Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity
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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Format: | Article/Letter to editor biblioteca |
Language: | English |
Subjects: | Object-based image analysis, Plant productivity, Plant-soil feedback, Precision agriculture, Remote sensing, Segmentation, Template matching, Unmanned aerial systems, |
Online Access: | https://research.wur.nl/en/publications/using-unmanned-aerial-systems-uas-and-object-based-image-analysis |
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dig-wur-nl-wurpubs-5550102025-01-15 Nuijten, Rik J.G. Kooistra, Lammert De Deyn, Gerlinde B. Article/Letter to editor Drones 3 (2019) 3 ISSN: 2504-446X Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity 2019 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. en application/pdf https://research.wur.nl/en/publications/using-unmanned-aerial-systems-uas-and-object-based-image-analysis 10.3390/drones3030054 https://edepot.wur.nl/503914 Object-based image analysis Plant productivity Plant-soil feedback Precision agriculture Remote sensing Segmentation Template matching Unmanned aerial systems https://creativecommons.org/licenses/by/4.0/ Wageningen University & Research |
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Object-based image analysis Plant productivity Plant-soil feedback Precision agriculture Remote sensing Segmentation Template matching Unmanned aerial systems Object-based image analysis Plant productivity Plant-soil feedback Precision agriculture Remote sensing Segmentation Template matching Unmanned aerial systems |
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Object-based image analysis Plant productivity Plant-soil feedback Precision agriculture Remote sensing Segmentation Template matching Unmanned aerial systems Object-based image analysis Plant productivity Plant-soil feedback Precision agriculture Remote sensing Segmentation Template matching Unmanned aerial systems Nuijten, Rik J.G. Kooistra, Lammert De Deyn, Gerlinde B. Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity |
description |
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. |
format |
Article/Letter to editor |
topic_facet |
Object-based image analysis Plant productivity Plant-soil feedback Precision agriculture Remote sensing Segmentation Template matching Unmanned aerial systems |
author |
Nuijten, Rik J.G. Kooistra, Lammert De Deyn, Gerlinde B. |
author_facet |
Nuijten, Rik J.G. Kooistra, Lammert De Deyn, Gerlinde B. |
author_sort |
Nuijten, Rik J.G. |
title |
Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity |
title_short |
Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity |
title_full |
Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity |
title_fullStr |
Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity |
title_full_unstemmed |
Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity |
title_sort |
using unmanned aerial systems (uas) and object-based image analysis (obia) for measuring plant-soil feedback effects on crop productivity |
url |
https://research.wur.nl/en/publications/using-unmanned-aerial-systems-uas-and-object-based-image-analysis |
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