An enhanced machine vision system for smart poultry farms using deep learning

This study confronts some major issues in current poultry production such as disease spread, lack of labor, lack of efficient monitoring, and increased demand for greater animal welfare. To address all these problems, a sophisticated machine vision system using deep learning is suggested, in which the YOLOv11 algorithm has been used to monitor and manage poultry automatically. The data set of 330 images of chickens was obtained from farms in Thailand with diverse environmental conditions and augmented to 1,716 images through rotation, modification of brightness, and saturation modification techniques. The model YOLOv11 was trained using the NVIDIA Jetson Orin Nano Developer Kit and evaluated in terms of precision, recall, and mean average precision (mAP). The model was highly accurate, with 0.964, 0.938, and 0.963 in precision, recall, and mAP, respectively. Optical and thermal imaging together enabled the system to overcome difficulties in varying lighting and environmental conditions. Real-time monitoring and tracking of multiple chickens were demonstrated by implementation results, which assisted in disease prevention, feed efficiency, and overall farm management. This research contributes to smart farming development through the provision of a fully automated, scalable, and very accurate poultry monitoring system to promote sustainability, efficiency of operation, and increased animal welfare in modern poultry farming.