Leaf Wetness Duration Estimation Research Articles

Improved Estimation of Leaf Wetness Duration Using Deep-Learning-Based Time-Resolution Technique

Plant diseases are one of the leading causes of the loss of crops. Monitoring the plant growth conditions could help prevent the spread of diseases and ensure healthy agricultural productivity. A leaf wetness sensor (LWS) is used to detect wetness on the surface of the leaves. Leaf wetness duration (LWD) is an essential parameter that governs the possibility of the occurrence of diseases in plants. The sensor system used in this work for monitoring the environmental conditions transmits data every 30 min due to power consumption considerations. To improve the time resolution of the signal and accurate estimation of LWD, a deep-learning-based architecture is proposed. The data from LWS has been used to train the architecture and the results obtained have been compared with the existing methods to analyze its performance. Using the proposed architecture, the signal from LWS was benchmarked with the commercially available Phythos-31. The time resolution of data has been improved from ±30 to ±4 min. The average signal-to-noise ratio (SNR) values for three test signals with improved time resolution have increased from 19.36 to 22.16 dB. Similarly, the average root means square error (RMSE) of the estimated LWD values after time resolution improved from 0.55 to 0.20. During the experimental analysis, it has been observed that the proposed architecture estimates the LWD values better compared to the other time-resolution techniques.

A CFD transient model of leaf wetness duration on greenhouse cucumber leaves

The estimation of leaf wetness duration (LWD) is important for crop disease monitoring and early warning, because LWD provides the necessary conditions for pathogen infection. Crop canopy condensation caused by high humidity in greenhouses is one of the main causes of LWD formation, and measuring LWD in greenhouses is difficult. A simulation model based on agricultural meteorological parameters is typically used to replace field measurements. This study was conducted in a Chinese solar greenhouse. A 2D transient model based on computational fluid dynamics (CFD) was used to estimate the distribution of cucumber leaf condensation in a solar greenhouse during early summer nights in Beijing. The LWD was estimated by considering the duration of the simulated leaf condensation at each point and simulating the dehumidification effect under ventilation conditions. The visual observations of leaf condensation were compared with the simulation results from May 31 to June 1 and from June 3 to June 4, 2021 (cloudy and clear days, respectively). The horizontal leaf condensation was observed first near the south roof, whereas the vertical canopy had a longer LWD at 1 m from the ground (average value of 8 h). LWD was estimated using relative humidity thresholds (RMSE of 1.944 h on cloudy days and RMSE of 0.5 h on clear days), and the good agreement between measurements and estimation indicated that the 2D CFD model combined with the relative humidity threshold method could be used to estimate the temporal and spatial distribution of canopy LWD.

Emulators of a Physical Model for Estimating Leaf Wetness Duration

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.

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

A limitation for widespread implementation of Asian soybean rust (ASR) warning system is the scarcity of leaf wetness duration (LWD) data. The objective of this study was to assess the feasibility of using LWD estimated by different empirical methods as input data for an ASR-warning system. The study was carried out with data from field experiments conducted in Ponta Grossa, Parana State, Campo Verde and Pedra Preta, both in Mato Grosso State, Brazil, throughout the 2014–15 and 2015–16 seasons. The estimated LWD values were used as input in an ASR-warning system. More reliable estimations of LWD were obtained using the Number of Hours with Relative Humidity above 90% (NHRH≥90%). The use of estimated LWD resulted in disease overestimation in Ponta Grossa, underestimation in Pedra Preta and moments of under and overestimation in Campo Verde. Analyzing the ASR-warning system with the more conservative threshold, five sprays per season were recommended for all sites and the mean fractions of correct estimates (θ1) in Ponta Grossa were higher (θ1 = 0.876) than those obtained in Pedra Preta (θ1 = 0.632) and Campo Verde (θ1 = 0.662). Otherwise, for the less conservative spray threshold, a reduction of one spray was observed in Ponta Grossa (θ1 = 0.912) when compared to Pedra Preta (θ1 = 0.826) and Campo Verde (θ1 = 0.767), where five sprays were still recommended. Thus, LWD estimated by NHRH≥90% has enough accuracy and precision for being used as input in the ASR-warning system in the most traditional soybean regions of Brazil.

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

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.

