• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.130-133
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• 2020

Smart pig farm which is based on IoT has been widely adopted by many pig farmers. In order to achieve optimal control of smart pig farm, the relation between environmental conditions and performance metric should be characterized. In this study, the relation between multiple environmental conditions including temperature, humidity and various performance metrics, which are daily gain, feed intake, and MSY, is analyzed based on data obtained from 55 real pig farm. Especially, based on preprocessing of data, various regression based machine learning algorithms are considered. Through performance evaluation, we show that the performance can be predicted with high precision, which can improve the efficiency of management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1560-1566
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• 2017

Recently, accumulation of data on pig farm is enabled through the wide spread of smart pig farm equipped with Internet-of-Things based sensors, and various machine learning algorithms are applied on the data in order to improve the productivity of pig farm. Herein, multiple machine learning schemes are used to predict the water usage in pig farm which is known to be one of the most important element in pig farm management. Especially, regression algorithms, which are linear regression, regression tree and AdaBoost regression, and classification algorithms which are logistic classification, decision tree and support vector machine, are applied to derive a prediction scheme which forecast the water usage based on the temperature and humidity of pig farm. Through performance evaluation, we find that the water usage can be predicted with high accuracy. The proposed scheme can be used to detect the malfunction of water system which prevents the death of pigs and reduces the loss of pig farm.

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

• 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.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Convergence Security Journal
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• v.22 no.5
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• pp.69-80
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• 2022

Now that climate change and food resource security are becoming issues around the world, smart farms are emerging as an alternative to solve them. In addition, changes in the production environment in the primary industry are a major concern for people engaged in all primary industries (agriculture, livestock, fishery), and the resulting food shortage problem is an important problem that we all need to solve. In order to solve this problem, in the primary industry, efforts are made to solve the food shortage problem through productivity improvement by introducing smart farms using the 4th industrial revolution such as ICT and BT and IoT big data and artificial intelligence technologies. This is done through the public and private sectors.This paper intends to consider the minimum requirements for the smart farm data collection system for the development and utilization of smart farms, the establishment of a sustainable agricultural management system, the sequential system construction method, and the purposeful, efficient and usable data collection system. In particular, we analyze and improve the problems of the data collection system for building a Korean smart farm standard model, which is facing limitations, based on in-depth investigations in the field of livestock and livestock (pig farming) and analysis of various cases, to establish an efficient and usable big data collection system. The goal is to propose a method for collecting big data.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.1
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• pp.61-72
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• 2023

The increase in the rearing intensity of pigs has caused deterioration in the pig house's internal environment such as temperature, humidity, ammonia gas, and so on. Traditionally, the widely used method to control the internal environment was through the manipulation of the ventilation system. However, the conventional ventilation system had a limitation to control the internal environment, prevent livestock disease, save energy, and reduce odor emission. To overcome this problem, the air-recirculated ventilation system was suggested. This system has a semi-closed loop ventilation type. For designing this system, it was essential to evaluate the ventilation rates considering the pressure loss of ducts. Therefore, in this study, pressure loss calculation and experiment were conducted for the quantitative ventilation design of a semi-closed loop system. The results of the experiment showed that the inlet through which external air flows should always be opened. In addition, it was also found that for the optimum design of the semi-closed loop ventilation system, it was appropriate to install a damper or a backflow prevention device rather than a ventilation fan.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.177-182
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• 2021

The fourth industrial revolution based on information and communication technology (ICT) becomes the center of society, and the overall industrial structure is also changing significantly. ICT refers to the hardware of information devices and the software technologies required for the operation and information management of these devices, and any means of collecting, producing, processing, preserving, communicating and utilizing them. ICT is integrated into industries and services or combined with new technologies in various fields such as robotics and nanotechnology to connect all products and services to the network. The development of ICT, which continuously creates new products and services, has spread to all sectors of the industry, affecting not only daily life but also the livestock sector recently. In agriculture, ICT technology can reduce production costs by efficiently managing labor and energy because it can improve quality and yield based on data on environmental and growth information such as temperature, humidity, light and soil. In particular, smart livestock is considered suitable for achieving livestock management goals because it can reduce labor force and improve productivity by remotely and automatically managing accurate information necessary for raising and breeding livestock with ICT devices. The purpose of this study is to propose the need for ICT technology by comparing farm productivity before and after ICT is introduced. The method of the study is to compare the productivity before and after the introduction of ICT in Korean beef farms, pig farms, and poultry farms. The effectiveness of the study proved the excellence of ICT technology through the production results before ICT introduction and the productivity improvement case of livestock farms that efficiently operated manpower management and reduced labor force after ICT introduction. The conclusion of this paper is to present the need for smart livestock through ICT adoption through case study results.

• Journal of agriculture & life science
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• v.46 no.5
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• pp.137-141
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• 2012

The foot and mouth disease(FMD) was occurred from Andong city on November 23, 2010 and spread out the whole country except Jeju island and Jeolla-do. About 3.4 million livestock such as cow and pig was buired at 4,200 sites during preventive measures of FMD. Government did not effectively respond to the FMD crisis management so FMD spread out the whole country. To Prevent the spread FMD, Farms have to fast approaching and respond directly to smartphones and Tablet PC applications. Resolve the difficulties of using smart devices and easy to operate for the effective utilization of the development of simple applications. This application of FMD, developed for the prevention and alarm applications, foot and mouth disease will be caused, farmers around the farm in case of risk and the seriousness of the FMD will notify smartphone, FMD prevent additional damage due to be interested in preventing further that allows your application is for development purposes.