• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1898-1908
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• 2017

Accurately Diagnosing and managing disease in livestock can help sustainable livestock productivity and maintain human health. Maintaining the health of livestock is an important part of human health. The prediction of calf diseases is carried out by pre-processing the calf biometric data. calf information is used as information for calf birth history, calf biometric information, environmental information on housing, and disease management. It can be developed as an ontology and used as a knowledge base. The Bayesian network was used and inferred in the process of analyzing the correlations of calf diseases. Prediction of diseases based on knowledge of calf disease on calf diseases name, causes, occur timing, care and symptoms, etc., will be able to respond to accurate disease treatment and prevent other livestock from being infected in advance.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.335-343
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• 2019

Healthy living is an essential element of human happiness. Quality eating provides the basis for life, and the health of livestock, which provides meat and dairy products, has a direct impact on human health. In the case of calves, diarrhea is the cause of all diseases.In this paper, we use a sensor to measure calf 's biometric data to diagnose calf diarrhea. The collected biometric data is subjected to a preprocessing process for use as meaningful information. We measure calf birth history and calf biometrics. The ontology is constructed by inputting environmental information of housing and biochemistry, immunity, and measurement information of human body for disease management. We will build a knowledge base for predicting calf diarrhea by predicting calf diarrhea through logical reasoning. Predict diarrhea with the knowledge base on the name of the disease, cause, timing and symptoms of livestock diseases. These knowledge bases can be expressed as domain ontologies for parent ontology and prediction, and as a result, treatment and prevention methods can be suggested.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.401-404
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• 2008

In livestock, diagnosis of disease is found on body temperature variation like a human. In case of cattle, body temperature variation can estimate disease that milk fever, toxication, diarrhea, dyspepsia, chronic enteritis, influenza, pneumonia, anthrax. So we are suggested the temperature measurement system for livestock. This system will be useful to a stock farmer and alternative that a worker.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.294-297
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• 2015

Recently, big data analysis which is based on machine learning has been gained a lot of attentions in various fields. Especially, agriculture is considered as one promising field that machine learning algorithm can be efficiently utilized and accordingly, lots of works have been done so far. However, most of the researches are focusing on the forecast of weather or analysis of genome, and machine learning algorithm for livestock management, especially which uses individual data of livestocks, e.g., temperature and movement, are not properly investigated yet. In this work, we propose fast abnormal livestock detection algorithm based on machine learning, more specifically expectation maximization, such that livestock which has problem can be efficiently and promptly found. In our proposed scheme, livestocks are divided into two clusters using expectation maximization based on their bionic data and the abnormal livestock can be detected by comparing the size of two clusters. Especially, we divide the case in which single livestock has problem and the case in which livestocks have epidemic such that fast response is enabled when epidemic case. Moreover, our algorithm does not need statistical information.

• 월간낙농육우
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• v.32 no.1
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• pp.91-94
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• 2012

향후 국내 낙농업은 소비자들의 전반적인 소득향상 및 '동물복지' 개념의 부각에 의하여, 품질이 우수하고 위생적인 안전한 축산물에 대한 요구가 증가할 것으로 예측되며, 하루 속히 동물복지 및 친환경축산 개념 등이 결합된 가축 생산성 극대화 기술들을 개발하여 치열한 무한 국제 경쟁력시대에 대비할 것이 요망된다고 하겠으며, 이번 호에서는 국내 동물복지형 낙농업의 과제와 동물질병발생의 원인 등에 대한 관련 지식들을 소개하고자 한다.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.223-224
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• 2015

구제역은 소, 돼지 등 발굽이 두 개로 갈라진 가축들에게 감염을 유발하는 전염성이 매우 높은 바이러스성 질병이다. 구제역 감염 시 입 주변, 구강 내, 코, 발굽사이 등에 수포가 생기며 고열과 식욕이 저하되어 심하게 앓거나 죽게 되는데, 강한 감염 전파력을 가졌음에도 치료제가 없고, 감염확인 즉시 확산 방지를 위한 살 처분만이 이루어지고 있다. 따라서 무엇보다도 빠른 감염여부 진단이 중요하다. 현재까지 구제역을 진단하는 방법으로는 감염 된 가축의 혈액에서 구제역 항원 단백질에 대한 항체형성 여부를 확인하는 항체진단법과 수포액과 같은 체액을 채취하여 세포배양을 통한 구제역 바이러스 분리방법이 있지만 두 가지 모두 짧은 잠복기를 갖는 구제역 바이러스를 빠른 시간 내 진단하기는 어렵다. 따라서 본 연구에서는 보다 빠른 구제역 진단 키트개발을 위해 NCBI Pubmed를 이용하여 구제역바이러스가 가지는 6개의 주요 단백질을 확인하였고, NCBI BLAST를 이용하여 6개의 단백질 중 구제역 바이러스에 특이적인 항원 단백질 peptidase C28을 선정하였다. 선정된 단백질의 아미노산 서열을 이용하여 IEDB analysis resource를 통해 peptidase C28의 epitope 부위를 예측하였다. 예측 된 부위의 아미노산 서열을 NCBI BLAST에서 정상 동물과 비교하여 구제역바이러스 특이 항원 단백질 epitope peptide를 최종 선정하였다. 이를 이용한 구제역 바이러스 진단키트 제작은 보다 빠른 진단을 통해 감염 확산을 조기에 차단하고 경제적 손실과 피해를 최소화 할 수 있다.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.140-155
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• 2020

