• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.133-143
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• 2023

In this study, we propose a big data set of strawberry pests collected directly for diagnosis model learning and an automatic pest diagnosis model architecture based on deep learning. First, a big data set related to strawberry pests, which did not exist anywhere before, was directly collected from the web. A total of more than 12,000 image data was directly collected and classified, and this data was used to train a deep learning model. Second, the deep-learning-based automatic pest diagnosis module is a module that classifies what kind of pest or disease corresponds to when a user inputs a desired picture. In particular, we propose a model architecture that can optimally classify pests based on a convolutional neural network among deep learning models. Through this, farmers can easily identify diseases and pests without professional knowledge, and can respond quickly accordingly.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.87-97
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• 2021

To investigate the current pest management status in Korea, a survey was conducted from 151 students and graduates in the Korea National College of Agriculture and Fisheries (KNCAF) by on-line. The questionnaire consists of two divisions, basic questions and pest control questions. The basic questions were including the respondent's age, academic status, cultivating crops and cultivating area. The pest control questions were including pest control methods, pesticide selection rationale, and pest forecasting methods. As a summary of basic questions, the respondents in their 20s accounted for 91.2%. Moreover, 34.5% of the respondents had over 3 hectares of cultivating area. The cultivating methods were differed by cultivating crops. As a summary of pest control questions, major control methods were using the conventional chemicals (>66%). To understand the pesticide selection rationale, farmers/respondents made their own decisions based on existing control techniques (30%) or depended on the decisions of pesticide vendors (29%). As for the pest forecasting method, it was mainly conducted by the Rural Development Administration affiliated organization (29%) and the National Crop Pest Management System (27%). Regarding the reliability of the pest diagnosis and pesticide prescription of pesticide vendors, 97% of the respondents marked above average. However, there was no choice on strong reliability. Interestingly, 79% of the respondents agreed to train experts for pest diagnosis and pesticide prescription with high necessity and, in particular, 47% of respondents were very strongly supported. These results suggest that the farmers might be need more qualified experts in pest diagnosis and pesticide prescriptions. Taken together, these survey results would provide important information to understand the current status of pest management by farmers' point of view and useful to set the direction of pest control.

• Korean journal of applied entomology
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• v.57 no.3
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• pp.161-164
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• 2018

Pristiphora (Pristiphora) apricoti Zinovjev, 1993, pest of Prunus armeniaca var. ansu Max, belonging to Nematinae of Tenthredinidae is newly recognized in South Korea. The host plant is recorded for the first time from South Korea. Diagnosis, rearing notes, and photographs of the diagnostic characters and oviposition are provided.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.27-35
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• 2007

The majority of farm managers growing the garden products in greenhouse concern massively about the diagnosis and prevention of the breeding and extermination for pests. especially, the managing problem for pests turns up as main issue. In the paper, we first build a wireless sensor network with soil and environment sensors such as illumination, temperature and humidity. And then we design and implement multimedia pest predication and management system which is able to predict and manage various pest of garden products in greenhouse. The proposed system can support the database with information about the pests by building up wireless sensor network in greenhouse compared with existing high-priced PLC device as well as collect various environment information from soil, the interior of greenhouse, and the exterior of greenhouse. To verify the good capability of our system, we implemented several GUI interface corresponding desktop. web, and PDA mobile platform based on real greenhouse model. Finally, we can confirm that our system work well prediction and management of pest of garden products in greenhouse based on several platforms.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.

• Proceedings of the Korean Society of Crop Science Conference
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• 2000.11a
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• pp.128-129
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• 2000

• The Journal of Bigdata
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• v.1 no.1
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• pp.19-22
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• 2016

Many Studies have been actively conducted on the early diagnosis of the crop pest utilizing IT technology. The purpose of the paper is to discuss on the image processing method capable of detecting the crop leaf pest prematurely by analyzing the image of the leaf received from the camera sensor. This paper proposes an algorithm of diagnosing leaf infection by utilizing an improved K means clustering method. Leaf infection grouping test showed that the proposed algorithm illustrated a better performance in the qualitative evaluation.

• Journal of Microbiology and Biotechnology
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• v.20 no.1
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• pp.193-201
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• 2010

A specific and rapid real-time PCR assay for detecting Ralstonia solanacearum in horticultural soil and plant tissues was developed in this study. The specific primers RSF/RSR were designed based on the upstream region of the UDP-3-O-acyl-GlcNAc deacetylase gene from R. solanacearum, and a PCR product of 159 bp was amplified specifically from 28 strains of R. solanacearum, which represent all genetically diverse AluI types and all 6 biovars, but not from any other nontarget species. The detection limit of $10^2\;CFU/g$ tomato stem and horticultural soil was achieved in this real-time PCR assay. The high sensitivity and specificity observed with field samples as well as with artificially infected samples suggested that this method might be a useful tool for detection and quantification of R. solanacearum in precise forecast and diagnosis.

• Research in Plant Disease
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• v.30 no.1
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• pp.99-102
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• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.185-192
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• 2020

The number of dementia patients in Korea is expected to increase to 3.30 million in 2050, and the cost of dementia management will increase sharply to KRW 106.5 trillion of GDP. In August 2017, the Moon Jae-in government announced the 'Dementia National Responsibility System' through a five-year plan for government operation and expanded the Dementia Peace Center nationwide. However, for this, strategic dementia-related technology development strategies should be established and given the role of government and the role of the private sector. Therefore, in order to derive the corresponding strategy, this study developed the government's 'dementia' response technology development strategy through the situation analysis from the political, economic, social, and technological perspective and the environmental (PEST) analysis of the strengths, weaknesses, opportunities, and threats (SWOT). As a result, the direction of technology development in the dementia-related medical device market is expected to become a trend of developing dementia self-measurement by developing low-cost and high-efficiency diagnostic technology products. It has been shown that the development of various products for consumers should begin. As a result, the dementia market approach strategy should be premised, the related technical support and legal restrictions should be minimized, and the education of related experts should be strengthened to solve the government's development of dementia technology and the social problems of dementia. In addition, by developing joint projects with major companies around the world and actively participating in the technology platform, it is important to naturally build up skills accumulation for the development of dementia technology and competence skills of dementia technology experts in the long term.