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Ajuste ligero para la detección de tos en cerdos

Xu Zhang1,2, Baoming Li1,3,4, Xiaoliu Xue1

  • 1Department of Agricultural Structure and Bioenvironmental Engineering, College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China.

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|January 28, 2026
PubMed
Resumen

Este estudio presenta un sistema ligero de reconocimiento de tos de cerdo que utiliza aprendizaje por transferencia para la detección temprana de enfermedades respiratorias en la cría intensiva. El método identifica eficazmente la tos de cerdo incluso con datos limitados y entornos de granja ruidosos.

Palabras clave:
PANNs-CNN14TFDSmodelo de alerta tempranareconocimiento de tos de cerdoaprendizaje por transferencia

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Área de la Ciencia:

  • Ciencia Agrícola
  • Salud Animal
  • Aprendizaje Automático

Sus antecedentes:

  • Las enfermedades respiratorias son una preocupación importante en la cría intensiva de cerdos, lo que afecta el bienestar y la productividad animal.
  • La detección temprana de la tos de cerdo es crucial para una intervención oportuna, pero se ve obstaculizada por datos etiquetados limitados y acústicas de granja desafiantes.

Objetivo del estudio:

  • Desarrollar un método ligero y preciso de reconocimiento de tos de cerdo para la detección temprana de enfermedades en entornos agrícolas con recursos limitados.
  • Abordar los desafíos de los tamaños de muestra pequeños y los entornos acústicos complejos en la cría de cerdos.

Principales métodos:

  • Se utilizó una red neuronal de audio preentrenada, congelando su backbone y ajustando el clasificador para la transferencia de conocimiento y la adaptación de dominio.
  • Se incorporó un módulo de doble flujo tiempo-frecuencia para mejorar la captura de características temporales-espectrales específicas de la tos.
  • Se evaluó el método en un conjunto de datos de toses de cerdo y clips de ruido ambiental.

Principales resultados:

  • Logró una precisión del 94,59 % y una puntuación F1 del 92,86 % en el conjunto de datos de prueba, superando a los modelos de referencia.
  • La validación cruzada demostró una precisión media del 96,99 %, lo que indica una generalización robusta.
  • El enfoque propuesto de ajuste ligero demostró ser eficaz para el reconocimiento de audio de muestras pequeñas en contextos agrícolas.

Conclusiones:

  • El marco desarrollado ofrece una solución técnica fiable para la alerta temprana de enfermedades respiratorias en granjas porcinas a través del reconocimiento preciso de la tos.
  • El aprendizaje por transferencia presenta una estrategia viable para el reconocimiento de audio de muestras pequeñas en entornos agrícolas con recursos limitados.
  • El estudio destaca el potencial de la IA para mejorar la monitorización y gestión de la salud animal en la cría intensiva.