scholarly journals Model to Enhance Site-Specific Estimation of Leaf Wetness Duration

Plant Disease ◽  
2002 ◽  
Vol 86 (2) ◽  
pp. 179-185 ◽  
Author(s):  
K. S. Kim ◽  
S. E. Taylor ◽  
M. L. Gleason ◽  
K. J. Koehler
Keyword(s):  
Wind Speed ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Estimation Accuracy ◽  
Leaf Wetness ◽  
Wind Model ◽  
Site Specific ◽  
Leaf Wetness Duration ◽  
Linear Discriminant ◽  
The Hierarchical Structure

The ability of empirical models to enhance accuracy of site-specific estimates of leaf wetness duration (LWD) was assessed for 15 sites in Iowa, Nebraska, and Illinois during May to September of 1997, 1998, and 1999. Enhanced estimation of LWD was obtained by applying a 0.3-m height correction to SkyBit wind-speed estimates for input to the classification and regression tree/stepwise linear discriminant (CART/SLD) model (CART/SLD/Wind model), compared to either a proprietary model (SkyBit wetness) or to the CART/SLD model using wind speed estimates for a 10-m height. The CART/SLD/Wind model estimated LWD more accurately than the other models during dew-eligible (20:00 to 9:00) as well as dew-ineligible (10:00 to 19:00) periods, and for the period 20:00 to 9:00 regardless of rain events. Improvement of LWD estimation accuracy was ascribed to both the hierarchical structure of decision-making in the CART procedure and wind speed correction. Accuracy of the CART/SLD/Wind model identifying hours as wet or dry varied little among the 15 sites, suggesting that this model may be desirable for estimating LWD from site-specific data throughout the midwestern United States.

scholarly journals Forecasting Site-Specific Leaf Wetness Duration for Input to Disease-Warning Systems

Plant Disease ◽  
2006 ◽  
Vol 90 (5) ◽  
pp. 650-656 ◽  
Author(s):  
K. S. Kim ◽  
M. L. Gleason ◽  
S. E. Taylor
Keyword(s):  
Regression Tree ◽  
Empirical Models ◽  
Classification And Regression Tree ◽  
Weather Data ◽  
Leaf Wetness ◽  
Warning Systems ◽  
Site Specific ◽  
Leaf Wetness Duration ◽  
Regression Tree Analysis ◽  
Cart Model

Empirical models based on classification and regression tree analysis (CART model) or fuzzy logic (FL model) were used to forecast leaf wetness duration (LWD) 24 h into the future, using site-specific weather data estimates as inputs. Forecasted LWD and air temperature then were used as inputs to simulate performance of the Melcast and TOM-CAST disease-warning systems. Overall, the CART and FL models underpredicted LWD with a mean error (ME) of 2.3 and 3.9 h day-1, respectively. The CFL model, a corrected version of the FL model using a weight value, reduced ME in LWD forecasts to -1.1 h day-1. In the Melcast and TOM-CAST simulations, the CART and CFL models predicted timing of occurrence of action thresholds similarly to thresholds derived from on-site weather data measurements. Both models forecasted the exact spray dates for approximately 45% of advisories derived from measurements. When hindcast and forecast estimates derived from site-specific estimates provided by SkyBit Inc. were used as inputs, the CART and CFL models forecasted spray advisories within 3 days for approximately 70% of simulation periods for the Melcast and TOM-CAST disease-warning systems. The results demonstrate that these models substantially enhance the accuracy of commercial site-specific LWD estimates and, therefore, can enhance performance of disease-warning systems using LWD as an input.

scholarly journals Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 158 ◽  
Author(s):  
Hui Wang ◽  
Jorge Sanchez-Molina ◽  
Ming Li ◽  
Francisco Rodríguez Díaz
Keyword(s):  
Threshold Model ◽  
Estimation Error ◽  
Characteristic Curve ◽  
Regression Tree ◽  
Independent Set ◽  
Classification And Regression Tree ◽  
Leaf Wetness ◽  
Tree Model ◽  
Leaf Wetness Duration ◽  
Input Variables

Leaf wetness duration (LWD) is a key driving variable for peat and disease control in greenhouse management, and depends upon irrigation, rainfall, and dewfall. However, LWD measurement is often replaced by its estimation from other meteorological variables, with associated uncertainty due to the modelling approach used and its calibration. This study uses the decision learning tree method (DLT) for calibrating four LWD models—RH threshold model (RHM), the dew parameterization model (DPM), the classification and regression tree model (CART) and the neural network model (NNM)—whose performances in reproducing measured data are assessed using a large dataset. The relative importance of input variables in contributing to LWD estimation is also computed for the models tested. The LWD models were evaluated at two different greenhouse locations: in a Chinese (CN) greenhouse over three planting seasons (April 2014–October 2015) and in a Spanish (ES) greenhouse over four planting seasons (April 2016–February 2018). Based on multi-evaluation indicators, the models were given a ranking for their assessment capabilities during calibration (in the Spanish greenhouse from April 2016 to December 2016 and in the Chinese greenhouse from April 2014 to November 2014). The models were then evaluated on an independent set of data, and the obtained areas under the receiver operating characteristic curve (AUC) of the LWD models were over 0.73. Therein, the best LWD model in this case was the NNM, with positive predict values (PPVs) of 0.82 (SP) and 0.90 (CN), and mean absolute errors (MAEs) of 1.85 h (SP) and 1.30 h (CN). Consequently, the DLT can decrease LWD estimation error by calibrating the model threshold and choosing black box model input variables.

