• Journal of Practical Agriculture & Fisheries Research
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• v.26 no.2
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• pp.14-24
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• 2024

To enhance the usability of smart agriculture, methods for utilizing smart farm data are required. Therefore, this study proposes a scheme for utilizing regional smart farm data by linking it to services. The current status of domestic and foreign smart farm data collection and linkage services is analyzed. To collect and link regional smart farm data, necessary data collection, data cleaning, data storage structure and schema, and data storage and linkage systems are proposed. Based on the standards currently being implemented for regional smart farm internal data storage, a farm schema, environmental information schema, facility control information schema, and growth information schema are designed by extending the crop schema and crop main environmental factor information database schema. A data collection and management system structure based on the Hadoop Ecosystem is designed for data collection and management at regional smart farm data centers. Strategies are proposed for utilizing regional smart farm data to provide smart farm productivity improvement and revenue optimization services, image-based crop analysis services, and virtual reality-based smart farm simulation services.

• Journal of Information Technology Services
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• v.19 no.3
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• pp.139-149
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• 2020

There are now many different commercial weather sensors suitable for smart farms, and various smart farm devices are being developed and distributed by companies participating in the government-led smart farm expansion project. However, most do not comply with standard specifications and are therefore limited to use in smart farms. This paper proposed the connecting structure of operating non-standard node devices in smart farms following standard specifications supporting smart greenhouse. This connecting structure was proposed as both a virtual node module method and a virtual node wrapper method. In addition, the SoftFarm2.0 system was experimentally operated to analyze the performance of the implementation of the two methods. SoftFarm2.0 system complies with the standard specifications and supports non-standard smart farm devices. According to the analysis results, both methods do not significantly affect performance in the operation of the smart farm. Therefore, it would be good to select and implement the method suitable for each non-standard smart farm device considering environmental constraints such as power, space, distance of communication between the gateway and the node of the smart farm, and software openness. This will greatly contribute to the spread of smart farms by maximizing deployment cost savings.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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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.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.743-752
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• 2020

The purpose of this study was to estimate the economic evaluation of a smart farm investment for tomatoes and strawberries. In addition, the potential adoption rate of the smart farm was derived for different scenarios. This study analyzed the economic evaluation with the net present value (NPV) method and estimated the adoption potential of the smart farm with the trade-off analysis, minimum data (TOA-MD) model. The results were as follows: The analysis of the net present value shows that the smart farm investment for the two crops are economically feasible, and the minimum prices for the tomatoes and strawberries should be 1,179 and 3,797 won/kg to secure a sufficient economic feasibility for the smart farm investment. Next, the analysis of the potential adoption rates for smart farms through the TOA-MD model showed that when the support ratio for the adoption of a smart farm system was 50% and the price increase rates were, respectively, - 5, 2.5, 0, 2.5, and 5%, the conversion rates for tomato farms to switch to smart farms were 0.97, 1.78, 3.05, 4.91, and 7.47%, while the ratios of the strawberry farms to switch to smart farms were 0.12, 0.29, 0.65, 1.33, and 2.53%, respectively. This study has some known limitations, but it provides useful information on decision making about smart farm adoption and can contribute to government policies on smart farms.

• Journal of People, Plants, and Environment
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• v.23 no.6
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• pp.655-665
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• 2020

Background and objective: This study was conducted to compare the cultivation environment, growth of cut flowers, and management performance of conventional farms and smart farms growing the standard cut chrysanthemum, 'Jinba'. Methods: Conventional and smart farms were selected, and facility information, cultivation environment, cut flower growth, and management performance were investigated. Results: The conventional and smart farms were located in Muan, Jeollanam-do, and conventional farming involved cultivating with soil culture in a plastic greenhouse, while the smart farm was cultivating with hydroponics in a plastic greenhouse. The conventional farm did not have sensors for environmental measurement such as light intensity and temperature and pH and EC sensors for fertigation, and all systems, including roof window, side window, thermal screen, and shading curtain, were operated manually. On the other hand, the smart farm was equipped with sensors for measuring the environment and nutrient solution, and was automatically controlled. The day and night mean temperatures, relative humidity, and solar radiation in the facilities of the conventional and the smart farm were managed similarly. But in the floral differentiation stage, the floral differentiation was delayed, as the night temperature of conventional farm was managed as low as 17.7℃ which was lower than smart farm. Accordingly, the harvest of cut flowers by the conventional farm was delayed to 35 days later than that of the smart farm. Also, soil moisture and EC of the conventional farm were unnecessarily kept higher than those of the smart farm in the early growth stage, and then were maintained relatively low during the period after floral differentiation, when a lot of water and nutrients were required. Therefore, growth of cut flower, cut flower length, number of leaves, flower diameter, and weight were poorer in the conventional farm than in the smart farm. In terms of management performance, yield and sales price were 10% and 38% higher for the smart farm than for the conventional farm, respectively. Also, the net income was 2,298 thousand won more for the smart farm than for the conventional farm. Conclusion: It was suggested that the improved growth of cut flowers and high management performance of the smart farm were due to precise environment management for growth by the automatic control and sensor.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.203-215
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• 2022

