• Journal of Agricultural Extension & Community Development
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• v.17 no.3
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• pp.607-637
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• 2010

The goal of this study is to develop a skill system for the areas of agriculture, forestry and fisheries among the skill frameworks that require basic examination in the development of skill standards. More specifically, the study aims to classify skills in the areas of agriculture, forestry and fisheries and to develop respective skill level. We classified skills and created the skill level through a study of documents, interview with experts and in-depth discussions with expert group centering on terminologies commonly used in the industrial settings. As a result of skill classification, we were able to classify skills into four categories in medium-scale classification, 13 categories in small-scale classification, and again into total 42 categories. We classified the skill level in the areas of agriculture, forestry and fisheries into 8 stages. Based on the skill system, we provided definition of skill and skill group, definition of each different skill, and performance standard by skill and level.

• Journal of Soil and Groundwater Environment
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• v.22 no.3
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• pp.18-26
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• 2017

The increasing frequency of droughts has been increasing the necessity of utilizing subsurface dams as reliable groundwater resources in areas where it is difficult to supply adequate agricultural water using only surface water. In this study, we analyzed the current status and actual conditions of five agricultural subsurface dams as well as the effect of obtaining additional groundwater from subsurface dams operated as one aspect of the sustainable integrated water management system. Based on the construction methods and functions of each subsurface dam, the five subsurface dams are classified into three types such as those that derive water from rivers, those that prevent seawater intrusion, and those that link to a main irrigation canal. The classification is based on various conditions including topography, reservoir location, irrigation facilities, and river and alluvial deposit distributions. Agricultural groundwater upstream of subsurface dams is obtained from four to five radial collector wells. From the study, the total amount of groundwater recovered from the subsurface dam is turned out to be about 29~44% of the total irrigation water demand, which is higher than that of general agricultural groundwater of about 4.6%.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.6
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• pp.117-131
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• 2010

This study was conducted to utilize as basic information for the construction of conservation and estimation system for Palustrine wetland, which was badly managed and imprudently reclaimed, through the analysis of distribution characteristics and the estimation of conservation value for sample sites (eight wetlands) in rural area. As the result of wetland type classification, these wetlands was classified by 4 types (Permanent freshwater marshes/pools, ponds, Aquaculture ponds, and Seasonally flooded agricultural land) by Ramsar system, 3 types (Emergent Wetland, Aquatic Bed, and Scrub-Shrub Wetland) by NWI (Cowardin) System, 5 types (Farm Pond Depression, Under-flow wetland, Man-made Pond Depression, Abandoned Paddy Fields Wetland, and Reservoir Shore) by National Wetland's Categorical System, and 3 types (Aquatic Bed Wetland, Emergent Wetland, and Forested Wetland) by Lee (2000) System. These results suggest us developing the new type classification system for small Palustrine wetland in Korean rural areas. The score of function assessment (The Modified RAM) for small Palustrine wetlands was high at the wetlands nearby hills and rice paddy fields, and low at those nearby upper fields, which was mainly affected by land-use and vegetation. The functions as 'Flood/Storm Water Storage', 'Runoff Attenuation', 'Water Quality Protection' were resulted by the structural difference of inflow and outlet. Some functions as 'Wetland size', 'Wetland to immediate watershed ratio', 'Presence of boat traffic', 'Maximum water depth', 'Fetch of water's body' of RAM were not appropriate in evaluation of small wetlands in rural area. Which suggest us developing the new function assessment system for small Palustirne wetland in Korean rural areas.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.617-620
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• 2017

The periodic monitoring of crop conditions and timely estimation of crop yield are of great importance for supporting agricultural decision-makings, as well as for effectively coping with food security issues. Remote sensing has been regarded as one of effective tools for crop condition monitoring and crop type classification. Since 2010, RDA (Rural Development Administration) has been developing technology for monitoring on crop condition using remote sensing and model. These special papers address recent state-of-the-art of remote sensing and geospatial technologies for providing operational agricultural information, such as, crop yield estimation methods using remote sensing data and process-oriented model, crop classification algorithm, monitoring and prediction of weather and climate based on remote sensing data,system design and architecture of crop monitoring system, history on rice yield forecasting method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.145-152
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• 2020