Physical modeling of leaf wetness duration at the tree scale: considering leaf properties and tree architecture to estimate water interception

Fungal disease infection risk in fruit trees strongly depends on the climatic conditions in the orchard and on the microclimate within the tree. The Leaf Wetness Duration (LWD), i.e. the time that free water remains on leaves in relation to rain or dew events is for many fungal species the significant parameter linking the epidemic risk to environment humidity. Our goal is to provide a reliable LWD estimate model for plant protection decision support tools. Since LWD is the combination of two distinct phenomena: water interception and water evaporation, both have to be considered in the model's framework. Water interception is described by two main parameters: canopy gap fraction and leaf water storage capacity. They have been quantified in an experiment on 'McIntosh' apple trees in Quebec (Canada). The gap fraction was measured with a LIDAR detector, which allowed us to choose the desired complexity level of a 3D reconstruction of the canopy. Water storage capacity was measured in orchard on every type of leaf susceptible to apple scab. The obtained data are used to implement a water interception submodel aimed at improving LWD estimation in our physical model.

Modelling coffee leaf rust risk in Colombia with climate reanalysis data.

Many fungal plant diseases are strongly controlled by weather, and global climate change is thus likely to have affected fungal pathogen distributions and impacts. Modelling the response of plant diseases to climate change is hampered by the difficulty of estimating pathogen-relevant microclimatic variables from standard meteorological data. The availability of increasingly sophisticated high-resolution climate reanalyses may help overcome this challenge. We illustrate the use of climate reanalyses by testing the hypothesis that climate change increased the likelihood of the 2008–2011 outbreak of Coffee Leaf Rust (CLR, Hemileia vastatrix) in Colombia. We develop a model of germination and infection risk, and drive this model using estimates of leaf wetness duration and canopy temperature from the Japanese 55-Year Reanalysis (JRA-55). We model germination and infection as Weibull functions with different temperature optima, based upon existing experimental data. We find no evidence for an overall trend in disease risk in coffee-growing regions of Colombia from 1990 to 2015, therefore, we reject the climate change hypothesis. There was a significant elevation in predicted CLR infection risk from 2008 to 2011 compared with other years. JRA-55 data suggest a decrease in canopy surface water after 2008, which may have helped terminate the outbreak. The spatial resolution and accuracy of climate reanalyses are continually improving, increasing their utility for biological modelling. Confronting disease models with data requires not only accurate climate data, but also disease observations at high spatio-temporal resolution. Investment in monitoring, storage and accessibility of plant disease observation data are needed to match the quality of the climate data now available.This article is part of the themed issue ‘Tackling emerging fungal threats to animal health, food security and ecosystem resilience’.

Use of an Empirical Model to Estimate Leaf Wetness Duration for Operation of a Disease Warning System Under a Shade in a Ginseng Field.

Ginseng foliar diseases are typically controlled by spray application using periodic schedules. Few disease warning systems have been used for effective control of ginseng foliar diseases because ginseng is grown under shade nettings, which makes it difficult to obtain weather data for operation of the disease warning system. Using weather data measured outside the shade as inputs to an empirical leaf wetness duration (LWD) model, LWD was estimated to examine if operation of a disease warning system would be feasible for control of ginseng foliar diseases. An empirical model based on a fuzzy logic system (fuzzy model) was used to estimate LWD at two commercial ginseng fields located in Gochang-gun and Jeongeup-si, Korea, in 2011 and 2012. Accuracy of LWD estimates was assessed in terms of mean error (ME) and mean absolute error (MAE). The fuzzy model tended to overestimate LWD during dew eligible days whereas it tended to underestimate LWD during rainfall eligible days. Still, daily disease risk ratings of the TOM-CAST disease warning system, which are derived from estimates of wetness duration and temperature, had a tendency to coincide with that derived from measurements of weather variables. As a result, spray advisory dates for the TOM-CAST disease warning system were predicted within ±3 days for about 78% of time windows during which the action threshold for spray application was reached. This result suggested that estimates of LWD using an empirical model would be helpful in control of a foliar disease in a ginseng field. It was also found that a spray application time model using meteorological observations may prove successful without the requirement of leaf wetness sensors within the field. Development of empirical correction schemes to the fuzzy model and a physical model for LWD estimation in a ginseng field could improve accuracy of LWD estimates and, as a result, spray advisory date prediction, which merits further studies.

Assessment of microclimate conditions under artificial shades in a ginseng field

BackgroundKnowledge on microclimate conditions under artificial shades in a ginseng field would facilitate climate-aware management of ginseng production. MethodsWeather 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. ResultsAir 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°C and 27°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%. ConclusionWeather 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.

