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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
16/03/2022 |
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Actualizado : |
16/03/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
HIRIGOYEN, A.; ACOSTA-MUÑOZ, C.; SALAMANCA, A.J.A.; VARO-MARTINEZ, M.Á.; RACHID, C.; FRANCO, J.; NAVARRO-CERRILLO, R. |
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Afiliación : |
ANDRES EDUARDO HIRIGOYEN DOMINGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CRISTINA ACOSTA-MUÑOZ, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; ANTONIO JESÚS ARIZA SALAMANCA, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; MARIA ÁNGELES VARO-MARTINEZ, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain; ANA CECILIA RACHID CASNATI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JORGE FRANCO, Faculty of Agronomy, University of the Republic, Paysandú, Uruguay; RAFAEL NAVARRO-CERRILLO, Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Córdoba, Spain. |
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Título : |
A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Annals of Forest Research, 2021, Volume 64, Issue 2, Pages 165-183. OPEN ACCESS. doi: https://doi.org/10.15287/afr.2021.2073 |
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ISSN : |
1844-8135 |
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DOI : |
10.15287/afr.2021.2073 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received October 27, 2020; Revised December 14, 2021; Accepted December 21, 2021.
Corresponding author: Hirigoyen, A.; National Institute of Agricultural Research (INIA), Tacuarembó, Uruguay; email:ahirigoyen@inia.org.uy -- The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for supporting our research work and for help during the fieldwork. We are particularly grateful to Roberto Scoz, Demian Gomez, Leonidas Carrasco and Alicia Peduzzi for their assistance during this research. RMNC acknowledge the institutional support of the Ministerio de Ciencia, Innovaci?n y Universidades (Spain), through the ESPECTRAMED (CGL2017-86161-R). show significant changes. |
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Contenido : |
ABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied through machine learning algorithms provide an effective method to estimate LAI in eucalypt plantations. The methodology proposed is directly applicable for operational forest planning at the landscape level. © 2021, Editura Silvica. All rights reserved. MenosABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied throu... Presentar Todo |
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Palabras claves : |
Intensive silviculture; LAI canopy; Machine learning algorithms. |
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Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
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URL : |
https://www.afrjournal.org/index.php/afr/article/viewFile/2073/1177
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Marc : |
LEADER 03380naa a2200265 a 4500
001 1062842
005 2022-03-16
008 2021 bl uuuu u00u1 u #d
022 $a1844-8135
024 7 $a10.15287/afr.2021.2073$2DOI
100 1 $aHIRIGOYEN, A.
245 $aA machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data.$h[electronic resource]
260 $c2021
500 $aArticle history: Received October 27, 2020; Revised December 14, 2021; Accepted December 21, 2021. Corresponding author: Hirigoyen, A.; National Institute of Agricultural Research (INIA), Tacuarembó, Uruguay; email:ahirigoyen@inia.org.uy -- The authors thank the Instituto Nacional de Investigaciones Agropecuarias (INIA-Uruguay) for supporting our research work and for help during the fieldwork. We are particularly grateful to Roberto Scoz, Demian Gomez, Leonidas Carrasco and Alicia Peduzzi for their assistance during this research. RMNC acknowledge the institutional support of the Ministerio de Ciencia, Innovaci?n y Universidades (Spain), through the ESPECTRAMED (CGL2017-86161-R). show significant changes.
520 $aABSTRACT. - As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied through machine learning algorithms provide an effective method to estimate LAI in eucalypt plantations. The methodology proposed is directly applicable for operational forest planning at the landscape level. © 2021, Editura Silvica. All rights reserved.
653 $aIntensive silviculture
653 $aLAI canopy
653 $aMachine learning algorithms
700 1 $aACOSTA-MUÑOZ, C.
700 1 $aSALAMANCA, A.J.A.
700 1 $aVARO-MARTINEZ, M.Á.
700 1 $aRACHID, C.
700 1 $aFRANCO, J.
700 1 $aNAVARRO-CERRILLO, R.
