• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 2001.11a
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• pp.178-180
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• 2001

작물의 엽면적 등 다양한 생육정보를 간편하고 비파괴적으로 추정할 수 있다면 작물의 생리 생태학적 모델에의 적용을 통하여 다양한 작물 연구에 중요한 공헌을 할 수 있다. 본 연구에서는 오이 개개 잎의 형태정보를 이용하여 오이의 개개 엽면적, 생체중 및 건물중 예측하는 것을 목적으로 하였고, 이를 위하여 엽면적은 5가지 모델을 사용하였고, 생체중 및 건물중은 6가지의 모델을 사용하여 분석하였다. 또한 신경회로망은 3 layer의 back propagation method를 사용하여 분석하였다. 각 모델들은 독립변수로는 Robinson ＆ Pharr이 사용한 개개 잎의 폭 및 길이를 사용하였다. 회귀모델에 의한 추정 결과, 모델의 정확성 및 정밀성은 엽면적 > 생체중 > 건물중 순 이었지만, 특히 건물중의 경우는 상대적으로 낮은 상관관계를 가지는 것으로 나타났다. 회귀모델을 사용하여 건물중 추정하는 것에는 한계가 있는 것으로 생각되며, 신경회로망도 이와 유사한 관계를 나타냈지만 다양한 변수 수정을 통하여 상관계수를 증가시킬 수 있을 것이라고 생각된다.

• Journal of Bio-Environment Control
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• v.2 no.1
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• pp.9-15
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• 1993

Chinese white cabbage, Chinese flat cabbage, lettuce, garland chrysanthemum, and green perilla were grown in nutrient solution culture to investigate the effects of various media and nutrient solutions. The culture media were sand, mixed substrate(peatmoss : sand= 1 : 1), and non-media(deep-flow culture). The nutrient solutions were Cooper's, Hoagland's, and Yamazaki's solution. Plants were grown under different treatments for three weeks. Generally, the growth was greatest in non-media culture and followed mixed substrate culture, and poorest in sand culture. In non-media culture, the growth of Chinese white cabbage, Chinese flat cabbage, lettuce, and green perilla was good in Yamazaki's solution. And regardless of nutrient solution, garland chrysanthemum was good in non-media culture. Relative chlorophyll was not different among the treatments.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.56 no.3
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• pp.233-243
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• 2011

This experiment was conducted to investigate the effect of future climate change on growing period and temperature in different rice maturity types as global warming progressed, where Odaebyeo, Hwaseongbyeo, Ilpumbyeo were used as a representative cultivar of early, medium, and medium-late rice maturity type, respectively, and A1B scenario was applied to weather data for future climate change at 57 sites in Korea. When cropping season was not adjusted to climate change, entire growing period and growing temperature were shorten and risen, respectively, as global warming progressed. On the other side, when cropping season was adjusted to climate change, growing period and temperature after heading date were not changed in contrast to growing period and growing temperature before heading which were more seriously shortened and risen as global warming progressed than in not adjusted cropping season. It is supposed that adjusting cropping season to climate change can alleviate rice yield reduction and quality deterioration to some degree by improving growing temperature condition during grain-filling period, but also still have a limit such as seriously shortened growing period indicating that there need to develope actively new rice cultivation methods and varieties for future climate change.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.5
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• pp.386-395
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• 2006

