Estimating Nitrogen and Chlorophyll Content in Corn Using Spectral Vegetation Indices Derived From UAV Multispectral Imagery

Remote sensing is a unique and cost-effective tool that provides information about the nitrogen status of plants in a non-destructive way. The objective of this study is to evaluate the effectiveness of aerial multispectral imagery captured by UAV for estimating corn nitrogen (N) and chlorophyll (Ch...

Full description

Saved in:
Bibliographic Details
Main Authors: Nikrooz Bagheri, Mehryar Jaberi Aghdam, Hamidreza Ebrahimi
Format: Article
Language:English
Published: Shahid Bahonar University of Kerman 2024-06-01
Series:Biomechanism and Bioenergy Research
Subjects:
Online Access:https://bbr.uk.ac.ir/article_4301_4373af379a7ed0ae95d3028e13538f43.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1841545711813918720
author Nikrooz Bagheri
Mehryar Jaberi Aghdam
Hamidreza Ebrahimi
author_facet Nikrooz Bagheri
Mehryar Jaberi Aghdam
Hamidreza Ebrahimi
author_sort Nikrooz Bagheri
collection DOAJ
description Remote sensing is a unique and cost-effective tool that provides information about the nitrogen status of plants in a non-destructive way. The objective of this study is to evaluate the effectiveness of aerial multispectral imagery captured by UAV for estimating corn nitrogen (N) and chlorophyll (Chl) content at different growth stages. The study used a fully randomized experimental design with four treatments of nitrogen fertilizer (0, 50%, 100%, and 150%). Ten plants were randomly selected in each plot at the phenological stages of 8 leaves (V8) and tasseling growth stages (VT) for sampling. Leaf samples were taken to measure total nitrogen (N) and chlorophyll (Chl) content. Mathematical models were created using vegetation indices extracted from aerial multispectral images to estimate the amount of nitrogen and chlorophyll. The models were evaluated using the leave-one-out cross-validation method. The results showed that there is a significant positive relationship between the leaf dry weight (LDW), the Chl and N content with the amount of nitrogen fertilizer used. So, the results indicated that the REIP index is suitable for estimating chlorophyll content in both the V8 (R2 of 0.997) and VT (R2 of 0.980) growth stages. Additionally, the REIP index was found to be an appropriate index for estimating N content in both growth stages (R2 of 0.980). It can be concluded that aerial multispectral remote sensing technology is a reliable method for estimating corn nitrogen and chlorophyll content.
format Article
id doaj-art-ff563db737e4449bb498017363137e60
institution Kabale University
issn 2821-1855
language English
publishDate 2024-06-01
publisher Shahid Bahonar University of Kerman
record_format Article
series Biomechanism and Bioenergy Research
spelling doaj-art-ff563db737e4449bb498017363137e602025-01-11T18:55:35ZengShahid Bahonar University of KermanBiomechanism and Bioenergy Research2821-18552024-06-0131819310.22103/bbr.2024.23234.10824301Estimating Nitrogen and Chlorophyll Content in Corn Using Spectral Vegetation Indices Derived From UAV Multispectral ImageryNikrooz Bagheri0Mehryar Jaberi Aghdam1Hamidreza Ebrahimi2Agricultural Engineering Research Institute. Agricultural Research, Education and Extension Organization (AREEO). Karaj. Iran.Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran.Novin Niro Shahbaz Company.Remote sensing is a unique and cost-effective tool that provides information about the nitrogen status of plants in a non-destructive way. The objective of this study is to evaluate the effectiveness of aerial multispectral imagery captured by UAV for estimating corn nitrogen (N) and chlorophyll (Chl) content at different growth stages. The study used a fully randomized experimental design with four treatments of nitrogen fertilizer (0, 50%, 100%, and 150%). Ten plants were randomly selected in each plot at the phenological stages of 8 leaves (V8) and tasseling growth stages (VT) for sampling. Leaf samples were taken to measure total nitrogen (N) and chlorophyll (Chl) content. Mathematical models were created using vegetation indices extracted from aerial multispectral images to estimate the amount of nitrogen and chlorophyll. The models were evaluated using the leave-one-out cross-validation method. The results showed that there is a significant positive relationship between the leaf dry weight (LDW), the Chl and N content with the amount of nitrogen fertilizer used. So, the results indicated that the REIP index is suitable for estimating chlorophyll content in both the V8 (R2 of 0.997) and VT (R2 of 0.980) growth stages. Additionally, the REIP index was found to be an appropriate index for estimating N content in both growth stages (R2 of 0.980). It can be concluded that aerial multispectral remote sensing technology is a reliable method for estimating corn nitrogen and chlorophyll content.https://bbr.uk.ac.ir/article_4301_4373af379a7ed0ae95d3028e13538f43.pdfmultispectral imagerynitrogenprecision agricultureremote sensingunmanned aerial vehicleartificial intelligence
spellingShingle Nikrooz Bagheri
Mehryar Jaberi Aghdam
Hamidreza Ebrahimi
Estimating Nitrogen and Chlorophyll Content in Corn Using Spectral Vegetation Indices Derived From UAV Multispectral Imagery
Biomechanism and Bioenergy Research
multispectral imagery
nitrogen
precision agriculture
remote sensing
unmanned aerial vehicle
artificial intelligence
title Estimating Nitrogen and Chlorophyll Content in Corn Using Spectral Vegetation Indices Derived From UAV Multispectral Imagery
title_full Estimating Nitrogen and Chlorophyll Content in Corn Using Spectral Vegetation Indices Derived From UAV Multispectral Imagery
title_fullStr Estimating Nitrogen and Chlorophyll Content in Corn Using Spectral Vegetation Indices Derived From UAV Multispectral Imagery
title_full_unstemmed Estimating Nitrogen and Chlorophyll Content in Corn Using Spectral Vegetation Indices Derived From UAV Multispectral Imagery
title_short Estimating Nitrogen and Chlorophyll Content in Corn Using Spectral Vegetation Indices Derived From UAV Multispectral Imagery
title_sort estimating nitrogen and chlorophyll content in corn using spectral vegetation indices derived from uav multispectral imagery
topic multispectral imagery
nitrogen
precision agriculture
remote sensing
unmanned aerial vehicle
artificial intelligence
url https://bbr.uk.ac.ir/article_4301_4373af379a7ed0ae95d3028e13538f43.pdf
work_keys_str_mv AT nikroozbagheri estimatingnitrogenandchlorophyllcontentincornusingspectralvegetationindicesderivedfromuavmultispectralimagery
AT mehryarjaberiaghdam estimatingnitrogenandchlorophyllcontentincornusingspectralvegetationindicesderivedfromuavmultispectralimagery
AT hamidrezaebrahimi estimatingnitrogenandchlorophyllcontentincornusingspectralvegetationindicesderivedfromuavmultispectralimagery