Evaluación del potencial de datos espectrales para el diagnóstico de Marchitez Letal (ML) en Palma de Aceite (Elaeis guineensis Jacq)
ilustraciones, fotografías, gráficas, tablas
- Autores:
-
Giraldo Betancourt, Cristhian
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81322
- Palabra clave:
- 630 - Agricultura y tecnologías relacionadas::632 - Lesiones, enfermedades, plagas vegetales
Wilt diseases
Oil-palm
Botany
Marchitez (Patología vegetal)
Palma africana
Botánica
Respuestas hiperespectrales
Imágenes multiespectrales
Enfermedad
Índices de vegetación
Clasificación supervisada
Aprendizaje automático
Vegetation índices
Hyperspectral responses
Multispectral images
Disease
Supervised classification
Machine learning
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Evaluación del potencial de datos espectrales para el diagnóstico de Marchitez Letal (ML) en Palma de Aceite (Elaeis guineensis Jacq) |
| dc.title.translated.eng.fl_str_mv |
Evaluation of the potential of spectral data for the diagnosis of Lethal Wilt (LW) in Oil Palm (Elaeis guineensis Jacq) |
| title |
Evaluación del potencial de datos espectrales para el diagnóstico de Marchitez Letal (ML) en Palma de Aceite (Elaeis guineensis Jacq) |
| spellingShingle |
Evaluación del potencial de datos espectrales para el diagnóstico de Marchitez Letal (ML) en Palma de Aceite (Elaeis guineensis Jacq) 630 - Agricultura y tecnologías relacionadas::632 - Lesiones, enfermedades, plagas vegetales Wilt diseases Oil-palm Botany Marchitez (Patología vegetal) Palma africana Botánica Respuestas hiperespectrales Imágenes multiespectrales Enfermedad Índices de vegetación Clasificación supervisada Aprendizaje automático Vegetation índices Hyperspectral responses Multispectral images Disease Supervised classification Machine learning |
| title_short |
Evaluación del potencial de datos espectrales para el diagnóstico de Marchitez Letal (ML) en Palma de Aceite (Elaeis guineensis Jacq) |
| title_full |
Evaluación del potencial de datos espectrales para el diagnóstico de Marchitez Letal (ML) en Palma de Aceite (Elaeis guineensis Jacq) |
| title_fullStr |
Evaluación del potencial de datos espectrales para el diagnóstico de Marchitez Letal (ML) en Palma de Aceite (Elaeis guineensis Jacq) |
| title_full_unstemmed |
Evaluación del potencial de datos espectrales para el diagnóstico de Marchitez Letal (ML) en Palma de Aceite (Elaeis guineensis Jacq) |
| title_sort |
Evaluación del potencial de datos espectrales para el diagnóstico de Marchitez Letal (ML) en Palma de Aceite (Elaeis guineensis Jacq) |
| dc.creator.fl_str_mv |
Giraldo Betancourt, Cristhian |
| dc.contributor.advisor.spa.fl_str_mv |
Martínez Martínez, Luis Joel Torres León, Jorge Luis |
| dc.contributor.author.spa.fl_str_mv |
Giraldo Betancourt, Cristhian |
| dc.subject.ddc.spa.fl_str_mv |
630 - Agricultura y tecnologías relacionadas::632 - Lesiones, enfermedades, plagas vegetales |
| topic |
630 - Agricultura y tecnologías relacionadas::632 - Lesiones, enfermedades, plagas vegetales Wilt diseases Oil-palm Botany Marchitez (Patología vegetal) Palma africana Botánica Respuestas hiperespectrales Imágenes multiespectrales Enfermedad Índices de vegetación Clasificación supervisada Aprendizaje automático Vegetation índices Hyperspectral responses Multispectral images Disease Supervised classification Machine learning |
| dc.subject.lemb.eng.fl_str_mv |
Wilt diseases Oil-palm Botany |
| dc.subject.lemb.spa.fl_str_mv |
Marchitez (Patología vegetal) Palma africana Botánica |
| dc.subject.proposal.spa.fl_str_mv |
Respuestas hiperespectrales Imágenes multiespectrales Enfermedad Índices de vegetación Clasificación supervisada Aprendizaje automático Vegetation índices |
| dc.subject.proposal.eng.fl_str_mv |
Hyperspectral responses Multispectral images Disease Supervised classification Machine learning |
| description |
ilustraciones, fotografías, gráficas, tablas |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2022-03-22T20:56:50Z |
