• 품질경영학회지
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• 제52권2호
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• pp.201-220
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• 2024

Purpose: Smart farming is related to the low carbon certification system as it provides many opportunities to cultivate and manage crops in an eco-friendly, thereby reducing carbon footprint. However, there is a significant lack of consumer perception research on low carbon labels for smart farms vegetables. Therefore, this study aims to investigate consumer perceptions of smart farm vegetable and low carbon labels. Methods: This study manipulated cultivation type(general vs. smart farm) and low carbon labels (yes vs. no) as experimental stimuli. Measurement questions and the research model were validated through confirmatory factor analysis and reliability analysis. Hypotheses testing were conducted using SPSS 29.0, AMOS 28.0. Results: The results of the study showed no significant difference in consumers perceived naturalness based on cultivation types, and there was also no moderating effect of the low carbon label. There was no difference between environmental benefits and health benefits according to the cultivation type. Perceived naturalness had a significant effect on both environmental and health benefits, and environmental benefits showed a higher impact relationship. These benefits positively affected attitudes and willingness to pay a premium, Environmental benefits had a higher impact on attitudes, while health benefits had a higher impact on willingness to pay a premium. Lastly, attitudes were found to have a significant impact on the willingness to pay a premium. Conclusion: This study is valuable in that it investigated consumer perceptions of smart farms and low carbon labels that have not been previously studied. It compares the environmental and health benefits, confirming their influence on attitudes and willingness to pay a premium. The results suggest a potential expansion in academic research on smart farming and low carbon labels, offering practical insights for marketing strategies and policies for relevant companies.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.388-393
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• 2024

The convergence of machine learning and smart farm is becoming more and more important. The purpose of this research is to quantitatively analyze machine learning and smart farm with bibliographic data from 2013 to 2023. This study analyzed the 251 articles, filtered from the Web of Science, with regard to the article publication trend, the article citation trend, the top 10 research area, and the top 10 keywords representing the articles. The quantitative analysis results reveal the four points: First, the number of article publications in machine learning and smart farm continued growing from 2016. Second, the article citations in machine learning and smart farm drastically increased since 2018. Third, Computer Science, Engineering, Agriculture, Telecommunications, Chemistry, Environmental Sciences Ecology, Material Science, Instruments Instrumentation, Science Technology Other Topics, and Physics are top 10 research areas. Fourth, it is 'machine learning', 'smart farming', 'internet of things', 'precision agriculture', 'deep learning', 'agriculture', 'big data', 'machine', 'smart' and 'smart agriculture' that are the top 10 keywords composing authors' keywords in the articles in machine learning and smart farm from 2013 to 2023.

• 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.

• 한국환경과학회지
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• 제33권7호
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• pp.501-509
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• 2024

Prunus mume is a culturally significant fruit tree in East Asia that is widely used in traditional foods and medicines. The present study investigated the effects of sunlight exposure and leaf position on the photosynthetic efficiency of P. mume using SPAD values. The study was conducted at Cheongju National University of Education, Korea, under contrasting conditions between sunny (Site A) and shaded (Site B) areas on P. mume trees. Over three days, under varied weather, photosynthetic photon flux density (PPFD) and SPAD measurements were collected using a SPAD-502 plus chlorophyll meter and a smartphone PPFD meter application. The SPAD values of the 60 leaves were measured in triplicate for each tree. The results indicated that trees in sunny locations consistently exhibited higher SPAD values than those in shaded areas, implying greater photosynthetic efficiency. Moreover, leaves positioned higher in the canopy showed increased photosynthetic efficiency under different light conditions, underscoring the significance of leaf placement and light environment in photosynthetic optimization. Despite the daily sunlight variability, these factors maintained a consistent influence on SPAD values. This study concludes that optimal leaf positioning, influenced by direct sunlight exposure, significantly enhances photosynthetic efficiency in P. mume. These findings highlight the potential of integrating smart farming techniques, especially open-field smart farming technology, to improve photosynthesis and, consequently, crop yield and efficiency. The findings also highlight the need for further exploration of environmental factors affecting photosynthesis for agricultural advancement.