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Remote sensing of yields : Application of UAV imagery-derived ndvi for estimating maize vigor and yields in complex farming systems in Sub-Saharan Africa

Wahab, Ibrahim LU ; Hall, Ola LU and Jirström, Magnus LU (2018) In Drones 2(3).
Abstract

The application of remote sensing methods to assess crop vigor and yields has had limited applications in Sub-Saharan Africa (SSA) due largely to limitations associated with satellite images. The increasing use of unmanned aerial vehicles in recent times opens up new possibilities for remotely sensing crop status and yields even on complex smallholder farms. This study demonstrates the applicability of a vegetation index derived from UAV imagery to assess maize (Zea mays L.) crop vigor and yields at various stages of crop growth. The study employs a quadcopter flown at 100 m over farm plots and equipped with two consumer-grade cameras, one of which is modified to capture images in the near infrared. We find that UAV-derived GNDVI is a... (More)

The application of remote sensing methods to assess crop vigor and yields has had limited applications in Sub-Saharan Africa (SSA) due largely to limitations associated with satellite images. The increasing use of unmanned aerial vehicles in recent times opens up new possibilities for remotely sensing crop status and yields even on complex smallholder farms. This study demonstrates the applicability of a vegetation index derived from UAV imagery to assess maize (Zea mays L.) crop vigor and yields at various stages of crop growth. The study employs a quadcopter flown at 100 m over farm plots and equipped with two consumer-grade cameras, one of which is modified to capture images in the near infrared. We find that UAV-derived GNDVI is a better indicator of crop vigor and a better estimator of yields—r = 0.372 and r = 0.393 for mean and maximum GNDVI respectively at about five weeks after planting compared to in-field methods like SPAD readings at the same stage (r = 0.259). Our study therefore demonstrates that GNDVI derived from UAV imagery is a reliable and timeous predictor of crop vigor and yields and that this is applicable even in complex smallholder farms in SSA.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Green normalized difference vegetation index, Maize yields, Near infrared, Remote sensing, Unmanned aerial vehicles
in
Drones
volume
2
issue
3
article number
28
pages
16 pages
publisher
MDPI AG
external identifiers
  • scopus:85057123396
ISSN
2504-446X
DOI
10.3390/drones2030028
language
English
LU publication?
yes
id
6734c611-0bf7-474c-ab90-56379e1dd339
date added to LUP
2018-08-29 13:46:27
date last changed
2023-02-06 12:32:56
@article{6734c611-0bf7-474c-ab90-56379e1dd339,
  abstract     = {{<p>The application of remote sensing methods to assess crop vigor and yields has had limited applications in Sub-Saharan Africa (SSA) due largely to limitations associated with satellite images. The increasing use of unmanned aerial vehicles in recent times opens up new possibilities for remotely sensing crop status and yields even on complex smallholder farms. This study demonstrates the applicability of a vegetation index derived from UAV imagery to assess maize (Zea mays L.) crop vigor and yields at various stages of crop growth. The study employs a quadcopter flown at 100 m over farm plots and equipped with two consumer-grade cameras, one of which is modified to capture images in the near infrared. We find that UAV-derived GNDVI is a better indicator of crop vigor and a better estimator of yields—r = 0.372 and r = 0.393 for mean and maximum GNDVI respectively at about five weeks after planting compared to in-field methods like SPAD readings at the same stage (r = 0.259). Our study therefore demonstrates that GNDVI derived from UAV imagery is a reliable and timeous predictor of crop vigor and yields and that this is applicable even in complex smallholder farms in SSA.</p>}},
  author       = {{Wahab, Ibrahim and Hall, Ola and Jirström, Magnus}},
  issn         = {{2504-446X}},
  keywords     = {{Green normalized difference vegetation index; Maize yields; Near infrared; Remote sensing; Unmanned aerial vehicles}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{MDPI AG}},
  series       = {{Drones}},
  title        = {{Remote sensing of yields : Application of UAV imagery-derived ndvi for estimating maize vigor and yields in complex farming systems in Sub-Saharan Africa}},
  url          = {{http://dx.doi.org/10.3390/drones2030028}},
  doi          = {{10.3390/drones2030028}},
  volume       = {{2}},
  year         = {{2018}},
}