Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8%) and delineate C. endivia crop covered area (accuracy = 85.4%). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale.
Original languageEnglish
Article number54
JournalDrones
Volume3
Issue number3
DOIs
Publication statusPublished - 30 Jun 2019

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image analysis
crops
Cichorium endivia
soil
Lolium perenne
cover crops
Raphanus
endive
Vicia sativa
winter
Raphanus sativus
precision agriculture
biomass
Trifolium repens
Netherlands
summer
methodology

Cite this

@article{da2b0ea3306745288b2a8778eaa651a9,
title = "Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity",
abstract = "Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8{\%}) and delineate C. endivia crop covered area (accuracy = 85.4{\%}). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale.",
author = "Nuijten, {Rik J.G.} and Lammert Kooistra and {De Deyn}, {Gerlinde B.}",
year = "2019",
month = "6",
day = "30",
doi = "10.3390/drones3030054",
language = "English",
volume = "3",
journal = "Drones",
issn = "2504-446X",
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Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity. / Nuijten, Rik J.G.; Kooistra, Lammert; De Deyn, Gerlinde B.

In: Drones, Vol. 3, No. 3, 54, 30.06.2019.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Nuijten, Rik J.G.

AU - Kooistra, Lammert

AU - De Deyn, Gerlinde B.

PY - 2019/6/30

Y1 - 2019/6/30

N2 - Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8%) and delineate C. endivia crop covered area (accuracy = 85.4%). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale.

AB - Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8%) and delineate C. endivia crop covered area (accuracy = 85.4%). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale.

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DO - 10.3390/drones3030054

M3 - Article

VL - 3

JO - Drones

JF - Drones

SN - 2504-446X

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