The study is to investigate research trends and knowledge structures in the Smart Farm field. To achieve the research purpose, keywords and the relationship among keywords were analyzed targeting 104 Korean academic journals related to the Smart Farm in KCI(Korea Citation Index), and topics were analyzed using the LDA Topic Modeling technique. As a result of the analysis, the main keywords in the Korean Smart Farm-related research field were 'environment', 'system', 'use', 'technology', 'cultivation', etc. The results of Degree, Betweenness, and Eigenvector Centrality were presented. There were 7 topics, such as 'Introduction analysis of Smart Farm', 'Eco-friendly Smart Farm and economic efficiency of Smart Farm', 'Smart Farm platform design', 'Smart Farm production optimization', 'Smart Farm ecosystem', 'Smart Farm system implementation', and 'Government policy for Smart Farm' in the results of Topic Modeling. This study will be expected to serve as basic data for policy development necessary to advance Korean Smart Farm research in the future by examining research trends related to Korean Smart Farm.

• The Journal of Information Systems
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• v.31 no.2
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• pp.45-62
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• 2022

Purpose Smart farms play a leading role in changing the safety food culture for the citizens. The purpose of this study is to investigate the factors that are important to covid 19-response in the case of ICT smart farm. To do so, we classified the factors as operating effect aspect and industrial wave effect aspect of the smart farm. Design/methodology/approach This study was conducted by visiting Geumsan County, which is attempting to perform a smart farm. Through interviewing farmers representatives based on their operational effect expectations on the smart farm, we derived the industrial crash effect factors and thereafter designed the research model. This study applied AHP, which is an expert decision-making method cans be used to measure relative importance for determining priorities. After interviewing the experts with smart farm, we obtained the factors which are important to smart farm development. Findings According to analysis, the productivity improvement factor was ranked as the most important among the operational effect items. This is consistent with the ultimate goal of smart farms with ICT convergence technology, which is increase the profitability of agriculture. The second place is the factor in the development of infrastructure and infrastructure, and the third and fifth positions were export expansion, environmentally friendly management, and job creation in terms of operational effectiveness.

• Korean Journal of Organic Agriculture
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• v.28 no.3
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• pp.315-334
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• 2020

This study was conducted to analyze factors affecting acceptance of smart farm technology. Smart farm technology is rapidly being introduced to agriculture in accordance with the progress of the 4th Industrial Revolution, but research on this is still little. Therefore, in this study, based on the unified theory of acceptance and use of technology (UTAUT), a research model reflecting the characteristics of smart farm technology was constructed. To test this, empirical analysis was performed. A survey was conducted for students in smart farm technology education and adult male and female farmers who are currently planning to operate smart farms. Valid 204 sample were used for analysis. The hypothesis test was based on multiple regression analysis using SPSS 24 statistical package. For the mediating effect and moderating effect, Process Macro 3.4 based on the regression equation was used. The results of testing the hypothesis are as follows. First, in the causal hypothesis test, it was shown that performance expectancy, social influence and price value have a significant positive effect on the intention to use smart farm technology. On the other hand, effort expectancy, facilitating conditions were not tested for a significant influence on the use of smart farm technology. As a result of analyzing the mediating effect of trust, it was found that trust plays a mediating role between performance expectancy, effort expectancy, social influence, facilitating conditions, price value and intention to use smart farm technology. In particular, the effort expectancy has not been tested for a direct significant effect on intention to use smart farm technology, but it has been shown to have an impact through trust. Trust was found to be a full mediating between the effort expectancy and the intention to use the smart farm technology. The current IT level of prospective users has been shown to play a moderating role between performance expectancy, facilitating conditions and intention to use smart farm technology. In particular, the IT level was found to strengthen the relationship between performance expectancy and intention to use smart farm technology. Based on the results of these studies, academic and practical implications were suggested.

• Korean Journal of Organic Agriculture
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• v.30 no.2
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• pp.255-277
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• 2022

This study investigated consumers' perception of fruits and vegetables grown in smart farms to stimulate the sale of agricultural products grown in smart farms. To this end, a survey was conducted on 1,050 consumers. The main results are as follows. First, 58.6% of respondents knew about smart farms, and they perceived fruits and vegetables grown in smart farms as more valuable than those grown in conventional facilities. In the detailed values, values of safety and environmental damage reduction were perceived to be of greatest value among five values. Second, as a result of investigating the importance of smart farm cultivation information in comparison with price, the most respondents emphasized both smart farm cultivation information and price information, and smart farm cultivation information was compared with price information. Cases were investigated to be more important with slight differences. Third, 41.4% of respondents had the price premium payment intention for fruit and vegetables grown in smart farms. Fourth, as a result of analyzing variables affecting the premium intention, the higher the health value among five values was recognized and the more important the smart farm cultivation information was, the higher the premium payment intention was.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.