• Journal of Animal Environmental Science
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• v.5 no.2
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• pp.93-100
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• 1999

Pig's original image data was transformed to a binary image, an image excluding head and tail portion from the whole binary image, and a projected image associated with pig's height. Then the length of body, width of shoulder, and area of pig were calculated and the relationships among the above characteristics and pig's weight were analyzed. The results obtained from this study were as follows: 1. Whole binary image data was considered to be improper to determine the pig's weight because the movement of pig's head and tail portion affected the image data. 2. Binary image data excluding head and tail portion from the whole binary image showed a better estimation of the pig's weight than the whole binary image. 3. Pig's should width was analyzed to be improper factor to determine the pig's weight. 4. The projected image associated with pig's height showed the highest correlation between the pig's area of the image and pig's weight(R2=0.9965). From this research the projected image associated with pig's height, which is excluding head and tail portion from the whole body of pig's image, was considered to be the prime factor to measure the pig's weight by the noncontact measurement.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.105-110
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• 2021

The pig ICT automatic feeder is capable of automatically feeding feed, etc. according to the set conditions. However, there is a disadvantage that the setting condition itself must depend on the user's experience. Therefore, trial and error is caused, and there is a problem that the efficiency is lowered. Therefore, it is necessary to develop a system and implement a service model that can improve pig productivity by suggesting optimal feeding setting conditions based on data. Therefore, in this paper, a pig feeding management service model was developed using the performance analysis program such as the existing feeding data, breeding management data, and pig production management system. Through this, we developed a consumer-oriented feed management service model that can be efficiently utilized by analyzing pig data. In addition, it is possible to provide a service that contributes to a decrease in the mortality rate and an increase in the MSY of the farms with the intelligent automatic feeding management service, thereby improving the productivity of the pig farms and thereby increasing the income of the pig farms.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1107-1112
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• 2006

Geometry PIG (Pipeline Inspection Gauge)는 배관 내에 삽입되어 내부를 흐르는 매체에 의해서 추진되는 장치로서 배관의 기하학적 형상을 파악하기 위해 사용된다. Geometry PIG는 여러 종류의 센서를 지니고 배관 내부를 주행 하면서 탑재된 저장장치에 빠른 샘플링 속도로 데이터를 저장하기 때문에 획득된 많은 양의 데이터를 분석하기 위한 가시화 기법이 필요하다. 본 논문에서는 데이터의 특성을 고려하여 다양한 가시화 기법들의 스키마를 정의하고, 이러한 가시화 기법들을 이용해 geometry PIG 데이터 분석을 위한 통합된 가시화 기법을 제안한다. 통합된 가시화 기법은 각 가시화 기법들을 사용자가 원하는 형태로 배치하며 사용자가 원하는 시점에서 데이터를 파악할 수 있도록 가시화 기법에 따른 동기화와 사용자 인터페이스를 지원한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.130-133
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• 2020

Smart pig farm which is based on IoT has been widely adopted by many pig farmers. In order to achieve optimal control of smart pig farm, the relation between environmental conditions and performance metric should be characterized. In this study, the relation between multiple environmental conditions including temperature, humidity and various performance metrics, which are daily gain, feed intake, and MSY, is analyzed based on data obtained from 55 real pig farm. Especially, based on preprocessing of data, various regression based machine learning algorithms are considered. Through performance evaluation, we show that the performance can be predicted with high precision, which can improve the efficiency of management.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.71-81
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• 2020

Enclosed pig house are creating an environment with high concentrations of gas and dust. Poor conditions in pig farms reduce pig weight and increase disease and accidents for livestock workers. In the pig house, the high concentration of harmful gas may cause asphyxiation accidents to workers and chronic respiratory disease by long-term exposure. As pig farm workers have been aging and feminized, the damage to the health of the harsh environment is getting serious, and real-time monitoring is needed to prevent the damage. However, most of the measuring devices related to humidity, harmful gas, and fine dust except temperature sensors are exposed to high concentrations of gas and dust inside pig house and are difficult to withstand for a long time. The purpose of this study is to develop an wearable based device to monitor the hazardous environment exposed to workers working in pig farms. Based on the field monitoring and previous researches, the measurement range and basic specifications of the equipment were selected, and wearable based device was designed in terms of utilization, economic efficiency, size and communication performance. Selected H₂S and NH₃ sensors showed the average error of 5.3% comparing to standard gas concentrations. The measured data can be used to manage the working environment according to the worker's location and to obtain basic data for work safety warning.

