Machine intelligence and vision based autonomous controls.
Papers and Presentations
- De Silva, O., Heiwolt, K., Rogers, H., Wang, H., and Wang, N. (2021). RAS Counter: Non-invasive yield prediction for vineyards, AgriFoRwArdS CDT Annual Conference 2021, online.
I would like to apply my knowledge and expertise to agricultural robotics within the topics of machine intelligence and vision based autonomous controls.
Multi-robot crop monitor
Seeing Spectral Signatures
There are many application in agri-food production where it would be very useful to measure the spectrum of light reflected from a scene rather than measuring a camera RGB. Indeed spectral signatures are useful for identifying plant species, monitoring growth and to determine the presence of disease, among other tasks. Unfortunately, spectral measurement devices are expensive, cumbersome to use, and often unsuitable for deployment in the field. In contrast conventional RGB cameras are cheap and reliable and are easy to use outside the lab. The core aim of this project is to develop algorithms for mapping the RGB signal recorded by a conventional camera to corresponding spectral signatures that are useful for the diagnosis of plant diseases. The research will be based on calibration, measurement and inference. That is, we will understand the physical characteristics of our device, the spectra in the world we would like to estimate and then we will develop algorithms for mapping the RGB camera measurements to spectra. Our work will also consider mapping RGB+Near Infra Read camera systems and develop algorithms for predicting spectral signature that extend to the NIR part of the spectrum.