Detección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificial

Ilustraciones, fotografías, tablas

Autores:: Cortes Quiceno, Manuel Alejandro

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Detección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificial
dc.title.translated.eng.fl_str_mv	Indirect detection of phytosanitary, phenological, and productive parameters of Cayenne pepper cultivation through the use of phenotyping platforms and artificial intelligence
title	Detección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificial
spellingShingle	Detección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificial 630 - Agricultura y tecnologías relacionadas Capsicum annuum Fenotipado Phenotyping Inteligencia artificial Artificial intelligence Variación fenotípica Phenotypic variation Fotosíntesis Marchitez Vascular Fenología Componentes de rendimiento Fenotipado de alto rendimiento Número de frutos Aprendizaje automático Aprendizaje profundo Agricultura 4.0. Machine learning Deep learning Photosynthesis Vascular wilt Phenology Number of fruits High-throughput phenotyping Machine learning Deep learning Agriculture 4.0
title_short	Detección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificial
title_full	Detección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificial
title_fullStr	Detección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificial
title_full_unstemmed	Detección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificial
title_sort	Detección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificial
dc.creator.fl_str_mv	Cortes Quiceno, Manuel Alejandro
dc.contributor.advisor.none.fl_str_mv	Gómez López, Eyder Daniel Ramirez Gil, Joaquin Guillermo
dc.contributor.author.none.fl_str_mv	Cortes Quiceno, Manuel Alejandro
dc.contributor.researcher.none.fl_str_mv	Conejo Rodríguez Diego Felipe
dc.contributor.orcid.spa.fl_str_mv	0000-0002-8030-8624
dc.subject.ddc.spa.fl_str_mv	630 - Agricultura y tecnologías relacionadas
topic	630 - Agricultura y tecnologías relacionadas Capsicum annuum Fenotipado Phenotyping Inteligencia artificial Artificial intelligence Variación fenotípica Phenotypic variation Fotosíntesis Marchitez Vascular Fenología Componentes de rendimiento Fenotipado de alto rendimiento Número de frutos Aprendizaje automático Aprendizaje profundo Agricultura 4.0. Machine learning Deep learning Photosynthesis Vascular wilt Phenology Number of fruits High-throughput phenotyping Machine learning Deep learning Agriculture 4.0
dc.subject.agrovoc.none.fl_str_mv	Capsicum annuum Fenotipado Phenotyping Inteligencia artificial Artificial intelligence Variación fenotípica Phenotypic variation
dc.subject.proposal.spa.fl_str_mv	Fotosíntesis Marchitez Vascular Fenología Componentes de rendimiento Fenotipado de alto rendimiento Número de frutos Aprendizaje automático Aprendizaje profundo Agricultura 4.0.
dc.subject.proposal.eng.fl_str_mv	Machine learning Deep learning Photosynthesis Vascular wilt Phenology Number of fruits High-throughput phenotyping Machine learning Deep learning Agriculture 4.0
description	Ilustraciones, fotografías, tablas
publishDate	2023
dc.date.issued.none.fl_str_mv	2023-11
dc.date.accessioned.none.fl_str_mv	2024-02-09T13:40:38Z
dc.date.available.none.fl_str_mv	2024-02-09T13:40:38Z
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
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/85670
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/85670 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Palmira - Ciencias Agropecuarias - Maestría en Ciencias Agrarias
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias Agropecuarias
dc.publisher.place.spa.fl_str_mv	Palmira, Valle del Cauca, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Palmira
institution	Universidad Nacional de Colombia