Estimation of leaf wetness duration for greenhouse roses using a dynamic greenhouse climate model in Zimbabwe

Leaf wetness duration (LWD) is one of the most critical parameters involved in the development of plant diseases, since many pathogens require the presence of free water on plant organs to infect foliar tissue. For this reason LWD monitoring is extremely important in crop protection, particularly through the use of weather-related disease forecasting models. Because of the difficulties involved in the measurement of leaf wetness duration, simulation models, based on agrometeorological parameters, are often used as alternatives to field measurement. Furthermore, in greenhouse crop production, these LWD models can be used in predictive control of the greenhouse climate as to suppress the development and propagation of infectious plant diseases. In this study, the Gembloux Dynamic Greenhouse Climate Model (GDGCM) was applied to derive estimates of LWD and hence potential disease incidence, for a rose crop in a naturally ventilated Azrom type greenhouse in Zimbabwe. The model LWD estimates were compared both with data measured by sensors and with visual inspections of LWD conducted during the period May 2007–April 2008. When the GDGCM outputs were used to estimate LWD, the best agreement between measured and predicted LWDs (RMSE=3.2hd−1) were obtained when LWD was calculated as the number of hours that the dew point depression of the air remained below 2.3°C (for wetness onset) and 2.5°C (for drying off). The good agreement between measured and predicted LWD showed that the GDGCM can be used to predict LWD with acceptable accuracy. This could be of considerable value in helping to predict possible outbreaks of certain climate related diseases, like downy mildew, powdery mildew and botrytis. This information can be used by growers to decide on the optimal precautions to take to prevent possible epidemics of these diseases.

Estimation of leaf wetness duration for a short-grass surface

The measurement of leaf wetness duration (LWD) was investigated using subhourly dielectric, infrared surface temperature, dewpoint temperature, grass temperature and relative humidity (RH) measurements. Near real-time LWD data and information displays and alerts were made available timeously via a web-based system. LWD was estimated above a short-grass surface using five methods: dielectric leaf wetness sensors (LWS); a constant RH for which wetness events were registered for RH greater than 87%; RH between 70–87% if RH increased by more than 3% in 30 min; and two dewpoint depression-based methods for which surface-measured temperature, using an infrared thermometer (IRT), and grass temperature were compared with the dewpoint at either 0.1 or 2 m. The RH methods generally overestimated LWD compared to the other methods. There was reasonable agreement between IRT- and grass-temperature methods if rain days were excluded but these methods showed poor agreement with LWS measurements of LWD. Microclimatic and radiative conditions, during nocturnal condensing events, are reported. Automatic weather station data would have more value if grass temperature was included for determination of LWD by comparison of grass temperature with a measured dewpoint, with timeous alerts and web-based display of near real-time LWD data and graphics.

An integrated evaluation of thirteen modelling solutions for the generation of hourly values of air relative humidity

The availability of hourly air relative humidity (HARH) data is a key requirement for the estimation of epidemic dynamics of plant fungal pathogens, in particular for the simulation of both the germination of the spores and the infection process. Most of the existing epidemic forecasting models require these data as input directly or indirectly, in the latter case for the estimation of leaf wetness duration. In many cases, HARH must be generated because it is not available in historical series and when there is the need to simulate epidemics either on a wide scale or with different climate scenarios. Thirteen modelling solutions (MS) for the generation of this variable were evaluated, with different input requirements and alternative approaches, on a large dataset including several sites and years. A composite indicator was developed using fuzzy logic to compare and to evaluate the performances of the models. The indicator consists of four modules: Accuracy, Correlation, Pattern and Robustness. Results showed that when available, daily maximum and minimum air relative humidity data substantially improved the estimation of HARH. When such data are not available, the choice of the MS is crucial, given the difference in predicting skills obtained during the analysis, which allowed a clear detection of the best performing MS. This study represents the first step of the creation of a robust modelling chain coupling the MS for the generation of HARH and disease forecasting models, including the systematic validation of each step of the simulation.

Suitability of relative humidity as an estimator of leaf wetness duration

Leaf wetness duration (LWD) is a key parameter in agricultural meteorology since it is related to epidemiology of many important crops, controlling pathogen infection and development rates. Because LWD is not widely measured, several methods have been developed to estimate it from weather data. Among the models used to estimate LWD, those that use physical principles of dew formation and dew and/or rain evaporation have shown good portability and sufficiently accurate results, but their complexity is a disadvantage for operational use. Alternatively, empirical models have been used despite their limitations. The simplest empirical models use only relative humidity data. The objective of this study was to evaluate the performance of three RH-based empirical models to estimate LWD in four regions around the world that have different climate conditions. Hourly LWD, air temperature, and relative humidity data were obtained from Ames, IA (USA), Elora, Ontario (Canada), Florence, Toscany (Italy), and Piracicaba, São Paulo State (Brazil). These data were used to evaluate the performance of the following empirical LWD estimation models: constant RH threshold (RH ≥ 90%); dew point depression (DPD); and extended RH threshold (EXT_RH). Different performance of the models was observed in the four locations. In Ames, Elora and Piracicaba, the RH ≥ 90% and DPD models underestimated LWD, whereas in Florence these methods overestimated LWD, especially for shorter wet periods. When the EXT_RH model was used, LWD was overestimated for all locations, with a significant increase in the errors. In general, the RH ≥ 90% model performed best, presenting the highest general fraction of correct estimates ( F C), between 0.87 and 0.92, and the lowest false alarm ratio ( F AR), between 0.02 and 0.31. The use of specific thresholds for each location improved accuracy of the RH model substantially, even when independent data were used; MAE ranged from 1.23 to 1.89 h, which is very similar to errors obtained with published physical models for LWD estimation. Based on these results, we concluded that, if calibrated locally, LWD can be estimated with acceptable accuracy by RH above a specific threshold, and that the EXT_RH method was unsuitable for estimating LWD at the locations used in this study.

