Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits

Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha<sup<−1</sup< for the first cluster, 4.22 t ha<sup<−1</sup< for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha<sup<−1</sup< for cluster 1 and 4.74, 4.31, and 4.66 t ha<sup<−1</sup< for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha<sup<−1</sup<), followed by cluster 2 (5.6 t ha<sup<−1</sup<), cluster 3 (4.31 t ha<sup<−1</sup<), and cluster 4 (3.85 t ha<sup<−1</sup<). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Stefano Marino [verfasserIn] Arturo Alvino [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	UAV crop monitoring crop yield precision agriculture yield components

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 13(2021), 4, p 541
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:4, p 541

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs13040541

Katalog-ID:	DOAJ035572345

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allfields	10.3390/rs13040541 doi (DE-627)DOAJ035572345 (DE-599)DOAJ8a874ac375f341bf80acd38758a908ae DE-627 ger DE-627 rakwb eng Stefano Marino verfasserin aut Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha<sup<−1</sup< for the first cluster, 4.22 t ha<sup<−1</sup< for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha<sup<−1</sup< for cluster 1 and 4.74, 4.31, and 4.66 t ha<sup<−1</sup< for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha<sup<−1</sup<), followed by cluster 2 (5.6 t ha<sup<−1</sup<), cluster 3 (4.31 t ha<sup<−1</sup<), and cluster 4 (3.85 t ha<sup<−1</sup<). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops. UAV crop monitoring crop yield precision agriculture yield components Science Q Arturo Alvino verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 4, p 541 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:4, p 541 https://doi.org/10.3390/rs13040541 kostenfrei https://doaj.org/article/8a874ac375f341bf80acd38758a908ae kostenfrei https://www.mdpi.com/2072-4292/13/4/541 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 4, p 541
spelling	10.3390/rs13040541 doi (DE-627)DOAJ035572345 (DE-599)DOAJ8a874ac375f341bf80acd38758a908ae DE-627 ger DE-627 rakwb eng Stefano Marino verfasserin aut Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha<sup<−1</sup< for the first cluster, 4.22 t ha<sup<−1</sup< for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha<sup<−1</sup< for cluster 1 and 4.74, 4.31, and 4.66 t ha<sup<−1</sup< for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha<sup<−1</sup<), followed by cluster 2 (5.6 t ha<sup<−1</sup<), cluster 3 (4.31 t ha<sup<−1</sup<), and cluster 4 (3.85 t ha<sup<−1</sup<). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops. UAV crop monitoring crop yield precision agriculture yield components Science Q Arturo Alvino verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 4, p 541 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:4, p 541 https://doi.org/10.3390/rs13040541 kostenfrei https://doaj.org/article/8a874ac375f341bf80acd38758a908ae kostenfrei https://www.mdpi.com/2072-4292/13/4/541 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 4, p 541
allfields_unstemmed	10.3390/rs13040541 doi (DE-627)DOAJ035572345 (DE-599)DOAJ8a874ac375f341bf80acd38758a908ae DE-627 ger DE-627 rakwb eng Stefano Marino verfasserin aut Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha<sup<−1</sup< for the first cluster, 4.22 t ha<sup<−1</sup< for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha<sup<−1</sup< for cluster 1 and 4.74, 4.31, and 4.66 t ha<sup<−1</sup< for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha<sup<−1</sup<), followed by cluster 2 (5.6 t ha<sup<−1</sup<), cluster 3 (4.31 t ha<sup<−1</sup<), and cluster 4 (3.85 t ha<sup<−1</sup<). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops. UAV crop monitoring crop yield precision agriculture yield components Science Q Arturo Alvino verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 4, p 541 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:4, p 541 https://doi.org/10.3390/rs13040541 kostenfrei https://doaj.org/article/8a874ac375f341bf80acd38758a908ae kostenfrei https://www.mdpi.com/2072-4292/13/4/541 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 4, p 541
