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

Mashonjowa, Emmanuel; Ronsse, Frederik; Mubvuma, Michael Ticharwa; Milford, J. R.; Pieters, Jan

doi:10.1016/j.compag.2013.04.007

Cited by 25 publications

(12 citation statements)

References 34 publications

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“…Such information would be valuable for predicting occurrence of certain climate-related diseases and can be used by growers to help them decide on the optimal precautions to take to prevent possible epidemics (Zhang et al, 1997). Presently LWD is used as an input in many disease forecast models and warning systems in greenhouse crop production including cucumber (Huber and Gillespie, 1992;Korner and Holst, 2003;Zhao et al, 2011;Baptista et al, 2012;Mashonjowa et al, 2013).…”

Section: Leaf Wetness Duration (Lwd)mentioning

confidence: 99%

Factors Affecting the Performance of Greenhouse Cucumber Cultivation-A Review

Singh¹,

Singh²,

Pandey³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

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Section: Leaf Wetness Duration (Lwd)mentioning

confidence: 99%

Factors Affecting the Performance of Greenhouse Cucumber Cultivation-A Review

Singh¹,

Singh²,

Pandey³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

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“…Bassimba et al [14] calibrated the threshold of the RHM for 14 commercial citrus orchards in Spain by ROC curve analysis, and sensibility of the model was in the range of 0.43-0.93. Mashonjowa et al [12] proposed the calibration thresholds of the RHM (RH > 84%) and the DPM (DPD < 2.5 • C) for a Zimbabwe greenhouse, and the correct success index of each model was 0.59 and 0.76. Empirical leaf wetness models after calibration of thresholds, such as the RHM and DPM, did not perform better than the complex physical models.…”

Section: Discussionmentioning

confidence: 99%

“…Conversely, estimation of LWD is based on its relationships with meteorological variables available in standard agro-meteorological stations. Some examples of empirical LWD models are the simple relative humidity threshold model (RHM), which simulates the leaf wetness occurrence when humidity is above a threshold [10,11]; the dew point depression method (DPM), based on the principle of dew formation [12,13]; and the classification and regression tree (CART) model, which considers the non-linear relationship between leaf wetness, wind speed, rainfall, dew temperature and relative humidity in decision nodes to determine leaf wetness [14]. Kim et al demonstrated the spatial portability of the CART model through its application in different environments [15].…”

mentioning

confidence: 99%

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

Wang

Sánchez-Molina

et al. 2019

Water

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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 . 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. simulation were developed, and are routinely used to provide inputs to early disease warning systems of disease outbreaks [6,7]. The LWD models can be divided into two broad categories: empirical and physical models. The latter are data demanding because they require inputs that are not always available, such as cloud cover and net radiation [8,9]. Conversely, estimation of LWD is based on its relationships with meteorological variables available in standard agro-meteorological stations. Some examples of empirical LWD models are the simple relative humidity threshold model (RHM), which simulates the leaf wetness occurrence when humidity is above a threshold [10,11]; the dew point depression method (DPM), based on the principle of dew formation [12,13]; and the classification and regression tree (CART) model, which considers the non-linear relationship between leaf wetness, wind speed, rainfall, dew temperature and relative humidity in decision nodes to determine leaf wetness [14]. Kim et al. demonstrated the spatial portability of the CART model through its application in different environments [15]. Gillespie et al. achieved good results when estimating LWD by a dew point depression model [16]. Further attempts have been made for estimation of LWD using neural network models (NNMs), which can be considered black box models. These are self-adaption models trained with refe...

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“…Many mechanistic models have been developed, such as GDGCM model [2,3] There are also some mechanistic models which just describe the dynamic behaviors of greenhouse microclimate under a particular operating state, for example, Teitel and Tann studied the transient response of the greenhouse air temperature and humidity under the…”

Section: Introductionmentioning

confidence: 99%

“…Many mechanistic models have been developed, such as GDGCM model [2,3], KASPRO model [4,5], MICGREEN model [6], SimGreC model [7], SIMICROC model [8], etc. Both GDGCM model and KASPRO model are developed according to the climatic conditions in Western European countries (Belgium and the Netherlands), where the air temperature is not too high in summer, and the natural ventilation and the shading are enough for cooling.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Mechanistic Modeling of Air Temperature and Humidity in the Greenhouses with on-off Actuators

Xu¹,

Chen²,

Zhang³

et al. 2016

TELKOMNIKA

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IntroductionMany modern greenhouses have been imported in China as show-cases of modern agriculture from Western European countries in the past 30 years. However, few of such showcase greenhouses have been economically successful in China. These greenhouses consume too much energy. One major cause for the discrepancy in performance of these production systems in Europe and in China is the significant climatic differences between China (particularly in the subtropical regions) and the countries where the systems were developed [1]. Therefore, it's necessary to develop suitable control strategies for the greenhouses in China. However, it's inappropriate to test new control methods in an actual greenhouse directly, because it's laborious and costly, and it even causes damage to the actuators and crops. It's better to test new control methods in simulation at first, which is convenient and safe. The premise is that the mechanistic models of greenhouse microclimate must be built in advance. The purpose of this paper was to develop suitable mechanistic models of greenhouse air temperature and humidity, according to the characteristics of actuators in most greenhouses in China.The mechanistic model of greenhouse microclimate is a group of dynamic energy and mass balance equations, the function of which is to simulate the dynamic behaviors of greenhouse microclimate. Many mechanistic models have been developed, such as GDGCM model [2,3] There are also some mechanistic models which just describe the dynamic behaviors of greenhouse microclimate under a particular operating state, for example, Teitel and Tann studied the transient response of the greenhouse air temperature and humidity under the

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

Cited by 25 publications

References 34 publications

Factors Affecting the Performance of Greenhouse Cucumber Cultivation-A Review

Factors Affecting the Performance of Greenhouse Cucumber Cultivation-A Review

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

Dynamic Mechanistic Modeling of Air Temperature and Humidity in the Greenhouses with on-off Actuators

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