2. Adaptive greenhouse model

The key to an adaptive greenhouse is the computer model that drives specific implementations of other components. An adaptive management framework for microclimate evaluation and control in greenhouse production systems is proposed in Figure 2. A diagram of the steps in the analysis process is shown by rectangles. The arrows are the direction of the process, and the central spiral highlights the goal of arriving at a compromising decision based on a shared set of objectives developed through the iterative process. The three essential elements in this structure are (i) data entry and retrieval, (ii) computer model (expressed by mathematical equations), and (iii) data

Figure 2. Diagram of the adaptive management framework for monitoring, data processing, evaluating, and adjusting greenhouse microclimate with an iterative analysis approach for scenario analysis with greenhouse crop production.

analysis components. The data entry component may be implemented by direct interfacing with real-time data acquisition system or by using web-based and desktop application software. Computer model is application specific; it can be updated and is usually condensed and produced from previous extensive research works in crop physiology. Data analysis comprises implementing relevant techniques within the retrieval component (i.e., programmable spread sheets) or by integrating with third-party applications (i.e., Simulink blocks). The proposed framework can be adapted to new research projects for working with different culture classes and objects by which many specific scenarios may be modeled and analyzed. It carefully monitors the possible outcomes of the system for better understanding of the process in order to adjust control parameters through an iterative learning process.

It describes design and concepts of an adaptive management framework for evaluating optimality degrees and comfort ratios of air temperature (T), relative humidity (RH), and vapor pressure deficit (VPD), as well as prediction of the expected yield in greenhouse cultivation of tomato. The presented framework was designed to allow production managers to ask "what-if" type of questions for further quantitative inclusion to avoid possible detriment decisions. It also provides an in-depth rigorous analysis tool for decision making or decision procrastination when facing uncertainties. It can assist in enhancing scheduling efficiency and guiding investments through different simulated scenarios that are based on information analysis to support optimal restoration strategies. In the rest of this chapter, we refer to the term "microclimate parameters T, RH, or VPD" by M. We also use Optð Þ¼ M α, and Cf tð Þ¼ M, t, α β, to refer to the terms "optimality degree" and "comfort ratio," respectively, defined in Sections 3.1 and 3.2.

The key to an adaptive greenhouse is the computer model that drives specific implementations of other components. An adaptive management framework for microclimate evaluation and control in greenhouse production systems is proposed in Figure 2. A diagram of the steps in the analysis process is shown by rectangles. The arrows are the direction of the process, and the central spiral highlights the goal of arriving at a compromising decision based on a shared set of objectives developed through the iterative process. The three essential elements in this structure are (i) data entry and retrieval, (ii) computer model (expressed by mathematical equations), and (iii) data

Figure 2. Diagram of the adaptive management framework for monitoring, data processing, evaluating, and adjusting greenhouse microclimate with an iterative analysis approach for scenario analysis with greenhouse crop production.

2. Adaptive greenhouse model

170 Plant Engineering

The framework utilizes a custom-designed data acquisition, and control system [17] that was built using Arduino Uno prototype microcontroller board for monitoring and manipulating of the microclimate parameters. Three computer models were employed by the framework for evaluation and adjusting of optimality degrees Optð Þ M , comfort ratio Cf tð Þ M, t, α , and prediction of the expected yield. The framework was implemented in MATLAB® (The MathWorks Inc, Natick, MA, USA) environment through Simulink blocks and coding of various main functions and sub-functions that were stored as "m-files." Different toolboxes were developed for the immense data-analyzing tasks as shown in Figure 3. The framework structure was designed in a way that end users can create (or update) entries in database, select report type (1-day or multidays report), and proceed with a specific analysis procedure. The database is a dynamic flat file type that can be created by entering collected data, either manually from previously stored sources such as excel sheets or directly from the hardware interface. The computer models presented in this chapter are focused on tomato (Lycopersicon

Figure 3. Arrangement of the process in the framework toolboxes.

esculentum); however, with slight modification, the framework can be reprogrammed to work with other greenhouse crops provided that their yield prediction and growth response models are available. Results of microclimate evaluation and set-point manipulation discussed in Sections 3 and 4 can contribute to dynamic greenhouse climate control strategies [18] such as the one in Ref. [19]. An example is provided by comparing a model reference-adaptive greenhouse microclimate controller with conventional closed-loop feedback shown in Figure 4. In this scheme, the control law is adapted with the new greenhouse states based on the optimized set points as shown in the diagram of Figure 5 [19] for a specific microclimate

Figure 4. Demonstration of conventional greenhouse controller (left) versus model reference adaptive controller (right).

Figure 5. Adaptive control of greenhouse air temperature based on manipulated set point as discussed in Ref. [19].

parameter (i.e., air temperature), causing other microclimate parameter (i.e., humidity) to be actually controlled via set-point manipulation.
