The existence of dead broilers in flat broiler houses poses significant challenges to large-scale and welfare-oriented broiler breeding. To ensure the timely identification and removal of dead broilers, a mobile device based on visual technology for grasping them was meticulously designed in this study. Among the multiple recognition models explored, the YOLOv6 model was selected due to its exceptional performance, attaining an impressive 86.1% accuracy in identification. This model, when integrated with a specially designed robotic arm, forms a potent combination for effectively handling the task of grasping dead broilers. Extensive experiments were conducted to validate the efficacy of the device. The results reveal that the device achieved an average grasping rate of dead broilers of 81.3%. These findings indicate that the proposed device holds great potential for practical field deployment, offering a reliable solution for the prompt identification and grasping of dead broilers, thereby enhancing the overall management and welfare of broiler populations.

With the increasing urgency for digital transformation in large-scale caged layer farms, traditional methods for monitoring the environment and chicken health, which often rely on human experience, face challenges related to low efficiency and poor real-time performance. In this study, we focused on caged layer chickens and proposed an improved abnormal beak detection model based on the You Only Look Once v8 (YOLOv8) framework. Data collection was conducted using an inspection robot, enhancing automation and consistency. To address the interference caused by chicken cages, an Efficient Multi-Scale Attention (EMA) mechanism was integrated into the Spatial Pyramid Pooling-Fast (SPPF) module within the backbone network, significantly improving the model’s ability to capture fine-grained beak features. Additionally, the standard convolutional blocks in the neck of the original model were replaced with Grouped Shuffle Convolution (GSConv) modules, effectively reducing information loss during feature extraction. The model was deployed on edge computing devices for the real-time detection of abnormal beak features in layer chickens. Beyond local detection, a digital twin remote monitoring system was developed, combining three-dimensional (3D) modeling, the Internet of Things (IoT), and cloud-edge collaboration to create a dynamic, real-time mapping of physical layer farms to their virtual counterparts. This innovative approach not only improves the extraction of subtle features but also addresses occlusion challenges commonly encountered in small target detection. Experimental results demonstrate that the improved model achieved a detection accuracy of 92.7%. In terms of the comprehensive evaluation metric (mAP), it surpassed the baseline model and YOLOv5 by 2.4% and 3.2%, respectively. The digital twin system also proved stable in real-world scenarios, effectively mapping physical conditions to virtual environments. Overall, this study integrates deep learning and digital twin technology into a smart farming system, presenting a novel solution for the digital transformation of poultry farming.