Aplicación de redes neuronales convolucionales para la detección del tizón tardío Phytophthora infestans en papa Solanum tuberosum
Application of convolutional neural networks for detection of the late blight Phytophthora infestans in potato Solanum tuberosum
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La presencia del tizón tardío o gota en el cultivo de papa afecta directamente el crecimiento de la planta y el desarrollo del tubérculo, por ello, es importante la detección temprana de la enfermedad. Actualmente, la aplicación de redes neuronales convolucionales es una oportunidad orientada a la identificación de patrones en la agricultura de precisión, incluyendo el estudio del tizón tardío, en el cultivo de papa. Este estudio describe un modelo de aprendizaje profundo capaz de reconocer el tizón tardío en el cultivo de papa, por medio de la clasificación de imágenes de las hojas. Se utilizó, en la aplicación de este modelo, el conjunto de datos aumentado de PlantVillage, para entrenamiento. El modelo propuesto ha sido evaluado a partir de métricas de rendimiento, como precisión, sensibilidad, puntaje F1 y exactitud. Para verificar la efectividad del modelo en la identificación y la clasificación del tizón tardío y comparado en rendimiento con arquitecturas. como AlexNet, ZFNet, VGG16 y VGG19. Los resultados experimentales obtenidos con el conjunto de datos seleccionado mostraron que el modelo propuesto alcanza una exactitud del 90 % y un puntaje F1, del 91 %. Por lo anterior, se concluye que el modelo propuesto es una herramienta útil para los agricultores en la identificación del tizón tardío y escalable a plataformas móviles, por la cantidad de parámetros que lo comprenden.
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