Early Detection of Drought Stress in Arabidopsis Thaliana Utilsing a Portable Hyperspectral Imaging Setup

Puneet Mishra, Torsten Feller, Martin Schmuck, Andreas Nicol, Alison Nordon

Research output: Chapter in Book/Report/Conference proceedingConference paper

4 Citations (Scopus)

Abstract

Close-range hyperspectral imaging (HSI) of plants is now a potential tool for non-destructive extraction of plant functional traits. A major motivation is the plant phenotyping related applications where different plant genotypes are explored for different environmental conditions. HSI of Arabidopsis thaliana is of particular importance as it is a model organism in plant biology. In the present work, a portable HSI setup has been used for the monitoring of a set of 6 Arabidopsis thaliana plants. The plants were monitored under controlled watering conditions where 3 plants were watered as normal and the other 3 plants were given 50% of the normal volume of water. The images were pre-processed utilising the standard normal variate (SNV) and changes over time were evaluated using unsupervised clustering over the time series. The results showed an early detection of stress from day 4 onwards compared to the commonly used normalised difference vegetation index (NDVI), which provided detection from day 9.

Original languageEnglish
Title of host publication2019 10th Workshop on Hyperspectral Imaging and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2019
PublisherIEEE computer society
ISBN (Electronic)9781728152943
DOIs
Publication statusPublished - Sep 2019
Externally publishedYes
Event10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 - Amsterdam, Netherlands
Duration: 24 Sep 201926 Sep 2019

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2019-September
ISSN (Print)2158-6276

Conference

Conference10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019
CountryNetherlands
CityAmsterdam
Period24/09/1926/09/19

Keywords

  • agriculture
  • automatic
  • digital
  • phenotyping
  • visible-near infrared

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