• International journal of advanced smart convergence
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• v.9 no.1
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.505-507
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• 2021

Image recognition technology is a technology that recognizes an image object by using the generated feature descriptor and generates object feature points and feature descriptors that can compensate for the shape of the object to be recognized based on artificial intelligence technology, environmental changes around the object, and the deterioration of recognition ability by object rotation. The purpose of the present invention is to implement a power management framework required to increase profits and minimize damage to livestock farmers by preventing accidents that may occur due to the improvement of efficiency of the use of livestock house power and overloading of electricity by integrating and managing a power fire management device installed for analyzing a complex environment of power consumption and fire occurrence in a smart safety livestock house, and to develop and disseminate a safe and optimized intelligent smart safety livestock house.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.109-126
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• 2018

With massive and dense production, the livestock industry is rapidly moving into a large-scale, capital-intensive industry especially in swine, poultry, and ducks. However, livestock epidemics can pose a serious threat to the livestock industry and the lives of the people. The government has established and operates the National Animal Protection and Prevention System (KAHIS) since 2013 in order to control the threat, in accordance with the five stages. The digitalized data and information are excellent in ease of management, but it is also pointed out that it is difficult to take countermeasures through linkage with the data in an emergency situation. Recently, the technology of the fourth industrial revolution such as Internet of Things (IoT), Big Data, Artificial intelligence (AI) has been rapidly implemented to the livestock industry, which makes smart livestock disease control system possible. Therefore, this study investigated the domestic and overseas cases which apply 4th Industrial Revolution technology in the industry, and derived 13 possible candidate tasks in the near future. In order to ascertain the priority of policy formulation, we surveyed the expert groups and examined the priority of each of the five stages of the prevention and the priority of each stage. The results of this study are expected to contribute to the establishment of policies for the advancement of smart livestock disease control research and livestock protection.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.129-136
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• 2022

The most important part of the smart livestock building system is to maintain a breeding environment so that livestock can grow to high quality despite changes in the internal and external atmospheric environment. Especially, it is very important to maintain the temperature and humidity in the livestock building because various diseases occur during the summer and winter. To manage the environment suitable for livestock, a smartfarm system for livestock building is applied, but it is very expensive. In this study, we propose a hardware design and control method for low cost system based on HMI and fuzzy control. To evaluate the performance of the proposed system, we did a simulation experiment in the atmospheric conditions of summer and winter. As a result, it showed the performance of minimizing the temperature and humidity stress of livestock. And when applied to the livestock building, the proposed system showed stable control performance even in the change of the external atmospheric environment. Therefore, as with these results, if proposed system in this study is applied to the smart farm system, it will be effective in managing the environment of livestock building.

• Smart Media Journal
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• v.3 no.1
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• pp.16-21
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• 2014

The demand for food rises proportionally as population grows. To be able to achieve sustainable supply of livestock products, efficient farm management is a necessity. With the advancement in technology it also brought innovations that could be harness in order to achieve better productivity in animal production and agriculture. Precision Livestock Farming (PLF) is a budding concept of making use of smart sensors or available devices to automatically and continuously monitor and manage livestock production. With this concept, this paper introduces a swine management system that integrates image processing technique for weight monitoring. This system captures pig images using camera, evaluate and estimate the weight base on the captured image. It is comprised of Pig Module, Breeding Module, Health and Medication Module, Weighr Module, Data Analysis Module and Report Module to help swine farm administrators better understand the performance and situation of the swine farm. This paper aims to improve the management in both small and big livestock raisers.

• Smart Media Journal
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• v.12 no.10
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• pp.85-92
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• 2023

In December 2014, Livestock and Livestock Products Traceability Act was established, allowing consumers to receive livestock traceability information. While the Livestock Traceability System provides consumers with transparent and fair information about their food, it has brought increased workload and penalty burdens to stakeholders in the livestock industry. In this paper, we propose an IoT system for inventory management based on traceability numbers to enable sellers to conveniently provide livestock traceability information to consumers. We analyzed the protocol for managing data from electronic scales and conducted functional testing and verification on mobile devices. Furthermore, we implemented the design and system functionality, taking into account UI/UX on Android OS-based devices to synchronize and interconnect traceability and product information with electronic scales. We anticipate that the proposed approach will minimize user inconvenience and raise production efficiency in the existing market.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.287-292
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• 2024

