Automatic scoring of lateral and sternal lying posture in grouped pigs using image processing and Support Vector Machine

Nasirahmadi, Abozar; Sturm, Barbara; Olsson, Anne-Charlotte; Jeppsson, Knut-Håkan; Müller, Simone; Edwards, Sandra; Hensel, Oliver

doi:10.1016/j.compag.2018.12.009

Cited by 87 publications

(58 citation statements)

References 28 publications

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“…6 b). Our method of measuring standing and lying postures is data-driven and does not require calibration, such as setting thresholds of depth 10 or the number of segments 37 . The method learns the task only by using many examples of pig images exhibiting said behaviours.…”

Section: Discussionmentioning

confidence: 99%

“…Recent advancements focus on monitoring behaviour where the task of detecting problems correspond to users of the system 4 . Developments towards systems that automatically alert staff to behavioural changes are limited to limited behaviours, e.g., lying (or not) 41 , which maybe arbitrary 21 , 23 , 37 . Changes in relevant behaviours are impractical to quantify manually and early detection, through automation, allows for timely intervention to prevent a further reduction in animal welfare and associated economic losses.…”

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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“…Support vector machine (SVM) is an effective modeling tool for classification and was used for regression, pattern classification, prediction, and problem detection (Nasirahmadi et al, ). In SVM, data input space is mapped into a high dimensional feature space through a kernel function by using minimal training data (Huang, Tang, Yang, & Zhu, ).…”

Section: Methodsmentioning

confidence: 99%

“…Also, the receiver operating characteristic (ROC) was computed in MATLAB based on true‐positive and false‐negative rates. The area under the ROC curve which ranges from 0.5 (no discrimination ability) to 1 (best discrimination ability) was also calculated (Nasirahmadi et al, ).…”

Section: Methodsmentioning

confidence: 99%

Nondestructive classification of saffron using color and textural analysis

Moghadam

Taghizadeh

Sadrnia

et al. 2020

Food Science & Nutrition

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Saffron classification based on machine vision techniques as well as the expert's opinion is an objective and nondestructive method that can increase the accuracy of this process in real applications. The experts in Iran classify saffron into three classes Pushal, Negin, and Sargol based on apparent characteristics. Four hundred and forty color images from saffron for the three different classes were acquired, using a mobile phone camera. Twenty‐one color features and 99 textural features were extracted using image analysis. Twenty‐two classifiers were employed for classification using mentioned features. The support vector machine and Ensemble classifiers were better than other classifiers. Our results showed that the mean classification accuracy was up to 83.9% using the Quadratic support vector machine and Subspace Discriminant classifier.

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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 scoring of lateral and sternal lying posture in grouped pigs using image processing and Support Vector Machine

Cited by 87 publications

References 28 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

Nondestructive classification of saffron using color and textural analysis

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

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