• 스마트미디어저널
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• 제13권2호
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• pp.136-144
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• 2024

최근 IT 기술의 급속한 발전과, 인구노령화에 따른 노동력 부족에 대한 다양한 방안들이 나타난다. 축산업에서도 인공지능 기술을 활용하여 관리하는 시스템들이 늘어나고 있다. 제안하는 메타버스 기반의 스마트 축사 시스템은 디지털 가상 세계와 첨단 농업 기술의 결합으로 구성된 시스템이다. 이 시스템을 통해, 특별한 상황에서는 농부들은 축사에 직접 방문하지 않고도 동물의 상태를 실시간으로 모니터링할 수 있으며, 센서와 카메라를 통해 수집된 데이터를 분석하여 보다 효율적인 농업 경영이 가능하다. 추가적으로, 원격 제어 기능을 통해 축사 환경의 조절이 가능하며, 이는 노동력 절감과 축산업의 활성화에 기여할 것이다.

• 스마트미디어저널
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• 제11권8호
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• pp.84-92
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• 2022

Precision livestock monitoring promises greater management efficiency for farmers and higher welfare standards for animals. Recent studies on video-based animal activity recognition and tracking have shown promising solutions for understanding animal behavior. To achieve that, surveillance cameras are installed diagonally above the barn in a typical cattle farm setup to monitor animals constantly. Under these circumstances, tracking individuals requires addressing challenges such as occlusion and visual appearance, which are the main reasons for track breakage and increased misidentification of animals. This paper presents a framework for multi-cattle tracking in closed barns with appearance and motion models. To overcome the above challenges, we modify the DeepSORT algorithm to achieve higher tracking accuracy by three contributions. First, we reduce the weight of appearance information. Second, we use an Ensemble Kalman Filter to predict the random motion information of cattle. Third, we propose a supplementary matching algorithm that compares the absolute cattle position in the barn to reassign lost tracks. The main idea of the matching algorithm assumes that the number of cattle is fixed in the barn, so the edge of the barn is where new trajectories are most likely to emerge. Experimental results are performed on our dataset collected on two cattle farms. Our algorithm achieves 70.37%, 77.39%, and 81.74% performance on HOTA, AssA, and IDF1, representing an improvement of 1.53%, 4.17%, and 0.96%, respectively, compared to the original method.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1519-1530
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• 2020

As livestock farms grow in size, the number of breeding individuals increases, making it difficult to manage livestock. Livestock farms require an integrated management system such as a monitoring system, an access control system, and an abnormal behavior detection system to manage livestock houses. In this paper, a smart korean cattle livestock management system using IoT and AI technology was proposed for livestock management in livestock farms. The smart korean cattle farm management system consists of a monitoring and control system, a vehicle access management system, and an abnormal cattle behavior detection system. It is expected that this will help manage large-scale livestock houses, and additional research is needed to improve the performance of abnormal behavior detection in the future.

• 대한임베디드공학회논문지
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• 제11권1호
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• pp.15-20
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• 2016

Recently, the u-IT applications for plants and livestock become larger and control of livestock farm environment has been used important in the field of industry. We implemented wireless sensor networks and farm environment automatic control system for applying to the breeding barn environment by calculating the THI index. First, we gathered environmental information like livestock object temperature, heart rate and momentum. And we also collected the farm environment data including temperature, humidity and illuminance for calculating the THI index. Then we provide accurate control action roof open and electric fan in of intelligent farm to keep the best state automatically by using collected data. We believed this technology can improve industrial competitiveness through the u-IT based smart integrated management system introduction for industry aversion and dairy industries labor shortages due to hard work and old ageing.