• KOREAN JOURNAL OF CROP SCIENCE
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• v.28 no.3
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• pp.340-344
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• 1983

Taxonomic distances and Q correlations of all possible comparisons among thirty-two collected soybean varieties were calculated from the standardized mean of twenty-one characters. Ten varietal groups were classified by the single linkage clustering based on Q correlations. The means of Q correlations of intra-group were higher than those of inter-group. Each groups were characteristic in each mean of characters within varietal groups.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.10a
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• pp.35-35
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• 2017

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.618-620
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• 2004

This paper explores the use of IKONOS imagery of 1 m resolution panchromatic (PAN) band and 4 m resolution multi-spectral (MS) band in the development of agriculture­related data inventories. Three images (May 25, 2001, December 25, 2001, October 23, 2003) were used to obtain temporal distributions in crop cover characteristics such as rice, pear, grape, red pepper, corn, barley, garlic and surface water cover of reservoir with field investigations. The availability and cost problems are expected to solve by KOMPSAT-2 that is scheduled to launch in 2005. The capability of KOMPSAT-2 image for crop and rural water resources management will increase by accumulating temporal data inventories as a database.

• Proceedings of the Korean Society of Crop Science Conference
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• 2021.10a
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• pp.17-17
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• 2021

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• Journal of Agricultural Extension & Community Development
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• v.14 no.2
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• pp.301-328
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• 2007

The purposes of this study were to classify the silver farming types and to investigate proper crops for silver farmers. Data were collected from 408 farmers returned from the urban area. Descriptive statistics were employed using SPSS/PC windows program. After then, researchers discussed the silver farming patterns and their proper crops, and tried to analyze the silver farming model. Major results were as follows: The farming aims of retirees, urban residents as well as farmers returned from the urban area were various from hobby/ leisure to income generating activities. Fourteen types of silver farming were classified by farming aim and residential area of retirees. Retirees in the hobby/leisure-oriented silver farming type can select and enjoy the various plants, animals, and insects as well as crops due to their small scale. Silver farmers in the self-sufficiency type may select crops for their food. Income-oriented silver farmers may have difficulties in choosing the proper crop. They must consider their income needs, health status and field location. Profit-oriented silver farmers with venture mind can have some business opportunities in the agricultural sectors in spite of severe competition. As the aged silver farmers have poor health, they must keep work safety rules, use the proper work-aids, and utilize the labor-saving farming system.

• 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.

• Korean Journal of Medicinal Crop Science
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• v.10 no.5
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• pp.366-374
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• 2002

Pen-tsao-kang-mu(Bon-cho-kang-mok), chinese medicinal plant book, was written by Lee sijin(1578), China. The subject of this study were 158 articles of vegetable part in Pen-tsao-kang-mu. Among them only 139 articles were able to be identified by authority of several references. By Engler's system they were classified into 8 divisions, 10 classes, 6 subclasses, 31 orders, 22 suborders, 52 families, 85 genera, 99 species, 12 varieties and 2 forma, and were confirmed 113 kinds of original plants. Among the divisions, Angiospermae was the most numerous division with 92 kinds(80.70%) and the second division was Fungi with 12 kinds(10.53%). The next was Rhodophyta with 4(3.51%) kinds. Other 19 articles were unable to be classified because of their ambiguous name.

• Korean Journal of Agricultural Science
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• v.49 no.1
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• pp.121-128
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• 2022

Fusarium wilt disease caused by Fusarium species is a major problem affecting cultivated cucurbit plants worldwide. Fusarium species are well-known soil-borne pathogenic fungi that cause Fusarium wilt disease in several cucurbit plants. In this study, we aimed to identify and classify pathogenic Fusarium species from cultivated Korean cucurbit plants, specifically watermelon and cucumber. Thirty-six Fusarium isolates from different regions of Korea were obtained from the National Institute of Horticulture and Herbal Science Germplasm collection. Each isolate was morphologically and molecularly identified using an internal transcribed spacer of ribosomal DNA, elongation factor-1α, and the beta-tubulin gene marker sequence. Fusarium species that infect the cucurbit plant family could be divided into three groups: Fusarium oxysporum (F. oxysporum), Fusarium solani (F. solani), and Fusarium equiseti (F. equieti). Among the 36 isolates examined, six were non-pathogenic (F. equiseti: 15-127, F. oxysporum: 14-129, 17-557, 17-559, 18-369, F. solani: 12-155), whereas 30 isolates were pathogenic. Five of the F. solani isolates (11-117, 14-130, 17-554, 17-555, 17-556) were found to be highly pathogenic to both watermelon and cucumber plants, posing a great threat to cucurbit production in Korea. The identification of several isolates of F. equiseti and F. oxysporum, which are both highly pathogenic to bottle gourd, may indicate waning resistance to Fusarium species infection.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.93-96
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• 2001

Estimation of leaf wetness duration (LWD) facilitates assessment of the likelihood of outbreaks of many crop diseases. Models that estimate LWD may be more convenient and grower-friendly than measuring it with wetness sensors. Empirical models utilizing statistical procedures such as CART (Classification and Regression Tree; Gleason et al., 1994) have estimated LWD with accuracy comparable to that of electronic sensors.(omitted)