Hyperspectral imaging for phenotyping plant drought stress and nitrogen interactions using multivariate modeling and machine learning techniques in wheat

Simple item page
dc.contributor.authorOkyere, Frank Gyan
dc.contributor.authorCudjoe, Daniel Kingsley
dc.contributor.authorVirlet, Nicolas
dc.contributor.authorCastle, March
dc.contributor.authorRiche, Andrew Bernard
dc.contributor.authorGreche, Latifa
dc.contributor.authorMohareb, Fady
dc.contributor.authorSimms, Daniel
dc.contributor.authorMhada, Manal
dc.contributor.authorHawkesford, Malcolm John
dc.date.accessioned2025-03-21T12:32:54Z
dc.date.available2025-03-21T12:32:54Z
dc.date.freetoread2025-03-21
dc.date.issued2024-09-17
dc.date.pubOnline2024-09-17
dc.description.abstractAccurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.
dc.description.journalNameRemote Sensing
dc.description.sponsorshipRothamsted Research
dc.description.sponsorshipThis research was supported by the OCP S.A. under the University of Mohammed VI Polytechnic, Rothamsted Research and Cranfield University project (FP04).
dc.identifier.citationOkyere FG, Cudjoe DK, Virlet N, et al., (2024) Hyperspectral imaging for phenotyping plant drought stress and nitrogen interactions using multivariate modeling and machine learning techniques in wheat. Remote Sensing, Volume 16, Issue 18, September 2024, Article number 3446
dc.identifier.eissn2072-4292
dc.identifier.elementsID554025
dc.identifier.issn2072-4292
dc.identifier.issueNo18
dc.identifier.paperNo3446
dc.identifier.urihttps://doi.org/10.3390/rs16183446
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23636
dc.identifier.volumeNo16
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI AG
dc.publisher.urihttps://www.mdpi.com/2072-4292/16/18/3446
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectdrought stress
dc.subjectgas exchange measurements
dc.subjecthyperspectral imaging
dc.subjectmachine learning
dc.subjectvegetation indices
dc.subject3709 Physical Geography and Environmental Geoscience
dc.subject37 Earth Sciences
dc.subject40 Engineering
dc.subjectMachine Learning and Artificial Intelligence
dc.subject2 Zero Hunger
dc.subject3701 Atmospheric sciences
dc.subject3709 Physical geography and environmental geoscience
dc.subject4013 Geomatic engineering
dc.titleHyperspectral imaging for phenotyping plant drought stress and nitrogen interactions using multivariate modeling and machine learning techniques in wheat
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-09-05

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Hyperspectral_Imaging_for_Phenotyping_Plant-2024.pdf
Size:
3.32 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (SWEE)