Detección temprana de estrés biótico y abiótico usando modelos de clasificación de datos de espectroscopía de reflectancia VIS/NIR: Aplicación en plantas de Banano Gros Michel
ilustraciones, diagramas
- Autores:
-
Díaz Herrera, Cristian Camilo
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86300
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación
Estrés de sequia
Estrés abiótico
Estrés biótico
Fusarium oxysporum
Espectroscopia
drought stress
abiotic stress
biotic stress
Fusarium oxysporum
spectroscopy
Detección temprana
VIS / NIR
Clasificación
Estrés hídrico
Fusarium oxysporum
Ralstonia solanacearum
Early detection
VIS/NIR
Classification
Water stress
Fusarium oxysporum
Ralstonia solanacearum
Banana
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Detección temprana de estrés biótico y abiótico usando modelos de clasificación de datos de espectroscopía de reflectancia VIS/NIR: Aplicación en plantas de Banano Gros Michel |
| dc.title.translated.eng.fl_str_mv |
Early detection of biotic and abiotic stress using classification models of VIS/NIR reflectance spectroscopy data: Application in Gros Michel banana plants |
| title |
Detección temprana de estrés biótico y abiótico usando modelos de clasificación de datos de espectroscopía de reflectancia VIS/NIR: Aplicación en plantas de Banano Gros Michel |
| spellingShingle |
Detección temprana de estrés biótico y abiótico usando modelos de clasificación de datos de espectroscopía de reflectancia VIS/NIR: Aplicación en plantas de Banano Gros Michel 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación Estrés de sequia Estrés abiótico Estrés biótico Fusarium oxysporum Espectroscopia drought stress abiotic stress biotic stress Fusarium oxysporum spectroscopy Detección temprana VIS / NIR Clasificación Estrés hídrico Fusarium oxysporum Ralstonia solanacearum Early detection VIS/NIR Classification Water stress Fusarium oxysporum Ralstonia solanacearum Banana |
| title_short |
Detección temprana de estrés biótico y abiótico usando modelos de clasificación de datos de espectroscopía de reflectancia VIS/NIR: Aplicación en plantas de Banano Gros Michel |
| title_full |
Detección temprana de estrés biótico y abiótico usando modelos de clasificación de datos de espectroscopía de reflectancia VIS/NIR: Aplicación en plantas de Banano Gros Michel |
| title_fullStr |
Detección temprana de estrés biótico y abiótico usando modelos de clasificación de datos de espectroscopía de reflectancia VIS/NIR: Aplicación en plantas de Banano Gros Michel |
| title_full_unstemmed |
Detección temprana de estrés biótico y abiótico usando modelos de clasificación de datos de espectroscopía de reflectancia VIS/NIR: Aplicación en plantas de Banano Gros Michel |
| title_sort |
Detección temprana de estrés biótico y abiótico usando modelos de clasificación de datos de espectroscopía de reflectancia VIS/NIR: Aplicación en plantas de Banano Gros Michel |
| dc.creator.fl_str_mv |
Díaz Herrera, Cristian Camilo |
| dc.contributor.advisor.spa.fl_str_mv |
Botero Fernandez, Veronica Catalina |
| dc.contributor.author.spa.fl_str_mv |
Díaz Herrera, Cristian Camilo |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación |
| topic |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantación Estrés de sequia Estrés abiótico Estrés biótico Fusarium oxysporum Espectroscopia drought stress abiotic stress biotic stress Fusarium oxysporum spectroscopy Detección temprana VIS / NIR Clasificación Estrés hídrico Fusarium oxysporum Ralstonia solanacearum Early detection VIS/NIR Classification Water stress Fusarium oxysporum Ralstonia solanacearum Banana |
| dc.subject.agrovoc.spa.fl_str_mv |
Estrés de sequia Estrés abiótico Estrés biótico Fusarium oxysporum Espectroscopia |
| dc.subject.agrovoc.eng.fl_str_mv |
drought stress abiotic stress biotic stress Fusarium oxysporum spectroscopy |
| dc.subject.proposal.spa.fl_str_mv |
Detección temprana VIS / NIR Clasificación Estrés hídrico Fusarium oxysporum Ralstonia solanacearum |
| dc.subject.proposal.eng.fl_str_mv |
Early detection VIS/NIR Classification Water stress Fusarium oxysporum Ralstonia solanacearum Banana |
