• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.126-136
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• 2020

Recently, unmanned aerial vehicles (UAV, Drone) are highly regarded for their potential in the agricultural field, and research and development are actively conducted for various purposes. Therefore, in this study, to present a framework for tracking research trends in UAV use in the agricultural field, we secured a keyword search strategy and analyzed social network, a methodology used to analyze recent research trends or technological trends as an analysis model applied. This study consists of three stages. As a first step in data acquisition, search terms and search formulas were developed for experts in accordance with the Keyword Search Strategy. Data collection was conducted based on completed search terms and search expressions. As a second step, frequency analysis was conducted by country, academic field, and journal based on the number of thesis presentations. Finally, social network analysis was performed. The analysis used the open source programming language 'Python'. Thanks to the efficiency and convenience of unmanned aerial vehicles, this field is growing rapidly and China and the United States are leading global research. Korea ranked 18th, and bold investment in this field is needed to advance agriculture. The results of this study's analysis could be used as important information in government policy making.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.355-362
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• 2022

Smart Farm, which combines information and communication technologies with agriculture is moving from simple monitoring of the growth environment toward discovering the optimal environment for crop growth and in the form of self-regulating agriculture. To this end, it is important to collect related data, but it is more important for farmers with cultivation know-how to analyze the collected data from various perspectives and derive useful information for regulating the crop growth environment. In this study, we developed a web service that allows farmers who want to obtain necessary information with data related to crop growth to easily analyze data. Web-based data analysis serivice developed uses R language for data analysis and Express web application framework for Node.js. As a result of applying the developed data analysis service together with the growth environment monitoring system in operation, we could perform data analysis what we want just by uploading a CSV file or by entering raw data directly. We confirmed that a service provider could provid various data analysis services easily and could add a new data analysis service by newly adding R script.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.79-79
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• 2023

With the development of digital technology, the size of the smart agriculture market at home and abroad is rapidly expanding. It is necessary to establish a foundation for sustainable precision agriculture in order to respond to the aging of rural areas and labor shortages. This study was conducted to establish an automated digital agricultural test bed for soybean production management using data suitable for agricultural environmental conditions in Korea and to demonstrate the field of leading complexes. In order to manage water smartly, we installed a subsurface drip irrigation system in the upland field and an underground water level control system in the paddy field. Based on data collected from sensors, water management was controlled by utilizing an integrated control system. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. The main growth characteristics and yield, such as stem length, number of branches, and number of nodes of the main stem, were investigated during the main growth period. During the operation of the test bed, drought appeared during the early vegetative growth period and maturity period, but in the open field smart agriculture test bed, water was automatically supplied, reducing labor by 53% and increasing yield by 2%. A test bed was installed for each field digital farming element technology, and it is planned to verify it once more this year. In the future, we plan to expand the field digital farming technology developed for leading farmers to the field.

• Smart Media Journal
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• v.3 no.1
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• pp.16-21
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• 2014

The demand for food rises proportionally as population grows. To be able to achieve sustainable supply of livestock products, efficient farm management is a necessity. With the advancement in technology it also brought innovations that could be harness in order to achieve better productivity in animal production and agriculture. Precision Livestock Farming (PLF) is a budding concept of making use of smart sensors or available devices to automatically and continuously monitor and manage livestock production. With this concept, this paper introduces a swine management system that integrates image processing technique for weight monitoring. This system captures pig images using camera, evaluate and estimate the weight base on the captured image. It is comprised of Pig Module, Breeding Module, Health and Medication Module, Weighr Module, Data Analysis Module and Report Module to help swine farm administrators better understand the performance and situation of the swine farm. This paper aims to improve the management in both small and big livestock raisers.

• Agricultural and Biosystems Engineering
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• v.7 no.1
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• pp.27-41
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• 2006

Assessment of nitrogen and chlorophyll content from crop leaves can help growers adjust N fertilizer rates to meet the demands of the crop. Numerous researchers have presented their studies about spectral signature of plant leaves to characterize the plant features. However, interrelational review and summary were limited and a communication gap exists between the plant science and optical engineering. Understanding the mechanism of leaf interaction to electromagnetic radiation and factors affecting spectrophotometric measurements can enhance the foundation of optical remote sensing technologies. This paper provides extensive review of previous works in optical sensing and explains the basics of plant optics, spectral measurements for plant stress, factors that affect sensitivity to spectral analysis, and applications that deploy optical remote sensing technologies.

• International Journal of Advanced Culture Technology
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• v.2 no.2
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• pp.15-18
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• 2014

This paper presents a sensor location decision making method respect to Container-Grown Seedlings in view of precision agriculture (PA) when sensors involved in tree container measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the container-grown seedlings system have a known location, whereas the remaining locations must be estimated. We derive Rao-Cramer bounds and maximum-likelihood estimators under Gaussian and log-normal models for the TOA and RSS measurements, respectively.

• Journal of Biosystems Engineering
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• v.31 no.1 s.114
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• pp.24-32
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• 2006

Compaction is becoming a greater concern in crop production and the environment because it can have deleterious effects on growing conditions that are difficult to remediate. Because compaction can vary considerably from point to point within a field, and also from depth to depth within the soil profile, it is important to consider quantification and management of the spatial and vertical variability in soil compaction when developing an overall site-specific crop management plan. In this paper, the importance of soil compaction, techniques for quantification of its variability, and the concept of site-specific tillage are examined. Methods and systems to detect within-field variation in soil strength as a surrogate measure of soil compaction and related soil properties are also compared and discussed. Quantification of variability in soil compaction and site-specific compaction management was motivated recently, and sensors and control systems are still under development. Future study will need to address a number of issues related to understanding and applying the sensor measurements.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.47-58
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• 1994

The farm population survey and the livestock survey are sample surveys related to agriculture. Two new sample designs for these surveys are considered. Shi-Gun(county) estimates in the farm population survey and Shi-Do(county) estimates in the livestock survey can be obtained. Also the sample sizes are reduced. To increase the precision of the estiamtes strarified simple random samples are used and particularly purposive samples are introduced in livestock survey. Lastly the method of management and replacement of samples are investigated for successive occasion survey.

• Agricultural and Biosystems Engineering
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• v.6 no.2
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• pp.39-46
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• 2005

Because soil compaction is a concern in crop production and environmental pollution, quantification and management of spatial and vertical variability in soil compaction for soil strength) would be a useful aspect of site -specific field management. In this paper, a soil strength profile sensor (SSPS) that could take measurements continuously while traveling across the field was developed and the performance was evaluated through laboratory and field tests. The SSPS obtained data simultaneously at 5 evenly spaced depths up to 50 em using an array of load cells, each of which was interfaced with a soil-cutting tip. Means of soil strength measurements collected in adjacent, parallel transects were not significantly different, confirming the repeatability of soil strength sensing with the SSPS. Maps created with sensor data showed spatial and vertical variability in soil strength. Depth to the restrictive layer was different for different field locations, and only 5 to 16% of the tested field areas were highly compacted.

• Agricultural and Biosystems Engineering
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• v.1 no.2
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• pp.88-94
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• 2000

Nitrogen (N) management is critical for corn production. On the other hand, N leaching into the groundwater creates serious environmental problems. There is a demand for sensors that can assess the plant N deficiency throughout the growing season to allow producers to reach their production goals, while maintaining environmental quality. This paper reports on the performance of a vision-based reflectance sensor for real-time assessment of N stress level of corn crops. Data were collected representing the changes in crop reflectance in various spectral ranges over several stages of development in the growing season. The performance of this non-contact sensor was validated under various field conditions with reference measurement from a Minolta SPAD meter and stepped nitrogen treatments.