The problem of odor and environmental pollution caused by livestock manure is spreading greatly as a social issue. To reduce the odor of livestock raised in livestock farms and improve the farm environment, raw materials of Abelmoschus manihot Jinhuakui were put into feed additives to measure the state of odor. It is characterized by being non-toxic and sweet, and Abelmoschus manihot Jinhuakui, which contains abundant nutrients that are beneficial to health in all parts such as roots, stems, and flowers, is a medicinal plant that cannot be discarded. In particular, it has the effect of helping bowel movements because it stimulates bowel movements. Ammonia levels were investigated through the KS X 3279 national standard-applied smart livestock IoT hub sensor pack installed at Flower Garden and Ugil Farm. The purpose of this paper is to reduce the odor that is the most problematic on farms and improve the environment, and it is planned to expand research into deodorants after feed additives. It is hoped that the research results will solve the livestock problem and help livestock farmers.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.151-157
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• 2021

Livestock farmers are using animal carcasses to dispose of the carcasses of livestock that have died of natural causes or disease. Most of the existing livestock carcass processors are mechanical in their structure without considering the environment. It has a function of sterilizing dead bodies at high pressure after processing them and causes environmental problems such as carbon monoxide emissions. If livestock carcasses occur, livestock farmers have to purchase their own livestock carcasses or entrust them to the outside world, which is costly. For this reason, the possibility of environmental pollution, infectious diseases, and spread has been increased recently by frequent dumping of dead bodies. The carcass of livestock mixed with manure not only serves as a medium for infectious diseases but also needsto be buried on a large scale as foot-and-mouth disease and avian influenza spread. As a result, the possibility of environmental pollution, such as contamination of groundwater, is increasing, so research is needed to protect and improve the environment. We aim to improve the process of processing livestock carcasses and purify the agricultural environment through development results on the form, structure and function of eco-friendly livestock carcasses. Its shape is applied with naturalshapessuch asstones and seeds. The material used in the dead body processis a brown beggar biocouple and is applied with an eco-friendly industrial animal recycling process. As a result of the study, it is expected to improve odors and the environment, and to be used as data to improve and help the livestock industry in the future.

• Smart Media Journal
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• v.13 no.6
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• pp.16-23
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• 2024

Pigs express their reactions to their environment and health status through a variety of sounds, such as grunting, coughing, and screaming. Given the significance of pig vocalizations, their study has recently become a vital source of data for livestock industry workers. To facilitate this, we propose a lightweight deep learning model based on MobileNet that analyzes pig vocal patterns to distinguish pig voices from farm noise and differentiate between vocal sounds and coughing. This model was able to accurately identify pig vocalizations amidst a variety of background noises and cough sounds within the pigsty. Test results demonstrated that this model achieved a high accuracy of 98.2%. Based on these results, future research is expected to address issues such as analyzing pig emotions and identifying stress levels.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.13-25
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• 2021

The particulate matters (PM₁₀ and PM_2.5) and ammonia emitted from livestock farms as dispersed to urban and residential areas can increase the public's concern over the health problem, social conflicts, and air quality. Understanding the atmospheric dispersion of such matters is important to prevent the problems for the regulatory purposes. In this study, AERMOD modeling was performed to predict the dispersion of livestock particulate matters and ammonia in Gwangju metropolitan city and five surrounding cities. The five cities were divided into 40 sub-zones to model the area-based emissions which varied with the number of livestock farms, species and growth stages of the animals. As a result, the concentrations of PM₁₀, PM_2.5 and ammonia resulted from livestock farms located in the surrounding cities were 2.00 ㎍ m^-3, 0.30 ㎍ m^-3 and 0.04 ppm in the southwestern part of Gwangju based on the average concentration of 1 hour. These values accounted for 0.7% of PM₁₀ concentration, 0.5% of PM_2.5 concentration, and 0.4% of the ammonia concentration in Gwangju, contributing to a small amount of air pollution compared to other sources. As preventive measures, the plantation was applied to high emission source areas to reduce particulate matters and ammonia emissions by 35% and 31%, respectively, and resulted in decrease of the area of influence by 57% for particulate matters and 59% for ammonia.