Controlling Greenhouse Light to a Consistent Daily Integral

Albright, L. D.; Both, Arend-Jan; Chiu, A. J.

doi:10.13031/2013.2721

Cited by 105 publications

(56 citation statements)

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“…Gutman et al (1993) solve a 96 hour temperature integral problem with linear programming and present an in-depth analysis of the solution using co-states and Pontryagin's maximum principle. Albright, Both, and Chiu (2000) produce a contracted amount of lettuce heads of a specific weight per unit time by maintaining at constant temperature a daily light integral, using supplementary lighting. Further developments of this idea are reported by Ferentinos, Albright, and Ramani (2000) and by Seginer, Albright, and Ioslovich (2006).…”

Section: Exploiting the Integrating Capacity Of The Crop Temperature mentioning

confidence: 99%

Optimal Greenhouse Cultivation Control: Survey and Perspectives

Straten

Henten

2010

IFAC Proceedings Volumes

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Abstract:A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well.

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Section: Exploiting the Integrating Capacity Of The Crop Temperature mentioning

confidence: 99%

Optimal Greenhouse Cultivation Control: Survey and Perspectives

Straten

Henten

2010

IFAC Proceedings Volumes

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“…Energy savings of up to 30% have been reported, ensuring a quick payback period based on today's fuel prices (Plaisier & Svensson, 2005). Plants grow and pass from its initial transplanting state to its harvest state in 25 days requiring of a constant daily light integral, which can be achieved only through shading or supplemental lighting (Albright et al, 2000). Seginer et al, (2006) revealed that light control signals may use 3-day light integrals rather than a single-day integral.…”

Section: Movable Shade Curtains Controller In Greenhousesmentioning

confidence: 99%

Self Powered Instrumentation Equipment and Machinery Using Solar Panels

Hahn¹

2010

Solar Collectors and Panels, Theory and Applications

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“…The set points for temperature were 24°C during the day (6 a.m. to 10 p.m.) and 19°C during the night. The control zone for relative humidity was from 30% to 70%, and the light integral was controlled to 17 moles m -2 d -1 , with the algorithm presented in Albright et al (2000). The EC was not controlled by the computer, but was adjusted manually to keep the nutrient solution between 1150 and 1250 mS cm -1 by adding reverse-osmosis water and solution stocks every two days.…”

Section: Network Design and Data Setsmentioning

confidence: 99%

PREDICTIVE NEURAL NETWORK MODELING OF pH AND ELECTRICAL CONDUCTIVITY IN DEEPTROUGH HYDROPONICS

Ferentinos¹,

Albright²

2002

Transactions of the ASAE

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ABSTRACT. A model is presented that predicts pH and electrical conductivity (EC) changes in the rootany research reports describe nutrient uptake by plants and the dynamics of growth (see, for example, Barber and Silberbush, 1984;Wild and Breeze, 1981;Marshall and Porter, 1991;Lawlor, 1991). Models of certain dynamic processes (such as average shoot or root concentration, transpiration, nutrient uptake, etc.) exist, but generally not at the whole-plant level and seldom in a form useful for environmental control or fault detection.Neural networks (NN) have been used to model a variety of biological and environmental processes (e.g., Bhat et al., 1990;Seginer et al., 1994;Thompson and Kramer, 1994;Sridhar et al., 1996;Lacroix et al., 1997;Lin and Jang, 1998;Altendorf et al., 1999;Hong et al., 2000), but not in the specific area of plant cultivation. Knowledge of the dynamics of interacting biological systems within hydroponically grown plants is limited. Moreover, hydroponic systems can be monitored with a level of detail that permits one to collect extensive sets of data about the system. Thus, the NN approach shows promise as a means to avoid the need to model internal plant processes and still achieve a prediction capability suitable for control and fault-detection algorithms.Hydroponic systems provide an opportunity to monitor and control the growing process of plants and characterize their interactions with their surrounding microenvironments. Multiple sensors in the root zone make monitoring of nutrient solution characteristics possible. Information about the shoot environment is also available from sensors that monitor environmental conditions inside the greenhouse. This information, in the form of data rather than analytical expressions and dynamical relationships, provides the NN model the ability to optimize its parameters (weights and thresholds) in order to identify and simulate the real process in the best possible way.The objective of this work was to develop and validate a neural network model able to predict pH and EC values in the nutrient solution of hydroponically grown lettuce. Use of the model will provide a first step toward solving the greater problem of using such information to develop fault-detection methods for greenhouse crops. MATERIALS AND METHODSThe growing plant of the modeled system was lettuce (Lactuca sativa cv. Vivaldi), and the growing system was deep-trough hydroponics in a greenhouse. Information about the physical process was collected by measuring the most important parameters that affect the dynamics of nutrient solution pH and EC ( fig. 1). These parameters can be divided into three categories: S System variables. These are the measured parameters of the system: pH, electrical conductivity (EC), and the temperature of the nutrient solution. S Indoor environment disturbances. These are measured environmental parameters that affect the growing plants: air temperature and relative humidity, which are measured above the canopy, and the photosynthetically active radiation (PAR...

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Controlling Greenhouse Light to a Consistent Daily Integral

Cited by 105 publications

References 0 publications

Optimal Greenhouse Cultivation Control: Survey and Perspectives

Optimal Greenhouse Cultivation Control: Survey and Perspectives

Self Powered Instrumentation Equipment and Machinery Using Solar Panels

PREDICTIVE NEURAL NETWORK MODELING OF pH AND ELECTRICAL CONDUCTIVITY IN DEEPTROUGH HYDROPONICS

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Controlling Greenhouse Light to a Consistent Daily Integral

Cited by 105 publications

References 0 publications

Optimal Greenhouse Cultivation Control: Survey and Perspectives

Optimal Greenhouse Cultivation Control: Survey and Perspectives

Self Powered Instrumentation Equipment and Machinery Using Solar Panels

PREDICTIVE NEURAL NETWORK MODELING OF pH AND ELECTRICAL CONDUCTIVITY IN DEEPTROUGH HYDROPONICS

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Product

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PREDICTIVE NEURAL NETWORK MODELING OF pH AND ELECTRICAL CONDUCTIVITY IN DEEPTROUGH HYDROPONICS