Goose Surface Temperature Monitoring System Based on Deep Learning Using Visible and Infrared Thermal Image Integration

Owing to increased biosecurity and industrial demands, the poultry houses in Taiwan are generally nonopen and closed types, with automatic environmental control and sensor equipment gradually being installed in such houses. Environmental sensors and poultry health monitoring systems are necessary to improve poultry feeding efficiency and safety. In this work, we developed a goose surface temperature monitoring system based on deep learning using visible and infrared thermal images. The goose images were first collected in a poultry house to form the deep learning training data set. The Mask R-convolutional neural network algorithm was then employed to train the goose detection model. The developed goose surface temperature monitoring system consisted of an embedded system with the trained goose detection model, a visible camera, and an infrared thermal camera. The visible camera captured visible images in the poultry house, in which the geese could be identified by the trained goose detection model. The individual surface temperatures of the geese were obtained through integration of the visible and infrared thermal images. The developed monitoring systems were installed in the land and pool areas of a commercial goose house to monitor the surface temperature of the geese and achieved a precision of 97.1% and recall of 95.1%. In addition, the goose surface temperature of the pool area was observed to be lower than that of the land area.