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The increasing demand for efficiently growing vegetation in greenhouses requires continual improvement of the control of the growth environment experienced by the plants. The single most important factor in maximizing the crop's growth is the quantity, quality, and geometrical distribution of radiation intercepted at every moment. Few available greenhouse coverings are capable of responding to changes in sunlight conditions by themselves, limiting the grower's control: they must use additional technologies, including screens, artificial lighting, heating, and cooling. This review considers existing efforts in providing adaptable greenhouse covering systems and advanced optical materials for controlling the color, intensity, and/or distribution of sunlight transmitted into greenhouse‐like structures by describing existing static materials and their responsive equivalents. This work also offers speculation on potential applications of other light‐control elements, mostly designed for use in the urban environment that can be adapted for greenhouse use in the future. [ABSTRACT FROM AUTHOR]

Greenhouses shelter the crop from unfavourable environmental conditions and the covering largely contributes to creating beneficial growing conditions inside. There is no perfect greenhouse cover for all combinations of crop and climatic regions. Usually a greenhouse cover has permanent optical properties determining the amount of solar radiation entering the greenhouse. Consequently during crop growth, the amount and quality (spectrum, direct/diffuse ratio) of the solar radiation is not ideal for the crop. Growers try to compensate for this by using different additional techniques such as temporary coatings, screens (mobile or fixed, etc.) and heating or cooling. New materials are currently being developed, whose optical properties can be (almost) instantaneously changed (materials with switchable properties). This will allow growers to gain real-time control on the quantity and quality of the light entering the greenhouse to match crop requirement. The present study uses advanced simulation models to predict the potential of covers with switchable properties to improve tomato yield and use of resources in different climatic regions (mild winter and tropical) and with different greenhouse types (artisan and industrial type). Results indicate that covers with switchable properties have advantages over permanent properties for most combinations of filter type/location. Only in very extreme tropical climates will covering materials with permanent filter properties have advantages. Furthermore, simulations models can play a major role in optimising the switchable filter design. • No single type of greenhouse cover has optimum performance in every climate & location. • Switching radiometric properties looks promising for high light demand crops. • Switchable materials have no advantage over permanent filters in tropical climates. • Simulation could be a powerful tool to determine optimum radiometric properties. • Simulation should include specific combination of crop, greenhouse location & type. [ABSTRACT FROM AUTHOR]

Crop modelling is an essential part of biosystems engineering; selecting or developing a crop model for a specific application, having its requirements and desires, is difficult if not impossible without the required domain knowledge. This paper presents a fundamentally different model selection approach based on biological functionalities. This is enabled by a common structure, which allows for a combining of components, yielding new models. This increased design space allows the development of models which are better suited to the application than the original models. The use of a common structure, and its potential, are demonstrated by a use-case involving the selection of a tomato crop model for a model-based control application, but the rationales and methodologies can apply to other crops and applications as well. In this paper, 27 valid model combinations have been created from 4 models. In the use-case presented, the models are compared to data originating from a real system. The predictive performance of a model is quantified by the Root-Mean-Squared-Error (RSME) between the predictions and data. One trade-off is model accuracy versus computational speed. With the model set used, a 13% decrease in RSME was obtained by allowing a 7.5% increase in model computation time compared to one of the original models. • A fundamentally different model selection approach using biological functionalities. • 27 valid model combinations have been created from 4 models. • A model combination has been found which predicts better than the original models. [ABSTRACT FROM AUTHOR]