Visión artificial para la detección de plagas en cultivos de tomate: revisión sistemática de la literatura
DOI:
https://doi.org/10.36825/RITI.14.34.010Palabras clave:
Agricultura Protegida, Manejo de Plagas, Horticultura, Monitoreo Agrícola, Visión ComputacionalResumen
La vigilancia fitosanitaria constituye una actividad esencial para disminuir pérdidas productivas ocasionadas por plagas en cultivos de tomate desarrollados bajo condiciones de agricultura protegida. En años recientes, la visión artificial y los métodos de aprendizaje profundo han adquirido relevancia como herramientas capaces de automatizar procesos de detección y análisis de imágenes agrícolas. A pesar de estos avances, todavía existe la necesidad de conocer qué técnicas se han utilizado, en qué condiciones han sido evaluadas y qué desempeño han reportado, particularmente en contextos de agricultura protegida y producción de pequeña escala. El presente trabajo tiene como finalidad examinar las investigaciones publicadas sobre estas tecnologías y valorar su posible incorporación en esquemas de monitoreo agrícola participativo. Para ello, se realizó una revisión sistemática de literatura científica publicada entre 2020 y 2026, siguiendo los lineamientos metodológicos de Kitchenham. La revisión se desarrolló mediante las etapas de planificación, búsqueda y selección de estudios, y síntesis de la información, considerando criterios de inclusión y exclusión previamente establecidos. Como resultado, se seleccionaron y analizaron cincuenta estudios relacionados con procesamiento de imágenes, inteligencia artificial y monitoreo fitosanitario. Entre los principales hallazgos de la revisión se identificó que diversos estudios reportan niveles elevados de desempeño de modelos de aprendizaje profundo en tareas de identificación automática de síntomas asociados con plagas y otras afectaciones del cultivo. La síntesis realizada permitió identificar las principales técnicas, datos, contextos de aplicación y desempeños reportados, proporcionando elementos de referencia para valorar su posible incorporación en esquemas de monitoreo agrícola participativo.
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