Recognition of aggressive episodes of pigs based on convolutional neural network and long short-term memory

Chen, Chen; Zhu, Weixing; Steibel, Juan P.; Siegford, Janice M.; Wurtz, Kaitlin; Han, Junjie; Norton, Tomás

doi:10.1016/j.compag.2019.105166

Cited by 89 publications

(54 citation statements)

References 26 publications

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“… To improve upon the speed and precision of existing pig detection and behaviour estimation methods 31 , we reframed the goal of the method to directly obtain pig behaviours from images. This was instead of first detecting pigs and then inferring the corresponding behaviour at different stages 21 , 32 . We investigated the characteristics of two well-known detectors 28 , 29 , in terms of speed, pig behaviour detection precision and miss rate, showing that YOLO is clearly superior for this task.…”

Section: Discussionmentioning

confidence: 99%

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Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Alameer

Kyriazakis

Bacardit

2020

Sci Rep

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Changes in pig behaviours are a useful aid in detecting early signs of compromised health and welfare. In commercial settings, automatic detection of pig behaviours through visual imaging remains a challenge due to farm demanding conditions, e.g., occlusion of one pig from another. Here, two deep learning-based detector methods were developed to identify pig postures and drinking behaviours of group-housed pigs. We first tested the system ability to detect changes in these measures at group-level during routine management. We then demonstrated the ability of our automated methods to identify behaviours of individual animals with a mean average precision of , under a variety of settings. When the pig feeding regime was disrupted, we automatically detected the expected deviations from the daily feeding routine in standing, lateral lying and drinking behaviours. These experiments demonstrate that the method is capable of robustly and accurately monitoring individual pig behaviours under commercial conditions, without the need for additional sensors or individual pig identification, hence providing a scalable technology to improve the health and well-being of farm animals. The method has the potential to transform how livestock are monitored and address issues in livestock farming, such as targeted treatment of individuals with medication.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Alameer

Kyriazakis

Bacardit

2020

Sci Rep

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“…In another study, Lao et al (93) defined a classification tree for identification of several sow behaviors using videos from 3D cameras with high (99%) accuracy for lying, sitting, and drinking behaviors and lower for kneeling (78%) and shifting (64%). Machine learning techniques have also shown to be powerful for the identification of social interactions among animals, such as mounting and aggressive behavior (92,94). Viazzi et al (92) achieved a mean accuracy of 0.88 when using linear discriminant analysis for classifying aggressive behavior in pigs, while Chen et al (94) achieved an accuracy of 0.97 on the validation set using a convolution neural network and long shortterm memory approach.…”

Section: Evaluation Of Body Composition Meat and Carcass Traits In mentioning

confidence: 99%

“…Machine learning techniques have also shown to be powerful for the identification of social interactions among animals, such as mounting and aggressive behavior (92,94). Viazzi et al (92) achieved a mean accuracy of 0.88 when using linear discriminant analysis for classifying aggressive behavior in pigs, while Chen et al (94) achieved an accuracy of 0.97 on the validation set using a convolution neural network and long shortterm memory approach. Even though there was an improvement in accuracy in the latter study, it did not include an automated strategy for the identification of individual animals.…”

Section: Evaluation Of Body Composition Meat and Carcass Traits In mentioning

confidence: 99%

Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

2020

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Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Applications of CSV are currently a growing research area and there are already commercial products available. However, there are still challenges that demand research for the successful development of autonomous solutions capable of delivering critical information. This review intends to present significant developments that have been made in CVS applications in animal and veterinary sciences and to highlight areas in which further research is still needed before full deployment of CVS in breeding programs and commercial farms.

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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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Recognition of aggressive episodes of pigs based on convolutional neural network and long short-term memory

Cited by 89 publications

References 26 publications

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

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