• 한국농림기상학회:학술대회논문집
• /
• 한국농림기상학회 2001년도 춘계 학술발표논문집
• /
• pp.93-96
• /
• 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)

• 한국농림기상학회:학술대회논문집
• /
• 한국농림기상학회 2001년도 춘계 학술발표논문집
• /
• pp.163-166
• /
• 2001

One of the most important factors influencing the outbreak and severity of foliar diseases is the duration of wetness from dew deposition, rainfall, or irrigation. Models may provide good alternatives for assessing leaf wetness duration (LWD) without the labor, cost, and inconvenience of making measurements with sensors.(omitted)

• 한국농림기상학회:학술대회논문집
• /
• 한국농림기상학회 2003년도 춘계 학술발표논문집
• /
• pp.50-53
• /
• 2003

Models have been developed to estimate leaf wetness duration (LWD) using conventional weather observations, e.g., air temperature, water vapor pressure, and wind speed, which are relatively invariant over space (Pedro and Gillespie, 1982; Gleason et al., 1994; Francl and Panigrahi, 1997).(omitted)

• 한국농림기상학회:학술대회논문집
• /
• 한국농림기상학회 2003년도 춘계 학술발표논문집
• /
• pp.54-57
• /
• 2003

Implementation of disease-warning systems often results in substantial reduction of spray frequency (Lorente et al., 2000; Madden et al., 2000). This change reduces the burden of pesticide sprays on the environment and can also delay the development of fungicide and bactericide resistance. To assess the risk of outbreaks of many foliar diseases, it is important to quantify leaf wetness duration(LWD) since activities of foliar pathogen depend on the presence of free water on host crop surface for sufficient periods of time to allow infection to occur.(omitted)

• The Plant Pathology Journal
• /
• 제17권6호
• /
• pp.372.2-372
• /
• 2001

• 한국농림기상학회:학술대회논문집
• /
• 한국농림기상학회 2019년도 하계 학술발표초록집
• /
• pp.188-189
• /
• 2019

• Journal of Ginseng Research
• /
• 제40권1호
• /
• pp.90-96
• /
• 2016

Background: Knowledge on microclimate conditions under artificial shades in a ginseng field would facilitate climate-aware management of ginseng production. Methods: Weather data were measured under the shade and outside the shade at two fields located in Gochang-gun and Jeongeup-si, Korea, in 2011 and 2012 seasons to assess temperature and humidity conditions under the shade. An empirical approach was developed and validated for the estimation of leaf wetness duration (LWD) using weather measurements outside the shade as inputs to the model. Results: Air temperature and relative humidity were similar between under the shade and outside the shade. For example, temperature conditions favorable for ginseng growth, e.g., between $8^{\circ}C$ and $27^{\circ}C$, occurred slightly less frequently in hours during night times under the shade (91%) than outside (92%). Humidity conditions favorable for development of a foliar disease, e.g., relative humidity > 70%, occurred slightly more frequently under the shade (84%) than outside (82%). Effectiveness of correction schemes to an empirical LWD model differed by rainfall conditions for the estimation of LWD under the shade using weather measurements outside the shade as inputs to the model. During dew eligible days, a correction scheme to an empirical LWD model was slightly effective (10%) in reducing estimation errors under the shade. However, another correction approach during rainfall eligible days reduced errors of LWD estimation by 17%. Conclusion: Weather measurements outside the shade and LWD estimates derived from these measurements would be useful as inputs for decision support systems to predict ginseng growth and disease development.

