• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.12
• /
• pp.1611-1617
• /
• 2018

The body temperature of livestock is directly related to the health of livestock such that it changes immediately when there exists health problem. Accordingly, the monitoring of livestock's temperature is one of most important tasks in farm management. However, the temperature of livestock is usually measured using skin-attached sensor which is significantly affected by the outside temperature and the condition of attachment which results in the inaccurate measurement of temperature. Herein we have proposed new scheme which estimates the body core temperature of cow based on measured data from neck-attached smart sensor. Especially, we have considered both schemes which estimate the exact temperature and which detect the unusually high temperature based on machine learning. We have found that the occurrence of high temperature can be detected accurately. The proposed scheme can be used in monitoring of health condition of cow and improving the efficiency of farm management.

• Smart Media Journal
• /
• v.13 no.2
• /
• pp.23-31
• /
• 2024

In precision cattle farm, reliably tracking the identity of each cattle is necessary. Effective tracking of cattle within farm environments presents a unique challenge, particularly with the need to minimize the occurrence of excessive tracking trajectories. To address this, we introduce a trajectory playback decision tree algorithm that reevaluates and cleans tracking results based on spatio-temporal relationships among trajectories. This approach considers trajectory as metadata, resulting in more realistic and accurate tracking outcomes. This algorithm showcases its robustness and capability through extensive comparisons with popular tracking models, consistently demonstrating the promotion of performance across various evaluation metrics that is HOTA, AssA, and IDF1 achieve 68.81%, 79.31%, and 84.81%.

• Food Science and Industry
• /
• v.54 no.3
• /
• pp.184-195
• /
• 2021

Smart HACCP is a system that can check the monitoring of critical control point (CCP) in real time to implement improvement measures immediately after departure from limit criteria and prevent falsification of data by digitizing handwritten records. In this study, we developed the analysis model for the effectiveness ofsmart HACCP to compare and analyze with existing HACCP. By introducing of smart HACCP system, the evaluation index value of HACCP effectiveness for HACCP-certificated companies on a small scale increased by 9.25 points, corresponding to 11.52% of increase rate. General HACCP-certificated companies showed 4.52 point and 5.00% of increase rate by introducing of smart HACCP system. Thus, it was confirmed that the introduction of smart HACCP system contributes to the improvement of food safety management and especially it would be more effective for HACCP-certificated companies on a small scale than general HACCP-certificated companies.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.4
• /
• pp.743-748
• /
• 2021

Amid the global population growth and climate change, high-tech smart farm technology that combines agriculture and ICT is actively being researched in Korea to solve sustainable crises such as declining population of agricultural and livestock industries. Existing smart farms are growing mainly on crops with low price competitiveness. Food consumption structures are becoming more sophisticated and diverse, and as agricultural consumption patterns change, the smart farm system also needs to be optimized for growing high-value special crops. To this end, an integrated ICT management system was designed and implemented by establishing a containerized smart farm environment specialized in growing sprout ginseng. Through this, it is possible to implement high-tech agricultural production and lead new future convergence industries through the convergence of ICT, agriculture, and the latest technologies and farming.

• International journal of advanced smart convergence
• /
• v.13 no.2
• /
• pp.276-283
• /
• 2024

Currently, the overall tendency for green and sustainable economic development is creating a circular economy. In actuality, agricultural output is currently benefiting greatly from the growth of the circular economy. The creation of a circular economy helps address resource scarcity, save the environment, combat climate change, and increase economic efficiency. Vietnam's economy can grow quickly and sustainably by shifting to a circular economy production model. Comparing prior growth techniques to the digital age and implementing circular economic development connected with high technology will be a fantastic opportunity to boost growth efficiency. In actuality, Vietnam currently has a large number of agricultural circular economy models. These are models: Creating and using gas from waste and wastewater in livestock and farming; model combining cultivation, livestock, and aquaculture; agro-forestry model; garden-forest model; Circular model using agricultural by-products as a catalyst or creating other valuable products; model of moderation, linked to reducing the use of growth hormones, veterinary medications, pesticides, and artificial fertilizers in agriculture and animal husbandry. Unfortunately, there have been few studies and applications of the aforementioned models, which has made it difficult to build the agricultural sector sustainably. In this paper, we outline the current situation and propose solutions to develop a circular economy model in agriculture in Vietnam for sustainable development.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.2
• /
• pp.437-447
• /
• 2020

