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 Spatial variability of leaf wetness duration in cotton, coffee and banana crop canopies

2008 ◽  
Vol 65 (spe) ◽  
pp. 18-25 ◽  
Author(s):  
Eduardo Alvarez Santos ◽  
Paulo Cesar Sentelhas ◽  
José Eduardo Macedo Pezzopane ◽  
Luiz Roberto Angelocci ◽  
José Eduardo Boffino Almeida Monteiro
Keyword(s):  
Spatial Variability ◽  
Plant Disease ◽  
Leaf Wetness ◽  
Weather Station ◽  
Cotton Crop ◽  
Leaf Wetness Duration ◽  
Disease Epidemiology ◽  
Plant Disease Management ◽  
Upper Third ◽  
Banana Crop

Despite the importance of leaf wetness duration for plant disease epidemiology, there has been little attention paid to research on how its variability relates to different cropping situations. The objective of this study was to evaluate the spatial variability of leaf wetness duration (LWD) in three crops, comparing these measurements with turfgrass LWD, obtained in a standard weather station. LWD was measured by electronic sensors in three crops with different canopy structures and leaf area: cotton, coffee and banana. For the cotton crop, cylindrical sensors were deployed at the lower third and on the top of the canopy, facing southwest. For the coffee crop, flat plate sensors were installed in the lower third of the canopy facing northeast and southwest; in the middle third facing northeast and southwest; and inside and on the top of the canopy. For the banana canopy, cylindrical sensors were used to measure LWD in the lower third of the canopy and in the upper third of the plant. Turfgrass LWD was simultaneously measured in a nearby standard weather station. The LWD showed different patterns of variation in the three crop canopies. For coffee plants, the longest LWD was found in the lower portions of the canopy; for the banana crop, the upper third of the canopy showed the longest LWD; whereas for the cotton crop no difference was observed between the top and lower third of the canopy. Turfgrass LWD presented a good relationship with LWD measured on the top or in the upper third of the crops. Thus, the estimate of crop LWD can be perfomed based on turfgrass LWD, this being a useful tool for plant disease management purposes for crops in which the longer LWD occurs at the upper canopy portion.

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 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 Spatial variability of leaf wetness duration in a 'Niagara Rosada' vineyard

2008 ◽  
Vol 28 (1) ◽  
pp. 104-114
Author(s):  
Jorge Lulu ◽  
Paulo C. Sentelhas ◽  
Mário J. Pedro Júnior ◽  
José R. M. Pezzopane ◽  
Gabriel C. Blain
Keyword(s):  
Spatial Variability ◽  
Leaf Wetness ◽  
Crop Canopy ◽  
Vitis Labrusca ◽  
Leaf Wetness Duration ◽  
Canopy Position ◽  
Significant Difference ◽  
The Relationship ◽  
Electronic Sensors ◽  
Niagara Rosada

Despite considerable efforts to develop accurate electronic sensors to measure leaf wetness duration (LWD), little attention has been given to studies about how is LWD variability in different positions of the crop canopy. In order to evaluate the influence of 'Niagara Rosada' (Vitis labrusca) grapevine structure on the spatial variability of LWD, the objective of this study was to determine the canopy position of the ‘Niagara RosadaÂ’ table grape with longer LWD and its correlation with measured standard LWD over turfgrass. LWD was measured in four different canopy positions of the vineyard (sensors deployed at 45º with the horizontal): at the top of the plants, with sensors facing southwest and northeast (Top-SW and Top-NE), and at the grape bunches height, with sensors facing southwest and northeast (Bottom-SW and Bottom-NE). No significant difference was observed between the top (1.6 m) and the bottom (1.0 m) of the canopy and also between the southwest and northeast face of the plants. The relationship between standard LWD over turfgrass and crop LWD in different positions of the grape canopy showed a define correlation, with R² ranging from 0.86 to 0.89 for all period, from 0.72 to 0.77 for days without rain, and from 0.89 to 0.91 for days with rain.

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.

