Stomatal Conductance Patterns and Advanced Automatic Control

Abstract

Humanitarian, environmental, and political concerns have contributed to the evolution of agricultural technology, also known as AgTech. Researchers from various scientific backgrounds are diving into AgTech to ensure the world's food security, create a sustainable future for agriculture, and pave the way for autonomous cultivation methods. This thesis project attempts to contribute to the aforementioned subjects from the control engineering perspective. The thesis objective is the design and testing of a novel predictive climate controller, for tomato greenhouses agriculture, able to conclude on the optimal yield-energy consumption ratio with limited intervention by the human factor. The main novelty introduced by this algorithm is the use of crop variables in the decision-making process according to the Speaking Plant Approach (SPA). However, no straightforward recipe indicates which crop signals could be used. In the context of this study, it is explored how crop variables, measurable by thermal imaging, can be used for the formulation of a SPA-based objective function. Specifically, the research focuses on stomatal conductance, the canopy, and the mean canopy temperature, for the SPA-based objective function formulation. The cost function generation entails the definition of the necessary state constraints. Except for the objective function definitions and the determination of constraints, a predictive controller requires a system representation that acts as a predictor. Nevertheless, the complex and non-linear nature of the climate-crop system complicates the system identification process. Concurrently, data science is blooming and new data-driven system representation techniques are breaching. Data-Enabled Predictive Control is a novel control policy based on systems behavioral theory which uses a non-parametric system representation enabling the omission of the system identification process. This approach has not been tested for the description of highly complex climate-crop systems. Therefore, another target of this project is to examine the capabilities of this data-driven predictor for the representation of the climate-crop model and evaluate if and how can be used as a predictor in a climate control regime.