A Monte Carlo Approach for estimating the Uncertainty of Predictions with the Tomato Plant Growth Model, Tomgro

Cooman, Alexander; Schrevens, Eddie

doi:10.1016/j.biosystemseng.2006.05.005

Cited by 30 publications

(14 citation statements)

References 8 publications

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“…Although these parameters changed with the greenhouse structure, the type of crops, etc., all these changes had little influence on the final results of the model. Therefore, these parameters can be considered as fixed, and their values are set as recommended values or average values according to some studies [21]. The last remaining value S CO 2 was a fixed value, and its impact on the final results of the model was very small, verifying the idea of ignoring this parameter in the previous section.…”

Section: Parameter Analysis Resultsmentioning

confidence: 93%

“…According to Jones et al (1991), the appearance rate of new nodes was GENR S = GENRAT · F N (T) · F(CO 2 ), in which GENRAT is a constant depending on tomato varieties. However, Cooman et al (2006) [21] stated that GENRAT is a linear function related to the number of stems. Given that the data of nodes in the conditions of specific temperature and humidity cannot be obtained in the actual greenhouse, and relevant studies [22] found that CO 2 inhibition function in TOMGRO had little effect on the appearance rate of the nodes, the integrated model used the calculation equation of the nodes as shown in Equation (4).…”

Section: Integrated Modelmentioning

confidence: 99%

“…The experimental data of the potential growth rate of fruit and leaves at different organ development stages were given in TOMGRO, and the empirical equation at the development stages was used in Vanthoor. They were more precise than the discrete data in TOMGRO, so using a combination of Vanthoor and other related researches [21,23], the potential growth rates of fruit (POF) and leaves (POL) in the integrated model were as shown in Equations (18) and (19):…”

Section: Integrated Modelmentioning

confidence: 99%

“…However, according to related studies [23] and simulations, POF has a great influence on the yield, so it needs to be optimized by default). The parameter range was set according to the average value of the parameters provided by Cooman [21], and it took reference to the +/−10% principle [34][35][36]. In this range, the parameters were evenly distributed [37,38], as shown in Table 1.…”

Section: Sensitivity Analysismentioning

confidence: 99%

See 3 more Smart Citations

An Integrated Yield Prediction Model for Greenhouse Tomato

Lin

Wei

2019

Agronomy

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The commonly used greenhouse crop yield prediction models today have their specific application scenarios, which may not ensure the accuracy of the results if the greenhouse environment changes. This greatly restricts their use in the greenhouse environment. To solve this problem, two widely used tomato growth models were compared in the study: TOMGRO and Vanthoor, and then an integrated model was obtained. Through the extended Fourier amplitude sensitivity test (EFAST), the model parameters were divided into three categories: optimized, fixed and ignored. In addition, Bayesian optimization was used as an optimization algorithm, through which the parameters applicable to the greenhouse can be optimized based on the greenhouse data. Compared with TOMGRO and Vanthoor, the output of the integrated model was more reasonable and universal, and the RMSE in the integrated model was 2.5974 while that in TOMGRO and Vanthoor both were over 17, reflecting the fact that the model output was closer to the actual value. According to the verification results of four-year greenhouse data, the model had high performance in predicting yield.

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Section: Parameter Analysis Resultsmentioning

confidence: 93%

Section: Integrated Modelmentioning

confidence: 99%

Section: Integrated Modelmentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

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An Integrated Yield Prediction Model for Greenhouse Tomato

Lin

Wei

2019

Agronomy

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“…Cuando el numero de parámetros en un modelo incrementa tambien incrementa la incertidumbre de las salidas debido al incremento de incertidumbres de las entradas (parametros). Este procedimiento cobra importancia para determinar el dominio que tiene cada parametro (Cooman and Schrevens 2006). El objetivo de esta investigación fue realizar un analisis de sensibilidad global mediante el metodo de Sobol para el modelo HORTSYST con la finalidad de conocer la dinámica de los indices de sensibilidad de los parametros en cinco etapas de desarrollo del cultivo de jitomate hidropónico en invernadero: a los diez días después de trasplante (10 DDT), durante la etapa vegetativa (25 DDT), inicio de la fructificación (40 DDT), cosecha (80 DDT) y al final (119 DDT).…”

Section: Introductionunclassified

Variación temporal de los índices de sensibilidad de un modelo de cultivo para jitomate en invernadero

Ruiz

Jiménez

Perez

et al. 2020

Ingeniería y Región

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Los sistemas soporte de decisiones (SSD) son herramientas que pueden tener de base un modelo de cultivo y se utilizan para el manejo de ciertos aspectos en un agro-sistema como: programación de riego, control climático, fertilización y rendimientos. HORTSYST es un modelo dinámico de crecimiento, desarrollado para implementarse en un sistema experto para manejar el riego y nutrición de cultivos en sistemas intensivos. Cuenta con 16 parámetros, tres variables de estado: Producción de materia seca (DMP), índice fototérmico (PTI) y nitrógeno absorbido (Nup), además de dos variables de salidas; Transpiración del cultivo (ETc) e índice de Área foliar (LAI). Las variables de entrada son: La radiación solar global (Rg), temperatura del aire (Ta) y humedad relativa (Hr). La parte central del modelo es la estimación de un índice fototérmico, que representa al tiempo de desarrollo del cultivo con el que se simula LAI. La ETc se estima con una ecuación de balance de masa y energía. DMP se simula con el enfoque de la eficiencia del uso de la radiación (RUE). Nup se determina con la curva de dilución del nitrógeno y DMP simulado previamente. El objetivo de este trabajo es realizar un análisis de sensibilidad global mediante el método de Sobol, para conocer la importancia de cada parámetro en las variables de salida, a los diez días después de trasplante (DDT), durante la etapa vegetativa (25 DDT), inicio de la fructificación (40 DDT), cosecha (80 DDT) y al final (119 DDT) del ciclo del cultivo de jitomate (Solanum lycopersicum L.), cultivado en hidropónia en invernadero durante primavera-verano (S-S). Se encontró que los índices de efectos principales y totales, como medida de influencia de los parámetros, no siguen un orden establecido con forme avanza el ciclo de cultivo. Esta variación temporal está en función de la etapa de desarrollo y debe considerarse al realizar un análisis de sensibilidad.

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Nitrogen Removal in an Ecological Ditch Based on an Orthogonal Test

Wang

et al. 2016

Water Air Soil Pollut

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A Monte Carlo Approach for estimating the Uncertainty of Predictions with the Tomato Plant Growth Model, Tomgro

Cited by 30 publications

References 8 publications

An Integrated Yield Prediction Model for Greenhouse Tomato

An Integrated Yield Prediction Model for Greenhouse Tomato

Variación temporal de los índices de sensibilidad de un modelo de cultivo para jitomate en invernadero

Nitrogen Removal in an Ecological Ditch Based on an Orthogonal Test

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