A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis
The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Juan Manuel Ponce [verfasserIn] Arturo Aquino [verfasserIn] Diego Tejada [verfasserIn] Basil Mohammed Al-Hadithi [verfasserIn] José Manuel Andújar [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Agronomy - MDPI AG, 2012, 12(2021), 1, p 43 |
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| Übergeordnetes Werk: |
volume:12 ; year:2021 ; number:1, p 43 |
| Links: |
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| DOI / URN: |
10.3390/agronomy12010043 |
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DOAJ087078716 |
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10.3390/agronomy12010043 doi (DE-627)DOAJ087078716 (DE-599)DOAJ9926499b03f34a628cc480be4832efc5 DE-627 ger DE-627 rakwb eng Juan Manuel Ponce verfasserin aut A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards. aerial imagery canopy cover morphological image analysis crop tree unmanned aerial vehicle (UAV) Agriculture S Arturo Aquino verfasserin aut Diego Tejada verfasserin aut Basil Mohammed Al-Hadithi verfasserin aut José Manuel Andújar verfasserin aut In Agronomy MDPI AG, 2012 12(2021), 1, p 43 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2021 number:1, p 43 https://doi.org/10.3390/agronomy12010043 kostenfrei https://doaj.org/article/9926499b03f34a628cc480be4832efc5 kostenfrei https://www.mdpi.com/2073-4395/12/1/43 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 1, p 43 |
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10.3390/agronomy12010043 doi (DE-627)DOAJ087078716 (DE-599)DOAJ9926499b03f34a628cc480be4832efc5 DE-627 ger DE-627 rakwb eng Juan Manuel Ponce verfasserin aut A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards. aerial imagery canopy cover morphological image analysis crop tree unmanned aerial vehicle (UAV) Agriculture S Arturo Aquino verfasserin aut Diego Tejada verfasserin aut Basil Mohammed Al-Hadithi verfasserin aut José Manuel Andújar verfasserin aut In Agronomy MDPI AG, 2012 12(2021), 1, p 43 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2021 number:1, p 43 https://doi.org/10.3390/agronomy12010043 kostenfrei https://doaj.org/article/9926499b03f34a628cc480be4832efc5 kostenfrei https://www.mdpi.com/2073-4395/12/1/43 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 1, p 43 |
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10.3390/agronomy12010043 doi (DE-627)DOAJ087078716 (DE-599)DOAJ9926499b03f34a628cc480be4832efc5 DE-627 ger DE-627 rakwb eng Juan Manuel Ponce verfasserin aut A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards. aerial imagery canopy cover morphological image analysis crop tree unmanned aerial vehicle (UAV) Agriculture S Arturo Aquino verfasserin aut Diego Tejada verfasserin aut Basil Mohammed Al-Hadithi verfasserin aut José Manuel Andújar verfasserin aut In Agronomy MDPI AG, 2012 12(2021), 1, p 43 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2021 number:1, p 43 https://doi.org/10.3390/agronomy12010043 kostenfrei https://doaj.org/article/9926499b03f34a628cc480be4832efc5 kostenfrei https://www.mdpi.com/2073-4395/12/1/43 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 1, p 43 |
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10.3390/agronomy12010043 doi (DE-627)DOAJ087078716 (DE-599)DOAJ9926499b03f34a628cc480be4832efc5 DE-627 ger DE-627 rakwb eng Juan Manuel Ponce verfasserin aut A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards. aerial imagery canopy cover morphological image analysis crop tree unmanned aerial vehicle (UAV) Agriculture S Arturo Aquino verfasserin aut Diego Tejada verfasserin aut Basil Mohammed Al-Hadithi verfasserin aut José Manuel Andújar verfasserin aut In Agronomy MDPI AG, 2012 12(2021), 1, p 43 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2021 number:1, p 43 https://doi.org/10.3390/agronomy12010043 kostenfrei https://doaj.org/article/9926499b03f34a628cc480be4832efc5 kostenfrei https://www.mdpi.com/2073-4395/12/1/43 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 1, p 43 |
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10.3390/agronomy12010043 doi (DE-627)DOAJ087078716 (DE-599)DOAJ9926499b03f34a628cc480be4832efc5 DE-627 ger DE-627 rakwb eng Juan Manuel Ponce verfasserin aut A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards. aerial imagery canopy cover morphological image analysis crop tree unmanned aerial vehicle (UAV) Agriculture S Arturo Aquino verfasserin aut Diego Tejada verfasserin aut Basil Mohammed Al-Hadithi verfasserin aut José Manuel Andújar verfasserin aut In Agronomy MDPI AG, 2012 12(2021), 1, p 43 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:12 year:2021 number:1, p 43 https://doi.org/10.3390/agronomy12010043 kostenfrei https://doaj.org/article/9926499b03f34a628cc480be4832efc5 kostenfrei https://www.mdpi.com/2073-4395/12/1/43 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 1, p 43 |
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Juan Manuel Ponce |
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Juan Manuel Ponce misc aerial imagery misc canopy cover misc morphological image analysis misc crop tree misc unmanned aerial vehicle (UAV) misc Agriculture misc S A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis |
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A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis aerial imagery canopy cover morphological image analysis crop tree unmanned aerial vehicle (UAV) |
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methodology for the automated delineation of crop tree crowns from uav-based aerial imagery by means of morphological image analysis |
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A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis |
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The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards. |
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The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards. |
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The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards. |
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