Early detection of asymptomatic anthracnose disease in mango by hyperspectral imaging and machine learning
dc.contributor.advisor | Prieto Ortiz, Flavio Augusto | spa |
dc.contributor.advisor | Aleixos Borrás, Nuria | spa |
dc.contributor.advisor | Blasco Ivars, Jose | spa |
dc.contributor.author | Velásquez Hernández, Carlos Alberto | spa |
dc.contributor.cvlac | Velasquez, Carlos [0001505007] | spa |
dc.contributor.googlescholar | Velasquez, Carlos [Carlos Alberto Velasquez Hernandez] | spa |
dc.contributor.orcid | Velasquez, Carlos [0000000295527395] | spa |
dc.contributor.researchgate | Velasquez, Carlos [Carlos-Velasquez-Hernandez] | spa |
dc.contributor.researchgroup | Grupo de Automática de la Universidad Nacional Gaunal | spa |
dc.contributor.scopus | Velasquez, Carlos [58651134500] | |
dc.date.accessioned | 2025-09-02T19:52:03Z | |
dc.date.available | 2025-09-02T19:52:03Z | |
dc.date.issued | 2025-06-24 | |
dc.description | ilustraciones, diagramas, fotografías | spa |
dc.description.abstract | One of the most relevant diseases in mango is anthracnose, mainly caused by Colletotrichum sp. The relevance of anthracnose is due to its high incidence in mango crops, causing about 60% of mango losses worldwide, although, in regions with periods of high rainfall and humidity, the incidence has reached 100% of mango fruit production. The current control of anthracnose in mango is done visually, which means that detection is done at a late (visible) stage and is subject to the expertise, training and visual acuity of a human expert. With the rise of machine learning techniques, hyperspectral vision systems have found the complement for developing robust object detection and classification applications. This has triggered a technological revolution in fruit inspection and quality control, driving the research and development of new solutions that address latent challenges in agro-industry such as early detection of fruit diseases. Therefore, this research has aimed to develop a methodology for detecting anthracnose in mango (fruits and leaves) at an early stage using Vis-NIR hyperspectral imaging and machine learning techniques. Furthermore, this research has assessed the potential of spectral data to characterise anthracnose symptoms in mango to establish a set of relevant wavelengths representing the main spectral changes induced by the disease. This research has also defined a methodology that involved selecting representative samples of mango fruits and leaves of 3 varieties, the controlled inoculation of Colletotrichum sp. and the acquisition of hyperspectral images under laboratory conditions. Additionally, a framework was defined to implement different spectral pre-processing filters and machine learning models for classifying and detecting anthracnose-diseased samples. Also, several feature selection techniques to select a representative set of wavelengths (10) representing the different stages of disease symptom development were implemented. As a result, classification models based on neural networks and quadratic discriminant analysis were obtained with metrics above 95% and were shown to be efficient in detecting anthracnose-diseased samples 48-72 hours before visible symptoms became distinguishable. Furthermore, it was found that there are several spectral regions in the Vis-NIR range related to anthracnose symptoms, allowing their characterisation from early (non-visible) to late stages. The findings reported in this research have evidenced the enormous potential of hyperspectral imaging and machine learning models for early detection of anthracnose disease in mango with full or reduced spectrum and could serve as a basis for the development of novel detection systems in mango fruits with multispectral vision systems. | eng |
dc.description.abstract | Una de las enfermedades más relevantes en mango es la antracnosis, causada principalmente por Colletotrichum sp. La relevancia de la antracnosis se debe a su alta incidencia en los cultivos de mango, causando alrededor del 60% de las pérdidas de mango en todo el mundo, aunque, en regiones con periodos de alta pluviosidad y humedad, la incidencia ha llegado a alcanzar el 100% de la producción de fruta de mango. El control actual de la antracnosis en mango se realiza de forma visual, lo que significa que la detección se realiza en una fase tardı́a (visible) y está sujeta a la pericia, entrenamiento y agudeza visual de un experto humano. Con el auge de las técnicas de aprendizaje automático, los sistemas de visión hiperespectral han encontrado el complemento para el desarrollo de aplicaciones robustas de detección y clasificación de