• Journal of Korea Multimedia Society
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• v.25 no.4
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• pp.583-599
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• 2022

Although the object detection accuracy with a single image has been significantly improved with the advance of deep learning techniques, the detection accuracy for pig monitoring is challenged by occlusion problems due to a complex structure of a pig room such as food facility. These detection difficulties with a single image can be mitigated by using a video data. In this research, we propose a method in pig detection for video monitoring environment with a static camera. That is, by using both image processing and deep learning techniques, we can recognize a complex structure of a pig room and this information of the pig room can be utilized for improving the detection accuracy of pigs in the monitored pig room. Furthermore, we reduce the execution time overhead by applying a pruning technique for real-time video monitoring on an embedded board. Based on the experiment results with a video data set obtained from a commercial pig farm, we confirmed that the pigs could be detected more accurately in real-time, even on an embedded board.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.890-902
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• 2021

Although the object detection accuracy with still images has been significantly improved with the advance of deep learning techniques, the object detection problem with video data remains as a challenging problem due to the real-time requirement and accuracy drop with occlusion. In this research, we propose a method in pig detection for video monitoring environment. First, we determine a motion, from a video data obtained from a tilted-down-view camera, based on the average size of each pig at each location with the training data, and extract key frames based on the motion information. For each key frame, we then apply YOLO, which is known to have a superior trade-off between accuracy and execution speed among many deep learning-based object detectors, in order to get pig's bounding boxes. Finally, we merge the bounding boxes between consecutive key frames in order to reduce false positive and negative cases. Based on the experiment results with a video data set obtained from a pig farm, we confirmed that the pigs could be detected with an accuracy of 97% at a processing speed of 37fps.

• Animal Bioscience
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• v.34 no.8
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• pp.1350-1364
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• 2021

Objective: Adequate vitamin and trace mineral intake for pigs are important to achieve satisfactory growth performance. There are no data available on the vitamin and trace mineral intake across pig producers in China. The purpose of this study was to investigate and describe the amount of vitamin and trace minerals used in Chinese pig diets. Methods: A 1-year survey of supplemented vitamin and trace minerals in pig diets was organized in China. A total of 69 producers were invited for the survey, which represents approximately 90% of the pig herd in China. Data were compiled by bodyweight stages to determine descriptive statistics. Nutrients were evaluated for vitamin A, vitamin D, vitamin E, vitamin K, thiamine, riboflavin, vitamin B₆, vitamin B₁₂, pantothenic acid, niacin, folic acid, biotin, choline, copper, iron, manganese, zinc, selenium, and iodine. Data were statistically analyzed by functions in Excel. Results: The results indicated variation for supplemented vitamin (vitamin A, vitamin D, vitamin E, vitamin K, vitamin B₁₂, pantothenic acid, niacin, and choline) and trace minerals (copper, manganese, zinc, and iodine) in pig diets, but most vitamins and trace minerals were included at concentrations far above the total dietary requirement estimates reported by the National Research Council and the China's Feeding Standard of Swine. Conclusion: The levels of vitamin and trace mineral used in China's pig industry vary widely. Adding a high concentration for vitamin and trace mineral appears to be common practice in pig diets. This investigation provides a reference for supplementation rates of the vitamins and trace minerals in the China's pig industry.

• Journal of Veterinary Clinics
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• v.32 no.1
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• pp.78-84
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• 2015

Although researches have highlighted the important role of enhanced farm biosecurity to reduce the severity and prevalence of diseases in livestock, to date there has been little study in Korea on farmers' adoption of biosecurity measures to control porcine reproductive and respiratory syndrome virus (PRRSV) infection. To mitigate the risk of PRRSV infection in pigs, the risk factors by which PRRSV is introduced in pig farms must be determined. The primary aim of this study was to investigate pig producers' perceptions about on-farm biosecurity practices. We also analyzed data obtained from a cross-sectional study on 196 farrow-to-finish farms conducted between March 2013 and February 2014 to identify risk factors for PRRSV infection at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. Farms were classified as negative or positive through the use of infection profiles that combined data on PCR positive pigs and serological testing including antibody titer, sero-conversion pattern at each age category, and vaccination status. Data on biosecurity practices, farm management and environmental characteristics were analyzed using multivariate ordinal logistic regression. Generally, the biosecurity level in the pig farms included in this study were insufficient to reduce/prevent the risk of PRRSV infection given the high pig density areas and the considerable extent of vehicle movement. Factors associated with PRRSV infection were those where owners used on-farm vaccination programs had a lower risk of infection (OR = 0.19, 95% CI 0.06-0.61). The results from the analysis may guide to tailor biosecurity measures in the reduction or prevention of PRRS to the specific circumstances of pig farms in different localities of the world. To the best knowledge of the authors, this is the first study to report information on the biosecurity practices currently implemented on Korean pig farms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1560-1566
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• 2017

Recently, accumulation of data on pig farm is enabled through the wide spread of smart pig farm equipped with Internet-of-Things based sensors, and various machine learning algorithms are applied on the data in order to improve the productivity of pig farm. Herein, multiple machine learning schemes are used to predict the water usage in pig farm which is known to be one of the most important element in pig farm management. Especially, regression algorithms, which are linear regression, regression tree and AdaBoost regression, and classification algorithms which are logistic classification, decision tree and support vector machine, are applied to derive a prediction scheme which forecast the water usage based on the temperature and humidity of pig farm. Through performance evaluation, we find that the water usage can be predicted with high accuracy. The proposed scheme can be used to detect the malfunction of water system which prevents the death of pigs and reduces the loss of pig farm.