• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.313-313
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• 2017

Cold stress is one of the most influenced factors to rice yield. In order to identify genes related to cold stress in fertility stage, genome-wide association study (GWAS) was conducted. Cultivated 129 rice germplasm were moved in the growth chamber under the condition of $12^{\circ}C/RH70%$(12h day/12h night when the rice plant was grown in 10 DBH(days before heading). Also, rice plant as control was moved in the green house under condition of $28^{\circ}C/RH70%$(12h day/12h night). After 4 days the plants were moved in a greenhouse. The fertility of rice plant were monitored after the grain were fully grown. The most tolerant rice germplasm to cold stress were Cheongdo-Hwayang-12 and IR38 as 63.1 and 61.8 of fertility and the most recessive rice germplasm were Danyang38 and 8 rice germplasm as 0. As a result of GWAS with re-sequencing data and fertility after cold treatment germplasm using genome association and prediction integrated tool (GAPIT), 99 single-nucleotide polymorphisms (SNPs) were observed by applying a significance threshold of -logP>4.5 determined by QQ plot. With SNPs region, 14 candidate genes responded to cold stress in fertility stage were identified.

• 대한원격탐사학회지
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• 제35권1호
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• pp.57-81
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• 2019

Satellite images can be integrated into a crop model to strengthen the advantages of each technique for crop monitoring and to compensate for weaknesses of each other, which can be systematically applied for monitoring inaccessible croplands. The objective of this study was to outline the productivity of paddy rice based on simulation of the yield of all paddy fields in North Korea, using a grid crop model combined with optical satellite imagery. The grid GRAMI-rice model was used to simulate paddy rice yields for inaccessible North Korea based on the bidirectional reflectance distribution function-adjusted vegetation indices (VIs) and the solar insolation. VIs and solar insolation for the model simulation were obtained from the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) sensors of the Communication Ocean and Meteorological Satellite (COMS). Reanalysis data of air temperature were achieved from the Korea Local Analysis and Prediction System (KLAPS). Study results showed that the yields of paddy rice were reproduced with a statistically significant range of accuracy. The regional characteristics of crops for all of the sites in North Korea were successfully defined into four clusters through a spatial analysis using the K-means clustering approach. The current study has demonstrated the potential effectiveness of characterization of crop productivity based on incorporation of a crop model with satellite images, which is a proven consistent technique for monitoring of crop productivity in inaccessible regions.

• 한국농공학회논문집
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• 제59권1호
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• pp.71-79
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• 2017

Climate change phenomenon is posing a serious threat to sustainable corn production in Ghana. This study investigated the impacts of climate change on the rain-fed corn yield in the Dangme East district, Ghana by using Aquacrop model with a daily weather data set of 22-year from 1992 to 2013. Analysis of the weather data showed that the area is facing a warming trend as the numbers of years hotter and drier than the normal seemed to be increasing. Aquacrop model was assessed using the limited observed data to verify model's sufficiency, and showed credible results of $R^2$ and Nash-Sutcliffe efficiency (NSE). In order to simulate the corn yield response to climate variability four climate change scenarios were designed by varying long-term average temperature in the range of ${\pm}1^{\circ}C{\sim}{\pm}3^{\circ}C$ and average annual rainfall to ${\pm}5%{\sim}{\pm}30%$, respectively. Generally, the corn yield was negatively correlated to temperature rise and rainfall reduction. Rainfall variations showed more prominent impacts on the corn yield than that of temperature variations. The reduction in average rainfall would instantly limit the crop growth rate and the corn yield irrespective of the temperature variations.

• 한국측량학회지
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• 제39권6호
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• pp.419-427
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• 2021

지속적인 기후변화의 영향과 코로나 19로 인한 노동력 감소 등의 영향으로 매년 농작물의 작황 및 수확시기, 재배면적 등이 급격히 변화하고 있다. 이러한 상황에 탄력적으로 대응하기 위해서 ICT (Information and Communication Technology)를 기반으로 한 스마트팜 기술을 개별 농가에 적용하는 시도가 증가하고 있다. 한편, 다양한 기관에서 인공지능 기술 및 IoT 기술을 적용한 농작물의 수확량 예측을 시도하고 있으나 학습데이터의 부족으로 인해서 정확한 예측이 어려운 실정이다. 본 연구에서는 특정 기관에 국한된 데이터 수집의 한계를 극복하기 위해 농민들이 직접 참여하여 정확한 데이터를 입력하고 공유하여 생산량을 예측하는 커뮤니티 매핑 기반의 스마트 파머 서비스 기술을 개발하였다. 그 과정에서 생산량에 비하여 가격 변동이 큰 작물인 배추를 대상으로 분석을 수행하였다.