| dc.date.available.none.fl_str_mv |
2022-03-22T20:56:50Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
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http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/81322 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/81322 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
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A., & Skidmore, A. K. (2006). A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment, 101(2), 181–193. https://doi.org/10.1016/j.rse.2005.12.011 Chuvieco, E. (1990). Fundamentos de teledetection espacial. Convenio Empresarial de Manejo Sanitario. (2021). Informe interno - Subcomité Local. Cenipalma. Dangond Lacouture, L. F. (2019). El cultivo de la palma de aceite: oportunidad o amenaza para Colombia. Revista Palmas, 40(2), 44–60. Dash, J., & Curran, P. J. (2004). The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing, 25(23), 5403–5413. https://doi.org/10.1080/0143116042000274015 Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. International Journal of Remote Sensing, 20(14), 2741–2759. https://doi.org/10.1080/014311699211778 Datt, Bisun. (1998). Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves. Remote Sensing of Environment, 66(2), 111–121. https://doi.org/10.1016/S0034-4257(98)00046-7 Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey, J. E. (2000). Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment, 74, 229–239. De Maesschalck, R., Estienne, F., Verdu-Andres, J., Candolfi, a, Centner, V., Despagne, F., Jouan-Rimbaud, D., Walczak, B., Massart, D. L., De Jong, S., De Noord, O. E., Puel, C., & Vandeginste, B. M. G. (1999). The development of calibration models for spectroscopic data using principal component regression [Review]. Internet Journal of Chemistry, 2(19). Diebel, J., Norda, J., & Kretchmer, O. (2016). The Typical Weather Anywhere on Earth - Weather Spark. https://weatherspark.com/ Fedepalma. (2016). Balance económico del sector palmero colombiano en 2015. Boletín Económico, Figura 1, 8. http://web.fedepalma.org/sites/default/files/files/BTE 2016_en baja.pdf Fedepalma, F. N. de C. de P. de A. (2019). La agroindustria de la Palma de Aceite en Colombia. 1. http://web.fedepalma.org/sites/default/files/files/Infografía CESAR.pdf Giraldo-Betancourt, C., Velandia-Sánchez, E. A., Fischer, G., Gómez-Caro, S., & Martínez, L. J. (2020). Hyperspectral response of cape gooseberry (Physalis peruviana L.) plants inoculated with Fusarium oxysporum f. sp. physali for vascular wilt detection. Revista Colombiana de Ciencias Hortícolas, 14(3), 301–313. https://doi.org/10.17584/rcch.2020v14i3.10938 Giraldo, R., Molina, A., Torres-León, J., Acosta, M., & Martínez, S. (2016). Study of the Spectral Signatures of Oil Palms Affected with Lethal Wilt, Using Functional Data Statistic Analysis. 37, 131–139. Gitelson, A. A., Merzlyak, M. N., & Lichtenthaler, H. K. (1996). Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology, 148(3–4), 501–508. https://doi.org/10.1016/S0176-1617(96)80285-9 Glinskis, E. A., & Gutiérrez-Vélez, V. H. (2019). Quantifying and understanding land cover changes by large and small oil palm expansion regimes in the Peruvian Amazon. Land Use Policy, 80(September 2018), 95–106. https://doi.org/10.1016/j.landusepol.2018.09.032 Griffith, D. M., & Anderson, T. M. (2019). The ‘plantspec’ r package: A tool for spectral analysis of plant stoichiometry. Methods in Ecology and Evolution, 10(5), 673–679. https://doi.org/10.1111/2041-210X.13143 Guyot, G., & Baret, F. (1988). Utilisation de la haute resolution spectrale pour suivre l’etat des couverts vegetaux. ESA Special Publication, 278, 278–286. Hasmadi, M., Pakhriazad, H., & Shahrin, M. (2009). Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. Geografia: Malaysian Journal of Society and Space, 5(1), 1–10. Hernandez, R. (2017). Metodología para discriminación de malezas basada en la respuesta espectral de la vegetación. 