• 한국농림기상학회지
• /
• 제4권1호
• /
• pp.23-28
• /
• 2002

CART(Classification and Regression Tree) 모형을 이용해서 결로시간 예측에 필요한 기상변수들을 평가하였다. 입력 기상 변수들은 0.3m와 1.5m에서 측정된 기온, 상대습도, 풍속의 시간별 측정값으로서 이 관측 값들은 1997년부터 1999년 5월에서 9월 사이에 미국의 Iowa, Illinois 및 Nebraska주에 위치한 15개 자동 기상 관측소에서 관측된 것이다. 0.3 m에서 측정된 기온, 상대습도, 그리고 풍속을 이용해서 얻어진 모형이 가장 높은 결로시간의 예측 적중율(85.5%)을 보였으며, 이 모형은 Gleason 등(1994)의 CART/SLD 모형의 적중률(84.7%) 보다 다소 높았다. 그러나 새로운 변수를 추가한 경우에 정확도의 향상이 다소 있었으나 CART/SLD 모형을 대체할 정도는 아니었다. 따라서, 기온, 상대습도, 풍속들의 종관 기상관측값들을 입력변수로 사용하는 CART/SLD 모형이 종관 기상관측 자료 이외의 추가적인 자료를 필요로 하는 모형으로 결로시간을 예측하는 것보다 합리적일 것으로 보인다.

• 한국농림기상학회지
• /
• 제20권3호
• /
• pp.262-276
• /
• 2018

2016년부터 2017년까지 제주 감귤과수원 11개 지점에서 관측한 기상 및 이슬 자료를 이용하여 이슬지속시간 예측 모델을 평가하였다. 이슬지속시간 모델의 민감도와 예측 정확도 평가에는 4 가지 모델(Number of Hours of Relative Humidity, Classification And Regression Tree/Stepwise Linear Discriminant, Penman-Monteith, Deep-learning Neural Network)이 사용되었다. 모델의 민감도는 강우와 계절 변화에 따라 평가하였다. 전체 자료에서 강우일 자료를 제외하면 이슬지속시간 모델들은 평균 오차(평균제곱근오차 약 1.5 hours)가 적게 나타났다. 기계학습 모델은 겨울을 제외한 계절별 오차가 비슷한 크기(평균제곱근오차 약 3 hours)로 나타났다. 나머지 모델들은 여름에 오차(평균제곱근오차 약 9.6 hours)가 가장 크고 겨울에 가장 작은 것(평균제곱근오차 약 3.3 hours)으로 나타났다. 모델 예측 정확도 평가 방법은 통계적 오차 분석 방법과 평균 제곱 편차 회귀 분석 방법을 사용하였다. 통계오차를 통한 모델 성능은 DNN 모델이 가장 우수한 반면에 CART/SLD 모델은 예측 정확도가 가장 낮게 나타났다. 평균제곱 편차(MSD)는 모델의 선형성을 세 가지(제곱 바이어스(SB), 비균일성 기울기(NU), 상관관계 부족(LC)) 구성요소로 구분하여 분석하는 방법이다. 모델 성능이 우수할수록 SB와 LC는 감소하였고 NU는 증가하는 경향이 나타났다. MSD 분석 결과 DNN 모델이 가장 우수하였으며 다음으로 PM, NHRH, CART/SLD 순으로 나타났다. 본 연구에서 활용된 기계학습 모델은 기상 정보를 이용한 다른 농업정보 생산의 정확도 개선에 크게 기여할 것으로 판단된다.

• 한국작물학회지
• /
• 제42권4호
• /
• pp.473-482
• /
• 1997

Temperature, humidity and wetness duration were monitored for fully developed paddy rice canopies with 3 different structures induced by the seeding method(puddled-soil drill seeding, DS ; hand broadcasting, HB ; machine broadcasting, MB). Within-canopy air temperature averaged over "clear sky" hours during the study period(maximum tillering through heading) was lower than the screen temperature at a nearby standard weather station, especially in the night. The same trend was true for "overcast sky" hours except the diurnal distinction. Vapor pressure within the canopy was high during the daytime and low in the night, making the daytime deviation from outside the canopy more significant on clear days. Under the overcast sky, the canopy maintained a steady 5 to 10％ higher vapor pressure than the outside regardless of day or night. Daily maximum temperature was observed to be higher within the canopies with more leaf mass, making MB the highest, HB the lowest, and DS in between. Relative humidity was over 90％ in the night and dropped to 70％ in the mid-afternoon, but vapor pressure within the canopy was highest at around 13:00 LST. Dew point depression was lowest and, combined with the temperature, the relative humidity was highest in HB. Mean period of wetting duration was in the order of DS＞HB＞MB, while the dew point depression was greatest in DS.