• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.1-7
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• 2020

Early detection and classification of crop diseases play significant role to help farmers to reduce disease spread and to increase agricultural productivity. Recently, many researchers have used deep learning techniques like convolutional neural network (CNN) classifier for crop disease inspection with dataset of crop leaf images (e.g., PlantVillage dataset). These researches present over 90% of classification accuracy for crop diseases, but they have ability to detect only the pre-trained diseases. This paper proposes an efficient disease inspection CNN model for new crops not used in the pre-trained model. First, we present a benchmark crop disease classifier (CDC) for the crops in PlantVillage dataset using VGG16. Then we build a modified crop disease classifier (mCDC) to inspect diseases for untrained crops. The performance evaluation results show that the proposed model outperforms the benchmark classifier.

• Journal of the Korean Society of Hazard Mitigation
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• v.2 no.4 s.7
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• pp.77-85
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• 2002

Korean government compensates 100% for natural disaster relief fund to public facilities, and provides 70 percents to crops through unit price calculation. This study estimated disaster damage according to the standard production cost of crops. Disaster damage was estimated according to the unit price of crops. Those crops are lettuce, pumpkin, red pepper, spinach, tomato and cucumber. As a result, the ratio of natural disaster relief fund to damage was revealed as $3.3%{\sim}13.8%$ with present method, 5.7%{$\sim}34.1%$ with suggested method. Suggested method is the first one considered damage ratio of crops, producer price and raising period.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.225-225
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• 2019

건물, 차량, 사회인프라시설과 달리 농작물이란 물리적인 시설이 아닌 농업경제 활동의 결과물로서 최종적으로 판매를 통해 수익을 창출하는 행위에서 재난으로 인하여 지장을 받게 되는 경제적 피해가 고려되어야 한다. 이를 위해 본 연구는 홍수로 인한 농작물 피해를 "생산비 매몰비용"과 "순수익 손해"를 농작물 피해추정의 척도로 하였다. 생산비 매몰비용이란 경작시작부터 피해발생까지 투입된 생산비의 회수불가에 따른 피해이며, 순수익 손해란 피해발생에 따른 기대 순수익 하락에 따른 피해를 의미한다. 다양한 작물 가운데 10종의 대표작물을 선택하고, 각 작물의 표준생산비와 표준순수익을 농업생산 및 수익과 관련한 통계자료로부터 결정하였다. 이로부터 생육경과율과 홍수 발생시기(6~9월)를 고려하여 월별 투입생산비 및 기대순수익을 결정하였다. 대상지역 내 재난에 노출된 작물정보를 정의하는 농작물 인벤토리는 농림축산식품부에서 제작된 스마트 팜 맵(농경지 전자지도)을 활용하였고, 다양한 작물이 혼재된 밭의 경우 농업면적조사 결과를 토대로 결정한 밭작물 재배현황비를 고려하였다. 홍수에 따른 취약성을 설명하는 농작물 손상함수는 영향인자는 침수심, 침수기간이며, 이를 기준으로 한 손상함수는 농림부의 농업재해피해조사요령과 일본 치수경제조사메뉴얼을 참고하여 제시하였다. 본 연구에서 제시한 농작물 피해 추정 방법은 기존 방법인 다차원 홍수피해산정법(MD-FDA)과 비교할 때 대표작물의 현실화, 국내 실정을 고려한 손상함수, 그리고 면적 기반의 원단위를 사용함으로써 실무적으로 명확하고 실용적으로 사용될 것으로 기대된다.

• Life and Agrochemicals
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• s.277
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• pp.22-25
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• 2012

농작물병해충을 효율적으로 대처하기 위해 벼 병해충 예찰정보시스템, 과수종합정보시스템, 농작물 생육상황시스템, 농작물병 해충 잡초정보등 4개의 병해충 관련 전산시스템을 통합하고, 국가병해충 관리시스템(NPMS)을 구축했다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.1
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• pp.71-74
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• 2001

최근 지구온난화 등 이상기상으로 인하여 금년에 들어서도 벌써 한파와 폭설로 한해를 시작하고 있다. 이러한 재해는 '90년대 들어서서부터 금년까지 한해, 냉해 등을 비롯하여 무려 9차례나 되어(표 1), 거의 매년 기상재해로 인한 농작물의 피해가 급증하고 농가의 안정적인 삶과 농작물의 안정공급에 막대한 지장을 주고 있다.(중략)

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.861-864
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• 2016

이 논문에서는 스마트기기를 활용하여 농작물 재배가 체계적으로 이루어질 수 있도록 하이브리드 앱을 구현하여 사물인터넷 기반의 농작물 관리 시스템 서비스를 제안한다. 사물인터넷 기반 농장 관리 시스템에 관한 다양한 연구가 있었으나 농작물 생장 환경 모니터링을 통한 에너지 절감, 데이터 수집을 통한 활용방안에 대한 연구에 그쳤다. 그 결과 주말농장을 이용하는 도시 농부와 농장주들이 가지고 있는 주말농장의 문제점은 해결되지 않았다. 제안 서비스는 도시 농부와 농장주가 가지고 있는 문제점을 파악하여 체계적인 농작물 재배와 원활한 농장관리 서비스를 모바일 기기에서 제공한다. 특히, 농작물 재배에 필수적인 정보와 위치기반 주말농장 검색기능을 제공함으로써 관리의 정확성과 편의성을 제공한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.939-948
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• 2020

There are many studies to incorporate virtual reality technology into agricultural education. Interest in virtual reality is increasing not only for direct education such as agricultural machinery education but also for indirect education using games. A key technology in virtual reality is to model objects to feel as real as they are. Plants grown on a farm are equally affected by the environment of the farm. Plants also have their independent characteristics. In this paper, we propose a method to model plants in three dimensions in NPC form by reflecting these characteristics. The proposed method implements how individual plants reflect common weather and farm characteristics, and their unique characteristics. Implementation results show that the proposed method is simple but expresses the plants very realistically.

• Journal of the Korean association of regional geographers
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• v.12 no.6
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• pp.748-756
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• 2006

This study mainly deals with price behavior developed in a agricultural location model (or closed model) considering the production and demand aspects. The short-run situation of price and output is associated with the yearly fluctuation of yield from agricultural production. Demand is generally regarded as constant in the short-run because of being inelastic over short time. The long-run situation is associated with a period in which all related variables can be varied. Then a price behaviors from the two contrasting closed models have been further explored in the long-run economy. Agricultural price for each activity in the closed model is affected by change in agricultural production. Also, falling agricultural price is connected with lower rents and lower land values.

• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.138-147
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• 2017

In this paper, we introduce smart multi-sensor and environment monitoring system for managing growth and development of the agricultural produces. This system is able to help to collect the sensor information about the growing environment and to monitor in wired and wireless environments. Existing systems installed each kinds of sensor to gather information. In this case, installation cost was incurred about each sensor and the position of sensor set up manually. Therefore, this paper designed and implemented smart multi-sensor that simplify sensors in order to minimize the cost for installing each sensor. In addition, this system is able to monitor position and data information of smart multi-sensor using RFID communication