Análisis de imágenes multiespectrales para la detección de cultivos y detección de plagas y enfermedades en la producción de café

This research shows a method for the detection of coffee crops and the presence of pests and diseases in the production of these plants, using multispectral images from the Landsat 8 satellite. The study area is located between the coffee regions of Misantla and Coatepec, in the state of Veracruz. The development of the method involves the processes of: pre-processing of images of the area involved; digital interpretation through the process of supervised classification, implementation of principal component analysis; field sampling for training, validation and evaluation of results. An error matrix was applied to evaluate the accuracy of the data, showing an acceptable value in the

• Publication year

  2018

• Venue

  Research on computing science

• Publication date

  2018-12-31

• Fields of study

  Agricultural and Food Sciences, Computer Science, Environmental Science

• Identifiers
  DOI 10.13053/RCS-147-7-24
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions

  2017cited by this paper
• Observation of plant-pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements.

  2016cited by this paper
• Aplicación de la tecnología de imágenes hiperespectrales al control de calidad de productos agroalimentarios de la región de Amazonas (Perú)

  2016cited by this paper
• Análisis de imágenes hiperespectrales

  2013cited by this paper
• Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases

  2012cited by this paper
• Retos del productor cafetalero frente al contexto económico y político, en la región de Coatepec, Veracruz.

  2012influential reference
• Using nondeterministic learners to alert on coffee rust disease

  2011cited by this paper
• Método para identificación de cultivos de arroz (Oryza sativa L.) con base en imágenes de satélite

  2010cited by this paper
• Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves

  2008cited by this paper
• Estimación de clases de edad de las plantaciones de la provincia de Corrientes, Argentina, con base en datos satelitales Landsat

  2007cited by this paper
• Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging

  2007cited by this paper
• Hyperspectral image data analysis

  2002cited by this paper
• Introductory Digital Image Processing: A Remote Sensing Perspective

  1986cited by this paper

• Generación de un modelo óptimo para evaluar la productividad y el rendimiento de viñedos. Caso de estudio: Bodegas Viña Arnaiz

  2025cites this paper
• Application of machine learning models for asbestos cement detection in multispectral images

  2025cites this paper
• Spatiotemporal Land Use and Land Cover Changes and Their Impact on Landscape Patterns in the Colombian Coffee Cultural Landscape (2014–2034)

  2025cites this paper
• The Widespread Use of Remote Sensing in Asbestos, Vegetation, Oil and Gas, and Geology Applications

  2023cites this paper
• A Machine Learning Model for the Diagnosis of Coffee Diseases

  2022cites this paper
• Review of Remote Sensing Methods to Map Coffee Production Systems

  2020cites this paper