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Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery


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 & Technology, 28 (3). pp. 779-795. ISSN 0128-7680; ESSN: 2231-8526


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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Agriculture
Faculty of Engineering
Publisher: Universiti Putra Malaysia Press
Keywords: Multispectral imagery; Object-based analysis; Red edge band; Vegetation index
Depositing User: Mohamad Jefri Mohamed Fauzi
Date Deposited: 10 Sep 2021 08:50
Last Modified: 10 Sep 2021 08:50
URI: http://psasir.upm.edu.my/id/eprint/90414
Statistic Details: View Download Statistic

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