Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius

Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Luciano Ortenzi [verfasserIn] Simona Violino [verfasserIn] Federico Pallottino [verfasserIn] Simone Figorilli [verfasserIn] Simone Vasta [verfasserIn] Francesco Tocci [verfasserIn] Francesca Antonucci [verfasserIn] Giancarlo Imperi [verfasserIn] Corrado Costa [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	precision agriculture EVOO digital methods tree canopy image analysis yield prediction

Übergeordnetes Werk:	In: Drones - MDPI AG, 2018, 5(2021), 4, p 118
Übergeordnetes Werk:	volume:5 ; year:2021 ; number:4, p 118

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/drones5040118

Katalog-ID:	DOAJ014174588

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520			\|a Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production.
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allfields	10.3390/drones5040118 doi (DE-627)DOAJ014174588 (DE-599)DOAJca61712cae604ccfb8df250c0cbe69a1 DE-627 ger DE-627 rakwb eng TL1-4050 Luciano Ortenzi verfasserin aut Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production. precision agriculture EVOO digital methods tree canopy image analysis yield prediction Motor vehicles. Aeronautics. Astronautics Simona Violino verfasserin aut Federico Pallottino verfasserin aut Simone Figorilli verfasserin aut Simone Vasta verfasserin aut Francesco Tocci verfasserin aut Francesca Antonucci verfasserin aut Giancarlo Imperi verfasserin aut Corrado Costa verfasserin aut In Drones MDPI AG, 2018 5(2021), 4, p 118 (DE-627)1025498356 2504446X nnns volume:5 year:2021 number:4, p 118 https://doi.org/10.3390/drones5040118 kostenfrei https://doaj.org/article/ca61712cae604ccfb8df250c0cbe69a1 kostenfrei https://www.mdpi.com/2504-446X/5/4/118 kostenfrei https://doaj.org/toc/2504-446X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2021 4, p 118
spelling	10.3390/drones5040118 doi (DE-627)DOAJ014174588 (DE-599)DOAJca61712cae604ccfb8df250c0cbe69a1 DE-627 ger DE-627 rakwb eng TL1-4050 Luciano Ortenzi verfasserin aut Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production. precision agriculture EVOO digital methods tree canopy image analysis yield prediction Motor vehicles. Aeronautics. Astronautics Simona Violino verfasserin aut Federico Pallottino verfasserin aut Simone Figorilli verfasserin aut Simone Vasta verfasserin aut Francesco Tocci verfasserin aut Francesca Antonucci verfasserin aut Giancarlo Imperi verfasserin aut Corrado Costa verfasserin aut In Drones MDPI AG, 2018 5(2021), 4, p 118 (DE-627)1025498356 2504446X nnns volume:5 year:2021 number:4, p 118 https://doi.org/10.3390/drones5040118 kostenfrei https://doaj.org/article/ca61712cae604ccfb8df250c0cbe69a1 kostenfrei https://www.mdpi.com/2504-446X/5/4/118 kostenfrei https://doaj.org/toc/2504-446X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2021 4, p 118
allfields_unstemmed	10.3390/drones5040118 doi (DE-627)DOAJ014174588 (DE-599)DOAJca61712cae604ccfb8df250c0cbe69a1 DE-627 ger DE-627 rakwb eng TL1-4050 Luciano Ortenzi verfasserin aut Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production. precision agriculture EVOO digital methods tree canopy image analysis yield prediction Motor vehicles. Aeronautics. Astronautics Simona Violino verfasserin aut Federico Pallottino verfasserin aut Simone Figorilli verfasserin aut Simone Vasta verfasserin aut Francesco Tocci verfasserin aut Francesca Antonucci verfasserin aut Giancarlo Imperi verfasserin aut Corrado Costa verfasserin aut In Drones MDPI AG, 2018 5(2021), 4, p 118 (DE-627)1025498356 2504446X nnns volume:5 year:2021 number:4, p 118 https://doi.org/10.3390/drones5040118 kostenfrei https://doaj.org/article/ca61712cae604ccfb8df250c0cbe69a1 kostenfrei https://www.mdpi.com/2504-446X/5/4/118 kostenfrei https://doaj.org/toc/2504-446X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2021 4, p 118
allfieldsGer	10.3390/drones5040118 doi (DE-627)DOAJ014174588 (DE-599)DOAJca61712cae604ccfb8df250c0cbe69a1 DE-627 ger DE-627 rakwb eng TL1-4050 Luciano Ortenzi verfasserin aut Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production. precision agriculture EVOO digital methods tree canopy image analysis yield prediction Motor vehicles. Aeronautics. Astronautics Simona Violino verfasserin aut Federico Pallottino verfasserin aut Simone Figorilli verfasserin aut Simone Vasta verfasserin aut Francesco Tocci verfasserin aut Francesca Antonucci verfasserin aut Giancarlo Imperi verfasserin aut Corrado Costa verfasserin aut In Drones MDPI AG, 2018 5(2021), 4, p 118 (DE-627)1025498356 2504446X nnns volume:5 year:2021 number:4, p 118 https://doi.org/10.3390/drones5040118 kostenfrei https://doaj.org/article/ca61712cae604ccfb8df250c0cbe69a1 kostenfrei https://www.mdpi.com/2504-446X/5/4/118 kostenfrei https://doaj.org/toc/2504-446X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2021 4, p 118