• 스마트미디어저널
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• 제11권4호
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• pp.19-29
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• 2022

IoT 기술의 발전에 따라 스마트팜을 활용하는 농가가 증가하고 있다. 스마트팜은 환경을 모니터링하고, 원격 또는 자동으로 최적의 내부 환경을 조성하여 작물의 생산량 및 품질을 향상시킨다. 이를 위해 수집되는 농업 디지털 데이터를 활용하여 작물의 생산성을 예측하는 기술에 대한 연구가 활성화되고 있다. 그러나 생산량 예측을 위한 연구에서는 기존의 통계자료를 바탕으로 하는 통계모델 기반의 연구가 대부분이며, 이에 따라 예측 정확도가 낮은 문제점이 존재한다. 본 연구에서는 시설 원예 스마트팜에 수집된 농업 디지털 데이터를 활용하여 다양한 머신러닝 모델을 통해 생산량 및 판매 수익금을 예측하고, 성능을 비교하였다. 성능을 비교한 모델은 다중선형회귀, 서포트벡터머신, 인공신경망, 순환신경망, LSTM, ConvLSTM이다. 성능 비교 결과 ConvLSTM가 R2 값 및 RMSE 값에서 가장 우수한 성능을 나타내었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.133-152
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• 2022

Rice is a fundamental staple food commodity all around the world. Globally, it is grown over 167 million hectares and occupies almost 1/5^th of total cultivated land under cereals. With a total production of 782 million metric tons in 2018. In Pakistan, it is the 2^nd largest crop being produced and 3^rd largest food commodity after sugarcane and rice. The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop. Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature. Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers. this study helps to find out the hot spots of insect pest in rice field with a control of farmer's palm. Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions. result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds. This system can be good IoT based solution for pest attack prediction which is cost effective and accurate.

• 한국농림기상학회지
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• 제19권4호
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• pp.203-214
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• 2017

본 연구에서는 진주, 수원, 춘천의 정보로만 추정한 유전 모수(New1~New3)와 지역 조합으로 추정한 유전 모수(New4~New7), NICS (2010)와 Kim et al. (2004)의 유전 모수의 개화시기 및 잠재수량의 예측력을 평가하여 기존의 유전 정보와 새로운 유전 정보에 대한 불확실 정도를 알고 다음 후속 연구에 활용 가능성을 알아보고자 수행했다. 결과적으로, 개별 및 지역조합 유전 모수에서 모수 추정 지점 혹은 참여한 지점의 유전 모수의 평가 지표들은 비교적 좋은 결과를 보여 주었지만 뚜렷하게 나타나지 않았다. 대구, 밀양, 전주에서 New7 유전 모수의 개화시기의 예측력은 NICS (2010)나 Kim et al. (2004)의 유전 모수의 개화 시기 예측력보다 개선되지 않았다. 그러나 New7 유전 모수의 잠재수량의 예측력은 큰 차이는 아니지만 NICS (2010)나 Kim et al. (2004)의 유전 모수의 잠재 수량 예측력보다 개선되는 현상을 보였다. 예를 들면, 밀양에서 NICS (2010)와 Kim et al. (2004)의 유전 모수의 잠재수량 결정계수가 0.00과 0.01로 전혀 예측력이 없는 것으로 평가하였지만 New7 유전 모수의 잠재수량 결정계수는 0.31로 나타났다. 반면, 전주에서 NICS (2010)과 Kim et al. (2004)의 유전 모수의 잠재수량 결정계수는 0.66과 0.41로 평가되었는데, New7 유전 모수의 잠재수량 결정계수는 0.00으로 예측력이 없는 것으로 평가되었다. 새로운 유전 모수의 예측력(New1~New7)이 기존의 유전 모수(NICS (2010)과 Kim et al. (2004))의 예측력보다 크게 개선되지는 않았지만, 평가 결과가 좋은 지역 조합 유전 모수를 지역별 개화시기 및 잠재수량을 예측하는 데에는 활용할 수 있을 것으로 판단된다.

