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Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery

Precision agriculture is a concept of agricultural management, based on analyzing, measuring, and reacting to inter and intra-field variability in crops. One of the tools deployed for crop monitoring in precision agriculture is the use of an unmanned aerial vehicle, able to obtain high flexibility w...

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Main Authors: Ang, Yuhao, Che'ya, Nik Norasma, Roslin, Nor Athirah, Ismail, Mohd Razi
Format: Article
Language:English
Published: Universiti Putra Malaysia Press 2020
Online Access:http://psasir.upm.edu.my/id/eprint/81025/1/RICE.pdf
http://psasir.upm.edu.my/id/eprint/81025/
http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-1950-2020
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spelling my.upm.eprints.810252021-08-18T01:19:59Z http://psasir.upm.edu.my/id/eprint/81025/ Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery Ang, Yuhao Che'ya, Nik Norasma Roslin, Nor Athirah Ismail, Mohd Razi Precision agriculture is a concept of agricultural management, based on analyzing, measuring, and reacting to inter and intra-field variability in crops. One of the tools deployed for crop monitoring in precision agriculture is the use of an unmanned aerial vehicle, able to obtain high flexibility with fewer restrictions, and high spatial and spectral resolution in comparison to airborne and spaceborne system. In this paper, the assessment of various vegetation indices were performed for paddy stress monitoring using red edge band from multispectral imagery. The objective of the study was to create rice field maps with the use of aerial imagery and object-based image analysis technique to validate vegetative indices in rice field maps by using soil plant analysis development (SPAD) data. The result showed Normalized Difference Vegetation Index (R=0.957), Normalized Difference Red Edge (NDRE) (R=0.974), Soil Adjusted Vegetation Index (R=0.964), and Optimized Soil Adjusted Vegetation Index (R=0.966), all of which provided positive linear correlations with SPAD readings. NDRE showed higher correlation compared with other vegetation indices, exhibiting a better measure ment for farmers to make decisions. This paper has demonstrated how aerial imagery can be used to collect an accurate mapping in real time that can be analyzed to monitor conditions of crop and chlorophyll content by using SPAD to enable farmers to make informed decisions. Further investigations need to be carried out by validating the real chlorophyll content to improve existing correlations. Universiti Putra Malaysia Press 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/81025/1/RICE.pdf Ang, Yuhao and Che'ya, Nik Norasma and Roslin, Nor Athirah and Ismail, Mohd Razi (2020) Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery. Pertanika Journal of Science and Technology, 28 (3). pp. 779-795. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-1950-2020
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Precision agriculture is a concept of agricultural management, based on analyzing, measuring, and reacting to inter and intra-field variability in crops. One of the tools deployed for crop monitoring in precision agriculture is the use of an unmanned aerial vehicle, able to obtain high flexibility with fewer restrictions, and high spatial and spectral resolution in comparison to airborne and spaceborne system. In this paper, the assessment of various vegetation indices were performed for paddy stress monitoring using red edge band from multispectral imagery. The objective of the study was to create rice field maps with the use of aerial imagery and object-based image analysis technique to validate vegetative indices in rice field maps by using soil plant analysis development (SPAD) data. The result showed Normalized Difference Vegetation Index (R=0.957), Normalized Difference Red Edge (NDRE) (R=0.974), Soil Adjusted Vegetation Index (R=0.964), and Optimized Soil Adjusted Vegetation Index (R=0.966), all of which provided positive linear correlations with SPAD readings. NDRE showed higher correlation compared with other vegetation indices, exhibiting a better measure ment for farmers to make decisions. This paper has demonstrated how aerial imagery can be used to collect an accurate mapping in real time that can be analyzed to monitor conditions of crop and chlorophyll content by using SPAD to enable farmers to make informed decisions. Further investigations need to be carried out by validating the real chlorophyll content to improve existing correlations.
format Article
author Ang, Yuhao
Che'ya, Nik Norasma
Roslin, Nor Athirah
Ismail, Mohd Razi
spellingShingle Ang, Yuhao
Che'ya, Nik Norasma
Roslin, Nor Athirah
Ismail, Mohd Razi
Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery
author_facet Ang, Yuhao
Che'ya, Nik Norasma
Roslin, Nor Athirah
Ismail, Mohd Razi
author_sort Ang, Yuhao
title Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery
title_short Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery
title_full Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery
title_fullStr Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery
title_full_unstemmed Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery
title_sort rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery
publisher Universiti Putra Malaysia Press
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/81025/1/RICE.pdf
http://psasir.upm.edu.my/id/eprint/81025/
http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-1950-2020
_version_ 1709668987204272128
score 13.211869

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