Surveying the Agricultural Landscape using a hyperspectral camera

Scientific Background

In the context of the AgriFoRwArdS CDT (students are funded in the area of Agri-food robotics), computer vision technology plays a crucial role in a multitude of sectors, from agriculture to coastal monitoring and often relies on images captured from RGB cameras. Hyperspectral cameras, however, capture much richer information often not visible to the eye including both spatial and spectral information.

The additional information will enhance substrate identification, object detection, object tracing, etc in variable lighting and will also possibly help when images are adversely affected by shadows and glare. The application of hyperspectral cameras in a new environment such as coastal surveying presents challenges but also potential high rewards. Our project, in collaboration with Living Optics who develop such cameras and the Centre for Environment, Fisheries and Aquaculture Science (CEFAS), who will provide the case study in terms of coastal surveying tasks will help us understand how this new technology can be applied in the real world.

This timely research will provide a pioneering step towards increased accuracy and efficiency in sectors reliant on coastal surveying. Research Methodology

The project will follow a comprehensive, hands-on research approach. The student involved will undergo training in hyperspectral camera use and in the data they generate. They will work closely with CEFAS to produce an annotated image set for a number of their coastal surveying challenges.

The research will take place both at the lab and in the field, offering a diverse and enriching experience. The developed hyperspectral systems will be benchmarked against the existing RGB workflow used by Cefas. Training

In addition to gaining experience in cutting-edge computer vision research, the student will develop a robust set of skills in several key areas. They will receive training in the fundamentals of hyperspectral cameras and get hands-on experience with field deployment of those. Through the development and implementation of algorithms, they will hone their programming and data analysis skills.

They will also be exposed to interdisciplinary collaboration, working closely with professionals from agritech, geophysics, ecology, and computational science sectors. Their scientific research skills will be enhanced through the production of academic papers and contribution to public domain source code. This project offers a unique springboard for a future inter-disciplinary career in the AgriFoRwArdS area, providing a rich blend of theoretical knowledge and practical, industry-relevant experience.

Join us as we redefine the frontiers of computer vision technology, paving the way for more accurate, efficient, and reliable environmental surveying solutions. Your participation could help shape the future of agricultural and coastal monitoring systems, making a lasting impact on these crucial sectors.