130. Hijmans, R., Etten, J., Sumner, M., Cheng, J., Baston, D., Bevan, A., Bivand, R., Busetto, L., Canty, M., Fasoli, B., Forrest, D., Ghosh, A., Greenberg, J., Hiemstra, P., Karney, C., & Mattiuzzi, M. (2021). Package ‘raster.’ Hijmans, R., Phillips, S., Leathwick, J., & Elith, J. (2021). Package ‘dismo.’ Jones, H. G., & Vaughan, R. A. (2010). Remote sensing of vegetation: principles, techniques and applications. In University Press Oxford, United Kingdom, 1 edition. https://doi.org/10.1111/j.1654-1103.2011.01319.x Küng, O., Strecha, C., Beyeler, A., Zufferey, J.-C., Floreano, D., Fua, P., & Gervaix, F. (2011). The Accuracy of Automatic Photogrammetric Techniques on Ultra-Light Uav Imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. https://doi.org/10.5194/isprsarchives-xxxviii-1-c22-125-2011 Lehnert, L. W., Meyer, H., Obermeier, W. A., Silva, B., Regeling, B., Thies, B., & Bendix, J. (2019). Hyperspectral data analysis in R: The hsdar package. Journal of Statistical Software, 89(12). https://doi.org/10.18637/jss.v089.i12 León-Rueda, W. A., León, C., Caro, S. G., & Ramírez-Gil, J. G. (2021). Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools. Tropical Plant Pathology, 0123456789. https://doi.org/10.1007/s40858-021-00460-2 Liang, S. (2004). Quantitative Remote Sensing of Land Surfaces. In Wiley Series on Remote Sensing. https://doi.org/10.1002/047172372X Maccioni, A., Agati, G., & Mazzinghi, P. (2001). New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. Journal of Photochemistry and Photobiology B: Biology, 61(1–2), 52–61. https://doi.org/10.1016/S1011-1344(01)00145-2 Mahlein, A.-K. (2016). Present and Future Trends in Plant Disease Detection. Plant Disease, 100(2), 1–11. https://doi.org/10.1007/s13398-014-0173-7.2 Malvern Panalytical. (n.d.). Retrieved February 8, 2021, from https://www.malvernpanalytical.com/es/products/product-range/asd-range/fieldspec-range/fieldspec-4-standard-res-spectroradiometer Mamaghani, B., & Salvaggio, C. (2019). Multispectral sensor calibration and characterization for sUAS remote sensing. Sensors (Switzerland), 19(20). https://doi.org/10.3390/s19204453 Martínez, G., Arango, C. M., Rairán, N., Cadena, W., Castiblanco R, J. S., Sierra M, L. J., Aldana de la Torre, R. C., & Tovar M, J. P. (2017). GUÍA DE BOLSILLO PARA EL MANEJO DE LA MARCHITEZ LETAL (ML) DE LA PALMA DE ACEITE. Ml, 32. Martínez, G., Arango, M., Torres, G. A., Sarria, G. A., Vélez, D. C., Rodríguez, J., Mestizo, Y., Aya- Castañeda, H. A., Noreña, C., Varón, F. H., Drenth, A., & Guest, D. I. (2013). Avances en la investigación sobre las dos enfermedades mas importantes de la palma de aceite en Colombia: la pudrición del cogollo y la marchitez letal. Palmas, 34(1), 39–47. Martínez, L. J. (2017). Relationship between crop nutritional status, spectral measurements and Sentinel 2 images. Agronomia Colombiana, 35(2), 205–215. https://doi.org/10.15446/agron.colomb.v35n2.62857 Meyer, H., & Lehnert, L. W. (2020). Introduction to “ hsdar “. 1–52. MicaSense Multispectral Sensors. (n.d.). Retrieved February 8, 2021, from https://micasense.com/ Milborrow, S. (2021). Package ‘rpart.plot.’ Mission Planner Documentation. (n.d.). Retrieved February 8, 2021, from https://ardupilot.org/planner/index.html Montero, D., & Rueda, C. (2018). Detection of palm oil bud rot employing artificial vision. IOP Conference Series: Materials Science and Engineering, 437(1). https://doi.org/10.1088/1757-899X/437/1/012004 Murillo, P. J., & Carbonell, J. A. (2012). Principios y aplicaciones de Ia percepción remota en el cultivo de Ia caña de azücar en Colombia. Ortiz, N. (2014). Plataforma Hardware para la detección de la Pudrición de Cogollo en Palma Africana. Ose, K., Corpetti, T., & Demagistri, L. (2016). Multispectral Satellite Image Processing. Optical Remote Sensing of Land Surface: Techniques and Methods, 58–124. https://doi.org/10.1016/B978-1-78548-102-4.50002-8 Pardo, L., & Ocampo-Peñuela, N. (2019). Contexto actual del impacto ambiental de la palma de aceite en Colombia*. Palmas, 40(3), 79–88. Perdomo, N. Á. (2019). Método para la identificación temprana de la Pudrición del Cogollo en palma de aceite a partir de sensores remotos no tripulados. 