• Asian-Australasian Journal of Animal Sciences
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• 제18권3호
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• pp.340-344
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• 2005

In high-income Asian countries like Korea and Japan, per capita rice consumption has declined because of the change in consumer' favorite foods from rice to high-cost quality foods. This has forced farmers to reduce rice production. Although fodder rice could be another option to be cultured by farmers, available information concerning rice grain silage has been limited. In the present study, therefore, the difference in the quality of fodder rice silage prepared from either whole crop or grain at different ripening stages was compared. Various supplements were also added into whole crop and grain silages of fodder rice before ensiling, and thereafter, the palatability of prepared silages was determined by beef cattle. At ear emergence stage, the pH values for both grain and whole crop silages were approximately 4.5. In both grain and whole crop silages, the pH values were significantly increased by progressing ripening stages from milk-ripe stage to yellow-ripe stages, and the increase in pH value for grain silage was faster than that for whole crop silage. In the grain silage, the higher lactic acid (LA) content in grain silage seemed to be, the lower pH value was. Both in grain and whole crop silages, pH was significantly decreased by supplementation with LA bacteria. There were no significant differences in feed intake among any treatment groups.

• Journal of Veterinary Science
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• 제23권1호
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• pp.17.1-17.10
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• 2022

Background: Inspection of livestock farms using surveillance cameras is emerging as a means of early detection of transboundary animal disease such as African swine fever (ASF). Object tracking, a developing technology derived from object detection aims to the consistent identification of individual objects in farms. Objectives: This study was conducted as a preliminary investigation for practical application to livestock farms. With the use of a high-performance artificial intelligence (AI)-based 3D depth camera, the aim is to establish a pathway for utilizing AI models to perform advanced object tracking. Methods: Multiple crossovers by two humans will be simulated to investigate the potential of object tracking. Inspection of consistent identification will be the evidence of object tracking after crossing over. Two AI models, a fast model and an accurate model, were tested and compared with regard to their object tracking performance in 3D. Finally, the recording of pig pen was also processed with aforementioned AI model to test the possibility of 3D object detection. Results: Both AI successfully processed and provided a 3D bounding box, identification number, and distance away from camera for each individual human. The accurate detection model had better evidence than the fast detection model on 3D object tracking and showed the potential application onto pigs as a livestock. Conclusions: Preparing a custom dataset to train AI models in an appropriate farm is required for proper 3D object detection to operate object tracking for pigs at an ideal level. This will allow the farm to smoothly transit traditional methods to ASF-preventing precision livestock farming.

• 한국정보전자통신기술학회논문지
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• 제10권5호
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• pp.442-454
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• 2017

우리나라 식품소비 패턴이 육류 위주로 증가함으로써 축산업이 크게 성장하였으나, 수입 축산물 시장의 개방으로 국내 축산 제품의 가격 경쟁력 확보 방안이 시급한 시점이다. 따라서 생산비의 가장 큰 부분을 차지하고 있는 사료 원가 절감의 문제는 축산업 생태계에서 해결해야 하는 중요한 과제이다. 본 연구의 목적은 축산업의 스마트 팜을 지원하는 것이다. 이에 본 연구는 IoT에 기반 하여 축산 사료의 측정 장치를 개발하고, 자동 주문 및 배송 시스템을 구현하여 관련 영역의 효율성을 극대화할 수 있는 방안을 제안하였다. 구체적으로는 첫째, 초음파 센서와 온도/습도 측정 센서를 이용하여 사료 저장소인 피드빈 내 사료 잔량과 온/습도 정보를 수집하는 센서를 개발하였다. 둘째, 그 정보를 축산농가에 설치된 게이트웨이를 거쳐 SCM 서버에 전송할 수 있는 방법을 구현하였다. 셋째, 서버에 저장된 축산농가의 정보를 활용하여 사료의 자동 주문 및 배송이 가능한 서비스 방안을 구현하였다. 본 연구는 기존의 방법보다 용이하고 가격 경쟁력이 있는 방안으로서, 축산업의 다양한 영역에서 보다 다양한 IoT 활용 서비스와 경쟁력 강화 방안이 도출되는데 도움이 될 것이다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2005년도 하계 학술대회 논문집
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• pp.254-258
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• 2005

• 한국동물번식학회:학술대회논문집
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• 한국동물번식학회 2000년도 국제심포지움
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• pp.7-8
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• 2000