allfieldsSound	10.3390/drones5040118 doi (DE-627)DOAJ014174588 (DE-599)DOAJca61712cae604ccfb8df250c0cbe69a1 DE-627 ger DE-627 rakwb eng TL1-4050 Luciano Ortenzi verfasserin aut Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production. precision agriculture EVOO digital methods tree canopy image analysis yield prediction Motor vehicles. Aeronautics. Astronautics Simona Violino verfasserin aut Federico Pallottino verfasserin aut Simone Figorilli verfasserin aut Simone Vasta verfasserin aut Francesco Tocci verfasserin aut Francesca Antonucci verfasserin aut Giancarlo Imperi verfasserin aut Corrado Costa verfasserin aut In Drones MDPI AG, 2018 5(2021), 4, p 118 (DE-627)1025498356 2504446X nnns volume:5 year:2021 number:4, p 118 https://doi.org/10.3390/drones5040118 kostenfrei https://doaj.org/article/ca61712cae604ccfb8df250c0cbe69a1 kostenfrei https://www.mdpi.com/2504-446X/5/4/118 kostenfrei https://doaj.org/toc/2504-446X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2021 4, p 118
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Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. 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callnumber-first	T - Technology
author	Luciano Ortenzi
spellingShingle	Luciano Ortenzi misc TL1-4050 misc precision agriculture misc EVOO misc digital methods misc tree canopy misc image analysis misc yield prediction misc Motor vehicles. Aeronautics. Astronautics Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius
authorStr	Luciano Ortenzi
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topic_title	TL1-4050 Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius precision agriculture EVOO digital methods tree canopy image analysis yield prediction
topic	misc TL1-4050 misc precision agriculture misc EVOO misc digital methods misc tree canopy misc image analysis misc yield prediction misc Motor vehicles. Aeronautics. Astronautics
topic_unstemmed	misc TL1-4050 misc precision agriculture misc EVOO misc digital methods misc tree canopy misc image analysis misc yield prediction misc Motor vehicles. Aeronautics. Astronautics
topic_browse	misc TL1-4050 misc precision agriculture misc EVOO misc digital methods misc tree canopy misc image analysis misc yield prediction misc Motor vehicles. Aeronautics. Astronautics
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title	Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius
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title_full	Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius
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author_browse	Luciano Ortenzi Simona Violino Federico Pallottino Simone Figorilli Simone Vasta Francesco Tocci Francesca Antonucci Giancarlo Imperi Corrado Costa
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author-letter	Luciano Ortenzi
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title_sort	early estimation of olive production from light drone orthophoto, through canopy radius
callnumber	TL1-4050
title_auth	Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius
abstract	Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production.
abstractGer	Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production.
abstract_unstemmed	Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production.
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container_issue	4, p 118
title_short	Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius
url	https://doi.org/10.3390/drones5040118 https://doaj.org/article/ca61712cae604ccfb8df250c0cbe69a1 https://www.mdpi.com/2504-446X/5/4/118 https://doaj.org/toc/2504-446X
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author2	Simona Violino Federico Pallottino Simone Figorilli Simone Vasta Francesco Tocci Francesca Antonucci Giancarlo Imperi Corrado Costa
author2Str	Simona Violino Federico Pallottino Simone Figorilli Simone Vasta Francesco Tocci Francesca Antonucci Giancarlo Imperi Corrado Costa
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doi_str	10.3390/drones5040118
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up_date	2024-07-03T21:39:46.586Z
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Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. 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Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius

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