96. Perpiñan, O., & Hijmans, R. (2021). Package ‘rasterVis.’ R Core Team : The R Project for Statistical Computing. (2019). https://www.r-project.org/ Rincón-Romero, V., Camperos-Reyes, F. R., Anaya, M., Martínez, M. A., Sarria, G. A., Mestizo, Y. A., & Torres, J. L. (2019). ¿Las Fotografías aéreas permiten la deteccíon temprana de pudricíon del cogollo en palma de aceite? Rocha, P. J. (2007). Sanidad de la palma de aceite : diagnóstico e investigación integral liderada por el gremio palmero colombiano. Revista Palmas, 28(2), 87–98. http://publicaciones.fedepalma.org/index.php/palmas/article/view/1215 Rodríguez, J. (2015). Georreferenciación y Caracterización Multiespectral de Especies de Vegetación Usando un Sistema Aéreo de Percepción Remota No Tripulado. Universidad Distrital Francisco José de Caldas. Rodríguez, J., Lizarazo, I., Prieto, F., & Angulo-Morales, V. (2021). Assessment of potato late blight from UAV-based multispectral imagery. Computers and Electronics in Agriculture, 184(March). https://doi.org/10.1016/j.compag.2021.106061 Sandak, J., Sandak, A., & Meder, R. (2016). Assessing trees, wood and derived products with near infrared spectroscopy: Hints and tips. Journal of Near Infrared Spectroscopy, 24(6), 485–505. https://doi.org/10.1255/jnirs.1255 Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J., Miklas, P. N., Carter, A. H., Pumphrey, M. O., Knowles, R. R. N., & Pavek, M. J. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 70, 112–123. https://doi.org/10.1016/j.eja.2015.07.004 Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639. https://doi.org/10.1021/ac60214a047 Shafri, H. Z. M., Anuar, M. I., Seman, I. A., & Noor, N. M. (2011). Spectral discrimination of healthy and ganoderma-infected oil palms from hyperspectral data. International Journal of Remote Sensing, 32(22), 7111–7129. https://doi.org/10.1080/01431161.2010.519003 Sims, D. A., & Gamon, J. A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2–3), 337–354. https://doi.org/10.1016/S0034-4257(02)00010-X SISPA. (2021). Sistema de Información Estadística del Sector Palmero. http://sispa.fedepalma.org/sispaweb/ Skolik, P., McAinsh, M. R., & Martin, F. L. (2018). Biospectroscopy for Plant and Crop Science. In Comprehensive Analytical Chemistry (1st ed., Vol. 80). Elsevier B.V. https://doi.org/10.1016/bs.coac.2018.03.001 Stuart, B. H. (2004). Infrared Spectroscopy: Fundamentals and Applications. In Infrared Spectroscopy: Fundamentals and Applications. https://doi.org/10.1002/0470011149 Sylvain, T., & Cecile, L. (2018). Disease Identification : A Review of Vibrational Spectroscopy Applications. In Vibrational Spectroscopy for Plant Varieties and Cultivars Characterization (1st ed., Vol. 80). Elsevier B.V. https://doi.org/10.1016/bs.coac.2018.03.005 Tawfik, O. H., Mohd Shafri, H. Z., & Mohammed, A. A. (2013). Disease Detection From Field Spectrometer Data. IIUM Engineering Journal, 14(2), 133–143. https://doi.org/10.31436/iiumej.v14i2.409 Tharwat, A. (2018). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192. https://doi.org/10.1016/j.aci.2018.08.003 Therneau, T., Atkinson, B., & Ripley, B. (2019). Package ‘rpart.’ https://cran.r-project.org/package=rpart Tu, Y. H., Phinn, S., Johansen, K., Robson, A., & Wu, D. (2020). Optimising drone flight planning for measuring horticultural tree crop structure. ISPRS Journal of Photogrammetry and Remote Sensing, 160(December 2019), 83–96. https://doi.org/10.1016/j.isprsjprs.2019.12.006 Tugi, A., Rasib, A. W., Suri, M. A., Zainon, O., Mohd Yusoff, A. R., Abdul Rahman, M. Z., Sari, N. A., & Darwin, N. (2015). Oil palm tree growth monitoring for smallholders by using unmanned aerial vehicle. Jurnal Teknologi, 77(26), 17–27. https://doi.org/10.11113/jt.v77.6855 Viera-Torres, M., Sinde-González, I., Gil-Docampo, M., Bravo-Yandún, V., & Toulkeridis, T. (2020). Generating the baseline in the early detection of bud rot and red ring disease in oil palms by geospatial technologies. Remote Sensing, 12(19), 1–21. https://doi.org/10.3390/rs12193229 Vogelmann, J. E., Rock, B. N., & Moss, D. M. (1993). Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(8), 1563–1575. https://doi.org/10.1080/01431169308953986 Wright, M., Wager, S., & Probst, P. (2021). Package ‘ranger.’ Zarco-Tejada, P. J., Berjón, A., López-Lozano, R., Miller, J. R., Martín, P., Cachorro, V., González, M. R., & De Frutos, A. (2005). Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment, 99(3), 271–287. https://doi.org/10.1016/j.rse.2005.09.002 Zarco-Tejada, P. J., Pushnik, J. C., Dobrowski, S., & Ustin, S. L. (2003). Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment, 84(2), 283–294. https://doi.org/10.1016/S0034-4257(02)00113-X |
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Bogotá - Ciencias Agrarias - Maestría en Geomática |
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Escuela de posgrados |
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Facultad de Ciencias Agrarias |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Universidad Nacional de Colombia |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Martínez Martínez, Luis Joel94d011bd9a7f169197ab0a1837a443b9Torres León, Jorge Luis7c18ed585ff52149e2582c41b09664ac600Giraldo Betancourt, Cristhian346e92e5e1539a58e1d70225eccce12d2022-03-22T20:56:50Z2022-03-22T20:56:50Z2021https://repositorio.unal.edu.co/handle/unal/81322Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, gráficas, tablasLa Marchitez Letal (ML) es el problema fitosanitario más importantes para la palmicultura colombiana en zona Oriental, generando pérdidas económicas de más de 146 millones de dólares y la erradicación de más de 5.000 ha. Las técnicas tradicionales de diagnóstico y detección de la enfermedad no funcionan apropiadamente y son limitantes en grandes extensiones de tierra debido a la subjetividad, consumen mucho tiempo y requieren de un gran esfuerzo humano. El objetivo de este trabajo fue evaluar el potencial de las respuestas espectrales de sensores remotos (imágenes del sensor multiespectral Red-Edge M) y proximales (respuestas hiperespectrales del sensor FieldSpec4), para la discriminación de plantas sanas y enfermas en el cultivo de palma de aceite. El área de estudio se ubicó en el municipio de San Carlos de Guaroa (Meta-Colombia) en un cultivo comercial (cultivar IRHO), donde se tomaron datos en campo e imágenes con un vehículo aéreo no tripulado (UAV) a 60 m durante dos años. La metodología propuesta incluye, adquisición de imágenes, corrección radiométrica, generación de ortomosaicos e índices multiespectrales, extracción de datos y la clasificación supervisada mediante algoritmos de Machine Learning; los datos de referencia se obtuvieron a partir de variables fisiológicas, respuestas hiperespectrales y observaciones en campo de palmas sanas y enfermas. Se propone el uso de índices de vegetación como índice de clorofila terrestre MERIS (MTCI), longitud de onda del punto de inflexión (Lp), índice de Maccioni (MI), entre otros, como indicadores de palmas sanas y palmas enfermas en el cultivo; así mismo, se plantea el uso de índices de vegetación a partir de sensores multiespectrales como índice de diferencia normalizado del borde rojo (NDRE), NDVI modificado a 705 (mND705), índice de Vogelmann (VOG), entre otros, para clasificar palmas sanas y enfermas en imágenes de alta resolución. Los resultados mostraron que el algoritmo de Random Forest (RF) tuvo el mejor rendimiento en términos de métrica de Precision, Recall, F1, OA e índice Kappa, con valores superiores al 80%. El proyecto de investigación demostró que por medio de respuestas espectrales se pueden discriminar entre plantas con presencia o ausencia de síntomas de ML en el cultivo de palma de aceite. (Texto tomado de la fuente).Lethal Wilt (LW) is the most important