Reconocimiento automático de la actividad de vacunos en pastoreo
Resumen
El uso de podómetros o collares para registrar el comportamiento del ganado en períodos cortos de tiempo (e.g. 24 h) es costoso. En esta situación particular, el desarrollo de tecnologías de bajo costo y fáciles de usar es relevante. Al igual que las aplicaciones de teléfonos inteligentes para el reconocimiento de la actividad humana, las cuales analizan datos de sensores de aceleración integrados, en este trabajo desarrollamos una aplicación de Android para registrar la actividad del ganado. Para el desarrollo de esta aplicación, se siguieron cuatro pasos principales: a) adquisición de datos para el entrenamiento del modelo, b) entrenamiento del modelo, c) desarrollo de la aplicación y d) utilización de la aplicación. Para la adquisición de datos, desarrollamos un sistema en el que se utilizaron tres componentes: dos teléfonos inteligentes (uno en la vaca y otro para el observador) y una cuenta de Google Firebase para el almacenamiento de datos. Para el entrenamiento del modelo, la base de datos generada se utilizó para entrenar una red neuronal recurrente. El rendimiento del entrenamiento se evaluó mediante la matriz de confusión. Para todas las actividades, el modelo entrenado proporcionó una predicción alta (> 96 %). El modelo entrenado se utilizó para desarrollar una aplicación de Android con la API de TensorFlow. Finalmente, se utilizaron tres teléfonos celulares (LG gm730) para registrar la actividad de seis vacas Holstein (3 en producción y 3 secas). Se realizaron observaciones directas y no sistemáticas de los animales para contrastar las actividades registradas por el dispositivo. Los resultados mostraron coherencia entre las observaciones directas y la actividad registrada por el dispositivo.
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Derechos de autor 2022 Universidad del Cauca

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Datos de los fondos
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Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS),Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
Números de la subvención convocatoria 836-2019, proyecto 66737