Selective Harvesting

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Our Research

Robotic applications for crop harvesting. Developing systems which identify harvest ready crops to enable reliable and consistent picking/harvesting. This uses a mix of practical engineering solutions, robotics, vision and sensing systems, data collection sensors, and computer science, to design, develop, and deliver effective and consistent data and technology driven solutions.

Research Projects

The Augmented Agronomist - Synthesis of AI, ML and Robotics for Decision Support (UK Research and Innovation - Biotechnology and Biological Sciences Research Council – Career Transition Partnership Studentship)

This project sets out to provide agronomists with dedicated technological support in assessment and decision making. The project is closely linked with the RASberry project will have access to its software and hardware resources to minimise risks and maximise synergies.

Project Lead: Professor Marc Hanheide

Funder: National Institute for Agricultural Botany – East Malling Research

 

Optimising the light recipe for maximum photosynthesis yield and quality in strawberry (UK Research and Innovation - Biotechnology and Biological Sciences Research Council – Career Transition Partnership Studentship)

UK Research and Innovation - Biotechnology and Biological Sciences Research Council – Career Transition Partnership funding for a studentships on "Optimising the light recipe for maximum photosynthesis, yield and quality in strawberry".

Project Lead: Professor Simon Pearson

Funder: National Institute for Agricultural Botany – East Malling Research

 

Application of novel machine learning techniques and high-speed 3D vision algorithms for real time detection of fruit (UK Research and Innovation - Biotechnology and Biological Sciences Research Council – Career Transition Partnership Studentship)

UK Research and Innovation - Biotechnology and Biological Sciences Research Council – Career Transition Partnership funding for a studentships on "Optimising the light recipe for maximum photosynthesis, yield and quality in strawberry".

Project Lead: Professor Simon Pearson

Funder: National Institute for Agricultural Botany – East Malling Research

 

Feasibility study to develop a blueberry harvester

Development of a prototype blueberry picker.  This is a feasibility study that will enable the development of an automatic blueberry harvesting system, that uses high speed pneumatics controlled by image analysis to pick the fruit.

Project Lead: Ronald Bickerton

Funder: UK Research and Innovation – Innovate UK

 

GRASP berry - High speed picking soft fruit robots

This project aims to increase picking speed for soft fruit and to develop new ways to detect and pick occluded fruit. We aim to test the use of active manipulation to detect and pick occluded fruit, plus a novel high-speed robotic picking approach.

Project Lead: Dr Grzegorz Cielniak

Funder: UK Research and Innovation – Innovate UK

 

The Digital Sandwich - Digitised Food Supply Chain, fusing IoT, Blockchain and AI data layers

By fusing multiple industrial digital technologies (IDT's), we will develop a national, open demonstrator of a digital supply chain, using sandwich manufacturing as the use case. This demonstrator will step-change manufacturing productivity.

Project Lead: Professor Simon Pearson

Funder: UK Research and Innovation – Innovate UK

 

Building a better blueberry harvester (Award acceptance)

This project's objective is to develop and demonstrate a fully automatic blueberry harvesting machine, which removes berries from the bush by the use of innovative shaking systems and can fit inside the small greenhouses and polythene tunnels used by all UK and many EU blueberry producers.

Project Lead: Ronald Bickerton

Funder: UK Research and Innovation – Innovate UK

 

The First Fleet - The world's first fleet of multi modal soft fruit robots

This project addresses requirements for fleet robotics for soft fruit production in agriculture and horticulture.

Project Lead: Professor Marc Hanheide

Funder: UK Research and Innovation – Innovate UK

 

Robot Highways

This project aims to develop a knowledge exchange platform to empower transformation across UK and global supply chains. Our vision for future soft fruit farming encompasses fleets of electric robotic and autonomous systems powered by renewable energy that pick, transport, pack fruit whilst gathering data to maximise yield, reduce waste and environmental impacts.

Project Lead: Professor Marc Hanheide

Funder: UK Research and Innovation – Innovate UK

 

FRUITCAST

This project addresses the forecasting of readiness of commercially grown strawberries. Our technology led system integrates farm data on a deeply granular level, aiming to decrease the uncertainty of forecasts by monitoring the crop responses in real-time.

Project Lead: Professor Simon Pearson

Funder: UK Research and Innovation – Research England Connecting Capability Fund - CERES

 

In field logistics for fruit production

University of Lincoln and industrial partners have recently completed an IUK project using the Thorvald robotic platform as an autonomous vehicle to support picker logistics. The robot will now be framed to enable full scale and rapid deployment.

Project Lead: Professor Marc Hanheide

Funder: Berry Gardens Ltd

 

VEGCAST - Forecasting harvest profiles for broccoli

VEGCAST will develop state of the art crop forecasting technology that combines camera based machine learning technology with multi-ensemble meteorological predictions. The output will be real time forecasts of broccoli yield and its uncertainty.

Project Lead: Professor Simon Pearson

Funder: UK Research and Innovation – Research England Connecting Capability Fund - CERES

 

FASTPICK - Novel active vision and picking head to robotically harvest soft fruit

This project's focus is resolution of residual barriers to the robotic harvesting of soft fruit; picking fruit in complex, occluded and biologically variable clusters. FASTPICK will develop active vision systems including 3-D reconstruction of the scene and integrate it into a novel robotic picking head that aims to pick fruits in dense clusters. 

Project Lead: Dr Amir Ghalamzan E.
 
Funding: Innovate UK
 
Universal Robotic Fruit Picking Head "RoboFruit"
 
Selective Harvesting of soft fruits in dense clusters is a challenging technical and scientific problem. The existing technologies can only deal with simple picking scenarios. Robofruit aims at developing a novel picking head that enables dealing with many more picking scenarios. 
 
Project Lead: Dr Amir Ghalamzan E.
 
Funding: CERES Agri Tech