• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.603-606
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• 2015

The post-production of smart farming defines the stage that the final products are delivered from producer to consumers via market on ICT network. It deals with the process of product packaging and distribution from producer to consumer with marketing strategy. This focus on reference model for post-production service including specialization, centralization of product delivery, and just-in-time delivery, and marketing system on the network. It defines a significant function component on post-production stage. The producer plays a significant role in economy being one of the main contributors to the many customers. This articles suggest the effective product distribution service which requires delivering the right product, in the right quantity, in the right condition, to the right place, at the right time, for the right cost, and encompassing global marketing based on ICT network, will be provided[1].

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2017년도 추계학술대회
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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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• 제17권1호
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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.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2019년도 하계 학술발표초록집
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• pp.104-119
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• 2019

• 스마트미디어저널
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• 제5권3호
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• pp.17-24
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• 2016

우리나라 농업은 현재 농촌인구감소, 농촌인구의 고령화, 곡물자급률 하락, 기후변화 심화 등의 원인으로 어려움을 겪고 있으며, FTA 수입개방의 확대에 따른 우리나라의 농축산업의 경쟁력 확보가 필요하다. 낙후된 경쟁력 확보를 위해 정부에서는 한국형 스마트 팜 확대를 위해 1세대모델부터 3세대모델까지를 정의하고 있으며, 농업의 스마트화를 통해 농업의 성장한계를 극복하고 6차+${\alpha}$산업으로 발전하기 위한 노력하고 있다. 본 논문에서는 2세대 모델에 대한 IoF(Internet of Farming)-Cloud 기반의 실질적인 서비스들에 대한 정의 및 서비스를 검증하며, IoF-Cloud의 온실 테스트베드를 제시한다.

• 스마트미디어저널
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• 제3권1호
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• pp.16-21
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• 2014

세계 인구의 증가로 인하여 식량에 대한 요구 또한 이에 비례하여 증가하고 있는 가운데 지속적으로 안정적인 가축 공급을 위해서는 농장에 대한 효율적인 관리가 중요하다. 최근 여러 가지 기술적 진보와 혁신에 목축업이나 농업 분야의 생산성이 향상되고 있으며, 각종 스마트 센서와 여러 가지 자동화 디바이스를 이용하여 가축의 생육 상태를 지속적으로 모니터링하고 생산을 관리하는 PLF(Precision Livestock Farming)의 활용이 확산되고 있다. 본 논문은 이미지 프로세싱 기법을 이용하여 가축의 체중을 모니터링하는 swine 관리 시스템에 관한 것으로서 Pig Module, Breeding Module, Health and Medication Module, Weighr Module, Data Analysis Module 및 Report Module을 구현하여 카메라를 통해 획득한 이미지를 이용하여 체중을 자동으로 계산하고 먹이량을 조절하며 건강상태도 모니터링 할 수 있도록 하였다.

• Journal of Animal Science and Technology
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• 제66권1호
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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.

• 한국콘텐츠학회논문지
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• 제20권8호
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• pp.466-479
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• 2020

본 연구는 사회적 농업에서 발달장애인의 일자리 가능성을 모색하여 창출방안을 도출하고자 하였다. 이를 위해 해외 사회적 농업 활동 중에서 발달장애인 대상 사례를 참조하였다. 그리고 국내의 사회적 농장 다섯 곳을 방문하여 관찰하고, 담당자를 인터뷰하였다. 연구내용은 발달장애인의 일자리로서 사회적 농업의 의미와 가능성을 파악하고 사회적 농업에서 지속가능한 발달장애인 일자리 창출방안을 탐색하였다. 연구결과, 국내의 사회적 농업은 초기단계 있으며, 발달장애인에 대한 치유와 돌봄 중심의 농업체험 중심의 활동이 대부분이었다. 향후 지속적인 농업교육과 활동을 통해서 발달장애인에게 적합한 농업 일자리로서의 가능성이 충분하다는 결론을 도출하였다. 이러한 결과를 바탕으로 본 연구에서는 사회적 농업 분야에서 발달장애인 일자리 창출모형을 제안하였다. 본 연구에서 제시하는 일자리 창출모형은 크게 치유중심의 체험형, 돌봄 중심의 보호작업형, 사회적 일자리 모형으로 구분하였고, 사회적 일자리 모형에 스마트 팜 모형과 식물공장 모형을 추가하였다.

• 한국멀티미디어학회논문지
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• 제18권6호
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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.

• 한국컴퓨터정보학회논문지
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• 제24권8호
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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.