Modeling Dynamic Behavior of Leaf Temperature at Three-Dimensional Positions to Step Variations in Air Temperature and Light

Boonen, Christiaan; Joniaux, O; Janssens, Karl; Berckmans, Daniël; Lemeur, R; Kharoubi, A; Pien, Helga

doi:10.13031/2013.3078

Cited by 10 publications

(4 citation statements)

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“…Furthermore, these models are unable to account for the time varying nature of the plant system, as their parameters are assumed to remain constant once identified. The response of a plant will vary as a result of growth, biotic and abiotic factors, and adaptation processes (Boonen et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Dynamic modelling of lettuce transpiration for water status monitoring

Adeyemi

Peets

Domun

et al. 2018

Computers and Electronics in Agriculture

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Real-time information on the plant water status is an important prerequisite for the precision irrigation management of crops. The plant transpiration has been shown to provide a good indication of its water status. In this paper, a novel plant water status monitoring framework based on the transpiration dynamics of greenhouse grown lettuce plants is presented. Experimental results indicated that lettuce plants experiencing adequate water supply transpired at a higher rate compared to plants experiencing a shortage in water supply. A data-driven model for predicting the transpiration dynamics of the plants was developed using a system identification approach. Results indicated that a second order discrete-time transfer function model with incoming radiation, vapour pressure deficit, and leaf area index as inputs sufficiently explained the dynamics with an average coefficient of determination of 2 = 0.93 ± 0.04. The parameters of the model were updated online and then applied in predicting the transpiration dynamics of the plants in real-time. The model predicted dynamics closely matched the measured values when the plants were in a predefined water status state. The reverse was the case when there was a significant change in the water status state. The information contained in the model residuals (measured transpirationmodel predicted transpiration) was then exploited as a means of inferring the plant water status. This framework provides a simple and intuitive means of monitoring the plant water status in real-time while achieving a sensitivity similar to that of stomatal conductance measurements. It can be applied in regulating the water deficit of greenhouse grown crops, with specific advantages over other available techniques.

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Section: Introductionmentioning

confidence: 99%

Dynamic modelling of lettuce transpiration for water status monitoring

Adeyemi

Peets

Domun

et al. 2018

Computers and Electronics in Agriculture

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“…King and Shellie (2016) also reported improved predictions of the baseline temperatures using an artificial neural network (ANN), with air temperature, solar radiation, wind speed and VPD applied as input variables. The plant response will typically vary over the growth season due to crop growth and various adaptation processes (Boonen et al, 2000). Dhillon et al (2014) showed that baseline temperature prediction models for tree crops varied as the season progressed.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modelling of the baseline temperatures for computation of the crop water stress index (CWSI) of a greenhouse cultivated lettuce crop

Adeyemi

Peets

Domun

et al. 2018

Computers and Electronics in Agriculture

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The crop water stress index (CWSI) has been shown to be a tool that could be used for non-contact and real-time monitoring of plant water status, which is a key requirement for the precision irrigation management of crops. However, its adoption for irrigation scheduling is limited because of the need to know the baseline temperatures which are required for its calculation. In this study, the canopy temperature of greenhouse cultivated lettuce plants which were maintained as either well-watered or non-transpiring was continuously monitored along with prevailing environmental conditions during a five week period. This data was applied in developing a dynamic model that can be used for predicting the baseline temperatures. Input variables for the dynamic model included air temperature, shortwave irradiance, and air vapour pressure deficit measured at a 10 s interval. During a follow up study, the dynamic model successfully predicted the baseline temperatures producing mean absolute errors (MAE) that varied between 0.17°C and 0.29°C, and root mean squared errors (RMSE) that varied between 0.21°C and 0.35°C when comparing model predictions with measured values. The model predicted baseline temperatures were applied in calculating an empirical CWSI for lettuce plants receiving one of two irrigation treatments. The empirical CWSI consistently differentiated between the irrigation treatments and was significantly correlated with the theoretical CWSI with correlation coefficient (r) values greater than 0.9. The dynamic model presented in this study requires easily measured input parameters for the prediction of the baseline temperatures. This eliminates the need to maintain artificial reference surfaces required in other empirical approaches for the CWSI calculation and also eliminates the need for computing the complex theoretical CWSI.

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“…The few approaches to control crop temperature were designed for and tested with nondynamic (i.e., regular) greenhouse temperature regimes only. The few microclimate models for dynamic climate regimes (Boonen et al, 2000;Van Pee et al, 1998) were developed as black box models that are not generically usable. The aim of this work was therefore to develop and apply a simple generic deterministic microclimate model that can be used for dynamic greenhouse climate control.…”

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confidence: 99%

Microclimate Prediction for Dynamic Greenhouse Climate Control

Körner¹,

Aaslyng²,

Andreassen³

et al. 2007

horts

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Greenhouse energy-saving and biocide reduction can be achieved through dynamic greenhouse climate control with computerized model-based regimes. This can be optimized when next to greenhouse macroclimate (i.e., the aerial environment) also, the crop microclimate is predicted. The aim of this article was to design and apply a simple deterministic microclimate model for dynamic greenhouse climate control concepts. The model calculates crop temperature and latent heat of evaporation in different vertical levels of a dense canopy of potted plants. The model was validated with data attained from experiments on dynamic or nondynamic (regular) controlled greenhouse cultivation. Crop temperature was with a 95% confidence interval of 2 °C or 2.4 °C for sunlit or shaded leaves, respectively, accurately predicted in a simple greenhouse with predefined climate set points. With a more dynamic greenhouse control also including assimilation lighting and screens, the prediction quality decreased but still had a 95% confidence interval of crop temperature prediction of 3.8 °C for sunlit leaves. Simulations showed that controlling greenhouse temperature according to the predicted crop temperature rather than according to the air temperature can save energy. Energy-saving is highest during winter and 12% energy saving was attained during January under Danish climate conditions.

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Modeling Dynamic Behavior of Leaf Temperature at Three-Dimensional Positions to Step Variations in Air Temperature and Light

Cited by 10 publications

References 0 publications

Dynamic modelling of lettuce transpiration for water status monitoring

Dynamic modelling of lettuce transpiration for water status monitoring

Dynamic modelling of the baseline temperatures for computation of the crop water stress index (CWSI) of a greenhouse cultivated lettuce crop

Microclimate Prediction for Dynamic Greenhouse Climate Control

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