phytosanitary problem for Colombian palm cultivation in the eastern zone, generating economic losses of more than US$146 million and the eradication of more than 5,000 ha. Traditional techniques for diagnosis and detection of the disease do not work properly and are limited in large extensions of land due to subjectivity, are time-consuming and require great human effort. The objective of this work was to evaluate the potential of spectral responses from remote sensors (images from the Red-Edge M multispectral sensor) and proximal sensors (hyperspectral responses from the FieldSpec4 sensor), for the discrimination of healthy and diseased plants in oil palm cultivation. The study area was located in the municipality of San Carlos de Guaroa (Meta-Colombia) in a commercial crop (IRHO cultivar), where field data and images were taken with an unmanned aerial vehicle (UAV) at 60 m during two years. The proposed methodology includes image acquisition, radiometric correction, generation of orthomosaics and multispectral indices, data extraction and supervised classification using Machine Learning algorithms; reference data were obtained from physiological variables, hyperspectral responses and field observations of healthy and diseased palms. The use of vegetation indices such as MERIS terrestrial chlorophyll index (MTCI), wavelength of the inflection point (Lp), Maccioni index (MI), among others, is proposed as indicators of healthy and diseased palms in the crop; Likewise, the use of vegetation indices from multispectral sensors such as normalized difference red edge (NDRE), modified NDVI to 705 (mND705), Vogelmann index (VOG), among others, is proposed to classify healthy and diseased palms in high resolution images. The results showed that the Random Forest (RF) algorithm had the best performance in terms of Precision, Recall, F1, OA and Kappa index metrics, with values above 80%. The research project demonstrated that by means of spectral responses it is possible to discriminate between plants with presence or absence of ML symptoms in the oil palm crop.Incluye anexosMaestríaMagíster en GeomáticaGeo-información para el uso sostenible de los recursos naturalesxvii, 124 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaEscuela de posgradosFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá630 - Agricultura y tecnologías relacionadas::632 - Lesiones, enfermedades, plagas vegetalesWilt diseasesOil-palmBotanyMarchitez (Patología vegetal)Palma africanaBotánicaRespuestas hiperespectralesImágenes multiespectralesEnfermedadÍndices de vegetaciónClasificación supervisadaAprendizaje automáticoVegetation índicesHyperspectral responsesMultispectral imagesDiseaseSupervised classificationMachine learningEvaluación del potencial de datos espectrales para el diagnóstico de Marchitez Letal (ML) en Palma de Aceite (Elaeis guineensis Jacq)Evaluation of the potential of spectral data for the diagnosis of Lethal Wilt (LW) in Oil Palm (Elaeis guineensis Jacq)Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAbu Saria, N., Ahmada, A., Abu Saria, M., Sahiba, S., & Rasib, A. 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Remote Sensing of Environment, 84(2), 283–294. https://doi.org/10.1016/S0034-4257(02)00113-XEstudiantesInvestigadoresMaestrosPúblico generalORIGINAL1014250233.2021.pdf1014250233.2021.pdfTesis de Maestría en Geomáticaapplication/pdf7674539https://repositorio.unal.edu.co/bitstream/unal/81322/5/1014250233.2021.pdfc9d20160189c6b5ae473464fa511ac70MD55LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81322/6/license.txt8153f7789df02f0a4c9e079953658ab2MD56THUMBNAIL1014250233.2021.pdf.jpg1014250233.2021.pdf.jpgGenerated Thumbnailimage/jpeg5356https://repositorio.unal.edu.co/bitstream/unal/81322/7/1014250233.2021.pdf.jpg171984a6decd2ef2ed9cf09d7c0d7f65MD57unal/81322oai:repositorio.unal.edu.co:unal/813222023-08-03 23:03:55.262Repositorio Institucional Universidad Nacional de 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