773 $tAnnals of Forest Research, 2021, Volume 64, Issue 2, Pages 165-183. OPEN ACCESS. doi: https://doi.org/10.15287/afr.2021.2073
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
22/01/2021 |
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Actualizado : |
14/04/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
DELPIAZZO, R.; BARCELLOS, M.; BARROS, S.; BENTANCOR, L.; FRAGA, M.; GIL, J.; IRAOLA, G.; MORSELLA, C.; PAOLICCHI, F.; PÉREZ, R.; RIET-CORREA, F.; SANGUINETTI, M.; SILVA, A.; SILVEIRA, C.S.; CALLEROS, L. |
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Afiliación : |
RAFAEL DELPIAZZO, Universidad de la República Oriental del Uruguay. Facultad de Veterinaria. Estación Experimental "Dr. Mario A. Cassinoni". Departamento de Salud de los Sistemas Pecuarios. Paysandú, Uruguay.; MAILA BARCELLOS, Universidad de la República Oriental del Uruguay. Facultad de Ciencias. Sección Genética Evolutiva. Montevideo, Uruguay.; SOFÍA BARROS, Universidad de la República Oriental del Uruguay. Facultad de Ciencias. Sección Genética Evolutiva. Montevideo, Uruguay.; LAURA BENTANCOR, Universidad de la República Oriental del Uruguay. Facultad de Medicina. Instituto de Higiene. Departamento de Bacteriología y Virología. Montevideo, Uruguay.; MARTIN FRAGA COTELO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JORGE GIL, Universidad de la República Oriental del Uruguay. Facultad de Veterinaria. Estación Experimental "Dr. Mario A. Cassinoni". Departamento de Salud de los Sistemas Pecuarios. Paysandú, Uruguay.; GREGORIO IRAOLA, Institut Pasteur de Montevideo. Laboratorio de Genómica Microbiana, Montevideo, Uruguay. / Universidad Mayor. Facultad de Ciencias. Centro de Biología Integrativa. Santiago de Chile, Chile.; CLAUDIA MORSELLA, Estación Experimental INTA Balcarce. Laboratorio de Bacteriología. Balcarce, Buenos Aires, Argentina.; FERNANDO PAOLICCHI, Estación Experimental INTA Balcarce. Laboratorio de Bacteriología. Balcarce, Buenos Aires, Argentina.; RUBEN PEREZ, Universidad de la República Oriental del Uruguay. Facultad de Ciencias. Sección Genética Evolutiva. Montevideo, Uruguay.; FRANKLIN RIET-CORREA AMARAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Universidad de la República Oriental del Uruguay. Facultad de Ciencias. Sección Genética Evolutiva. Montevideo, Uruguay.; ALFONSO SILVA, Universidad de la República Oriental del Uruguay. Facultad de Ciencias. Sección Genética Evolutiva. Montevideo, Uruguay.; CAROLINE DA SILVA SILVEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA CALLEROS, Universidad de la República Oriental del Uruguay. Facultad de Ciencias. Sección Genética Evolutiva. Montevideo, Uruguay. |
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Título : |
Accurate and fast identification of Campylobacter fetus in bulls by real-time PCR targeting a 16S rRNA gene sequence. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Veterinary and Animal Science, January 2021, vol.11 no. 100165, 5 p. OPEN ACCESS. Doi: https://doi.org/10.1016/j.vas.2020.100163 |
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DOI : |
10.1016/j.vas.2020.100163 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 21 October 2020 / Received in revised form 20 December 2020 / Accepted 22 December 2020 / available online 24 December 2020.
