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Clasificación del estado de pH en plantas de stevia mediante redes neuronales supervisadas y visión artificial.

dc.contributor.authorBrizuela Ramírez, Jesús Emmanuel
dc.contributor.authorGarcía Díaz, Noel
dc.contributor.authorGarcía Virgen, Juan
dc.contributor.authorGutiérrez Magaña, Shanti Maryse
dc.contributor.authorRico Bautista, Dewar
dc.coverage.spatialColombiaen_US
dc.date.accessioned2025-10-22T13:18:03Z
dc.date.available2025-10-22T13:18:03Z
dc.date.issued2025-09-01
dc.identifier.issn2007-9737en_US
dc.identifier.urihttps://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5857
dc.description.abstractLa Inteligencia Artificial y la Visión Artificial han revolucionado la Agricultura de Precisión, permitiendo el monitoreo automatizado de cultivos. Este estudio propone un modelo basado en Redes Neuronales para clasificar el pH del suelo en cultivos de Stevia rebaudiana Bertoni, optimizando la gestión agrícola y la sostenibilidad de los cultivos. Para lograrlo, se procesaron imágenes de Stevia mediante técnicas de aumento de datos, extrayendo características de color en formatos RGB y hexadecimales. Posteriormente, se entrenó una Red Neuronal Artificial supervisada para clasificar el pH del suelo en categorías ácidas, óptimas y alcalinas. El modelo propuesto, StePHVIA, alcanzó una precisión del 99%, superando a arquitecturas preentrenadas como MobileNetV2 (97,38%) y ResNet-50 (76,38%). La evaluación se realizó utilizando métricas como el Coeficiente de Correlación de Matthews, la precisión, la recuperación y la puntuación F1. Estos resultados confirman la efectividad de la Visión Artificial y el Aprendizaje Profundo en la Agricultura de Precisión, ofreciendo una alternativa en tiempo real y de bajo costo para el monitoreo del suelo. StePHVIA facilita la detección temprana de desequilibrios en el suelo, optimizando la aplicación de fertilizantes y mejorando la productividad del cultivo de Stevia.en_US
dc.language.isoengen_US
dc.relationhttp://www.polibits.ojs.gelbukh.com/ojs/index.php/CyS/article/view/5902/4076en_US
dc.titleClasificación del estado de pH en plantas de stevia mediante redes neuronales supervisadas y visión artificial.en_US
dc.title.translatedClassification of pH Status in Stevia Plants Using Supervised Neural Networks and Computer Visionen_US
dc.contributor.cvlacNoel García Díaz [LWXTcE4AAAAJ]en_US
dc.contributor.cvlacDewar Rico Bautista [0000255815]en_US
dc.contributor.googlescholarJesús Emmanuel Brizuela Ramírez [g1nlh7gAAAAJ]en_US
dc.contributor.googlescholarNoel García Díaz [LWXTcE4AAAAJ]en_US
dc.contributor.googlescholarDewar Rico Bautista [q_ZtKjsAAAAJ]en_US
dc.contributor.orcidNoel García Díaz [0000-0002-7078-0941]en_US
dc.contributor.orcidShanti Maryse Gutiérrez Magaña [0009-0000-6048-712X]en_US
dc.contributor.orcidDewar Rico Bautista [0000-0002-1808-3874]en_US
dc.contributor.researchgateNoel García Díaz [Noel-Garcia-Diaz]en_US
dc.contributor.researchgateJuan García Virgen [Juan-Garcia-Virgen]en_US
dc.contributor.researchgateDewar Rico Bautista [Dewar-Rico-Bautista]en_US
dc.contributor.scopusNoel García Díaz [56891651900]en_US
dc.contributor.scopusJuan García Virgen [56891753600]en_US
dc.contributor.scopusShanti Maryse Gutiérrez Magaña [59752166400]en_US
dc.contributor.scopusDewar Rico Bautista [58689228900]en_US
dc.description.abstractenglishArtificial Intelligence and Computer Vision have revolutionized Precision Agriculture, enabling automated crop monitoring. This study proposes a model based on Neural Networks to classify soil pH in Stevia rebaudiana Bertoni crops, optimizing agricultural management and crop sustainability. To achieve this, Stevia images were processed using data augmentation techniques, extracting color features in RGB and hexadecimal formats. A supervised Artificial Neural Network was then trained to classify soil pH into acidic, optimal, and alkaline categories. The proposed model, StePHVIA, achieved 99% accuracy, outperforming pretrained architectures such as MobileNetV2 (97.38%) and ResNet-50 (76.38%). The evaluation was conducted using metrics such as Matthews Correlation Coefficient, accuracy, recall, and F1-score. These results confirm the effectiveness of Computer Vision and Deep Learning in Precision Agriculture, providing a real time and low cost alternative for soil monitoring. StePHVIA facilitates the early detection of soil imbalances, optimizing fertilizer application and improving Stevia crop productivity.en_US
dc.subject.proposalArtificial Intelligenceen_US
dc.subject.proposalcomputer visionen_US
dc.subject.proposalneural networksen_US
dc.subject.proposalprecision agricultureen_US
dc.subject.proposalsteviaen_US
dc.subject.keywordsInteligencia artificialen_US
dc.subject.keywordsvisión artificialen_US
dc.subject.keywordsredes neuronalesen_US
dc.subject.keywordsagricultura de precisiónen_US
dc.subject.keywordssteviaen_US
dc.publisher.facultyFacultad ingenieríasen_US
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTen_US
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