Rice leaf disease detection using the Stretched Neighborhood Effect Color to Grayscale method and Machine Learning

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Autor(es): dc.contributorCiudad Universitaria-
Autor(es): dc.contributorUniversidad César Vallejo-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorAguirre-Rodríguez, Elen Yanina-
Autor(es): dc.creatorGamboa, Alexander Alberto Rodriguez-
Autor(es): dc.creatorRodríguez, Elias Carlos Aguirre-
Autor(es): dc.creatorSantos-Fernández, Juan Pedro-
Autor(es): dc.creatorNascimento, Luiz Fernando Costa-
Autor(es): dc.creatorda Silva, Aneirson Francisco-
Autor(es): dc.creatorMarins, Fernando Augusto Silva-
Data de aceite: dc.date.accessioned2025-08-21T22:36:41Z-
Data de disponibilização: dc.date.available2025-08-21T22:36:41Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.17268/sci.agropecu.2025.011-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/301182-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/301182-
Descrição: dc.descriptionThe emergence of Machine Learning (ML) technologies and their integration into agriculture has demonstrated a significant impact on disease detection in crops, enabling continuous monitoring and enhancing risk planning and management. This study applied image processing techniques such as thresholding, gamma correction, and the Stretched Neighborhood Effect Color to Grayscale (SNECG) method, alongside ML, to develop a predictive model for identifying five types of rice diseases. The ML techniques used included Logistic Regression, Multilayer Perceptron, Support Vector Machines, Decision Trees, and Random Forests (RF). Hyperparameters were optimized and evaluated through 5-fold cross-validation. In the results, the SNECG method successfully converted images to grayscale, capturing essential features of lesions on rice leaves. The ML models developed with these techniques showed evaluation metrics exceeding 80%, with the RF model (precision = 88.31%) demonstrating superior performance. Additionally, the RF model was integrated into an interface designed for agricultural decision-making. The practical application of the developed model could significantly improve the ability to detect and manage diseases in rice crops.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFacultad de Ingeniería Universidad Nacional de Trujillo Ciudad Universitaria, Av. Juan Pablo II s/n-
Descrição: dc.descriptionPrograma de Investigación Formativa e Integridad Científica Universidad César Vallejo-
Descrição: dc.descriptionDepartment of Production São Paulo State University (UNESP), Guaratinguetá-
Descrição: dc.descriptionDepartment of Production São Paulo State University (UNESP), Guaratinguetá-
Descrição: dc.descriptionCAPES: CAPES - 001-
Descrição: dc.descriptionCNPq: CNPq - 304197/2021-1-
Formato: dc.format123-136-
Idioma: dc.languageen-
Relação: dc.relationScientia Agropecuaria-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectdisease classification-
Palavras-chave: dc.subjectdisease detection-
Palavras-chave: dc.subjectimage processing-
Palavras-chave: dc.subjectleaf disease-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectrandom forest-
Título: dc.titleRice leaf disease detection using the Stretched Neighborhood Effect Color to Grayscale method and Machine Learning-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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