• Magazine of the Korean Society of Agricultural Engineers
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• v.40 no.6
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• pp.37-45
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• 1998

A survey was conducted to get information on the utilization of irrigation systems for greenhoyses farming. Three regions were selected which represent geographical chatacteristics such as neighboring urban area, flat-field area, and mountainous area. The number of greenhouses farms interviewed was 432 in total. The contents of the survey consisted of general characteristics of greenhouse farmers, the size and location of greenhouses, cultuvated crops, irrigation method, irrigation scheduling, and irrigation automation. The analysis of the surveyed data showed that greenhouse farmers did not take technical assistances. Proper criteria or guidelines for selection and operation of irrigation systems were not available. Irrigation systems were operated by hand. Irrigation scheduling were executed by farmer's experience. Maintenance of irrigation systems in general were poor. Development of economically reasonable irrigation system is of importance.

• Journal of Families and Better Life
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• v.20 no.1
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• pp.75-86
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• 2002

The purpose of this study is to analyse the change of agricultural labor participation and decision-making involvement of rural women in Korea from 1960s to 1990s. For analysis of these changes, I used the data surveyed and collected by different researchers during those times. During last 40 years, rural society and the mode of agricultural production rapidly changed according to the development of Korean industry. Agricultural labor participation of rural women increased since the mid 1970s. Their agricultural labor expanded into full ranges of farming. Their decision-making involvement also expanded into all divisions of farming. However, they did not have decision making power as much as they contributed to farm labor The expansion of rural women's labor within the sphere of farm production has not substantially altered the decision-making power structure within the farm household.

• 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 Computer Information Conference
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• 2024.01a
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• pp.437-438
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• 2024

인구가 감소하고 있는 농어촌의 지역경제를 살림과 동시에 지역 소멸을 예방하고, 정착 지역 탐색에 많은 시간을 할애하는 귀농희망자들을 위해 도움을 줄 수 있는 데이터베이스를 설계하고 구축하였다. 사용자는 이 데이터베이스를 활용하여 사용자가 원하는 조건에 맞는 상위 5개 지역을 추천받을 수 있다. 정부와 기업이 귀농 지역 추천 데이터베이스를 활용하여 귀농을 희망하는 사람들의 행태를 알아보면 농촌 지역과 지방 소도시 지역 개발 및 활성화를 긍정적으로 예상할 수 있고, 이러한 관심이 결과적으로는 우리나라의 국토 균형발전에 큰 도움이 될 것이다.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.81-81
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• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• Journal of the Korean Geographical Society
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• v.9
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• pp.67-75
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• 1974

The vegetation activity of the Korean peninsula has been monitored temporal variations through a satellite remote sensing and the vegetation index was used to set up the vegetation data map of Korea. The AVHRR data sent by the NOAA-14 satellite was collected for 8 months between April and November, 1997 to calculate the normalized difference vegetation index(NDVI) which was combined the MVC(Maximum Value Composite). Then this NDVI composite map was prepared to review the temporal variations in the vegetation activity. The NDVI has been subject to the unsupervised classification for the growing season between May and October. And the vegetation type is divided into five classes ; urban, bare soil, grass, farming land, deciduous forest and coniferous forest. The unsupervised classificaion of vegetation distribution in the Korean Peninsula shows that the urban and bare soil take 4.14% of total national area, grass 4.49%, farming land 27.54%, deciduous forest 25.61% and coniferous forest 38.22%.

• Journal of IKEEE
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• v.22 no.4
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• pp.1152-1157
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• 2018

In this paper, we try to solve the difficulties of the location of the structure for the movement of the wire - based pesticide spraying equipment designed for field farming. To do this, we apply earth resistivity measurement method and analysis technique which can indirectly grasp the earth structure. Electrodes are installed on the field in a selected farming area, and multi-switches built in the control board are driven to automatically acquire ground resistivity data. Then, the optimal point suitable for the actual structure installation is selected through the site analysis using the 2D image restoration algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.403-405
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• 2019

The risk mitigation service based on network provides monitoring of the risk event data to be inputted and analyses its big data to be stored in real time. Furthermore, it performs the analysis of the plant disease risk such as a red tide, and livestock disease risk such a food-and-mouth disease, avian influenza, and rinderpest, and provides the mitigation service. The standardization road map for risk mitigation is the real time acquisition monitoring of risk events, and mitigation service for the risks.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.347-351
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• 2022

Farmers E-Market is a website that allows agricultural workers to direct market their products to buyers without the use of a middleman. That theory is blockchain system will be used by authors to accomplish this. The system, which is built on a public blockchain system, supports sustainability, shippers, and consumers. Farmers can keep track of their farming activities. Customers can review the product's history and track its journey through carriers to delivery after making a purchase. Farmers are encouraged to get information about their interests promptly in a blockchain-enabled system like this. This functionality is being used by small-scale farmers to form groups based on their location to attract large-scale customers, renegotiate farming techniques or volumes, and enter into contracts with buyers. The analysis shows the use of blockchain technology with a farmer's portal that keeps the video of trading data of crops, taking into account the qualities of blockchain such as values and create or transaction data. The proposal merges python as a programming language with a blockchain system to benefit farmers, vendors, and individuals by preserving transactions.