Transgenic animals are very useful for scientific, pharmaceutical, and agricultural purposes. In livestock, transgenic technology has been used forthe genetic alteration of farm animals, the production of human proteins inlarge quantities in the milk of transgenic farm animals, and the generation of animals with organs suitable for xenotransplantation. To date, the transfer of foreign genes into farm animals has been performed mainly by microinjection of DNA into the pronuclei of fertilized eggs. However, the overall success rate of transgenic animals in livestock so far has been disappointingly low, eg., the efficiency is 0∼5% in swine, and less than 1% in sheep and cattle, compared with the rate in mice where 5% microinjected develop into transgenic animals. Recently, McGreath et al. (2000) have succeeded in producing the gene targeted sheep by the use of nuclear transfer from cultured somatic cells transfected with a foreign gene in vitro. However, we may need plenty of time until currently employ this method for gene transfer to farm animals. We have been studying to exploit the method for improving production efficiencies of transgenic animals with emphasis of its application to farm animals. The present paper describes three approaches that we have made in our laboratory to improve production efficiencies of transgenic animals, based on the DNA microinjection method. 1. Co-injection of restriction enzyme with foreign DNA into the pronucleus for elevating production efficiencies of transgenic animals. 2. Efficient selection of transgenic mouse embryos using EGFP as a marker gene. 3. Phenotypes of tansgenic mice expressing WAP/hGH-CAG/EGFP fusion gene produced by selecting transgenic embryos. 4. Efficient site-specific integration of the transgene targeting an endogenous lox like site in early mouse embryos.

• 한국산업보건학회지
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• 제30권3호
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• pp.292-298
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• 2020

Objectives: The aim of this research is to establish Similar Exposure Groups (SEGs) for chemical and biological risk factors that occur in farm work involving 24 tasks among 15 crops. Methods: To categorize SEGs, work type, work environment, and similar tasks for each crop were considered. After confirming the chemical risk factors (pesticides, inorganic dust-total dust and PM₁₀, ammonia, and hydrogen sulfide) and biological factors (organic dust-total dust and PM₁₀, and endotoxins) that occur in the crops and tasks, similar crops and tasks were selected as SEGs. Results: Among chemical risk factors, pesticides was selected for the SEGs, which was categorized by open field, greenhouse, fruit, and specialty crops. For inorganic dust, open field (plowing harrowing, seedling, planting, harvest, and sorting and packing) and specialty crops (plowing harrowing, seedling, planting, and harvest) were selected as SEGs. For ammonia and hydrogen sulfide, livestock (preparation of farm, management of nursery bed, feeding, shipment and manure treatment) were selected as SEGs. For biological risk factors such as organic dust (total dust, PM₁₀) and endotoxins, open field (manure application), greenhouse (plowing harrowing, planting, manure application, and harvest), fruit (manure application), specialty crops (manure application, making furrows, mixing mushroom media, harvest, and sorting and packing), and livestock (preparation of farm, maintaining poultry litter, feeding, shipment and manure treatment) were selected as SEGs. Conclusions: To establish similar exposure groups in agriculture, it is important that the characteristics of each hazard factor are categorized by identifying risk factors occurring by tasks.

• 한국인터넷방송통신학회논문지
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• 제22권4호
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• pp.133-139
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• 2022

최근 국내 축산 농가들에서는 생산성 향상을 위해 축사 환경 및 사양관리, 안전한 축산물 생산, 동물 복지 등에 데이터를 기반으로 한 기술이 도입되고 있다. 또한, 정부는 정책적으로 축산물의 생산성 향상 및 축산 환경을 개선하기 위해 ICT기반의 축사시설 현대화를 통한 스마트 축사 보급 사업을 2017년부터 진행하고 있다. 그러나 현재 스마트 축사는 모니터링과 제어의 연계성, 다양성, 통합성 등에서 한계가 있다. 그러므로 다양한 모니터링과 제어의 연계 및 통합을 하기 위해 지능화된 알고리즘 및 원격 제어로 축산의 모든 과정을 지능적으로 시스템화하기 위해서는 사물인터넷, 빅데이터, 인공지능, 클라우드 컴퓨팅, 모바일 등 지능정보기술을 기반으로 한 스마트 축산 시스템 개발이 필요하다. 본 연구에서는 지능정보기술 기반의 스마트 축사 관련 국내외 연구동향을 소개하고 첨단기술의 국내 적용 한계를 분석하였다. 끝으로 축산분야에 적용 가능한 미래 지능정보기술에 대해서 살펴보았다.

