Our Research
Robotic Phenotyping is the process of identifying crop growth (e.g. size and shape of the plant) and understanding the complex physiological and genetic traits of crops using robotic technologies. Using robotic sensing, measurement, and analysis techniques, we can identify how a plant is performing against its predicted growth plan. By using robots to consistently and repeatably observe and measure crop growth, we are able to help inform plant breeders and growers about the performance of a type of plant or group of plants.
Research Projects
BerryPredictor - improving harvest forecasts, yield predictions and crop productivity by optimising zonal phyto-climates in covered strawberry production
This project aims to develop new soft fruit growing strategies from an improved understanding of how to optimise individual plant performance. BerryPredictor will also provide UK growers access to real-time accurate yield prediction profiles.
Project Lead: Professor Simon Pearson
Funder: UK Research and Innovation – Innovate UK
Data CAMPP (Innovative Training in Data Capture, Analysis and Management for Plant Phenotyping)
AI is revolutionising agriculture and agronomy. We will train people to develop and use, these tools. We aim to create an online learning environment and suite of course units targeting bioscientists covering topics from development and placement of robotics in the field, through to management of phenotyping image sets, and experimental design for machine learning systems.
Project Lead: Professor Elizabeth Sklar
Funder: UK Research and Innovation