Optical Projection Tomography for Plant Imaging
Lead Research Organisation:
University of Sheffield
Department Name: Chemistry
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
| Description | We demonstrate that OCT can be readily used to detect infections before any visible signs have appeared. |
| Exploitation Route | Others can benefit from our proof-of-concept and develop portable OCT fit for purpose. |
| Sectors | Agriculture Food and Drink |
| Description | This project has been used for outreach purposes |
| First Year Of Impact | 2023 |
| Sector | Education |
| Impact Types | Cultural Societal |
| Title | Machine Learning model of OCT image analysis for detection of infection |
| Description | The ML software segments the air-space within the mesophyll and extract average thickness of the gap. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2025 |
| Provided To Others? | No |
| Impact | The model proves that infection can be detected, automatically, in wheat plants before it reaches a symptomatic stage. |
| Description | OCT-plant project |
| Organisation | University of Sheffield |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The grant enabled a collaboration between Chemistry, the School of Bioscience, and Electrical Engineering, to supervise one international student. |
| Collaborator Contribution | Prof Rolfe, from Bioscience provided the knowhow of plant growth, inoculation and data interpretation. Prof Matcher, from Electrical Engineering, provided with insight onto the OCT setup and its application to plants. |
| Impact | A journal article is under review. A second article is in preparation. We hope to develop this proof-of-concept project into a future, larger research grant. |
| Start Year | 2021 |
| Title | Automated detection of infection |
| Description | Using our acquired data set of OCT images of wheat plant infected by septoria, we hired CIS, a software developer, to create a software for automatic detection of infection. We load OCT images of wheat leaf, and using a trained machine-learning algorithm, the software is able to tell us if the lead is infected or not with an accuracy >90%, before the leaf present any visual signs of infection. |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Impact | This software allows us to identify infection in crops before the plants show any visual signs of infection. If applied to farming, this technique will enable farmers to treat the infected crops in time to preserve yields. |
| Description | Pint of Science 2023 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | About 30 people attended my presentation, which was organised as part of the national/local Pint of Science. I was able to demonstrate the setup and the presentation triggered a range of follow-up questions and discussions. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://pintofscience.co.uk/event/seeing-the-invisible |
| Description | Tapton Secondary School |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Schools |
| Results and Impact | Outreach talk |
| Year(s) Of Engagement Activity | 2021 |