objetos. Esto ha desencadenado una revolución tecnológica en la inspección y el control de calidad de la fruta, impulsando la investigación y el desarrollo de nuevas soluciones que aborden retos latentes en la agroindustria como la detección temprana de enfermedades en frutas. Ası́ pues, el objetivo de esta investigación ha consistido en desarrollar una metodologı́a para la detección de antracnosis en mango (frutos y hojas) en una fase temprana utilizando imágenes hiperespectrales Vis-NIR y técnicas de aprendizaje automático. Además, esta investigación ha evaluado el potencial de los datos espectrales para caracterizar los sı́ntomas de antracnosis en mango con el fin de establecer un conjunto de longitudes de onda relevantes que representen los principales cambios espectrales inducidos por la enfermedad. Esta investigación también ha abarcado la selección de muestras representativas de frutos y hojas de mango de 3 variedades, la inoculación controlada de Colletotrichum sp. y la adquisición de imágenes hiperespectrales en condiciones de laboratorio. Además, ha definido un marco para implementar diferentes filtros de preprocesado espectral y modelos de aprendizaje automático para clasificar y detectar muestras enfermas de antracnosis. También se han implementado varias técnicas de selección de caracterı́sticas para seleccionar un conjunto representativo de longitudes de onda (10) que representaran las diferentes etapas de desarrollo de los sı́ntomas de la enfermedad. Como resultado, se obtuvieron modelos de clasificación, basados en redes neuronales y análisis discriminante cuadrático, con métricas superiores al 95%, mostrando ser eficientes en la detección de muestras enfermas de antracnosis 48-72 horas antes de que los sı́ntomas se hicieran distinguibles. Además, se ha encontrado que existen varias regiones espectrales en el rango Vis-NIR relacionadas con los sı́ntomas de antracnosis, permitiendo su caracterización desde los estadios tempranos (no visibles) hasta los tardı́os. Los hallazgos reportados en esta investigación han evidenciado el enorme potencial de las imágenes hiperespectrales y los modelos de aprendizaje automático para la detección temprana de la antracnosis en mango con espectro completo o reducido, pudiendo servir de base para el desarrollo de novedosos sistemas de detección en frutos de mango con sistemas de visión multiespectral. (Texto tomado de la fuente). | spa |
dc.description.abstract | Una de les malalties més rellevants del mango és l’ antracnosi, principalment causada per Colletotrichum sp. La importància de l’antracnosi és deguda a l’alta incidència en el cultiu de mangos, que causa al voltant del 60% de pèrdues a nivell mundial, tot i que en regions amb perı́odes d’alta pluviositat i humitat la incidència pot arribar al 100% de la producció de fruita. Actualment, el control de l’antracnosi en mango es fa visualment, la qual cosa implica que la detecció es produeix en una etapa (visual) tardana i que està condicionada a l’experiència, entrenament i agudesa visual de la persona experta. Amb l’auge de les tècniques d’aprenentatge automàtic, els sistemes de visió hiperespectral han trobat el complement per al desenvolupament d’aplicacions robustes de detecció i classificació d’objectes. Aquesta circumstància ha desencadenat una revolució tecnològica en la inspecció de fruita i el control de qualitat, i ha impulsat la recerca i el desenvolupament de noves solucions que adrecen reptes latents en la indústria agroalimentària com la detecció primerenca de malalties de la fruita. Aixı́ doncs, aquesta recerca ha consistit a desenrotllar una metodologia per a la detecció d’antracnosi en mango (fruits i fulles) en una etapa primerenca emprant imatge hiperespectral Vis-NIR i tècniques d’aprenentatge automàtic. A més, aquesta recerca ha avaluat el potencial de les dades espectrals per a caracteritzar els sı́mptomes d’antracnosi en mango per establir un conjunt rellevant de longituds d’ona que representen els principals canvis a nivell espectral introduı̈ts per la malaltia. Aquesta recerca ha definit una metodologia que ha inclòs la selecció de mostres representatives de fruits i fulles de mango de 3 varietats, la inoculació controlada de Colletotrichum i l’adquisició d’imatges hiperespectrals en condicions de laboratori. Addicionalment, s’ha definit un marc de treball per a implementar diferents filtres de preprocessament espectral i models d’aprenentatge automàtic per a la detecció i classificació de mostres amb la malaltia d’antracnosi. També s’han implementat diverses tècniques de selecció de caracterı́stiques per tal de triar un conjunt representatiu de longituds