CO2 enrichment driven model predictive control: a novel approach for precision agriculture in greenhouses located in hyper-arid regions

Abstract

One of the most pressing challenges of the 21st century is feeding nine billion people by 2050 and intensifying food production in a sustainable way to achieve food security. A common consensus is that more food needs to be produced with increasing limitations on natural resources. The dependency of the food sector on water resources is one of the most critical aspects of food production, especially in hot, arid and water scarce regions. The additional challenge of climate change induces more vulnerability in these systems. Precision agriculture has emerged as a promising solution for sustainable food production as it can efficiently manage system inputs by using targeted site-specific information. This work aims at contributing to food security and sustainable crop production in hot and arid regions by the implementation of sustainable agricultural practices along with precision agriculture. The implementation of sustainable agricultural systems and practices including greenhouses, hydroponics, and CO2 enrichment that aim at providing favorable growing conditions, protecting crops from external and negative weather conditions, enhancing yields and reducing resources consumption are considered in this study. Specifically, this research aims at investigating the effects of CO2 enrichment on plant responses, mainly transpiration rates, in agricultural greenhouses located in hot and arid climates facing high solar radiations such as the case of Qatar. Additionally, a hyperspectral-physiological data-driven predictive model for transpiration rates in greenhouses with CO2 enrichment is developed with a high temporal and spatial coverage. The transpiration model is then used in the prescriptive mode of optimising and controlling water irrigation scheduling in the greenhouse using model predictive control (MPC). This work also entails an economic performance study of CO2 enriched greenhouses with the integration of precision irrigation management under model predictive control. Consequently, this work will help farmers adopt precision irrigation practices in CO2 enriched greenhouses located in arid climates and will aid in the efficient utilisation and deployment of the aforementioned sustainable agricultural approaches in Qatar as well as regions with a similar climate.

Date of Award	2023
Original language	American English
Awarding Institution	HBKU College of Science and Engineering