Robotic Phenotyping

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

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

Hyper-NUE (EO4AgroClimate): Using hyperspectral imaging to estimate NUE in wheat

This project lays the foundations for next generation remote sensing of crops to determine nitrogen use efficiency (NUE) and associated canopy photosynthetic potential. We will develop a high-throughput novel hyperspectral (HS) analysis technique that directly extracts biological meaningful data from leaves and canopies at different scales, from leaf clip-on sensors, cameras mounted on field robots, and/or drones and satellite earth observation.

Project Lead: Dr Oorbessy Gaju

Co-Investigator: Simon Pearson

Funder: Science and Technologies Facilities Council

UKRI AI Centre for Doctoral Training in Sustainable Understandable agri-food Systems Transformed by Artificial INtelligence (SUSTAIN)

SUSTAIN imagines a system where data-driven AI transforms the production of crops (selective harvesting and weeding through precision agriculture) and raising of animals (livestock monitoring, reducing animal GHG emissions and improving animal welfare); enhances plant and animal breeding (AI informed genomics); stabilises supply chains (mechanism design and agent-based modelling); reduces food waste and loss (supply and demand matching) and enables fairer sharing of economic gains and understanding of environmental impacts (ethical and trustworthy AI). All the underlying methods need to be understandable by people so that decisions are trusted (explainable AI).

Project Lead: Professor Simon Parsons

Co-Director: Professor Elizabeth Sklar

Co-Investigator: Louise Manning

Funder: Engineering and Physical Sciences Research Council

Agri-Robotics Unleashed (ARU)

Agri-Robotics Unleashed (ARU) is a whole systems approach that drives soft fruit sustainable productivity by integrating robotic harvesting solutions into greenhouse design and novel canopy architectures.

Project Lead: Dr Marcello Calisti

Co-Investigators: Grzegorz Cielniak, Leonidas Rempelos, Simon Pearson

Funder: Biotechnology and Biological Sciences Research Council

High-throughput robotic phenotyping of fruit traits for automatic strawberry harvesting

The study will investigate techniques based on plant/fruit geometry (i.e. 3D) providing traits about the phenology of the variety and external fruit and plant characteristics. The approach will overcome the limitations of the laboratory-based phenotyping systems by exploiting an autonomous mobile robot to enable rapid identification of multiple traits in the field.

Project Lead: Professor Grzegorz Cielniak

Funder: Biotechnology and Biological Sciences Research Council

Qualicrop

The project will develop and introduce farm-gate, just in time automated crop grading for delicate produce (e.g. strawberries and grapes), adding value for tight-margin growers and ultimately disintermediating an inefficient supply chain, lowering costs to consumers.

Project Lead: Professor Grzegorz Cielniak

Co-Investigator: Simon Pearson

Funder: Innovate UK