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

dc.contributor.advisorGómez López, Eyder Daniel
dc.contributor.advisorRamirez Gil, Joaquin Guillermo
dc.contributor.authorCortes Quiceno, Manuel Alejandro
dc.contributor.orcid0000-0002-8030-8624spa
dc.contributor.researcherConejo Rodríguez Diego Felipe
dc.date.accessioned2024-02-09T13:40:38Z
dc.date.available2024-02-09T13:40:38Z
dc.date.issued2023-11
dc.descriptionIlustraciones, fotografías, tablasspa
dc.description.abstractEl 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)spa
dc.description.abstractChili 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.eng
dc.description.curricularareaCiencias Agropecuarias.Sede Palmiraspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ciencias Agrariasspa
dc.description.methodsse 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 Cayennespa
dc.description.researchareaProtección de cultivosspa
dc.format.extentxviii, 83 páginas + anexosspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/85670
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Palmiraspa
dc.publisher.facultyFacultad de Ciencias Agropecuariasspa
dc.publisher.placePalmira, Valle del Cauca, Colombiaspa
dc.publisher.programPalmira - Ciencias Agropecuarias - Maestría en Ciencias Agrariasspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.agrovocCapsicum annuum
dc.subject.agrovocFenotipado
dc.subject.agrovocPhenotyping
dc.subject.agrovocInteligencia artificial
dc.subject.agrovocArtificial intelligence
dc.subject.agrovocVariación fenotípica
dc.subject.agrovocPhenotypic variation
dc.subject.ddc630 - Agricultura y tecnologías relacionadasspa
dc.subject.proposalFotosíntesisspa
dc.subject.proposalMarchitez Vascularspa
dc.subject.proposalFenologíaspa
dc.subject.proposalComponentes de rendimientospa
dc.subject.proposalFenotipado de alto rendimientospa
dc.subject.proposalMachine learningeng
dc.subject.proposalDeep learningeng
dc.subject.proposalNúmero de frutosspa
dc.subject.proposalAprendizaje automáticospa
dc.subject.proposalAprendizaje profundospa
dc.subject.proposalAgricultura 4.0.spa
dc.subject.proposalPhotosynthesiseng
dc.subject.proposalVascular wilteng
dc.subject.proposalPhenologyeng
dc.subject.proposalNumber of fruitseng
dc.subject.proposalHigh-throughput phenotypingeng
dc.subject.proposalMachine learningeng
dc.subject.proposalDeep learningeng
dc.subject.proposalAgriculture 4.0eng
dc.titleDetección indirecta de parámetros fitosanitarios, fenológicos y productivos del cultivo de ají Cayenne mediante el uso de plataformas de fenotipado e inteligencia artificialspa
dc.title.translatedIndirect detection of phytosanitary, phenological, and productive parameters of Cayenne pepper cultivation through the use of phenotyping platforms and artificial intelligenceeng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
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dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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