Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Weight
This provides a high-precision, non-invasive method for poultry management, reducing animal stress and enabling intelligent livestock management, though it is domain-specific to agriculture.
This study tackled the problem of non-invasive estimation of duck body dimensions and weight by developing a deep learning model that fuses multimodal data (RGB images, depth images, and 3D point clouds), achieving a mean absolute percentage error of 6.33% and an R² of 0.953 across eight morphometric parameters.
Accurate body dimension and weight measurements are critical for optimizing poultry management, health assessment, and economic efficiency. This study introduces an innovative deep learning-based model leveraging multimodal data-2D RGB images from different views, depth images, and 3D point clouds-for the non-invasive estimation of duck body dimensions and weight. A dataset of 1,023 Linwu ducks, comprising over 5,000 samples with diverse postures and conditions, was collected to support model training. The proposed method innovatively employs PointNet++ to extract key feature points from point clouds, extracts and computes corresponding 3D geometric features, and fuses them with multi-view convolutional 2D features. A Transformer encoder is then utilized to capture long-range dependencies and refine feature interactions, thereby enhancing prediction robustness. The model achieved a mean absolute percentage error (MAPE) of 6.33% and an R2 of 0.953 across eight morphometric parameters, demonstrating strong predictive capability. Unlike conventional manual measurements, the proposed model enables high-precision estimation while eliminating the necessity for physical handling, thereby reducing animal stress and broadening its application scope. This study marks the first application of deep learning techniques to poultry body dimension and weight estimation, providing a valuable reference for the intelligent and precise management of the livestock industry with far-reaching practical significance.