Remote sensing of yields : Application of UAV imagery-derived ndvi for estimating maize vigor and yields in complex farming systems in Sub-Saharan Africa
(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.
(Less)
- author
- Wahab, Ibrahim LU ; Hall, Ola LU and Jirström, Magnus LU
- organization
- publishing date
- 2018
- 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}}, }