| description |
ilustraciones, diagramas |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-06-25T20:07:23Z |
| dc.date.available.none.fl_str_mv |
2024-06-25T20:07:23Z |
| dc.date.issued.none.fl_str_mv |
2024-06-24 |
| 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 |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/86300 |
| 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/86300 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
| dc.relation.indexed.spa.fl_str_mv |
Agrosavia Agrovoc |
| dc.relation.references.spa.fl_str_mv |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Botero Fernandez, Veronica Catalina97557c995747ce6e6d560ac9f59d802dDíaz Herrera, Cristian Camilo6bfa3dc8bd6fbd179c05737171e1b6936002024-06-25T20:07:23Z2024-06-25T20:07:23Z2024-06-24https://repositorio.unal.edu.co/handle/unal/86300Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasLa detección temprana de enfermedades y estrés hídrico (EH) en las plantas es crucial para la agricultura y la soberanía alimentaria de los países latinoamericanos. En este contexto, se han utilizado métodos de espectroscopía de reflectancia electromagnética visible (VIS) e infrarroja (NIR), que son no invasivos y han demostrado ser prometedores para identificar el estrés biótico y abiótico en las plantas incluso en su fase asintomática. Un ejemplo relevante de esto es la infección de las plantas de banano por enfermedades devastadoras, como la marchitez vascular causada por el hongo Fusarium oxysporum f.sp. cubense Raza 1 (FOCR1) y por la bacteria Ralstonia solanacearum Raza 2 (RSR2), que pueden resultar en pérdidas de hasta el 100% en las plantaciones. Para abordar este desafío, se llevó a cabo un estudio en el que se analizaron datos de reflectancia de 240 plantas de banano en el municipio de Carepa, ubicado en el departamento de Antioquia, Colombia. Estos datos incluyeron plantas sanas, aquellas sometidas a EH, infectadas con FOCR1, contagiadas con RSR2 y sus interacciones. El análisis se realizó utilizando un espectrómetro portátil ASD FieldSpec. Inicialmente, se aplicaron diversas técnicas de preprocesamiento a los datos de reflectancia en el rango espectral de 350-2500 nm, estas incluyen 2 de tratamiento de datos atípicos y 5 de suavizamiento. Luego, se llevaron a cabo diferentes enfoques para la selección de características, identificando las longitudes de onda que mejor discriminaban entre los diferentes tratamientos mediante la metodología RELIEF. Se emplearon métodos de clasificación supervisada, como Análisis Lineal Discriminante (ALD), Análisis Cuadrático Discriminante (ACD), Bosques Aleatorios (BA), Bayes ingenuo (BI), Máquinas de Soporte Vectorial (MSV), K vecinos más cercanos (KVC) y Perceptrón Multicapa (PM) con el objetivo de optimizar la exactitud de clasificación de los tratamientos, para esta medición se tuvo en cuenta una división de las plantas en el 75\% de entrenamiento y 25\% de prueba. Los resultados mostraron que, a pesar de que el período asintomático de las plantas de banano es de 20 días, con el ALD se logró un porcentaje de clasificación correcto del 86\% en el día 3 con métodos de preprocesamiento de Mínimos Cuadrados Asimétricos (MCAS) y la gestión de datos atípicos mediante el Método de la Bolsa (MB). Sin tener en cuenta las interacciones, la mejor metodología se obtiene al emplear un ALD con una precisión similar. Al día 6 post-inoculación, se obtienen precisiones similares con el ALD, siendo el más óptimo al usar los métodos de preprocesamiento como el de Corrección de Dispersión Multiplicativa (CDM) al tratar los datos atípicos con el MB. Estos resultados sugieren que la detección temprana de FOCR1, RSR2 y el EH en plantas de banano, mediante el uso de la espectroscopía de reflectancia, puede mejorar significativamente con la elección adecuada de metodologías de preprocesamiento, selección de características y clasificación de datos. (Texto tomado de la fuente).The early detection of diseases and water stress (WS) in plants is crucial for the agriculture and food sovereignty of Latin American countries. In this context, non-invasive methods such as visible-near infrared (VIS) and infrared (NIR) reflectance spectroscopy have been employed and proven promising for identifying biotic and abiotic stress in plants, even in asymptomatic phases. A relevant example is the infection of banana plants by devastating diseases like vascular wilt caused by the fungus Fusarium oxysporum f.sp. cubense Race 1 (FOCR1) and bacterial wilt caused by Ralstonia solanacearum Race 2 (RSR2), which can lead to losses of up to 100 % in plantations. To address this challenge, a study was conducted analyzing reflectance data from 240 banana plants in municipality of Carepa, located in the department from Antioquia, Colombia. The data included healthy plants, those subjected to WS, plants infected with FOCR1, contaged plants with RSR2, and their interactions. The analysis was performed using a portable ASD FieldSpec spectrometer. Initially, various preprocessing techniques were applied to the reflectance data in the spectral range of 350-2500 nm, including two for outlier treatment and five for smoothing. Different approaches for feature selection were then employed, identifying the wavelengths that best discriminated between the different treatments using the RELIEF methodology. Supervised classification methods such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forests (RF), Na¨ıve Bayes (NB), Support Vector Machines (SVM), k-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP) were employed to optimize the classification accuracy of treatments. For this, a division of plants into 75 % training and 25 % testing was considered. Results showed that despite the asymptomatic period of 20 days for banana plants, LDA achieved a correct classification rate of 86 % on day 3 with asymmetric least squares (ALS) preprocessing and outlier management using the Bag method. Excluding interactions, the best methodology was obtained using LDA with similar accuracy. On day 6 post-inoculation, similar accuracies were achieved with LDA being optimal when using preprocessing methodologies such as multiplicative scatter correction (MSC) and outlier treatment with the Bag method. These results suggest that early detection of FOCR1, RSR2, and WS in banana plants through reflectance spectroscopy can significantly improve with the appropriate choice of preprocessing, feature selection, and data classification methodologies.MaestríaMagíster en Ciencias - Estadísticaxxi, 85 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas630 - Agricultura y tecnologías relacionadas::633 - Cultivos de campo y de plantaciónEstrés de sequiaEstrés abióticoEstrés bióticoFusarium oxysporumEspectroscopiadrought stressabiotic stressbiotic stressFusarium oxysporumspectroscopyDetección tempranaVIS / NIRClasificaciónEstrés hídricoFusarium oxysporumRalstonia solanacearumEarly detectionVIS/NIRClassificationWater stressFusarium oxysporumRalstonia solanacearumBananaDetección temprana de estrés biótico y abiótico usando modelos de clasificación de datos de espectroscopía de reflectancia VIS/NIR: Aplicación en plantas de Banano Gros MichelEarly detection of biotic and abiotic stress using classification models of VIS/NIR reflectance spectroscopy data: Application in Gros Michel banana plantsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAgrosaviaAgrovoc[Abdulridha et al., 2016] Abdulridha, J., Ehsani, R., and De Castro, A. 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Computers and Electronics in Agriculture, 181:105966.InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86300/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1019036345.2024.pdf1019036345.2024.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf955128https://repositorio.unal.edu.co/bitstream/unal/86300/4/1019036345.2024.pdfa950adcc98f2c848348b7ec5a391f43bMD54THUMBNAIL1019036345.2024.pdf.jpg1019036345.2024.pdf.jpgGenerated Thumbnailimage/jpeg5491https://repositorio.unal.edu.co/bitstream/unal/86300/5/1019036345.2024.pdf.jpg3b8d03a71659f8fee22c0dfb1d63d9ecMD55unal/86300oai:repositorio.unal.edu.co:unal/863002024-08-25 23:11:58.706Repositorio Institucional Universidad Nacional de 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