IntelliGrow: a greenhouse component-based climate control system

The so-called closed greenhouse (closed ventilation windows) is a recent innovation in Dutch greenhouse industry. The technical concept consists of a heat pump, underground (aquifer) seasonal energy storage as well as daytime storage, air treatment units with heat exchangers, and air distribution ducts. Savings of up to 30% in fossil fuel and production increases by up to 20%, mainly because of the continuously high CO 2 concentration, have been reported. Economic feasibility of this innovative greenhouse highly depends on the yield increase that can be obtained. In this simulation study the effects of greenhouse climate on tomato yield in a closed greenhouse are presented. The explanatory model INTKAM was used, which has several submodels e.g. for light interception, leaf photosynthesis and biomass partitioning. The closed greenhouse offers possibilities for combinations of light, temperature, air humidity and CO 2 concentration that are impossible in a conventional greenhouse. At high CO 2 concentration and high light intensity, leaf photosynthesis shows a more narrow optimum for temperature than at high CO 2 and moderate light intensity. However, the response of crop photosynthesis to temperature has a much broader optimum than that of leaf photosynthesis. Besides photosynthesis, temperature also influences aspects like partitioning, leaf area development and fruit development. Yield potential reduces at temperatures above 26 o C, with fruit set being one of the first processes that is negatively influenced by supra-optimal temperatures. Based on actual climatic conditions in a conventional and a closed greenhouse (same crop management) measured during two years, INTKAM predicts an increase in yield by about 17%. Hence, in a closed greenhouse a higher stem density can be maintained for obtaining the same average fruit weight (size) as in a conventional greenhouse. In 2005 actual yield increase was similar to the simulated one (16%), but in 2004 only a 9% higher yield was realized, at least partly because of botrytis infection in the closed greenhouse.

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“…Therefore, optimizing greenhouse climate based on leaf photosynthetic response (e.g. Aaslyng et al, 2003) may lead to suboptimal conditions.…”

Section: Interaction Between Temperature and Comentioning

confidence: 99%

Climate and Yield in a Closed Greenhouse

Heuvelink¹,

Bakker²,

Marcelis³

et al. 2008

Acta Hortic.

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“…Various object-and component-oriented solutions have approached the issue of agricultural and environmental modeling, such as maize irrigation scheduling (Bergez et al 2001), multiple spatial scales ecosystems (Woodbury et al 2002), greenhouse control systems (Aaslyng et al 2003), weather modeling , households, landscape, and livestock integrated systems (Matthews 2006). In the same context of the agricultural and environmental modeling community, alternative frameworks have been made available to support modular model development through provision of libraries of core environmental modeling modules, as well as reusable tools for data manipulation, analysis, and visualization (Argent et al 2006).…”

Section: Modular Simulationmentioning

confidence: 99%

Deliberative processes for comprehensive evaluation of agroecological models. A review

Bellocchi

Rivington

Matthews

et al. 2014

Agron. Sustain. Dev.

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The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simulation experimentation to explore options and consequences, for example, future resource use efficiency (i.e., management in sustainable intensification), impacts of and adaptation to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the involvement of stakeholders to expand horizons beyond conventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided techniques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-structured, numerically based evaluation, but also on less tangible factors that may need to be addressed using complementary deliberative processes, (iii) comprehensive evaluation of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the deliberative process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs.

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“…The IntelliGrow climate control system introduced in [1] provides a control strategy for efficiently reducing the amount of energy required for heating. The system uses a mathematical model of the plants' photosynthesis process to optimize the temperature and CO 2 levels according to the actual light level in the greenhouse.…”

Section: Requirements In Greenhouse Climate Controlmentioning

confidence: 99%

“…To make this production ecologically and economically sustainable there is a critical need for energy-efficient climate control strategies that do not compromise product quality. Due to its urgency this issue has attracted the attention of an increasing number of researchers during the past decade and several research projects have produced promising control strategies [1,2,3,4,5]. Contrary to all expectations the industrial adoption pace of those control strategies has been very slow.…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Extensible Climate Control System for Sustainable Greenhouse Production

Sørensen

Jôrgensen

Klein

et al. 2011

Lecture Notes in Computer Science

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Abstract. The slow adoption pace of new control strategies for sustainable greenhouse climate control by industrial growers, is mainly due to the complexity of identifying and resolving potentially conflicting climate control requirements. In this paper, we present a multi-agent-based climate control system that allows new control strategies to be adopted without any need to identify or resolve conflicts beforehand. This is achieved by representing the climate control requirements as separate agents. Identifying and solving conflicts then becomes a negotiation problem among agents sharing the same controlled environment. Negotiation is done using a novel multi-objective negotiation protocol that uses a generic algorithm to find an optimized solution within the search space. The multi-agent-based control system has been empirically evaluated in an ornamental floriculture research facility in Denmark. The evaluation showed that it is realistic to implement the climate control requirements as individual agents, thereby opening greenhouse climate control systems for integration of independently produced control strategies.

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IntelliGrow: a greenhouse component-based climate control system

Cited by 67 publications

References 9 publications

Climate and Yield in a Closed Greenhouse

Climate and Yield in a Closed Greenhouse

Deliberative processes for comprehensive evaluation of agroecological models. A review

An Agent-Based Extensible Climate Control System for Sustainable Greenhouse Production

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