• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.1
• /
• pp.1-9
• /
• 2021

Number of duck and its breeding facilities have been steadily decreasing for financial and social issues in Korea. Therefore, the 'turning point' for duck industry is strongly demanded. In this study, the questionary survey was carried out to provide backgrounds for developing policy and technology for duck breeding farms. The questionary survey aimed to investigate the information of operation strategy of farm, ventilation, cooling and heating. The total number of survey respondents was 74. In case of facility type, 55.4% of respondents stated they used greenhouse type, 31.3% for winch-curtain type, and 2.7% for windowless type (mechanically ventilated facility). More than 85% of the facilities were using 'natural ventilation', it meant that these situation can restrict the not only environmental control but also the supply policy for 'smart farm' of the Government. 44.6% used the combination of the cross-ventilation method and roof-ventilation method for ventilation operation in summer season, and 31.1% followed only the cross-ventilation method. In case of winter season, 36.5% used the cross-ventilation method, and 33.3% used the combination of cross and roof-ventilation, method. For the ventilation strategy, about 86.5% depended on farmer's experience. In case of heating and cooling, 79.7% were using kerosene heater for winter season, and 43.2% were using mist-spray for summer season, respectively. More than 75% stated that cooling and heating strategies were based on farmer's experience. From the analyses of the survey results, a few proposals for developing policy and technology for duck breeding farm was suggested.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.5
• /
• pp.1003-1012
• /
• 2022

The horizontal force transfer to the turbine and substructure of a wind power generation system is a very important factor in maintaining the safety of the system, but it is inevitably vulnerable to large-scale coastal disasters such as earthquakes and typhoons. Wind power generation systems built on the coast or far offshore are very disadvantageous in terms of economic feasibility due to an increase in initial investment cost because a more robust design is required when installed in areas vulnerable to coastal disasters. In this study, the GIS method was used to select the optimal site for a wind farm from the viewpoint of reducing the risk of coastal disasters. The current status of earthquakes in the West and South Seas of Korea, and the path and intensity of typhoons affecting or passing through the West and South Seas were also analyzed. Accordingly, the optimal offshore wind farm site with the lowest risk of coastal disasters has been selected and will be used as basic research data for offshore wind power projects in the region in the future.

• Journal of Software Assessment and Valuation
• /
• v.17 no.1
• /
• pp.75-82
• /
• 2021

Mastitis in cows is a major factor that hinders dairy productivity of farms, and many attempts have been made to solve it. However, research on mastitis has been limited to diagnosis rather than prediction, and even this is mostly using a single sensor. In this study, a predictive model was developed using multivariate data including biometric data and environmental data. The data used for the analysis were collected from robot milking machines and sensors installed in farmhouses in Chungcheongnam-do, South Korea. The recurrent neural network model using three weeks of data predicts whether or not mastitis is diagnosed the next day. As a result, mastitis was predicted with an accuracy of 82.9%. The superiority of the model was confirmed by comparing the performance of various data collection periods and various models.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.56 no.1
• /
• pp.116-123
• /
• 2023

Flow-through aquaculture systems generate large amounts of wastewater containing compounds such as solids that can settle near aquafarms and cause eutrophication. The settled solids are often reintroduced into flow-through systems, and aquatic animals can be affected by the solids and pathogens associated with these solids. For a sustainable aquaculture operation, adequate wastewater treatment is required. Hydrocyclones are one of the most promising technologies for the removal of solids in aquaculture wastewater. In this study, a model for performance prediction of hydrocyclones was investigated under three different operating conditions: water temperature, solids concentration, and water inlet velocity. The synthetic solids solution was prepared using settled solids from abalone aquaculture farms. The daily solids removal rates of the tested hydrocyclones ranged from 0.18 to 26.0 g solids-m^-3-day^-1, and removal efficiency ranged from 5.1 to 34.4%. The inlet water velocity had the greatest effect on solids removal and hydrocyclone efficiencies. The following multiregression model equation was derived from the daily solids removal rate (g solids-m^-3-day^-1) results for water temperature (T, ℃), solids concentration (SS, mg-L^-1), and tangential inlet water velocity (TIV, m-sec^-1): daily solids removal rate: f(z)=4.465+0.809TIV-0.375T+0.217SS (r²=0.976).

• Journal of Internet of Things and Convergence
• /
• v.9 no.1
• /
• pp.41-47
• /
• 2023

Today, due to the increase in global population growth, the international community is discussing solving the food problem. The aquaculture industry is emerging as an alternative to solving the food problem. For the innovative growth of the aquaculture industry, smart fish farms that combine the fourth industrial technology are recently being distributed, and full-cycle digitalization is being promoted. Water quality sensors, which are important in the aquaculture industry, are electrochemical portable sensors that check water quality individually and intermittently, making it impossible to analyze and manage water quality in real time. Recently, optically-based monitoring sensors have been developed and applied, but the reliability of monitoring data cannot be guaranteed because the state information of the water quality sensor is unknown. Therefore, this paper proposes an algorithm representing self-diagnosis status such as Failure, Out of Specification, Maintenance Required, and Check Function based on monitoring data collected by water quality sensors to ensure data reliability.

