Detección del tizón tardío en folíolos de papa usando imágenes tomadas con dron y técnicas de Deep Learning

J. A. Cairampoma; Delia Gamarra-Gamarra; F. E.  Dionisio

doi:10.17268/sci.agropecu.2026.020

Autores/as

J. A. Cairampoma Universidad Nacional del Centro del Perú, Huancayo, Perú. https://orcid.org/0000-0002-1998-6178
Delia Gamarra-Gamarra Universidad Nacional del Centro del Perú, Huancayo, Perú. https://orcid.org/0000-0002-6262-237X
F. E. Dionisio Universidad Nacional del Centro del Perú, Huancayo, Perú. https://orcid.org/0000-0002-9769-6782

DOI:

https://doi.org/10.17268/sci.agropecu.2026.020

Palabras clave:

Detección del tizón tardío, Dron, Red neuronal convolucional, Mask R-CNN, Folíolos de papa

Resumen

Phytophthora infestans causa una de las enfermedades más devastadoras del cultivo de papa, también conocida como tizón tardío. Dado que la identificación temprana de este patógeno es crucial para el control efectivo de la enfermedad, este estudio tuvo como objetivo proponer una metodología automatizada para la identificación de sus lesiones en foliolos de papa, utilizando redes neuronales convolucionales llamadas "Mask R-CNN". Las evaluaciones se llevaron a cabo durante la temporada de lluvias, en cultivos realizados por agricultores en la localidad de Huasahuasi, en los Andes centrales del Perú. Se tomaron cien fotografías (5472 × 3078 píxeles) con un vehículo aéreo no tripulado (UAV) Phantom 4 Pro a una altitud de 3 m en cultivos con una incidencia de tizón tardío entre 2 y 3. Las imágenes se dividieron en cuatro partes y luego pasaron un riguroso control de calidad, dando como resultado 200 fotos (1825 × 1369 píxeles). Del total, el 75% se utilizó para el entrenamiento del modelo y el 25% para su validación. Los modelos se evaluaron en condiciones reales, utilizando métricas como la precisión y la recuperación. Se determinó que la red neuronal Mask R-CNN, basada en la arquitectura de red neuronal profunda ResNet 101, ofrece una precisión y efectividad aceptables (73,5%) en la identificación de lesiones de tizón tardío a nivel de foliolo. Esta metodología constituye una contribución significativa a la agricultura de precisión en los Andes, al validar una herramienta no invasiva capaz de superar las limitaciones topográficas de la zona. Su aplicación práctica optimizaría el uso de fungicidas mediante la detección dirigida, promoviendo así sistemas de producción de papa más sostenibles y rentables para los agricultores locales.

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