Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Weight
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
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.