d’ona (10) que representen les diferents fases del desenvolupament dels sı́mptomes de la malaltia. Com a resultat, s’han obtingut models de classificació basats en xarxes neuronals i anàlisi discriminant quadràtic amb mètriques per damunt del 95% i han mostrat ser eficients en detectar mostres amb antracnosi 48-72 hores abans que els sı́mptomes visibles esdevingueren distingibles. A més, es va trobar que hi ha diverses regions espectrals en el rang Vis-NIR relacionats amb sı́mptomes d’antracnosi, cosa que permet la caracterització des d’etapes primerenques (no visibles) fins a tardanes. Els resultats d’aquesta investigació evidencien l’enorme potencial de la imatge hiperespectral i els models d’aprenentatge automàtic per a la detecció primerenca de l’antracnosi en mango amb tot o part de l’espectre i pot servir de base per al desenvolupament de nous sistemes de detecció en fruit de mango amb sistemes de visió multiespectral | other |
dc.description.degreelevel | Doctorado | |
dc.description.degreename | Doctor en Ingeniería | |
dc.description.methods | En los capítulos 3, 4 y 5 del documento de tesis se explica de forma amplia y detallada | |
dc.description.researcharea | Machine learning and computer vision for spectral analysis | |
dc.description.researcharea | Advanced optical systems for fruit quality inspection and other applications | |
dc.description.researcharea | Precision agriculture | |
dc.description.sponsorship | Minciencias | |
dc.format.extent | xxix, 200 páginas | |
dc.format.mimetype | application/pdf | |
dc.identifier.instname | Universidad Nacional de Colombia | spa |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
dc.identifier.repourl | https://repositorio.unal.edu.co/ | spa |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/88555 | |
dc.language.iso | eng | |
dc.publisher | Universidad Nacional de Colombia | |
dc.publisher | Universitat Politècnica de València | |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | |
dc.publisher.faculty | Facultad de Ingeniería | |
dc.publisher.place | Bogotá, Colombia | |
dc.publisher.program | Bogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería Mecánica y Mecatrónica | |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
dc.rights.license | Reconocimiento 4.0 Internacional | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación | spa |
dc.subject.ddc | 630 - Agricultura y tecnologías relacionadas::632 - Lesiones, enfermedades, plagas vegetales | spa |
dc.subject.proposal | Machine learning | eng |
dc.subject.proposal | Hyperspectral imaging | eng |
dc.subject.proposal | Anthracnose detection | eng |
dc.subject.proposal | Wavelength selection | eng |
dc.subject.proposal | Aprendizaje automatico | spa |
dc.subject.proposal | Imagen hiperespectral | spa |
dc.subject.proposal | Detección de antracnosis | spa |
dc.subject.proposal | Selección de longitudes de ondas | spa |
dc.subject.proposal | Mangifera indica L. | eng |
dc.subject.proposal | Mango | spa |
dc.subject.proposal | Aprenentatge automàtic | other |
dc.subject.proposal | Imatge hiperespectral | other |
dc.subject.proposal | Detecció d’antracnosi | other |
dc.subject.proposal | Selecció de longituds d’ona | other |
dc.subject.proposal | Mango | other |
dc.subject.wikidata | artificial neural network | eng |
dc.subject.wikidata | red neuronal artificial | spa |
dc.subject.wikidata | canker | eng |
dc.subject.wikidata | Antracnosis | spa |
dc.subject.wikidata | Colletotrichum | eng |
dc.subject.wikidata | Colletotrichum | spa |
dc.subject.wikidata | machine learning | eng |
dc.subject.wikidata | aprendizaje automático | spa |
dc.title | Early detection of asymptomatic anthracnose disease in mango by hyperspectral imaging and machine learning | eng |
dc.title.translated | Detección temprana de antracnosis asintomática en mango mediante imágenes hiperespectrales y aprendizaje automático | spa |
dc.title.translated | Detecció precoç de antracnosi asimptomàtica en mànec mitjançant imatges hiperespectrales i aprenentatge automàtic | other |
dc.type | Trabajo de grado - Doctorado | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_db06 | |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | |
dc.type.content | Text | |
dc.type.driver | info:eu-repo/semantics/doctoralThesis | |
dc.type.redcol | http://purl.org/redcol/resource_type/TD | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | |
dcterms.audience.professionaldevelopment | Estudiantes | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | |
oaire.awardtitle | Convocatoria 785 - Doctorados nacionales | |
oaire.fundername | Minciencias | spa |
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