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spelling	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_abf2Gómez López, Eyder Daniel0ebbc74a-9df6-43c5-89b9-0f16bbd2e6cc600Ramirez Gil, Joaquin Guillermob5f71d9e-5831-4c59-8a9a-36e5b7526918600Cortes Quiceno, Manuel Alejandro 1f7eb784-760d-4ccc-ac84-92ed857145a2600Conejo Rodríguez Diego Felipe0000-0002-8030-86242024-02-09T13:40:38Z2024-02-09T13:40:38Z2023-11https://repositorio.unal.edu.co/handle/unal/85670Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones, fotografías, tablasEl ají (Capsicum annuum L.) es un cultivo relevante a nivel mundial, el cual en Colombia en los últimos años se ha convertido en una alternativa productiva debido a sus usos culinarios, propiedades medicinales y potencial de exportación. Sin embargo, este sistema productivo presenta limitantes productivos y tecnológicos, en especial enfrenta desafíos fitosanitarios, como el marchitamiento vascular (MV) asociado al agente causal Fusarium sp. Igualmente, aspectos asociados a la variabilidad del clima afectan la fenología de las plantas y los parámetros productivos, como el número de frutos, lo cual hace que se incremente la incertidumbre en las inversiones y la sostenibilidad en los sistemas agrícolas. En los últimos años se ha incrementado la capacidad de poder adquirir múltiples variables respuesta de forma masiva a nivel de plantas mediante un concepto denominado fenotipado de alto rendimiento (HTPP), la cual presenta múltiples aplicaciones, incluidas conocer y caracterizar las respuestas a fuentes de estrés bióticas y abióticas, parámetros fenológicos y productivos. Este enfoque, representa minería de rasgos fenotípicos y requiere métodos avanzados de análisis de datos como las herramientas de inteligencia artificial para la identificación de rasgos fenotípicos de mayor importancia a partir del uso de métodos como el aprendizaje automático (machine learning) y aprendizaje profundo (deep learning). El objetivo de nuestro trabajo fue detectar indirectamente parámetros fitosanitarios (MV), fenológicos (PF) y productivos (PP) del cultivo de ají Cayenne utilizando plataformas de fenotipado e inteligencia artificial. En un lote comercial de ají, el área de estudio fue de 1.145 m2 divididas en 96 parcelas iguales, midiendo 3 plantas por parcela, y registrando periódicamente múltiples rasgos fotosintéticos usando el sensor proximal MultispeQ. Igualmente se evaluaron las respuestas espectrales en tres etapas del ciclo del cultivo utilizando un Vehículo Aéreo no Tripulado (VANT) de tipo DJI Phantom 4 con una cámara multiespectral acoplada con 5 bandas. Estas bandas, incluyen el espectro visible (RGB) junto con la banda del infrarrojo cercano (NIR) y, la banda de borde rojo (RE). Se utilizó la función AutoML para evaluar diferentes modelos de aprendizaje automático (ML) y un enfoque de aprendizaje profundo (DL) para detectar la MV y predecir la fenología y el número de frutos. Los resultados mostraron que los rasgos fotosintéticos, espectrales y geométricos como Fv/Fm, NPQt, LDT, RelaChlo, Phi2, geometría del dosel, EVI, NDRE, CIRE y la banda de borde rojo fueron los más informativos y de mayor importancia para detectar la MV en el ají. Por su parte, para la estimación de PF y PP, los rasgos de mayor importancia fueron gH+, RelaChlo, PS1ActCent, FoPrime, EVI, VARI, CIrededge y CIRE. El enfoque basado en ML y el DL, demostró ser eficiente en la identificación de rasgos fotosintéticos clave que permiten la detección de MV y estimación de PF y PP. El presente trabajo presenta un avance relevante en aras de la implementación y validación de herramientas de agricultura 4.0, como base para mejorar las decisiones basadas en evidencia. (Texto tomado de la fuente)Chili pepper (Capsicum annuum L.) is a valuable crop around the world, and in Colombia, it has recently emerged as a viable alternative due to its culinary applications, medicinal benefits, and export potential. However, this production system has productivity and technological limits, particularly when dealing with phytosanitary issues such as vascular wilt (VW) caused by the causative agent Fusarium sp. Similarly, climate variability affects plant phenology and production parameters, such as fruit yield, increasing the uncertainty of investment and sustainability in agricultural systems. In recent years, the ability to collect multiple response variables at the plant level has increased thanks to a concept known as high-throughput