial and temporal features

Cited by 53 publications

References 35 publications

Transforming the Adaptation Physiology of Farm Animals through Sensors

Transforming the Adaptation Physiology of Farm Animals through Sensors

Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

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