Estimating hourly net radiation for leaf wetness duration using the Penman-Monteith equation

Leaf wetness duration (LWD) is related to plant disease occurrence and is therefore a key parameter in agrometeorology. As LWD is seldom measured at standard weather stations, it must be estimated in order to ensure the effectiveness of warning systems and the scheduling of chemical disease control. Among the models used to estimate LWD, those that use physical principles of dew formation and dew and/or rain evaporation have shown good portability and sufficiently accurate results for operational use. However, the requirement of net radiation (Rn) is a disadvantage foroperational physical models, since this variable is usually not measured over crops or even at standard weather stations. With the objective of proposing a solution for this problem, this study has evaluated the ability of four models to estimate hourly Rn and their impact on LWD estimates using a Penman-Monteith approach. A field experiment was carried out in Elora, Ontario, Canada, with measurements of LWD, Rn and other meteorological variables over mowed turfgrass for a 58 day period during the growing season of 2003. Four models for estimating hourly Rn based on different combinations of incoming solar radiation (Rg), airtemperature (T), relative humidity (RH), cloud cover (CC) and cloud height (CH), were evaluated. Measured and estimated hourly Rn values were applied in a Penman-Monteith model to estimate LWD. Correlating measured and estimated Rn, we observed that all models performed well in terms of estimating hourly Rn. However, when cloud data were used the models overestimated positive Rn and underestimated negative Rn. When only Rg and T were used to estimate hourly Rn, the model underestimated positive Rn and no tendency was observed for negative Rn. The best performance was obtained with Model I, which presented, in general, the smallest mean absolute error (MAE) and the highest C-index. When measured LWD was compared to the Penman-Monteith LWD, calculated with measured and estimated Rn, few differences were observed. Both precision and accuracy were high, with the slopes of the relationships ranging from 0.96 to 1.02 and R2 from 0.85 to 0.92, resulting in C-indices between 0.87 and 0.93. The LWD mean absolute errors associated with Rn estimates were between 1.0 and 1.5 h, which is sufficient for use in plant disease management schemes.

Evaluation of a Penman–Monteith approach to provide “reference” and crop canopy leaf wetness duration estimates

Leaf wetness duration (LWD) is a key parameter for plant disease-warning systems since the risk of outbreaks of many plant diseases is directly proportional to this environmental variable. However, LWD is not widely measured so several methods have been developed to estimate it from weather data. Methods based on the physical principles of dew deposition and dew or rain evaporation have shown good portability and sufficiently accurate results for operational use. A Penman–Monteith approach to modeling LWD on a “reference” wetness sensor located at a weather station was investigated as well as the use of an empirical wetness coefficient ( W) to convert “reference” LWD into crop LWD. This study was undertaken because recent observations revealed that an LWD sensor located about 30 cm above a turfgrass surface provided useful estimates of LWD in various nearby crops, suggesting that modeling such a sensor and location may be a simpler “reference” alternative to modeling LWD in a crop canopy. LWD was measured over mowed turfgrass at different heights (30, 110, and 190 cm above the ground) and at the top of the canopy of eight crops – apple, coffee, cotton, maize, muskmelon, grape, soybean, and tomato – using painted flat-plate sensors. At the same times and places, automatic weather stations measured air temperature, relative humidity, wind speed, and net radiation over turfgrass. A Penman–Monteith approach estimated sensor LWD over turfgrass with very good accuracy and precision, using an additional aerodynamic resistance based on wind speed to estimate LWD at 110 and 30 cm. The model overestimated LWD by 3.3% at 190 cm ( R 2 = 0.92), 1.5% at 110 cm ( R 2 = 0.87), and 5.7% at 30 cm ( R 2 = 0.89). When modeled LWD for a 30-cm height over turfgrass was correlated with LWD measured at the top of crop canopies, strong agreement was observed, with an average overestimation of 6.3% and a coefficient of determination of 0.92 for five crops combined. The use of both general and specific W coefficients reduced the average overestimation and the mean absolute error in LWD to less than 1 h/day. When independent data from four crops were use to evaluate crop LWD estimates by this two-step Penman–Monteith approach, mean absolute error was <1.6 h when both general and specific W coefficients were used. We concluded that a Penman–Monteith model for a fixed sensor size, albedo and exposure over turfgrass may be a very useful “reference” index to estimate crop LWD for use in plant disease management schemes.

