A Novel Dual-Network Approach for Real-Time Liveweight Estimation in Precision Livestock Management

The increasing demand for automation in livestock farming scenarios highlights the need for effective noncontact measurement methods. The current methods typically require either fixed postures and specific positions of the target animals or high computational demands, making them difficult to implement in practical situations. In this study, a novel dual-network framework is presented that extracts accurate contour information instead of segmented images from unconstrained pigs and then directly employs this information to obtain precise liveweight estimates. The experimental results demonstrate that the developed framework achieves high accuracy, providing liveweight estimates with an R² value of 0.993. When contour information is used directly to estimate the liveweight, the real-time performance of the framework can reach 1131.6 FPS. This achievement sets a new benchmark for accuracy and efficiency in non-contact liveweight estimation. Moreover, the framework holds significant practical value, equipping farmers with a robust and scalable tool for precision livestock management in dynamic, real-world farming environments. Additionally, the Liveweight and Instance Segmentation Annotation of Pigs dataset is introduced as a comprehensive resource designed to support further advancements and validation in this field.