The purpose of this study is to research the countermeasures and expected effects through the use of drones in the field of disaster prevention as a drone-based smart quarantine performance method. The environmental, market, and technological approaches to the review of the current quarantine performance task and its countermeasures are as follows. First, in terms of the environment, the effectiveness of the quarantine performance business using drone-based control is to broaden the utilization of forest, bird flu, livestock, facility areas, mosquito larvae, pests, and to simplify and provide various effective prevention systems such as AI and cholera. Second, in terms of market, the standardization of livestock and livestock quarantine laws and regulations according to the use of disinfection and quarantine missions using domestic standardized drones through the introduction of new technologies in the quarantine method, shared growth of related industries and discovery of new markets, and animal disease prevention It brings about the effect of annual budget savings. Third, the technical aspects are (1) on-site application of disinfection and prevention using multi-drone, a new form of animal disease prevention, (2) innovation in the drone industry software field, and (3) diversification of the industry with an integrated drone control / control system applicable to various markets. (4) Big data drone moving path 3D spatial information analysis precise drone traffic information ensures high flight safety, (5) Multiple drones can simultaneously auto-operate and fly, enabling low-cost, high-efficiency system deployment, (6) High precision that this was considered due to the increase in drone users by sector due to the necessity of airplane technology. This study was prepared based on literature surveys and expert opinions, and the future research field needs to prove its effectiveness based on empirical data on drone-based services. The expected effect of this study is to contribute to the active use of drones for disaster prevention work and to establish policies related to them.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.3
• /
• pp.117-124
• /
• 2023

Through reorganization in 2008, The ministry of Agriculture, Food and Rural Affairs integrated management of the food industry by transferred functions which was scattered in the Ministry of Health and Welfare, and established comprehensive policies covering the primary, secondary, and tertiary industries. In the agricultural industry sector, new business concepts such as smart farm and food tech have recently emerged alongside the fourth industrial revolution. In order for the Ministry of Agriculture, Food, and Rural Affairs to develop appropriate policies for the fourth industrial revolution, it is necessary to accurately estimate the size of agricultural and livestock-related businesses. In 2017, the Ministry of Agriculture, Food, and Rural Affairs initiated research for the agriculture, livestock and food industry's special classification, which was approved by the National Statistical Office in 2020. The estimation of the agriculture, livestock and food industry's size based on special classification is crucial because it has a substantial impact on the formulation and significance of policies. In this paper, the appropriate rate was derived from samples extracted from the special classification and the Korean standard industrial classification. Proposed are a method for estimating the population of the agricultural and livestock food industry, as well as a method for calculating the appropriate rate that more accurately reflects the population than the method currently in use.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.12
• /
• pp.2348-2354
• /
• 2016

In recent days, IoT (Internet of Things) technology has been widely used in the field of agriculture, which enables the collection of environmental data and biometric data into the database. The availability of big data on agriculture results in the increase of the machine learning based analysis. Through the analysis, it is possible to forecast agricultural production and the diseases of livestock, thus helping the efficient decision making in the management of smart farm. Herein, we use the environmental and biometric data of Smart Pig farm to derive the accurate relationship model between the environmental information and the daily weight increase of swine and verify the accuracy of the derived model. To this end, we applied the M5P tree algorithm of machine learning which reveals that the wind speed is the major factor which affects the daily weight increase of swine.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.62 no.6
• /
• pp.33-42
• /
• 2020

Emission factors for ammonia and particulate matters (PMs) from livestock buildings are of increasing importance in view of the environmental protection. While the existing emission factors were determined based on the emission inventory of other countries, in situ measurement of emission factors is required to construct an accurate emission inventory for Korea. This study is to report measurements of ammonia and PMs emissions from mechanically-ventilated pig houses, which are common types of pig barns in Korea. Ventilation rates and concentrations of ammonia and PMs were measured at the ventilation outlets of a weaner unit, a growing pig unit and a fattening pig unit to calculated the emission factors. The PMs emission was characterized with different aerodynamic diameters (PM_2.5, PM₁₀, and total suspended particulates (TSP)). The measured ammonia emission factors for weaners, growing pigs and fattening pigs were 0.225, 0.869 and 1.679 kg animal^-1 yr^-1, respectively, showing linear increase with pigs' age. The PMs emission factors for three growing stages were 0.023, 0.237 and 0.241 kg animal^-1 yr^-1, respectively for TSP, 0.017, 0.072 and 0.223 kg animal^-1 yr^-1, respectively for PM₁₀, and 0.011, 0.016 and 0.151 kg animal^-1 yr^-1, respectively for PM_2.5. PMs emissions were increased with pigs' age due to increasing feed supply and animal movement. The measured emission factors were smaller than those of the existing emission inventory indicating that the existing ones overestimate the emissions from pig buildings and also suggesting that long-term in situ monitoring at various livestock buildings is required to construct the accurate emission inventory.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.403-405
• /
• 2019

The risk mitigation service based on network provides monitoring of the risk event data to be inputted and analyses its big data to be stored in real time. Furthermore, it performs the analysis of the plant disease risk such as a red tide, and livestock disease risk such a food-and-mouth disease, avian influenza, and rinderpest, and provides the mitigation service. The standardization road map for risk mitigation is the real time acquisition monitoring of risk events, and mitigation service for the risks.