allfieldsGer	10.3390/rs13040541 doi (DE-627)DOAJ035572345 (DE-599)DOAJ8a874ac375f341bf80acd38758a908ae DE-627 ger DE-627 rakwb eng Stefano Marino verfasserin aut Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha<sup<−1</sup< for the first cluster, 4.22 t ha<sup<−1</sup< for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha<sup<−1</sup< for cluster 1 and 4.74, 4.31, and 4.66 t ha<sup<−1</sup< for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha<sup<−1</sup<), followed by cluster 2 (5.6 t ha<sup<−1</sup<), cluster 3 (4.31 t ha<sup<−1</sup<), and cluster 4 (3.85 t ha<sup<−1</sup<). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops. UAV crop monitoring crop yield precision agriculture yield components Science Q Arturo Alvino verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 4, p 541 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:4, p 541 https://doi.org/10.3390/rs13040541 kostenfrei https://doaj.org/article/8a874ac375f341bf80acd38758a908ae kostenfrei https://www.mdpi.com/2072-4292/13/4/541 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 4, p 541
allfieldsSound	10.3390/rs13040541 doi (DE-627)DOAJ035572345 (DE-599)DOAJ8a874ac375f341bf80acd38758a908ae DE-627 ger DE-627 rakwb eng Stefano Marino verfasserin aut Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha<sup<−1</sup< for the first cluster, 4.22 t ha<sup<−1</sup< for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha<sup<−1</sup< for cluster 1 and 4.74, 4.31, and 4.66 t ha<sup<−1</sup< for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha<sup<−1</sup<), followed by cluster 2 (5.6 t ha<sup<−1</sup<), cluster 3 (4.31 t ha<sup<−1</sup<), and cluster 4 (3.85 t ha<sup<−1</sup<). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops. UAV crop monitoring crop yield precision agriculture yield components Science Q Arturo Alvino verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 4, p 541 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:4, p 541 https://doi.org/10.3390/rs13040541 kostenfrei https://doaj.org/article/8a874ac375f341bf80acd38758a908ae kostenfrei https://www.mdpi.com/2072-4292/13/4/541 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 4, p 541
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title	Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits
ctrlnum	(DE-627)DOAJ035572345 (DE-599)DOAJ8a874ac375f341bf80acd38758a908ae
title_full	Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits
author_sort	Stefano Marino
journal	Remote Sensing
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author_browse	Stefano Marino Arturo Alvino
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author-letter	Stefano Marino
doi_str_mv	10.3390/rs13040541
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title_sort	vegetation indices data clustering for dynamic monitoring and classification of wheat yield crop traits
title_auth	Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits
abstract	Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha<sup<−1</sup< for the first cluster, 4.22 t ha<sup<−1</sup< for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha<sup<−1</sup< for cluster 1 and 4.74, 4.31, and 4.66 t ha<sup<−1</sup< for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha<sup<−1</sup<), followed by cluster 2 (5.6 t ha<sup<−1</sup<), cluster 3 (4.31 t ha<sup<−1</sup<), and cluster 4 (3.85 t ha<sup<−1</sup<). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops.
abstractGer	Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha<sup<−1</sup< for the first cluster, 4.22 t ha<sup<−1</sup< for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha<sup<−1</sup< for cluster 1 and 4.74, 4.31, and 4.66 t ha<sup<−1</sup< for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha<sup<−1</sup<), followed by cluster 2 (5.6 t ha<sup<−1</sup<), cluster 3 (4.31 t ha<sup<−1</sup<), and cluster 4 (3.85 t ha<sup<−1</sup<). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops.
abstract_unstemmed	Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha<sup<−1</sup< for the first cluster, 4.22 t ha<sup<−1</sup< for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha<sup<−1</sup< for cluster 1 and 4.74, 4.31, and 4.66 t ha<sup<−1</sup< for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha<sup<−1</sup<), followed by cluster 2 (5.6 t ha<sup<−1</sup<), cluster 3 (4.31 t ha<sup<−1</sup<), and cluster 4 (3.85 t ha<sup<−1</sup<). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops.
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container_issue	4, p 541
title_short	Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits
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