• Asian-Australasian Journal of Animal Sciences
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• 제11권2호
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• pp.117-120
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• 1998

Relationships of breeding values of sires for first lactation milk yield with pedigree information or indices were examined to identify the optimal criteria of selecting young dairy bulls for future use in artificial insemination (AI). Records of performance data on 1087 crossbred daughters (Holstein - Friesian, Jersey and Brown Swiss with Hariana) of 147 sires, generated at Livestock Production Research (Cattle and Buffaloes) Farm, IVRI, Izatnagar, U.P., during 1972 - 1995 were used to obtain the estimates of sire's breeding values (EBV) using the Best Linear Unbiased Prediction Procedures. The correlations between young bull's EBV and the dam's first lactation milk yield was non-significantly different from zero. However, the young bull's EBV was negatively and significantly related (r = - 0.275 ; P < 0.05) to the dam's best lactation milk yield, suggesting that the selection of young dairy bulls from high yielding elite dams is not a suitable criteria for genetic improvement. The correlations of sire's and paternal grandsire's EBV's with young bull's EBV were high and positive (0.532, 0.844; P < 0.01). The maternal grandsire's EBV was positively but non-significantly related to grandson's EBV. The pedigree index incorporating dam's milk records and sire's EBV's showed a negative and non-significant correlation with young bull's EBV. However, the correlation of a pedigree index $(I_3)$ combining information on sire's and paternal grand-sire's EBV's with young bull's EBV's was considerably high and positive (0.797; P < 0.01). The regression coefficients of young bull's EBV on pedigree index $I_3$, was higher than those on other pedigree information. These results revealed that there was no advantage in basing selection on dam's performance or maternal grand-sire's EBV and that sire's and paternal grandsire's EBV's were reliable pedigree information for selection of young dairy bulls for future use in AI.

• International journal of advanced smart convergence
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• 제9권1호
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• pp.215-221
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• 2020

The paradigm of agriculture is also changing to address the problem of food shortages due to the increase of the world population, climate conditions that are increasingly subtropical, and labor shortages in rural areas due to aging population. With the development of Information Communication Technology (ICT), our daily lives are changing rapidly and heralds a major change in agricultural management. In a hyper-connected society, the introduction of high-tech into traditional Agriculture of the past is absolutely necessary. In the development process of Agriculture, the first generation produced by hand, the second generation applied mechanization, and the third generation introduced automation. The fourth generation is the current ICT operation and the fifth generation is artificial intelligence. This paper investigated Smart Farm that increases productivity through convergence of Agriculture and ICT, such as smart greenhouse, smart orchard and smart Livestock. With the development of sustainable food production methods in full swing to meet growing food demand, Smart Farming is emerging as the solution. In overseas cases, the Netherlands Smart Farm, the world's second-largest exporter of agricultural products, was surveyed. Agricultural automation using Smart Farms allows producers to harvest agricultural products in an accurate and predictable manner. It is time for the development of technology in Agriculture, which benchmarked cases of excellence abroad. Because ICT requires an understanding of Internet of Things (IoT), big data and artificial intelligence as predicting the future, we want to address the status of theory and actual Agriculture and propose future development measures. We hope that the study of the paper will solve the growing food problem of the world population and help the high productivity of Agriculture and smart strategies of sustainable Agriculture.

• 한국컴퓨터정보학회논문지
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• 제21권10호
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• pp.91-98
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• 2016

Outbreaks of highly contagious livestock diseases can cause direct and indirect economic impacts such as lower productivity of cattle farms, fall in tourism in damaged areas and countries, and decline in exports. They also incur tremendous social costs associated with disease elimination and restoration work. Thus, it is essential to prevent livestock diseases through monitoring and prediction efforts. Currently, however, it is still difficult to provide accurate predictive information regarding occurrences of livestock diseases, because existing cattle health monitoring or forecasting systems are only limited to monitor environmental conditions of livestock barns and check activities of cattle by using a pedometer or thermal image. In this paper, we present a biosensor-based cattle health monitoring system capable of collecting bio-signals of farm animals in an effective way. For the presented monitoring system, we design an integrated monitoring device consisting of a sensing module to measure bio-signals of cattle such as the heartbeat, the breath rate and the momentum, as well as a Zigbee module designed to transmit the biometric data based on Wireless Sensor Network (WSN). We verify the validity of the monitoring system by the comparison of the correlations of designed device with a commercial ECG equipment through analyzing the R-peak of measured signals.