The lack of available data and data collection systems to build models for effective decision making in agronomy raises the complexity of optimising agricultural processes.
This means that much value gets lost and the environment is harmed unnecessary, just because agricultural workers lack software that visualises the health and environmental status of crops.
This could be met by management training systems that educate on documenting and optimising their farming practices, or through collaborative systems that assist agronomists with analysing the status quo, synthesising optimisation opportunities, and reporting crucial crop health information.
Many of the crucial information from sensory systems can provide crucial plant and soil data such as root temperature, moisture, and nutrients.
These systems are already deployed in our research facility and you can make a difference by implementing visualisations of these datasets.
The information of the data can then be fused to predict yield, diseases, and pests based on the plant, soil and environmental conditions.
You could get access to high-performance computers to explore how artificial intelligence models could help farmers in the field or use premade models from other researchers and focus on human-friendly visualisations.
For this project, it would be beneficial if you are already familiar with programming (especially development with Unity, TensorFlow or PyTorch).
However, feel free to get back to us if you are very eager to contribute to this project.
Your supervisor would be Paul-David Zuercher, who is researching with the University of Cambridge’s Cyber-Human Lab on immersive technologies (such as virtual reality and augmented reality).
He has many years of experience in programming, human-computer interaction and immersive technologies and is looking forward to meeting you to discuss details.
Feel free to reach out to us or contact Paul directly via e-mail.