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

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.

Spatial variability of leaf wetness duration in different crop canopies

The spatial variability of leaf wetness duration (LWD) was evaluated in four different height-structure crop canopies: apple, coffee, maize, and grape. LWD measurements were made using painted flat plate, printed-circuit wetness sensors deployed in different positions above and inside the crops, with inclination angles ranging from 30 to 45 degrees. For apple trees, the sensors were installed in 12 east-west positions: 4 at each of the top (3.3 m), middle (2.1 m), and bottom (1.1 m) levels. For young coffee plants (80 cm tall), four sensors were installed close to the leaves at heights of 20, 40, 60, and 80 cm. For the maize and grape crops, LWD sensors were installed in two positions, one just below the canopy top and another inside the canopy. Adjacent to each experiment, LWD was measured above nearby mowed turfgrass with the same kind of flat plate sensor, deployed at 30 cm and between 30 and 45 degrees. We found average LWD varied by canopy position for apple and maize (P<0.05). In these cases, LWD was longer at the top, particularly when dew was the source of wetness. For grapes, cultivated in a hedgerow system and for young coffee plants, average LWD did not differ between the top and inside the canopy. The comparison by geometric mean regression analysis between crop and turfgrass LWD measurements showed that sensors at 30 cm over turfgrass provided quite accurate estimates of LWD at the top of the crops, despite large differences in crop height and structure, but poorer estimates for wetness within leaf canopies.

Estimation of leaf wetness duration using empirical models in northwestern Costa Rica

Leaf wetness duration (LWD) estimation models developed in the midwestern U.S. were assessed for portability to northwestern Costa Rica during wet and dry seasons. The CART/SLD/Wind (CART) model overestimated LWD by 5.4 h at five sites in northwestern Costa Rica during the 1999 wet season. The Fuzzy (FL) model, in contrast, estimated LWD at the same sites within 3 h. During the 2000–2001 and 2002–2003 dry seasons, both models underestimated LWD at most sites, probably due to the prevalence of less humid conditions than in the temperate climate of the midwestern U.S. However, accuracy of the FL model was substantially improved when a correction factor was utilized during the 2002–2003 dry season, indicating that this model could be adjusted to estimate LWD in a semi-arid climate with acceptable accuracy. The results suggested that the adjusted FL model could estimate LWD accurately in other tropical regions with climate regimes similar to northwestern Costa Rica.

Neural network for the estimation of leaf wetness duration: application to a Plasmopara viticola infection forecasting

Leaf wetness duration (LWD) is one of the most important variables responsible for the outbreak of plant diseases but, in spite of its importance, the technology for measurement is not rather reliable. For this reason the modelling appears to be a valid support for LWD assessment. In this work a technique for LWD estimation that was applied in some agro-environmental studies from few years was used: artificial neural network (ANN). The ANN output then was used as input for an epidemiological model to predict Plasmopara viticola infections. The aim of this work was to carry out an ANN capable to find out the relationships between the agrometeorological input and LWD and to evaluate the impact of this estimated LWD when integrated in epidemiological simulations.

Development and validation of a leaf wetness duration model using a fuzzy logic system

A model to estimate leaf wetness duration (LWD) based on fuzzy logic was developed and validated using hourly weather measurements at 15 sites in the midwestern U.S. from 1997–1999. The Fuzzy model, which required relatively few input variables and simplified calculations compared with physical models, was comparable to sensor measurements in overall accuracy of LWD estimation (<1 h/day error). The Fuzzy model was also more portable than the CART/SLD/Wind model, an empirical model that used the same weather variables, in that the Fuzzy model classified presence or absence of wetness more accurately than the CART/SLD/Wind model at most sites. This suggests that incorporating energy balance principles and empirical computation methods in a fuzzy logic system makes it possible to accurately estimate LWD with a relatively small number of input variables. The accuracy of the Fuzzy model may be improved using correction factors when additional relevant inputs, e.g., solar radiation, become available. Therefore, the Fuzzy model may merit further study to verify this adaptability.