Detection of cold stressed maize seedlings for high throughput phenotyping using hyperspectral imagery

Chuanqi Xie, Ce Yang, Ali Moghimi

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

1 Scopus citations

Abstract

Hyperspectral imaging can provide hundreds of images at different wave bands covering the visible and near infrared regions, which is superior to traditional spectral and RGB techniques. Minnesota produced a lot of maize every year, while the temperature in Minnesota can change abruptly during spring. This study was carried out to use hyperspectral imaging technique to identify maize seedlings with cold stress prior to having visible phenotypes. A total of 60 samples were scanned by the hyperspectral camera at the wave range of 395-885 nm. The spectral reflectance information was extracted from the corrected hyperspectral images. By spectral reflectance information, support vector machine (SVM) classification models were established to identify the cold stressed samples. Then, the wavelengths which could play significant roles for the detection were selected using two-wavelength combination method. The classifiers were built again using the selected wavelengths. From the results, it can be found the selected wavelengths can even perform better than full wave range. The overall results indicated that hyperspectral imaging has the potential to classify cold stress symptoms in maize seedlings and thus help in selecting the corn genome lines with cold stress resistance.

Original languageEnglish (US)
Title of host publicationHyperspectral Imaging Sensors
Subtitle of host publicationInnovative Applications and Sensor Standards 2017
EditorsDavid P. Bannon
PublisherSPIE
ISBN (Electronic)9781510609273
DOIs
StatePublished - Jan 1 2017
EventHyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017 - Anaheim, United States
Duration: Apr 12 2017 → …

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10213
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceHyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017
CountryUnited States
CityAnaheim
Period4/12/17 → …

Keywords

  • Cold stress
  • Effective wavelengths
  • Hyperspectral imaging
  • Maize seedlings
  • Phenotyping

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  • Cite this

    Xie, C., Yang, C., & Moghimi, A. (2017). Detection of cold stressed maize seedlings for high throughput phenotyping using hyperspectral imagery. In D. P. Bannon (Ed.), Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017 [1021305] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10213). SPIE. https://doi.org/10.1117/12.2262781