• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.405-407
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• 2017

The environmental change is affecting the farm products like tomato, and pepper, etc. This affects to lead smart farming yield. What is more, this inconstant conditions cause the farms to be infected by variety diseases. Therefore ICT technology is needed to detect and prevent the crops from being effected by diseases. This article suggests the procedure to help producer for identifying farms disease based on the detected image. This detects the kind of diseases with comparing the trained image data before and after disease emergence. First step monitors an image of farms and resize it. Its features are extracted on parameters such as color, and morphology, etc. The next steps are used for classification to classify the image as infected or non-infected. on the bassis of detection algorithm.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.8-13
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• 2019

Smart Farming has been regarded as an important application in information and communications technology (ICT) fields. Selecting crops for cultivation at the pre-production stage is critical for agricultural producers' final profits because over-production and under-production may result in uncountable losses, and it is necessary to predict crop production to prevent these losses. The ITU-T Recommendation for Smart Farming (Y.4450/Y.2238) defines plan/production consultation service at the pre-production stage; this type of service must trace crop production in a predictive way. Several research papers present that machine learning technology can be applied to predict crop production after related data are learned, but these technologies have little to do with standardized ICT services. This paper clarifies the relationship between agricultural consultation services and predicting crop production. A prediction scheme is proposed, and the results confirm the usability and superiority of machine learning for predicting crop production.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.104-119
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• 2019

• Smart Media Journal
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• v.5 no.3
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• pp.17-24
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• 2016

Korean agriculture is currently experiencing difficulties as a cause of rural depopulation, aging of rural population, grain self-sufficiency rate decline, and deepening of climate change. It is necessary to ensure our country's agriculture industrial competitiveness in accordance with opening of FTA imports expanded. To ensure the underdeveloped competitive, Korean government defines the 3rd generation model from 1st generation model to extend the smart farms of Korean types. The agriculture smarting overcomes the growth limitations of agriculture, and efforts to develop 6th + ${\alpha}$ industry. In this paper, We define and verify the IoF(Internet of Farming)-Cloud based substantial services about 2rd generation model, and propose a greenhouse of IoF-Cloud testbed.

• 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.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.466-479
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• 2020

The purpose of this study was to explore the possibility of jobs for people with developmental disabilities in social farming and to derive job-creation plans. To this end, we analyzed the cases of social farms targeted for people with developmental disabilities among overseas social farming activities. And we visited and observed 5 social farms in Korea and interviewed the person in charge. The content of the study was to grasp the meaning and possibility of social farming as a job for people with developmental disabilities, and to explore ways to create a sustainable job for people with developmental disabilities in social farming. As a result of the study, social farming in Korea is in its infancy, and most of the activities are centered on agricultural experiences focused on healing and care for people with developmental disabilities. In the future, it was concluded that continuous agricultural education and activities are sufficient as suitable agricultural jobs for people with developmental disabilities. Based on these results, this study proposed a job model for people with developmental disabilities in social farming. The job model presented in this study is largely divided into a healing-oriented experience model, a care-oriented protective work model, and a social job model. In addition, a smart farm model and a plant factory model were added to the social job model.

• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.772-779
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• 2015

Advancing information technology have rapidly changed our service environment of life, culture, and industry. Computer information communication system is applied in medical, health, distribution, and business transaction. Smart is using new information by combining ability of computer and information. Although agriculture is labor intensive industry that requires a lot of hands, agriculture is becoming knowledge-based industry today. In agriculture field, computer communication system is applied on facilities farming and machinery Agricultural. In this paper, we designed and implemented application that provides personalized agriculture related information at the actual farming field. Also, this provides farmer a system that they can directly auction or sell their produced crops. We designed and implemented a system that parsing information of each seasonal, weather condition, market price, region based, crop, and disease and insects through individual setup on ubiquitous environment using location-based sensor network and processing data.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.37-43
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• 2019

The existing smart farm technology has been systematized for the mass production rather than the consumer. There are many problems such as economical aspect to apply to actual rural environment due to aging. The purpose of this study is to apply smart farm technology based on the applicability of population aged in rural areas. Due to the heat wave, the crops in general greenhouse cultivation facilities suffered from damage such as sunlight damage. To minimize such damage, adjust the temperature and humidity environment or install a light-shielding film. However, the workers in the rural areas are aging and the elderly who are farming alone have a lot of difficulties in doing so. In the case of people with weak physical strength, there is a danger that they may lead to safety accidents when carrying heavy loads. In this paper, we propose 'Smart Palm capable of automatic transportation function', applying small smart vehicles that follow workers to existing smart farms to improve and prevent these problems. It is a smart farm that performs the control functions of the existing smart greenhouse environment, installs the rail for each trough, and has a vehicle that follows the worker. The smart app can directly control the greenhouse and the vehicle remotely manually.

• Journal of Soil and Groundwater Environment
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• v.28 no.5
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• pp.51-58
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• 2023

In this study, an empirical experiment was conducted to assess the feasibility of replacing groundwater with rainwater in melon cultivation using a smart rainwater harvesting system. The rainwater harvesting efficiency was calculated under three different melon cultivation scenarios. After cultivation, the quality of the fruits grown with rainwater and groundwater was compared by examining the weight, degree of sweetness, and flesh hardness of the products. The results revealed that the water quality of the smart rainwater harvesting device was suitable for melon cultivation to provide better hardness and chloride levels than groundwater. It was also estimated that about 40% of the total water demand for full growth of the melon could be supplied by rainwater. The fruit weight and sweetness were equivalent or slightly better for the melons cultivated with rainwater than those cultivated with groundwater. In particular, the flesh hardness was significantly improved by rainwater cultivation. These results collectively suggest that rainwater can be used as a substitute for groundwater to preserve groundwater resources without compromizing the produced fruit quality.