In this paper, we propose the Crop Classification Management System (CCMS) that detects changes in growth environment to improve crop production rate. The CCMS consists of two modules. First, the Crop Classification Module (CCM) classifies crops through CNN. Second, the Farm Anomaly Detection Module (FADM) detects abnormal crops by comparing accumulated data of farms. The CCM recognizes crops currently grown on farms and sends them to the FADM, and the FADM picks up the weather data from the past to the present day of the farm growing the crops and applies them to the Nelson rules. The FADM uses the Nelson rules to find out weather data that has occurred and adjust farm conditions through IoT devices. The performance analysis of CCMS showed that the CCM had a crop classification accuracy of about 90%, and the FADM improved the estimated yield by up to about 30%. In other words, managing farms through the CCMS can help increase the yield of smart farms.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.21-28
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• 2017

The main goal of this paper is to assess application of UAV (Unmanned Aerial Vehicle) remote sensing and GIS based images in detection and measuring of rice field drought area in South Korea. Drought is recurring feature of the climatic events, which often hit South Korea, bringing significant water shortages, local economic losses and adverse social consequences. This paper describes the assesment of the near-realtime drought damage monitoring and reporting system for the agricultural drought region. The system is being developed using drought-related vegetation characteristics, which are derived from UAV remote sensing data. The study area is $3.07km^2$ of Wonbuk-myeon, Taean-gun, Chungnam in South Korea. UAV images were acquired three times from July 4 to October 29, 2015. Three images of the same test site have been analysed by object-based image classification technique. Drought damaged paddy rices reached $754,362m^2$, which is 47.1 %. The NongHyeop Agricultural Damage Insurance accepted agricultural land of 4.6 % ($34,932m^2$). For paddy rices by UAV investigation, the drought monitoring and crop productivity was effective in improving drought assessment method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2012-2027
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• 2019

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.5
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• pp.67-77
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• 2007

The images of MODerate resolution Imaging Spectroradiometer (MODIS) that provide wider swath and shorter revisit frequency than Land Satellite (Landsat) and Satellite Pour I' Observation de la Terre (SPOT) has been used fer land cover classification with better spatial resolution than National Oceanic and Atmosphere Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR)'s images. Due to the advantages of MODIS, several researches have conducted, however the results for the land cover classification using MODIS images have less accuracy of classification in small areas because of low spatial resolution. In this study, uncertainty of paddy fields classification using MODIS images was conducted in the region of Gyeonggi-do and the relation between this uncertainty of estimating paddy fields and topographical factors was also explained. The accuracy of classified paddy fields was compared with the land cover map of Environmental Geographic Information System (EGIS) in 2001 classified using Landsat images. Uncertainty of paddy fields classification was analyzed about the elevation and slope from the 30m resolution Digital Elevation Model (DEM) provided in EGIS. As a result of paddy classification, user's accuracy was about 41.5% and producer's accuracy was 57.6%. About 59% extracted paddy fields represented over 50 uncertainty in one hundred scale and about 18% extracted paddy fields showed 100 uncertainty. It is considered that several land covers mixed in a MODIS pixel influenced on extracted results and most classified paddy fields were distributed through elevation I, II and slope A region.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.

• The Korean Journal of Pesticide Science
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• v.15 no.2
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• pp.201-207
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• 2011

The use of chemical products to enhance and improve life is a widespread worldwide practice. In spite of the benefits of these products, there is the potential of chemicals for adverse effects to people or the environment. The globally harmonized system (GHS) of classifying and labeling chemicals that was recommended by the United Nations in 2003, has been introduced globally since 2008. Compare to the classification criteria of agricultural formulations today, classification criteria of GHS is different partly. One pictogram is removed and 3 pictograms are introduced newly. The classification criteria of GHS will be changed preferentially and implemented gradationally to hazard products.