The core questions for determining nitrogen topdress rate (Npi) at panicle initiation stage (PIS) are 'how much nitrogen accumulation during the reproductive stage (PNup) is required for the target rice yield or protein content depending on the growth and nitrogen nutrition status at PIS?' and 'how can we diagnose the growth and nitrogen nutrition status easily at real time basis?'. To address these questions, two years experiments from 2001 to 2002 were done under various rates of basal, tillering, and panicle nitrogen fertilizer by employing a rice cultivar, Hwaseongbyeo. The response of grain yield and milled-rice protein content was quantified in relation to RVIgreen (green ratio vegetation index) and SPAD reading measured around PIS as indirect estimators for growth and nitrogen nutrition status, the regression models were formulated to predict PNup based on the growth and nitrogen nutrition status and Npi at PIS. Grain yield showed quadratic response to PNup, RVIgreen around PIS, and SPAD reading around PIS. The regression models to predict grain yield had a high determination coefficient of above 0.95. PNup for the maximum grain yield was estimated to be 9 to 13.5 kgN/10a within the range of RVIgreen around PIS of this experiment. decreasing with increasing RVIgreen and also to be 10 to 11 kgN/10a regardless of SPAD readings around PIS. At these PNup's the protein content of milled rice was estimated to rise above 9% that might degrade eating quality seriously Milled-rice protein content showed curve-linear increase with the increase of PNup, RVIgreen around PIS, and SPAD reading around PIS. The regression models to predict protein content had a high determination coefficient of above 0.91. PNup to control the milled-rice protein content below 7% was estimated as 6 to 8 kgN/10a within the range of RVIgreen and SPAD reading of this experiment, showing much lower values than those for the maximum grain yield. The recovery of the Npi applied at PIS ranged from 53 to 83%, increasing with the increased growth amount while decreasing with the increasing Npi. The natural nitrogen supply from PIS to harvest ranged from 2.5 to 4 kg/10a, showing quadratic relationship with the shoot dry weight or shoot nitrogen content at PIS. The regression models to estimate PNup was formulated using Npi and anyone of RVIgreen, shoot dry weight, and shoot nitrogen content at PIS as predictor variables. These models showed good fitness with determination coefficients of 0.86 to 0.95 The prescription method based on the above models predicting grain yield, protein content and PNup and its constraints were discussed.

• Advanced Industrial SCIence
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• v.1 no.2
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• pp.24-29
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• 2022

Recently, smart farm research that applies IoT technology to various farms is being actively conducted to improve agricultural cooling power and minimize cost reduction. In particular, methods for automatically and remotely controlling environmental information data around smart farms through IoT devices are being studied. This paper proposes a processing model that can maintain an optimal growth environment by monitoring environmental information data collected from smart farms in real time based on machine learning. Since the proposed model uses machine learning technology, environmental information is grouped into multiple blockchains to enable continuous data collection through rich big data securing measures. In addition, the proposed model selectively (or binding) the collected environmental information data according to priority using weights and correlation indices. Finally, the proposed model allows us to extend the cost of processing environmental information to n-layer to a minimum so that we can process environmental information in real time.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.121-121
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• 2017

오늘날 시장에 유통되는 버섯은 대부분 환경이 조절되는 시설 내부에서 재배된다. 버섯은 다른 식물과 달리 버섯의 종류, 품종 등에 따라 요구되는 환경이 매우 다르다. 특히 대부분의 버섯은 버섯이 생육되는 공간의 온도, 수분, 이산화탄소, 조도 등의 관리가 필수적이다. 버섯의 단위면적당 생산량을 극대화하기 위해서는 이들 버섯이 필요로 하는 환경을 버섯의 생육특성에 따라 적합하게 유지해 주어야만 한다. 현재 대부분의 버섯재배사에는 이들 환경을 컨트롤하는 장치가 설치되어 있고, 이들 시스템의 환경 설정은 농업인이 현장에서 버섯의 상태를 확인 한 후 그때그때 경험에 의해 채득한 정보를 기반으로 환경설정을 하고 있는 실정이다. 이렇다 보니 버섯을 재배하는 기간에는 농업인이 재배사 내부의 환경을 관리하기 위해서 항상 버섯재배사에 머물러야 하고, 재배사의 환경관리 때문에 원거리 또는 장기 출타가 어려운 실정이다. 본 연구에서는 기존에 사람이 버섯재배 현장에서 컨트롤하던 환경관리를 원격에서 컴퓨터 또는 모바일로 구현할 수 있는 시스템을 개발하였다. 시스템에서 모니터링 및 제어하는 환경은 온도, 습도, 이산화탄소, 배기팬 및 입기팬의 가동 등 버섯재배 환경관리에 필요한 모든 요소를 모니터링 및 관리할 수 있도록 하였다. 이들 관리 요소는 인터넷이 연결된 컴퓨터에 접속하여 버섯재배사의 환경을 실시간으로 모니터링 하고 필요에 따라 제어를 할 수 있도록 하였다. 또한 컴퓨터에서 볼 수 있는 환경을 스마트폰을 통해서도 볼 수 있고 또한 제어할 수 있도록 하였다. 그리고 버섯배지를 입상 한 후부터 수확시기까지의 관리 환경을 데이터베이스로 만들어 농민이 버섯배지를 입상하고 데이터베이스와 연동한 제어가 되도록 설정하면 버섯재배사 내부의 환경은 데이터베이스의 정보를 읽어 들여 버섯의 생육단계에 따라 자동으로 내부환경이 제어되도록 하였다. 또한 농업인이 원격에서 버섯재배사 내부 버섯의 생육상황을 모니터링 할 수 있도록 재배사 상부에 CCD 카메라를 달아 실시간으로 버섯의 생육상활을 모니터링 할 수 있도록 구성하였다. 이와 같은 시스템을 사용하면 버섯재배 경험이 없는 농업인도 경험자와 같은 재배관리가 가능하고 원격에서 재배사 내부환경을 모니터링하고 제어 할 수 있기 때문에 농업인이 재배사에 매여 있지않아도 될 것으로 판단된다.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.8
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• pp.1296-1303
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• 2014