phenotyping (HTPP), which has a variety of applications, including understanding and characterizing responses to biotic and abiotic stress sources, as well as phenological and yield parameters. This strategy is known as phenotypic trait mining, and it involves advanced data analysis methods such as artificial intelligence tools to identify phenotypic traits of major importance using methods such as machine learning and deep learning. Our study aimed to use phenotyping and artificial intelligence platforms to indirectly detect phytosanitary (VW), phenological (PF), and productive (PP) factors in the Cayenne chili pepper crop. The study area in a commercial chili pepper plot was 1,145 m2 , divided into 96 identical plots, with three plants per plot and several photosynthetic traits recorded at regular intervals using the MultispeQ proximal sensor. Spectral responses were also assessed at three stages of the crop cycle using a DJI Phantom 4 Unmanned Aerial Vehicle (UAV) equipped with a multispectral sensor and 5 bands of light. These bands comprise the visible spectrum (RGB), near infrared (NIR), and rededge band (RE). The AutoML function was used to assess various machine learning (ML) models and a deep learning (DL) technique for detecting MV, predicting phenology, and fruit number. The results revealed that photosynthetic, spectral, and geometric features such as Fv/Fm, NPQt, LDT, RelaChlo, Phi2, canopy geometry, EVI, NDRE, CIRE, and red-edge band were the most informative and important for detecting MV in chili pepper. For FP and PP estimation, the most essential traits were gH+, RelaChlo, PS1ActCent, FoPrime, EVI, VARI, CIrededge, and CIRE. The ML and DLbased technique demonstrated to be efficient in identifying important photosynthetic traits that allow for MV detection and PF and PP quantification. The current effort represents a significant step forward in the application and validation of agriculture 4.0 tools as a foundation for better evidencebased decision-making.MaestríaMagíster en Ciencias Agrariasse uso fenotipado de alto rendimiento, minería de rasgos fenotípicos e inteligencia artificial para identificar rasgos clave que permiten la detección de parámetros fitosanitarios, fenológicos y productivos en cultivos comerciales de ají CayenneProtección de cultivosCiencias Agropecuarias.Sede Palmiraxviii, 83 páginas + anexosapplication/pdfspaUniversidad Nacional de ColombiaPalmira - Ciencias Agropecuarias - Maestría en Ciencias AgrariasFacultad de Ciencias AgropecuariasPalmira, Valle del Cauca, ColombiaUniversidad Nacional de Colombia - Sede Palmira630 - Agricultura y tecnologías relacionadasCapsicum annuumFenotipadoPhenotypingInteligencia artificialArtificial intelligenceVariación fenotípicaPhenotypic variationFotosíntesisMarchitez VascularFenologíaComponentes de rendimientoFenotipado de alto rendimientoNúmero de frutosAprendizaje automáticoAprendizaje profundoAgricultura 4.0.Machine learningDeep learningPhotosynthesisVascular wiltPhenologyNumber of fruitsHigh-throughput phenotypingMachine learningDeep learningAgriculture 4.0Detección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificialIndirect detection of phytosanitary, phenological, and productive parameters of Cayenne pepper cultivation through the use of phenotyping platforms and artificial intelligenceTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAgronet, (2023). 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HTPPs://doi.org/10.1016/j.ygeno.2021.04.031LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85670/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1113657355.2023.pdf1113657355.2023.pdfapplication/pdf3724070https://repositorio.unal.edu.co/bitstream/unal/85670/2/1113657355.2023.pdffb592559947bcbbd3486b6f6ff605472MD52THUMBNAIL1113657355.2023.pdf.jpg1113657355.2023.pdf.jpgGenerated Thumbnailimage/jpeg5546https://repositorio.unal.edu.co/bitstream/unal/85670/3/1113657355.2023.pdf.jpgf62a430d304fbffb88de4d5e6c5e77bfMD53unal/85670oai:repositorio.unal.edu.co:unal/856702024-02-09 23:10:42.711Repositorio Institucional Universidad Nacional de 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