Abstract
An automated non-contact weight measurement method for ducks is beneficial for preventing the stress response of ducks and, thus, promoting their healthy development. We propose a two-stream bidirectional interaction network that depends on RGB-D pictures to accurately determine the weight of ducks. We developed two-stream branches in the encoder to extract texture appearance information and spatial structure information from RGB images and depth images, respectively. Besides, we employed a cross-modality feature supplement module in the encoder to facilitate mutual learning and complementarity between these two modalities. Finally, a decoder is designed to combine the multi-scale characteristics of these two modalities and feed the fused features into the regression module to determine the final weight of the duck. For the experimental analysis of this study, we built a new dataset of RGB-D duck images consisting of 2865 pairs of RGB-D images captured from the bird-eye view. The comparative experimental results show that the proposed method could effectively estimate the duck weight with an MAE of only 0.1550, outperforming all the comparison methods on this dataset. This automated, non-contact weight measurement method can eliminate stress responses caused by human intervention. This method enables the automated collection of growth data, supporting precision feeding and health management decisions. It drives the digital and welfare-oriented transformation of the livestock industry, enhancing production efficiency while promoting animal welfare and sustainable agricultural practices.