This study was conducted to develop predictive models for the growth of Bacillus cereus on carrot treated with slightly acidic electrolyzed water (SAcEW) and ultrasonication (US) at different storage temperatures. In addition, the inactivation of B. cereus by US with SAcEW was investigated. US treatment with a frequency of 40 kHz and an acoustic energy density of 400 W/L at $40^{\circ}C$ for 3 min showed the maximum reduction of 2.87 log CFU/g B. cereus on carrot, while combined treatment of US (400 W/L, $40^{\circ}C$, 3 min) with SAcEW reached to 3.1 log CFU/g reduction. Growth data of B. cereus on carrot treated with SAcEW and US at different temperatures (4, 10, 15, 20, 25, 30, and $35^{\circ}C$) were collected and used to develop predictive models. The modified Gompertz model was found to be more suitable to describe the growth data. The specific growth rate (SGR) and lag time (LT) obtained from the modified Gompertz model were employed to establish the secondary models. The newly developed secondary models were validated using the root mean square error, bias factor, and accuracy factor. All results of these factors were in the acceptable range of values. After compared SGR and LT of B. cereus on carrot, the results showed that the growth of B. cereus on carrot treated with SAcEW and US was slower than that of single treatment. This result indicates that shelf life of carrot treated with SAcEW and US could be extended. The developed predictive models might also be used to assess the microbiological risk of B. cereus infection in carrot treated with SAcEW and US.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.788-791
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• 2009

본 연구는 가축의 생육상태를 온도 및 Ph 센서를 통해 실시간으로 감지하는 시스템의 설계를 다룬다. 농축산 환경에 적합한 요구사항을 토대로 센서와 임베디드 미들웨어, 그리고 백엔드 시스템에 필요한 소프트웨어 구조를 설계하고 평가한다. 특별히 논문에서는 출판-구독 모델을 통해 모듈의 추가 및 삭제가 용이한 모델을 제시하며, 이를 구현하기 위한 기법과 평가를 소개한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.205-208
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• 2023

양식장에서 최적의 생육환경을 유지할 수 있는 제어시스템 개발을 위해 수질에 영향을 미치는 요인들의 상관관계 분석을 위한 머신러닝 모델을 개발하고자 한다. 데이터간의 상관관계 분석 및 예측모델 생성을 위해 알고리즘의 결정계수와 MSE, RMSE 등의 수치를 통하여 데이터의 적합성을 검증하고자 한다.

• Korean Journal of Environment and Ecology
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• v.25 no.4
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• pp.590-600
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• 2011

The research was carried out for prediction of the potential habitats of warm-temperate evergreen broad-leaved trees under the current climate(1961~1990) and three climate change scenario(2081~2100) (CCCMA-A2, CSIRO-A2 and HADCM3-A2) using classification tree(CT) model. Presence/absence records of warm-temperate evergreen broad-leaved trees were extracted from actual distribution data as response variables, and four climatic variables (warmth index, WI; minimum temperature of the coldest month, TMC; summer precipitation, PRS; and winter precipitation, PRW) were used as predictor variables. Potential habitats(PH) was predicted 28,230$km^2$ under the current climate and 77,140~89,285$km^2$ under the three climate change scenarios. The PH masked by land use(PHLU) was predicted 8,274$km^2$ and the proportion of PHLU within PH was 29.3% under the current climate. The PH masked by land use(PHLU) was predicted 35,177~45,170$km^2$ and increased 26.9~36.9% under the three climate change scenarios. The expansion of warm-temperate evergreen broad-leaved trees by climate change progressed habitat fragmentation by restriction of land use. The habitats increase of warm-temperate evergreen broad-leaved trees had been expected competitive with warm-temperate deciduous broadleaf forest and suggested the expand and northward shift of warm-temperate evergreen broad-leaved forest zone.