Neural Networks-Based Models for Greenhouse Climate Control

[Abstract] Greenhouses are multivariable and nonlinear systems with high degree of complexity, so it is hard to build models that represent the whole dynamics of the system. This paper presents models of greenhouse climate based on neural networks. The models predict inside air temperature and relative humidity in the greenhouse as a function of the variables used as input for the network, as outside temperature, relative humidity, solar radiation, etc., and the actuators state signals, as window opening and others. Data sets used for modelling have been measured with real red pepper plants inside the greenhouse. The developed models are described and the achieved results are reported.[Resumen] Los invernaderos son sistemas multivariables y no lineales con un alto grado de complejidad, por lo que es difícil construir modelos que representen toda la dinámica del sistema. Este artículo presenta modelos de clima de invernadero basados en redes neuronales. Los modelos predicen la temperatura del aire interior y la humedad relativa en el invernadero en función de las variables utilizadas como entrada para la red, como la temperatura exterior, la humedad relativa, la radiación solar, etc., y las señales de estado de los actuadores, como la apertura de la ventana y otros. Los conjuntos de datos utilizados para el modelado se han medido con plantas reales de pimiento rojo dentro del invernadero. Se describen los modelos desarrollados y se reportan los resultados obtenidosJunta de Extremadura; GR1815

Comparación de modelos para estimar la presión real de vapor de agua

La presión real de vapor de agua es una variable básica para estimar la evapotranspiración de los cultivos, uno de los componentes del ciclo hidrológico;sin embargo es difícil y cara de medir de forma directa, por lo que se recurre enla práctica a estimaciones basadas en la temperatura y relaciones sicrométricas. Elobjetivo del presente trabajo fue realizar una comparación de diferentes métodosconvencionales para el cálculo de la presión real de vapor y compararlos con lasestimaciones realizadas con dos tipos de redes neuronales artificiales: feedforwardbackpropagation y radial basis function. Se usaron datos meteorológicos de cuatroestaciones del Distrito 075, localizadas en el Valle del Fuerte, al norte del estadode Sinaloa, México. Los resultados indican que la red neuronal artificial tipo radialbasis function (escenario E4) mostró ser el mejor método en la estimación de lapresión actual de vapor de agua

Modeling and optimization of environment in agricultural greenhouses for improving cleaner and sustainable crop production

Resource-use efficiency and crop yield are significant factors in the management of agricultural greenhouse. Appropriate modeling methods effectively improve the control performance and efficiency of the greenhouse system and are conducive to the design of water and energy-saving strategies. Meanwhile, the extreme environment could be forecasted in advance, which reduces pests and diseases as well as provides high-quality food. Accordingly, the interest of the scientific community in greenhouse modeling and optimizing has grown considerably. The objective of this work is to provide guidance and insight into the topic by reviewing 73 representative articles and to further support cleaner and sustainable crop production. Compared to the existing literature review, this work details the approaches to improve the greenhouse model in the aspects of parameter identification, structure and process optimization, and multi-model integration to better model complex greenhouse system. Furthermore, a statistical study has been carried out to summarize popular technology and future trends. It was found that dynamic and neural network techniques are most commonly used to establish the greenhouse model and the heuristic algorithm is popular to improve the accuracy and generalization ability of the model. Notably, deep learning, the combination of “knowledge” and “data”, and coupling between the greenhouse system elements have been considered as future valuable development