Digitized Autonomous Greenhouse Farms

Our project aims to revolutionize greenhouse farming by leveraging advanced technologies used in modern electronic chip manufacturing. We will develop a sophisticated digital twin of a greenhouse, focusing initially on one selected plant type such as pepper or tomato. This digital twin will serve as a dynamic virtual model of the greenhouse environment, accurately simulating plant growth under various conditions and optimizing processes in real time.

**Primary Goal**: We aim to gather real-time data on environmental conditions, plant growth, and operational workflows by partnering with a local greenhouse farm. This foundational dataset will monitor variables such as temperature, humidity and soil moisture. Using this data, we will construct a digital twin of the greenhouse, modelling the growth and development of the selected plant type. The digital twin will be validated through iterative testing and calibration against real-world data, ensuring accuracy and reliability.

**Secondary Goal**: We are aiming to demonstrate a pilot of robot autonomous navigation within the greenhouse. We will achieve this by purchasing an off-the-shelf robot and integrating it within the digital twin and training its operation. The training from the digital twin will be applied to the physical robot equipped for real-time processing and decision-making. Pilot tests will validate robot navigation, task execution, and overall integration with the digital twin, collecting performance data and feedback for further development.

**Technological Integration**: We will utilize the latest computational tools for high-performance AI training and inferencing, allowing us to develop and deploy advanced machine learning models that can predict and optimize plant growth and health. This combination of technologies will enhance operational efficiency and significantly reduce resource use and environmental impact.

**Addressing Critical Challenges**: Our approach addresses labour shortages, resource inefficiencies, and the need for sustainable farming practices. For instance, smart farming in Chile has cut water use by 70%, and AI-driven sprayers in California can reduce herbicide use by up to 96%. By automating labour-intensive tasks and optimizing resource use, our project aims to significantly reduce operational costs and environmental impact while enhancing crop yield and quality across farms in the UK and overseas.

**Impact and Vision**: The successful implementation of this project will demonstrate the feasibility and benefits of integrating digital twin technology, AI, and robotics in greenhouse farming. Our vision is to create a scalable and sustainable farming system adaptable to various crops and environments, contributing to global food security and sustainable agriculture practices.