This research project aims to tackle challenges related to greenhouse climate control by developing a smart and efficient AI-based control framework for greenhouse climate that minimizes costs while maintaining a suitable greenhouse climate for crop growth. In addition, this research will optimize use of resources such as energy and water which are of critical importance, particularly in the era of climate change, further highlighting the importance of efficient greenhouse climate control.
Sensing, robotics, and artificial intelligence (AI) are the leading edge of a technological revolution in agriculture. Present barriers unnecessarily destroy users’ interest in technology adoption, constraining HTP and DA applications to a narrow slice of what should be a broad, if not all-encompassing stakeholder base. This project proposes to develop a human-centered hyperspectral sensing robot and analysis system that enables not only critical research experiments but also confident adoption of AI technologies for data analysis and decision recommendations, moving from highly controlled to more complex systems.
This research addresses the complex challenges associated with operating such vertical farms in plant factories. The project focuses on automation for energy management and maximization of natural resources using techniques based on artificial intelligence (AI) that help to optimize the microclimate in plant factories. Due to scarcity of environmental data available, reinforcement learning based AI algorithms will be used to develop an optimal microclimate control scheme that focuses on maximizing yield while minimizing energy cost and pesticide usage.
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