Corresponding author: laurabet@higiene.edu.uy |
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Contenido : |
Campylobacter fetus is an important animal pathogen that causes infectious infertility, embryonic mortality and abortions in cattle and sheep flocks. There are two recognized subspecies related with reproductive disorders in livestock: Campylobacter fetus subsp. fetus (Cff) and Campylobacter fetus subsp. venerealis (Cfv). Rapid and reliable detection of this pathogenic species in bulls is of upmost importance for disease control in dairy and beef herds as they are asymptomatic carriers. The aim of the present work was to assess the performance a real-time PCR (qPCR) method for the diagnosis of Campylobacter fetus in samples from bulls, comparing it with culture and isolation methods. 520 preputial samples were both cultured in Skirrow?s medium and analyzed by qPCR. The estimated sensitivity of qPCR was 90.9% (95% CI, 69.4%?100%), and the specificity was 99.4% (95% CI, 98.6% - 100%). The proportion of C. fetus positive individuals was 2.1% by isolation and 2.5% by qPCR. Isolates were identified by biochemical tests as Cfv (n = 9) and Cff (n = 2). Our findings support the use of qPCR for fast and accurate detection of C. fetus directly from field samples of preputial smegma of bulls. The qPCR method showed to be suitable for massive screenings because it can be performed in pooled samples without losing accuracy and sensitivity. |
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Palabras claves : |
BOVINE GENITAL CAMPYLOBACTERIOSIS; CAMPYLOBACTER FETUS; MOLECULAR DIAGNOSIS; MOLECULAR DIAGNOSTICS; QPCR. |
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Asunto categoría : |
L73 Enfermedades de los animales |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14934/1/Veterinary-Animal-Science-2021-100163.pdf
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Marc : |
LEADER 02681naa a2200373 a 4500
001 1061678
005 2021-04-14
008 2021 bl uuuu u00u1 u #d
024 7 $a10.1016/j.vas.2020.100163$2DOI
100 1 $aDELPIAZZO, R.
245 $aAccurate and fast identification of Campylobacter fetus in bulls by real-time PCR targeting a 16S rRNA gene sequence.$h[electronic resource]
260 $c2021
500 $aArticle history: Received 21 October 2020 / Received in revised form 20 December 2020 / Accepted 22 December 2020 / available online 24 December 2020. Corresponding author: laurabet@higiene.edu.uy
520 $aCampylobacter fetus is an important animal pathogen that causes infectious infertility, embryonic mortality and abortions in cattle and sheep flocks. There are two recognized subspecies related with reproductive disorders in livestock: Campylobacter fetus subsp. fetus (Cff) and Campylobacter fetus subsp. venerealis (Cfv). Rapid and reliable detection of this pathogenic species in bulls is of upmost importance for disease control in dairy and beef herds as they are asymptomatic carriers. The aim of the present work was to assess the performance a real-time PCR (qPCR) method for the diagnosis of Campylobacter fetus in samples from bulls, comparing it with culture and isolation methods. 520 preputial samples were both cultured in Skirrow?s medium and analyzed by qPCR. The estimated sensitivity of qPCR was 90.9% (95% CI, 69.4%?100%), and the specificity was 99.4% (95% CI, 98.6% - 100%). The proportion of C. fetus positive individuals was 2.1% by isolation and 2.5% by qPCR. Isolates were identified by biochemical tests as Cfv (n = 9) and Cff (n = 2). Our findings support the use of qPCR for fast and accurate detection of C. fetus directly from field samples of preputial smegma of bulls. The qPCR method showed to be suitable for massive screenings because it can be performed in pooled samples without losing accuracy and sensitivity.
653 $aBOVINE GENITAL CAMPYLOBACTERIOSIS
653 $aCAMPYLOBACTER FETUS
653 $aMOLECULAR DIAGNOSIS
653 $aMOLECULAR DIAGNOSTICS
653 $aQPCR
700 1 $aBARCELLOS, M.
700 1 $aBARROS, S.
700 1 $aBENTANCOR, L.
700 1 $aFRAGA, M.
700 1 $aGIL, J.
700 1 $aIRAOLA, G.
700 1 $aMORSELLA, C.
700 1 $aPAOLICCHI, F.
700 1 $aPÉREZ, R.
700 1 $aRIET-CORREA, F.
700 1 $aSANGUINETTI, M.
700 1 $aSILVA, A.
700 1 $aSILVEIRA, C.S.
700 1 $aCALLEROS, L.
773 $tVeterinary and Animal Science, January 2021, vol.11 no. 100165, 5 p. OPEN ACCESS. Doi: https://doi.org/10.1016/j.vas.2020.100163
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