Leaf wetness duration estimation and its influence on a soybean rust warning system

2019 ◽  
Vol 48 (4) ◽  
pp. 395-408 ◽  
Author(s):  
Gustavo Castilho Beruski ◽  
Mark Lawrence Gleason ◽  
Paulo Cesar Sentelhas ◽  
André Belmont Pereira
Keyword(s):  
Warning System ◽  
Soybean Rust ◽  
Leaf Wetness ◽  
Leaf Wetness Duration ◽  
Duration Estimation

scholarly journals Influence of Temperature and Leaf Wetness Duration on Infection of Strawberry Leaves by Mycosphaerella fragariae

2000 ◽  
Vol 90 (10) ◽  
pp. 1120-1125 ◽  
Author(s):  
O. Carisse ◽  
G. Bourgeois ◽  
J. A. Duthie
Keyword(s):  
Leaf Surface ◽  
Leaf Wetness ◽  
Infection Model ◽  
Controlled Environment ◽  
Conidial Suspension ◽  
Precise Description ◽  
Leaf Wetness Duration ◽  
Influence Of Temperature ◽  
Mycosphaerella Fragariae ◽  
Weibull Equation

In controlled environment studies, the influence of temperature and wetness duration on infection of strawberry leaves by Mycosphaerella fragariae was quantified by inoculating plants with a conidial suspension and incubating them at various combinations of temperature (5 to 35°C) and leaf wetness duration (0 to 96 h). Infection was expressed as the number of lesions per square centimeter of leaf surface and relative infection was used to develop an infection model. Younger leaves were more susceptible to infection. Regardless of temperature and duration of leaf wetness, only few lesions developed on the oldest (19 to 21 days old) and intermediate leaves (12 to 15 days old), respectively (maximum of 1.7 and 2.3 lesions per cm2) as compared to the youngest leaves (5 to 7 days old; maximum of 12.6 lesions per cm2). On the youngest leaves, lesions developed at all temperatures except at 35°C, and the number of lesions, for all leaf wetness durations, increased gradually from 5 to 25°C and decreased sharply from 25 to 30°C. For temperatures of 15 and 20°C, the number of lesions increased gradually when leaf wetness duration increased from 12 to 96 h. At 25°C, the number of lesions increased with increasing leaf wetness from 12 to 48 h and then at a higher rate from 48 to 96 h. The optimal temperature for infection was 25°C. For most temperatures, a minimum of 12 h of leaf wetness was necessary for infection (more than 1 lesion per cm2). Relative infection was modeled as a function of both temperature and wetness duration using a modified version of the Weibull equation (R 2 = 0.98). The resulting equations provided a precise description of the response of M. fragariae to temperature. The model was sufficiently flexible to account for most characteristics of the response of M. fragariae to wetness duration. The model was used to construct a risk chart that can be used to estimate the potential risk for infection based on observed or forecasted temperature and leaf wetness duration.

Measurement of Specific Heat Using Bayesian Inference to Account for Uncertainties in the Calorimeter Performance

2006 ◽  
Author(s):  
A. F. Emery ◽  
K. Abernethy
Keyword(s):  
Thermal Conductivity ◽  
Bayesian Inference ◽  
Specific Heat ◽  
High Accuracy ◽  
Temperature History ◽  
Good Precision ◽  
Precise Characterization ◽  
Transient Conduction ◽  
Measured Specific Heat

One way of estimating thermal conductivity is to measure the temperature history for transient conduction. Unfortunately, the property estimated is usually not the conductivity but the thermal diffusivity, α = k/ρcp. While α can often be estimated with good precision, and ρ is usually well defined, the specific heat is often poorly known. As a consequence the uncertainty in the conductivity can be large. This paper reports the use of a calorimeter designed to measure the specific heat with high accuracy. The accuracy depends upon a precise characterization of the thermal performance of the calorimeter. Even when much care is taken, the calorimeter's behavior introduces uncertainty in the measured specific heat. The effects of these uncertainties are accounted for by using Bayesian inference to estimate the confidence intervals of the specific heat.

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