Mobile measuring system based on LabVIEW for pig body components estimation in a large-scale farm

Shi, Chuang; Zhang, Jianlong; Teng, Guanghui

doi:10.1016/j.compag.2018.11.042

Cited by 50 publications

(23 citation statements)

References 25 publications

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“…The estimation accuracy of this model is higher than the projection method [ 14 ], the 2D image method [ 15 , 16 , 17 , 18 ], and the ellipse fitting method [ 26 , 27 , 28 , 29 ], as this model estimates pig weight and body size based on the height and distance of all points in a 3D image rather than the individual information points extracted by these other methods. The accuracy is same when using the 3D image method [ 9 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], but the processing operation of the model is simpler. The estimation accuracy of pig weight and body size cannot be further improved because pig weight changes with eating, drinking, and excretion.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Pig Weight and Body Size Estimation Using a Multiple Output Regression Convolutional Neural Network: A Fast and Fully Automatic Method

Zhang

Zhuang

et al. 2021

Sensors

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Pig weight and body size are important indicators for producers. Due to the increasing scale of pig farms, it is increasingly difficult for farmers to quickly and automatically obtain pig weight and body size. Due to this problem, we focused on a multiple output regression convolutional neural network (CNN) to estimate pig weight and body size. DenseNet201, ResNet152 V2, Xception and MobileNet V2 were modified into multiple output regression CNNs and trained on modeling data. By comparing the estimated performance of each model on test data, modified Xception was selected as the optimal estimation model. Based on pig height, body shape, and contour, the mean absolute error (MAE) of the model to estimate body weight (BW), shoulder width (SW), shoulder height (SH), hip width (HW), hip width (HH), and body length (BL) were 1.16 kg, 0.33 cm, 1.23 cm, 0.38 cm, 0.66 cm, and 0.75 cm, respectively. The coefficient of determination (R2) value between the estimated and measured results was in the range of 0.9879–0.9973. Combined with the LabVIEW software development platform, this method can estimate pig weight and body size accurately, quickly, and automatically. This work contributes to the automatic management of pig farms.

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Section: Resultsmentioning

confidence: 99%

“…Therefore, this method is more promising than the 2D image method. The mean absolute error (MAE) of estimating pig body size for this method is 1.44–5.81% [ 9 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Ellipse fitting method.…”

Section: Introductionmentioning

confidence: 99%

Pig Weight and Body Size Estimation Using a Multiple Output Regression Convolutional Neural Network: A Fast and Fully Automatic Method

Zhang

Zhuang

et al. 2021

Sensors

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“…In this study, weight monitoring, with a relative error of around 3%, was achieved as pigs walked freely under a camera (without having to restrain the animals in any way). Hence, recent technological advances have enabled easy monitoring and estimation of pig fattening from images [154,155].…”

Section: Animal Performances and Feed Monitoringmentioning

confidence: 99%

Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions

Morrone

Dimauro

Gambella

et al. 2022

Sensors

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Precision livestock farming (PLF) has spread to various countries worldwide since its inception in 2003, though it has yet to be widely adopted. Additionally, the advent of Industry 4.0 and the Internet of Things (IoT) have enabled a continued advancement and development of PLF. This modern technological approach to animal farming and production encompasses ethical, economic and logistical aspects. The aim of this review is to provide an overview of PLF and Industry 4.0, to identify current applications of this rather novel approach in different farming systems for food producing animals, and to present up to date knowledge on the subject. Current scientific literature regarding the spread and application of PLF and IoT shows how efficient farm animal management systems are destined to become. Everyday farming practices (feeding and production performance) coupled with continuous and real-time monitoring of animal parameters can have significant impacts on welfare and health assessment, which are current themes of public interest. In the context of feeding a rising global population, the agri-food industry and industry 4.0 technologies may represent key features for successful and sustainable development.

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“…To overcome these limitations, as an alternative solution, the computer vision and AI based approaches start to attract interests, which has been used to automatically score pigs posture [6], recognise aggressive episodes of pigs [7] [8], estimate pig body components [9], predict tail-biting, fouling and diarrhoea in pigs [10], predict stress in piglets [11,12], count pigs [13], track outdoor animal [14], recognise feeding behavior [15], estimate pig weights from images [16], detect pigs in camera images [17], and measure pig body size [18]. For these approaches, only a few cameras are needed at specific places to identify and monitor the animals and the cost of the system has much less dependency on the number of pigs, which is especially attractive for large farms.…”

Section: Introductionmentioning

confidence: 99%

An adaptive pig face recognition approach using Convolutional Neural Networks

Marsot

Mei

Shan

et al. 2020

Computers and Electronics in Agriculture

111

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The evolution of agriculture towards intensive farming leads to an increasing demand for animal identification associated with high traceability, driven by the need for quality control and welfare management in agricultural animals. Automatic identification of individual animals is an important step to achieve individualised care in terms of disease detection and control, and improvement of the food quality. For example, as feeding patterns can differ amongst pigs in the same pen, even in homogenous groups, automatic registration shows the most potential when applied to an individual pig.

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Mobile measuring system based on LabVIEW for pig body components estimation in a large-scale farm

Cited by 50 publications

References 25 publications

Pig Weight and Body Size Estimation Using a Multiple Output Regression Convolutional Neural Network: A Fast and Fully Automatic Method

Pig Weight and Body Size Estimation Using a Multiple Output Regression Convolutional Neural Network: A Fast and Fully Automatic Method

Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions

An adaptive pig face recognition approach using Convolutional Neural Networks

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