5. Conclusion

growth stages). The performance curve in fact reveals how much a greenhouse microclimate parameter deviates from a perfectly controlled response. Deviation of the greenhouse from this ideal line at any α<sup>s</sup> can be used as an index factor of the perfect climate control task. The lesser deviation means the more perfect control task. Adaptability factor of the controller for microclimate parameter M at a preferred αs, denoted by ADPðM, αsÞ, is then defined as the ability of the controller to adapt itself with different preferred references and is calculated using Eq. (7).

Figure 17. Comfort ratio of microclimate parameters (left) and response of the climate controller (right) at 0 ≤ α<sup>s</sup> ≤ 1.

The optimum preferred reference border for parameter M, denoted by αOpt, is defined as the largest possible α<sup>s</sup> value for which the largest Cf tð Þ M, t, α can be achieved. In other words, it is the value of an unknown α<sup>i</sup> for which Cf tð Þ¼ M, t, α<sup>i</sup> β<sup>i</sup> has the minimum distance to Cf tð Þ¼ M, t, 1 1. In that sense, the cost function for this optimization problem is defined as

) on the Cft curve and the point of ideal microclimate (α ¼ 1 and β ¼ 1). The objective is

Figure 18. Demonstration of the algorithm for finding optimum preferred reference border for adjusting the climate controller. Data belongs to VPD response from a random data collection day in a tropical greenhouse experiment.

, which is the Euclidean distant between the unknown point (α<sup>i</sup>

4.3. Optimum reference borders

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð Þ <sup>α</sup><sup>i</sup> � <sup>1</sup> <sup>2</sup> <sup>þ</sup> <sup>β</sup><sup>i</sup> � <sup>1</sup> � �<sup>2</sup> <sup>q</sup>

Di ¼

186 Plant Engineering

and β<sup>i</sup>

An adaptive management framework was designed, developed, and introduced in this research to respond to the needs for an iterative processing tool that acknowledge complexity and uncertainty in microclimate control and management. A systematic approach was presented for automatic data collection and processing with the objective to produce knowledge-based information in achieving optimum microclimate for producing high-quality and high-yield tomato. Applications of computer models were demonstrated through case-study examples for measuring and adjusting optimality degrees, comfort ratios, and prediction of the expected yield. Several applications of the framework toolboxes were demonstrated through case-study examples for evaluating and comparing microclimate parameters as well as yield prediction in different greenhouse environments. Specific applications of the optimization toolbox of the framework were discussed for evaluating and adjusting greenhouse climate controller through manipulated set points. It was shown that using adaptive greenhouse model for tropical climate condition, efficient use of natural ventilation, or shading will cause up to 70% savings on other energy-consuming cooling systems without sacrificing fruit quality or yield. The presented approach can be used in cost-benefit analysis for providing best management decisions such as site selection, optimum growing season, scheduling efficiencies, energy management with different climate control systems, and risk assessments associated with each task. Results of microclimate evaluation and yield prediction that are generated by this framework can be used in other crop models that estimate plant responses to the environment, or contribute to task-planning algorithms for hierarchical decomposition of climate management, and in economic models of tomato for energy conservation and energy efficient greenhouse crop productions. The framework can also be used as a research tool in future studies such as evaluating effects of different greenhouse designs and shapes on comfort ratios of microclimate parameters, or finding optimum combination of ventilation and evaporative cooling systems for best fruit quality and yield.