• 한국농림기상학회지
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• 제25권4호
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• pp.398-403
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• 2023

과정 기반 작물모형인 K-배추 모델은 광합성, 생물 계절 등의 생리학적 과정을 기반으로 이전에 경험하지 못한 다양한 기후 조건에서 작물의 생장을 예측할 수 있게 해준다. 현재 1단계 프로세스 기반 모델은 기후 변화 시나리오에 따른 생산량 예측을 통해 기후영향을 평가하는 데 활용될 수 있지만, 지금까지 주산지 빅데이터와 모델 예측 간의 비교는 수행되지 않았다. 본 연구는 생산량 예측을 위해 현재 모델을 사용하고자 할 때 모델의 개선 방향을 검토하기 위해 수행되었다. 이를 위해, 강원 태백 및 삼척에서 수집된 관측 자료를 바탕으로 모델의 예측력을 평가하였다. 조사 대상 농가들은 정식일 및 토양관리 면에서 재배방법이 상이하였다. 분석 결과는 K-배추 모델을 사용하여 추정한 잠재적 바이오매스가 모든 경우에서 관측 값을 초과하는 것으로 나타났다. 한편, 모델 평가 과정에서 생체중 사용에 따른 한계 등으로, 수확 2주 전의 값을 기준으로 모델을 피팅했음에도 수확기 무렵 예측값과 관측값은 완전한 양의 상관관계를 보이지 않았다(R²=0.74, RMSE=866.4). 또한 생장적합지수는 농장별로 상이하였는데, 이러한 결과는 농가 간 토양특성 및 관리방식의 차이에 의한 것으로 추정된다. 따라서 농장별 토양 및 관리방식의 차이를 고려한 생산 예측기술 고도화를 위해서는 현재 K-배추 모델에서 임의의 생장적합지수를 사용하는 대신 동적 토양 양분 및 수분 모듈을 작물 모델에 통합하는 것이 필요하다.

• Journal of Biosystems Engineering
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• 제41권3호
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• pp.193-200
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• 2016

Purpose: New low-cost oilseeds are needed to meet an ever-increasing demand for oil for food, pharmaceutical, and industrial applications. African oil bean seed is a tropical crop that is underutilized and has high oil yields, but there have been no studies conducted on its mechanical oil expression up to now. The objective of this work was to investigate the effect of moisture content and seed dimensions on mechanical oil expression from the seeds. Methods: Fresh oil bean seeds were procured, de-hulled, and cleaned. Initial seed moisture content, obtained in accordance with the ASAE standard, was 12% dry basis (db). The seeds were further conditioned by dehydration and rehydration prior to oil expression to obtain four other moisture levels of 8, 10, 14, and 16% db. The major diameter of the seeds was measured using digital vernier calipers, and the seeds were classified into size dimensions (< 40, 41-45, 46-50, 51-55, and > 55 mm). The oil yield and expression efficiency were obtained in accordance with standard evaluation methods. Results: The highest oil yield and expression efficiency (47.74% and 78.96%, respectively) were obtained for a moisture content of 8% db and seed dimensions of < 40 mm, while the lowest oil yield and expression efficiency (41.35% and 68.28%, respectively) were obtained for a moisture content of 14% db and seed dimensions between 51-55 mm. A mathematical model was developed to predict oil yield for known moisture content and seed dimensions, with a coefficient of determination $R^2$ of 95% and the confidence level of the predictive model of 84.17%. The probability of prediction F ratio showed that moisture content influence was more significant than seed dimensions. Conclusions: The higher the moisture content and larger the seed dimensions, the lower the oil yield from African oil bean seeds.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.303-303
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• 2017

Japanese agriculture has faced to several threats: aging and decrease of farmer population, global competition, and the risk of climate change as well as harsh and variable weather. On the other hands, the number of large scale farms is increasing, because farm lands have been being aggregated to fewer numbers of farms. Cost cutting, development of efficient ways to manage complicatedly scattered farm lands, maintaining yield and quality under variable weather conditions, are required to adapt to changing environments. Information and communications technology (ICT) would contribute to solve such problems and to create innovative technologies. Thus we have been developing an early warning and decision support system to reduce weather and climate risks for rice, wheat and soybean production in Japan. The concept and prototype of the system will be shown. The system consists of a weather data system (Agro-Meteorological Grid Square Data System, AMGSDS), decision support contents where information is automatically created by crop models and delivers information to users via internet. AMGSDS combines JMA's Automated Meteorological Data Acquisition System (AMeDAS) data, numerical weather forecast data and normal values, for all of Japan with about 1km Grid Square throughout years. Our climate-smart system provides information on the prediction of crop phenology, created with weather forecast data and crop phenology models, as an important function. The system also makes recommendations for crop management, such as nitrogen-topdressing, suitable harvest time, water control, pesticide spray. We are also developing methods to perform risk analysis on weather-related damage to crop production. For example, we have developed an algorism to determine the best transplanting date in rice under a given environment, using the results of multi-year simulation, in order to answer the question "when is the best transplanting date to minimize yield loss, to avoid low temperature damage and to avoid high temperature damage?".