• Information Systems Review
• /
• v.22 no.1
• /
• pp.113-124
• /
• 2020

In the operation of farms, it is very important to evaluate the quality of harvested crops and to classify defective products. However, farmers have difficulty coping with the cost and time required for quality assessment due to insufficient capital and manpower. This study thus aims to detect defects by analyzing the epidermis of fruit using deep learning algorithm. We developed a model that can analyze the epidermis by applying YOLOv3 algorithm based on Region Convolutional Neural Network to video images of peach. A total of four classes were selected and trained. Through 97,600 epochs, a high performance detection model was obtained. The crop failure detection model proposed in this study can be used to automate the process of data collection, quality evaluation through analyzed data, and defect detection. In particular, we have developed an analytical model for peach, which is the most vulnerable to external wounds among crops, so it is expected to be applicable to other crops in farming.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.7
• /
• pp.1968-1985
• /
• 2024

At present, the world is witnessing a rapid change in all the fields of human civilization business interests and goals of all the sectors are changing very fast. Global changes are taking place quickly in all fields - manufacturing, service, agriculture, and external sectors. There are plenty of hurdles in the emerging technologies in agriculture in the modern days. While adopting such technologies as transparency and trust issues among stakeholders, there arises a pressurized necessity on food suppliers because it has to create sustainable systems not only addressing demand-supply disparities but also ensuring food authenticity. Recent studies have attempted to explore the potential of technologies like blockchain and practices for smart and sustainable agriculture. Besides, this well-researched work investigates how a scientific cum technological blockchain architecture addresses supply chain challenges in Precision Agriculture to take up challenges related to transparency traceability, and security. A robust registration phase, efficient authentication mechanisms, and optimized data management strategies are the key components of the proposed architecture. Through secured key exchange mechanisms and encryption techniques, client's identities are verified with inevitable complexity. The confluence of IoT and blockchain technologies that set up modern farms amplify control within supply chain networks. The practical manifestation of the researchers' novel blockchain architecture that has been executed on the Hyperledger network, exposes a clear validation using corroboration of concept. Through exhaustive experimental analyses that encompass, transaction confirmation time and scalability metrics, the proposed architecture not only demonstrates efficiency but also underscores its usability to meet the demands of contemporary Precision Agriculture systems. However, the scholarly paper based upon a comprehensive overview resolves a solution as a fruitful and impactful contribution to blockchain applications in agriculture supply chains.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.16 no.4
• /
• pp.403-417
• /
• 2014

Increased frequency of climate extremes is another face of climate change confronted by humans, resulting in catastrophic losses in agriculture. While climate extremes take place on many scales, impacts are experienced locally and mitigation tools are a function of local conditions. To address this, agrometeorological early warning systems must be place and location based, incorporating the climate, crop and land attributes at the appropriate scale. Existing services often lack site-specific information on adverse weather and countermeasures relevant to farming activities. Warnings on chronic long term effects of adverse weather or combined effects of two or more weather elements are seldom provided, either. This lecture discusses a field-specific early warning system implemented on a catchment scale agrometeorological service, by which volunteer farmers are provided with face-to-face disaster warnings along with relevant countermeasures. The products are based on core techniques such as scaling down of weather information to a field level and the crop specific risk assessment. Likelihood of a disaster is evaluated by the relative position of current risk on the standardized normal distribution from climatological normal year prepared for 840 catchments in South Korea. A validation study has begun with a 4-year plan for implementing an operational service in Seomjin River Basin, which accommodates over 60,000 farms and orchards. Diverse experiences obtained through this study will certainly be useful in planning and developing the nation-wide disaster early warning system for agricultural sector.

• Journal of Mushroom
• /
• v.15 no.3
• /
• pp.118-123
• /
• 2017

This study was carried out to develop the technology to manage the growth of mushrooms, which were cultivated based on long-term information obtained from quantified data. In this study, hardware that monitored and controlled the growth environment of the mushroom cultivation house was developed. An algorithm was also developed to grow mushrooms automatically. Environmental management for the growth of mushrooms was carried out using cultivation sites, computers, and smart phones. To manage the environment of the mushroom cultivation house, the environmental management data from farmers cultivating the highest quality mushrooms in Korea were collected and a growth management database was created. On the basis of the database value, the management environment for the test cultivar (hukthali) was controlled at $0.5^{\circ}C$ with 3-7% relative humidity and 10% carbon dioxide concentration. As a result, it was possible to produce mushrooms that were almost similar to those cultivated in farms with the best available technology.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.3
• /
• pp.141-148
• /
• 2021

Agrivoltaic systems, also called solar sharing, stated from an idea that utilizes sunlight above the light saturation point of crops for power generation using solar panels. It is expected that agrivoltaic systems can realize climate smart agriculture by reducing evapotranspiration and methane emission due to the reduction of incident solar radiation and the consequent surface cooling effect and bring additional income to farms through solar power generation. In this study, to evaluate that agrivoltaic systems are suitable for realization of climate smart agriculture, we conducted agro-environmental observations (i.e., downward/upward shortwave/longwave radiations, air temperature, relative humidity, water temperature, soil temperature, and wind speed) in a rice paddy under an agrivoltaic system and compared with the environment outside the system using automated meteorological observing systems (AMOS). During the observation period, the spatially averaged incoming solar radiation under the agrivoltaic system was about 70% of that in the open paddy field, and clear differences in the soil and water temperatures between the paddy field under the agrivoltaic system and the open paddy field were confirmed, although the air temperatures were similar. It is required in the near future to confirm whether such environmental differences lead to a reduction in water consumption and greenhouse gas emissions by flux measurements.