Ultrarapid real-time diagnostic tool of plant disease infection adapted for field deployment
Lead Research Organisation:
University of Nottingham
Department Name: Sch of Biosciences
Abstract
We will develop a rapid non-destructive technology for the detection of plant pathogens and insect damage. Uniquely this approach will simultaneously use a combination of machine vision and volatile biomarkers to diagnose the presence of plant pests, i.e. pathogens and insects, even before extensive damage and loss of plant yield occurs. This will enable faster detection of crop pests allowing farmers and growers to apply crop protection strategies at the correct time which will result in reduced yield loss and less pesticide spraying.
Technical Summary
This project uniquely brings together ultra-rapid imaging, chemical mapping and volatilome profiling to detect and diagnose plant pest insects and pathogens in real time, at a previously unavailable level of detail. Furthermore, we will develop proof-of-concept non-destructive diagnostic tools adapted for field deployment for the efficient real time, in-field detection of plant pathogens and insect damage that limit crop yields in the UK.
Publications
Caporaso N
(2022)
Prediction of coffee aroma from single roasted coffee beans by hyperspectral imaging.
in Food chemistry
Kirkwood A
(2025)
Mechanisms of aroma compound formation during the drying of Dendrobium nobile stems (Shihu).
in Food chemistry
Kirkwood A
(2023)
A flavour perspective of Tiepishihu (Dendrobium officinale) - an emerging food ingredient from popular traditional Chinese medicinal plants: a review.
in International journal of food science & technology
Kirkwood A
(2024)
Characterisation of aroma-active compounds in dried Dendrobium spp. stems (Shihu) using GC-Olfactometry and a modified NIF-SNIF method
in Journal of Food Composition and Analysis
| Description | Aphids hide under leaves, reproduce rapidly, and require early detection to prevent crop damage, disease transmission, and ensure effective pest management. This project created a novel approach to aphid detection by utilising hyperspectral imaging, multivariate classification methods and spectral information divergence (SID) analyses. The method successfully detected aphid colony infestation, both earlier and in locations that are invisible during standard human inspection. |
| Exploitation Route | Further research into method and field trials. |
| Sectors | Agriculture Food and Drink |
| Description | Findings are being communicated to interested stakeholders in the hope of future impact. |