This study was conducted to identify the location characteristics of infected farms in the areas where livestock diseases were clustered(southern Gyeonggi-do and Chungcheong-do), analyze the probability of disease occurrence in poultry farms, find out the areas corresponding to the conditions, and use them as the basis for prevention of livestock diseases, selection of discriminatory prevention zones, and establishment of prevention strategies and as the basic data for complementary measures. The increase of one poultry farm within a radius of 3-kilometers increases the risk of HPAI infection by 10.9% compared to the previous situation. The increase of 1m in distance from major roads with two lanes or more reduces the probability of HPAI infection by 0.001% compared to the previous situation. If the distance of the poultry farm located with 15 kilometers from a major migratory bird habitat increases by 15 to 30 kilometers, the chance of infection with HPAI is reduced by 46.0%. And if the distance of the same poultry farm increase by more than 30 kilometers, the chances of HPAI infection are reduced by 88.5%. Based on the results of logistic regression, the predicted probability was generated and the actual area of the location condition with 'more than 15 poultry farms within 3km a radius of, within 1km distance from major roads, and within 30km distance from major migratory birds habitat was determined and the infection rate was measured. It is expected that the results of this study will be used as basic data for preparing the data and supplementary measures when the quarantine authorities establish discriminatory quarantine areas and prevention strategies, such as preventive measures for the target areas and farms, or control of vehicles, by identifying the areas where livestock diseases are likely to occur in the region.

• Journal of the Korean Applied Science and Technology
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• v.38 no.1
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• pp.241-262
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• 2021

This paper reviewed the necessity of a information and communication technology (ICT)-based smart livestock system as a development strategy for the animal life industry in the future. It also predicted the trends of livestock and animal food until 2050, 30 years later. Worldwide, livestock raising and consumption of animal food are rapidly changing in response to population growth, aging, reduction of agriculture population, urbanization, and income growth. Climate change can change the environment and livestock's productivity and reproductive efficiencies. Livestock production can lead to increased greenhouse gas emissions, land degradation, water pollution, animal welfare, and human health problems. To solve these issues, there is a need for a preemptive future response strategy to respond to climate change, improve productivity, animal welfare, and nutritional quality of animal foods, and prevent animal diseases using ICT-based smart livestock system fused with the 4th industrial revolution in various aspects of the animal life industry. The animal life industry of the future needs to integrate automation to improve sustainability and production efficiency. In the digital age, intelligent precision animal feeding with IoT (internet of things) and big data, ICT-based smart livestock system can collect, process, and analyze data from various sources in the animal life industry. It is composed of a digital system that can precisely remote control environmental parameters inside and outside the animal husbandry. The ICT-based smart livestock system can also be used for monitoring animal behavior and welfare, and feeding management of livestock using sensing technology for remote control through the Internet and mobile phones. It can be helpful in the collection, storage, retrieval, and dissemination of a wide range of information that farmers need. It can provide new information services to farmers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.292-301
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• 2016

Because damages arising from the occurrence of foot-and-mouth disease (FMD) are very great, it is essential to make a preemptive diagnosis to cope with it in order to minimize those damages. The main symptoms of foot-and-mouth disease are body temperature increase, loss of appetite, formation of blisters in the mouth, on hooves and breasts, etc. in a cow or a bull, among which the body temperature check is the easiest and quickest way to detect the disease. In this paper, an algorithm to detect FMD from the hooves of cattle was developed and implemented for preemptive coping with foot-and-mouth disease, and a hoof check test is conducted after the installation of a high-resolution camera module, a thermo-graphic camera, and a temperature/humidity module in the cattle shed. Through the algorithm and system developed in this study, it is possible to cope with an early-stage situation in which cattle are suspected as suffering from foot-and-mouth disease, creating an optimized growth environment for cattle. In particular, in this study, the system to cope with FMD does not use a portable thermo-graphic camera, but a fixed camera attached to the cattle shed. It does not need additional personnel, has a function to measure the temperature of cattle hooves automatically through an image algorithm, and includes an automated alarm for a smart phone. This system enables the prediction of a possible occurrence of foot-and-mouth disease on a real-time basis, and also enables initial-stage disinfection to be performed to cope with the disease without needing extra personnel.

• Korean Journal of Veterinary Service
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• v.36 no.3
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• pp.187-192
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• 2013

For the monitoring of six viral disease (CIV: canine influenzavirus, CPIV: canine parainfluenzavirus, CHV: canine herpesvirus, CPV2: canine parvovirus type 2, CCoV: canine coronavirus, CNV: canine norovirus) inspections, a total of 300 samples were collected nasal or feces from the companion dogs of animal hospital (n=98) and the abandoned dogs of animal shelters (n=202) in Gwangju, Korea. Using PCR and RT-PCR, CPV2, CPIV and CHV were detected in 55 (18.3%), 11 (3.7%), 1 (0.3%), respectively. CPV2 was highly detected in May, October and November. and CPIV was highly detected in November. But those agents were not detected the virus in March and July. Based on the results of the investigation continuous monitoring for companion and abandoned dogs will be required.