scholarly journals Estimating leaf wetness duration over turfgrass, and in a 'Niagara Rosada' vineyard, in a subtropical environment

2008 ◽  
Vol 65 (spe) ◽  
pp. 10-17 ◽  
Author(s):  
Jorge Lulu ◽  
Paulo Cesar Sentelhas ◽  
Mário José Pedro Júnior ◽  
José Ricardo Macedo Pezzopane ◽  
Gabriel Constantino Blain
Keyword(s):  
High Accuracy ◽  
Training System ◽  
Dew Point ◽  
Classification And Regression Tree ◽  
Leaf Wetness ◽  
Good Precision ◽  
Weather Station ◽  
Leaf Wetness Duration ◽  
Confidence Index ◽  
Niagara Rosada

Leaf wetness duration (LWD) is a key parameter in agrometeorology because it is related to plant disease occurrence. As LWD is seldomly measured in a standard weather station it must be estimated to run warning systems for schedule chemical disease control. The objective of the present study was to estimate LWD over turfgrass considering different models with data from a standard weather station, and to evaluate the correlation between estimated LWD over turfgrass and LWD measured in a 'Niagara Rosada' vineyard, cultivated in a hedgerow training system, in Jundiaí, São Paulo State, Brazil. The wetness sensors inside the vineyard were located at the top of the plants, deployed at an inclination angle of 45º and oriented southwest, with three replications. The methods used to estimate LWD were: number of hours with relative humidity above 90% (NHRH > 90%), dew point depression (DPD), classification and regression tree (CART) and Penman-Monteith (PM). The CART model had the best performance to estimate LWD over turfgrass, with a good precision (R² = 0.82) and a high accuracy (d = 0.94), resulting in a good confidence index (c = 0.85). The results from this model also presented a good correlation with measured LWD inside the vineyard, with a good precision (R² = 0.87) and a high accuracy (d = 0.96), resulting in a high confidence index (c = 0.93), showing that LWD in a 'Niagara Rosada' vineyard can be estimated with data from a standard weather station.

scholarly journals Cytokine signature and COVID-19 prediction models in the two waves of pandemics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Serena Cabaro ◽  
Vittoria D’Esposito ◽  
Tiziana Di Matola ◽  
Silvia Sale ◽  
Michele Cennamo ◽  
...  
Keyword(s):  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Diagnostic Tools ◽  
Linear Discriminant ◽  
Diagnosis And Prognosis ◽  
Machine Learning Approach ◽  
Set Up ◽  
Definition Of ◽  
Second Wave

AbstractIn Europe, multiple waves of infections with SARS-CoV-2 (COVID-19) have been observed. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed, as compared with the first. IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools.

A site-specific sensor for measuring leaf wetness duration within turfgrass canopies

1996 ◽  
Vol 81 (1-2) ◽  
pp. 145-156 ◽  
Author(s):  
Loren J. Giesler ◽  
Garald L. Horst ◽  
Gary Y. Yuen
Keyword(s):  
Leaf Wetness ◽  
Site Specific ◽  
Leaf Wetness Duration ◽  
A Site

scholarly journals Emulators of a Physical Model for Estimating Leaf Wetness Duration

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 216
Author(s):  
Ju-Young Shin ◽  
Junsang Park ◽  
Kyu Rang Kim
Keyword(s):  
Wind Speed ◽  
Physical Model ◽  
Learning Algorithm ◽  
Physical Models ◽  
Leaf Wetness ◽  
Estimation Model ◽  
Leaf Wetness Duration ◽  
Speed Measurement ◽  
Wind Speed Measurement

Leaf wetness duration (LWD) has rarely been measured due to lack of standard protocol. Thus, empirical and physical models have been proposed to resolve this gap. Although the physical model provides robust performance in diverse conditions, it requires many variables. The empirical model requires fewer variables; nevertheless, its performance is specific to a given condition. A universal LWD estimation model using fewer variables is thus needed to improve LWD estimation. The objective of this study was to develop emulators of the LWD estimation physical model for use as universal empirical models. It is assumed that the Penman–Monteith (PM) model determines LWD and can be employed as a physical model. In this study, a simulation was designed and conducted to investigate the characteristics of the PM model and to build the emulators. The performances of the built emulators were evaluated based on a case study of LWD data obtained in South Korea. It was determined that a machine learning algorithm can properly emulate the PM model in LWD estimations based on the simulation. Moreover, the poor performances of some emulators that use wind speed may have been due to the limitation of wind speed measurement. The accuracy of the anemometer is thus critical to estimating LWD using physical models. A deep neural network using relative humidity and air temperature was found to be the most appropriate emulator of those tested for LWD estimation.

scholarly journals Cytokine Signature and COVID-19 Prediction Models in the Two Waves of Pandemics.

2021 ◽  
Author(s):  
Serena Cabaro ◽  
Vittoria D'Esposito ◽  
Tiziana Di Matola ◽  
Silvia Sale ◽  
Michele Cennamo ◽  
...  
Keyword(s):  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Diagnostic Tools ◽  
Linear Discriminant ◽  
Cart Analysis ◽  
Diagnosis And Prognosis ◽  
Machine Learning Approach ◽  
Set Up ◽  
Definition Of

Abstract In Europe, two waves of infections with SARS-CoV-2 (COVID-19) have been observed to date. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in the two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed. CART analysis revealed that IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide non-invasive biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools.

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