Combining spatial and spectral information to estimate chlorophyll contents of crop leaves with a field imaging spectroscopy system

Combining spatial and spectral information to estimate chlorophyll contents of crop leaves with a... Imaging spectroscopy is widely used in weed recognition, pest monitoring, agricultural product quality control and other precision agricultural fields. In the present study, an in-house-designed/developed field imaging spectroscopy system (FISS, 380–870 nm) was used to obtain the imaging spectra of soybean leaves at 344 wavelengths. The spatial and spectral information including the entropy, mean reflectivity and standard deviation of the leaf images at different wavelengths were extracted; the chlorophyll content was retrieved using multiple linear regression (MLR) together with the spatial information and spectral information, and the results were compared with the results derived with the Analytical Spectral Devices (ASD, FieldSpecFR spectrometer, Analytical Spectral Devices Inc., USA) data that were generated using conventional single sensor spectrometers. The results demonstrated that the entropy, standard deviation and other features of the image were very good indicators of the leaf chlorophyll content, confirming the idea that spatial information can be used to retrieve chlorophyll content, with an accuracy equivalent to that of spectral information, and can provide information that spectral reflectivity cannot provide. Thus, integrating spatial information and spectral information can greatly improve the chlorophyll content retrieval accuracy and reduce the estimation errors by 20 %. Due to the unique measurement method and image-spectrum-in-one feature, the field imaging spectroscopy system (FISS) data can be conveniently used to achieve accurate chlorophyll content retrieval, and the retrieval error was reduced by 30–45 % compared with that for the ASD data. FISS data and the proposed method of integrating both spectral and spatial information of imaging spectroscopy have potential advantages in quantitative spectral analysis applied in agricultural biochemistry related fields. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Combining spatial and spectral information to estimate chlorophyll contents of crop leaves with a field imaging spectroscopy system

Loading next page...
 
/lp/springer_journal/combining-spatial-and-spectral-information-to-estimate-chlorophyll-TiLS6HfRLm
Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-016-9466-5
Publisher site
See Article on Publisher Site

Abstract

Imaging spectroscopy is widely used in weed recognition, pest monitoring, agricultural product quality control and other precision agricultural fields. In the present study, an in-house-designed/developed field imaging spectroscopy system (FISS, 380–870 nm) was used to obtain the imaging spectra of soybean leaves at 344 wavelengths. The spatial and spectral information including the entropy, mean reflectivity and standard deviation of the leaf images at different wavelengths were extracted; the chlorophyll content was retrieved using multiple linear regression (MLR) together with the spatial information and spectral information, and the results were compared with the results derived with the Analytical Spectral Devices (ASD, FieldSpecFR spectrometer, Analytical Spectral Devices Inc., USA) data that were generated using conventional single sensor spectrometers. The results demonstrated that the entropy, standard deviation and other features of the image were very good indicators of the leaf chlorophyll content, confirming the idea that spatial information can be used to retrieve chlorophyll content, with an accuracy equivalent to that of spectral information, and can provide information that spectral reflectivity cannot provide. Thus, integrating spatial information and spectral information can greatly improve the chlorophyll content retrieval accuracy and reduce the estimation errors by 20 %. Due to the unique measurement method and image-spectrum-in-one feature, the field imaging spectroscopy system (FISS) data can be conveniently used to achieve accurate chlorophyll content retrieval, and the retrieval error was reduced by 30–45 % compared with that for the ASD data. FISS data and the proposed method of integrating both spectral and spatial information of imaging spectroscopy have potential advantages in quantitative spectral analysis applied in agricultural biochemistry related fields.

Journal

Precision AgricultureSpringer Journals

Published: Oct 12, 2016

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off