• Smart Media Journal
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• v.1 no.1
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• pp.53-57
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• 2012

Smart phone and its applications are currently bringing about significant changes in our lives and it is expected that applying such technology in the area of agriculture could increase the value added and productivity of agriculture with its various uses. This paper proposed a smartphone-based application for monitoring and managing livestock barn in real-time anytime, anywhere. In the proposed application, the livestock barn environment and video information collected in ubiquitous livestock barn based on wireless sensor networks can be used by user to monitor the livestock barn in real-time through the use of smart phone to control the livestock barn facilities anytime, anywhere. This application can provide user convenience and increase productivity by allowing users to control their livestock barn facilities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.408-411
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• 2017

This paper deals with how to operate sensor data in smart livestock barn environment through EPCIS event schema design based on GS1 standard. We defines the types of sensors used in the smart livestock barn environment, describes the information using the GS1 standard identifier, standard event / master data structure, and standard interface for operating the sensor information, and stores / shares various sensor information of smart livestock barn defined in the schema structure.

• Smart Media Journal
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• v.11 no.8
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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.

• Smart Media Journal
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• v.13 no.2
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• pp.136-144
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• 2024

In recent years, with the rapid development of IT technology and the aging of the population, various solutions to the labor shortage have emerged. In the livestock industry, there are an increasing number of management systems that utilize artificial intelligence technology. The Metaverse Smart Farm is a system that combines the digital virtual world with advanced agricultural technology. With this system, farmers can monitor the health of their animals in real time without having to visit the barns, and analyze the data collected through sensors and cameras for more efficient agricultural management. In addition, the barn environment can be adjusted through a remote control function, which is expected to reduce labor and revitalize the livestock industry.

• Smart Media Journal
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• v.13 no.10
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• pp.35-42
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• 2024

This study aims to develop a system that predicts the health status of cattle based on behavior patterns and environmental data within a smart barn using an autonomous driving system. Maintaining a unique ID for each cow using only a camera, without external devices (such as RFID tags), is essential. This enables the tracking of behavior patterns such as standing, sitting, and lying for each cow over time. Additionally, environmental data such as temperature and humidity are integrated to comprehensively assess the cows' health conditions. To achieve this, we propose a unique ID retention algorithm that combines object detection using YOLO, tracking with Deep SORT, and re-identification (ReID). Experimental results show that the YOLO + Deep SORT + ReID algorithm delivers the best performance in maintaining unique IDs, and the LSTM-based behavior analysis model demonstrates high accuracy in predicting behavior patterns. This system can serve as an effective tool for real-time prediction of livestock health conditions, such as disease or stress, through comprehensive analysis of environmental data and behavior patterns inside the barn.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.1
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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.

• Journal of Korea Multimedia Society
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• v.23 no.12
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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.