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 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 Regression tree analysis to predict body weight of South African non-descript goats raised at Syferkuil farm, Capricorn District of South Africa

2021 ◽  
Vol 37 (4) ◽  
pp. 293-304
Author(s):  
Thobela Tyasi ◽  
Amanda Tshegofatso Mkhonto ◽  
Madumetja Mathapo ◽  
Kagisho Molabe
Keyword(s):  
Body Weight ◽  
South African ◽  
Selection Criterion ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Develop Model ◽  
Data Mining Algorithm ◽  
Regression Tree Analysis ◽  
Males And Females ◽  
Cart Model

Regression tree is the data mining algorithm method which contains a series of calculations that creates a model from collected data. Present study aimed to develop model to estimate body weight (BW) from biometric traits viz. withers height (WH), sternum height (SH), body length (BL), heart girth (HG) and rump height (RH). A total of eighty-three (n = 83) South African non-descript indigenous goats ( 54 females and 29 males) aged three months and above were used in the study. Pearson?s correlations and classification and regression tree (CART) as statistical techniques were used for data analysis. Correlation results indicated that there was a positive highly statistical significant (P < 0.01) correlation between BW and all biometric traits in both males and females, the positive highly statistical significant correlation was observed between BW and WH (r = 0.82) in female goats while in males the highest positive statistical significant correlation was detected between BW and BL (r = 0.83). CART model indicated that the BW mean was 29.868 kilograms (kg) as dependent variable and BL had the highest remarkable role in BW followed by SH, RH while the age had the least remarkable role in BW. This study suggests that BL, SH and RH might be used by South African non-descript goats? farmers as a selection criterion during breeding to improve BW of animal. More completive studies and experiments need to be done using CART to predict BW in more sample size of South African nondescript goats or other goat breeds.

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 Obtaining weather data for input to crop disease-warning systems: leaf wetness duration as a case study

2008 ◽  
Vol 65 (spe) ◽  
pp. 76-87 ◽  
Author(s):  
Mark L. Gleason ◽  
Katrina B. Duttweiler ◽  
Jean C. Batzer ◽  
S. Elwynn Taylor ◽  
Paulo Cesar Sentelhas ◽  
...  
Keyword(s):  
Control Measures ◽  
Weather Data ◽  
Leaf Wetness ◽  
Warning Systems ◽  
Leaf Wetness Duration ◽  
Advantages And Disadvantages ◽  
Support Tools ◽  
Crop Disease ◽  
Weather Stations

Disease-warning systems are decision support tools designed to help growers determine when to apply control measures to suppress crop diseases. Weather data are nearly ubiquitous inputs to warning systems. This contribution reviews ways in which weather data are gathered for use as inputs to disease-warning systems, and the associated logistical challenges. Grower-operated weather monitoring is contrasted with obtaining data from networks of weather stations, and the advantages and disadvantages of measuring vs. estimating weather data are discussed. Special emphasis is given to leaf wetness duration (LWD), not only because LWD data are inputs to many disease-warning systems but also because accurate data are uniquely challenging to obtain. It is concluded that there is no single " best" method to acquire weather data for use in disease-warning systems; instead, local, regional, and national circumstances are likely to influence which strategy is most successful.

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.

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