Practical Monitoring of Undergrown Pigs for IoT-Based Large-Scale Smart Farm

Lee, Sung-Ju; Ahn, Hanse; Seo, Ji Hun; Chung, Yongwha; Park, Jooyoung; Pan, Sung Bum

doi:10.1109/access.2019.2955761

Cited by 28 publications

(23 citation statements)

References 30 publications

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“…Segmentation techniques without DL (1) are characterised by using thresholding for image binarization, separating background from foreground [16]. Otsu's method is a popular example for this, which has been used in a variety of use cases [17], [18], [19], [20]. This type of segmentation is usually only done as a pre-processing step, based on which the actual detection of the objects in the foreground takes place.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Deep Learning Instance Segmentation Models for Pig Precision Livestock Farming

et al. 2021

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In this paper, the deep learning instance segmentation architectures DetectoRS, SOLOv2, DETR and Mask R-CNN were applied to data from the field of Pig Precision Livestock Farming to investigate whether these models can address the specific challenges of this domain. For this purpose, we created a custom dataset consisting of 731 images with high heterogeneity and high-quality segmentation masks. For evaluation, the standard metric for benchmarking instance segmentation models in computer vision, the mean average precision, was used. The results show that all tested models can be applied to the considered domain in terms of prediction accuracy. With a mAP of 0.848, DetectoRS achieves the best results on the test set, but is also the largest model with the greatest hardware requirements. It turns out that increasing model complexity and size does not have a large impact on prediction accuracy for instance segmentation of pigs. DETR, SOLOv2, and Mask R-CNN achieve similar results to DetectoRS with a parameter count almost three times smaller. Visual evaluation of predictions shows quality differences in terms of accuracy of segmentation masks. DetectoRS generates the best masks overall, while DETR has advantages in correctly segmenting the tail region. However, it can be observed that each of the tested models has problems in assigning segmentation masks correctly once a pig is overlapped. The results demonstrate the potential of deep learning instance segmentation models in Pig Precision Livestock Farming and lay the foundation for future research in this area.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Deep Learning Instance Segmentation Models for Pig Precision Livestock Farming

et al. 2021

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“…Animal husbandry is shifting toward automation, intelligence, and precision [ 1 , 2 ]. Pig weight and body size, two of the most important indicators for pig producers, provide information about feed conversion ratio (FCR), growth rate, uniformity, and health conditions [ 3 , 4 ].…”

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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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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“…In the process of a large-scale installation, the problem of increased network bandwidth and heavy analyzation workload of the server arises due to the server requiring large-scale image data (i.e., video stream). In order to provide a practical method for pig monitoring system, it is efficient to analyze the data in a built-in device system that supports small-sized data processing after the data has been gathered [49]. Furthermore, we should consider practical issues, such as the monitoring cost, for large-scale pig farms.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a low-cost solution (rather than a typical PC-based solution) is required for "practical" monitoring of a pig room. Considering the effect of both the reduction in installation and the management costs through edge computing such as that shown in Reference [49], we can extend the solution for many high-level vision-based analyses, such as aggressive behavior analysis, in order to reduce damage to a pig farm by using a single embedded board.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Practical Monitoring of Undergrown Pigs for IoT-Based Large-Scale Smart Farm

Cited by 28 publications

References 30 publications

Evaluation of Deep Learning Instance Segmentation Models for Pig Precision Livestock Farming

Evaluation of Deep Learning Instance Segmentation Models for Pig Precision Livestock Farming

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

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

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