• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.93-96
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• 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)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.163-166
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• 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)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2003.09a
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• pp.50-53
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• 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)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2003.09a
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• pp.54-57
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• 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
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• v.17 no.6
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• pp.372.2-372
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• 2001

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.188-189
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• 2019

• Journal of Ginseng Research
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• v.40 no.1
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• pp.90-96
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• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.1
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• pp.23-28
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• 2002

Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.262-276
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• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.4
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• pp.473-482
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• 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.