Award details

Sinusoidally modulated fluorescence imaging for stress detection in plants

Reference	BB/X003299/1
Principal Investigator / Supervisor	Professor Stephen Rolfe
Co-Investigators / Co-Supervisors	Professor Bruce Grieve
Institution	University of Sheffield
Department	School of Biosciences
Funding type	Research
Value (£)	178,132
Status	Current
Type	Research Grant
Start date	01/01/2023
End date	31/12/2023
Duration	12 months

Abstract

Crops are subject to environmental stresses that limit their productivity. Sustaining food supplies requires continuous development of improved crop varieties. However, assessing the phenotype of new varieties is labour intensive and often involves subjective assessment of a limited number of traits by agronomists. Plant 'phenomics' aims to remove this bottleneck by providing quantitative data of multiple traits. There is a need for simple-to-use, autonomous and non-destructive in-field assessment tools. Such tools will be of use in plant breeding, predictive assessment during production and precision agriculture. We will develop a hand-held, portable, non-invasive chlorophyll fluorescence imaging device that uses sinusoidally-modulated light to probe subtle, stress-induced changes in photosynthetic function. A sinusoidal light input generates a complex output due to irradiance- and time-dependent quenching of chlorophyll fluorescence. As the frequency of the input irradiance is changed, these components alter in a complex manner reflecting the internal photosynthetic, physiological and metabolic processes in the leaf and can be used to detect subtle changes associated with stress. The proposal will develop sinusoidally modulated fluorescence imaging (SMFI) as a tool for the early, sensitive and specific detection of plant stress. We will (1) develop a handheld imaging device for SMFI application (2) use this to measure combinations of abiotic and biotic stresses in crop plants (3) develop analytical methods to detect, quantify and discriminate between specific stresses (4) relate changes in SMFI to underlying models of plant metabolism and (5) use machine learning/AI approaches to optimise acquisition protocols and analysis. This proposal will fill a measurement gap in the measurement, quantification and identification of plant stress and have an impacts academic and industrial research, and application in the agricultural and horticultural sectors.

Summary

Crop plants are subject to multiple environmental stresses that limit their productivity by impacting photosynthetic performance. Despite years of selective breeding, crops are not optimally adapted to the agricultural environment, a problem that is exacerbated by global climate change. Coupled with the increasing impact of pests and disease, stresses such as drought, flooding, salinity and temperature extremes are major limitations to agricultural productivity. The aim of this project is to develop a novel, highly sensitive, non-invasive approach for imaging plant responses to stress. It is a highly interdisciplinary project that brings together plant biology, engineering and computational techniques to deliver novel systems to measure plant health and thus improve food production and security. The maintenance and improvement of sustainable global food supplies requires continuous development and assessment of improved crop varieties that provide greater yields and are also resistant to biotic and climate-change related abiotic stresses. However, assessing the phenotype of new crop varieties is highly labour intensive and often involves subjective assessment of plant performance by agronomists. Plant 'phenomics' aims to remove this bottleneck in crop improvement and provide quantitative data at high temporal resolution of multiple crop traits in a changing environment. To fully exploit this approach, there is a need for simple-to-use measurement tools. The usefulness of such tools is not limited to plant breeding alone, as they allow crop monitoring for farmers or the identification and early intervention of stresses in precision agriculture. In this proposal, we will develop a hand-held, portable, non-invasive chlorophyll fluorescence imaging device that uses oscillating light to probe subtle, stress-induced changes in photosynthetic function. When plants are exposed to light, a small proportion of the lights absorbed by chlorophyll is re-emitted as fluorescence which can be used to probe the internal functions of the leaf. When exposed to a fluctuating light source, some parts of the photosynthetic apparatus can keep pace with these changes, whereas others lag behind. This generates a complex output reflecting the internal photosynthetic, physiological and metabolic processes in the leaf. The aim of this proposal is to develop sinusoidally modulated fluorescence imaging (SMFI) as a tool for the early, sensitive and specific detection of plant stress. This approach will deliver a new analysis approach that is more sensitive and rapid at detecting sub-lethal plant stress than existing approaches, providing new functionality for plant scientists, breeders and producers. We will develop a handheld device for SMFI imaging, use this to detect, quantify and discriminate between specific stresses, relate these results to underlying models of plant metabolism and then use machine learning/artificial intelligence approaches to optimise acquisition protocols and analysis. This proposal will fill a measurement gap in the measurement, quantification and identification of plant stress and have an impacts academic and industrial research, and application in the agricultural and horticultural sectors.

Committee	Not funded via Committee
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	UKRI Basic Technologies [2022]
Funding Scheme	X – not Funded via a specific Funding Scheme

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