Individual Pig Identification Using Back Surface Point Clouds in 3D Vision

Abstract

The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig’s back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig’s back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming.

An Improved Pig Counting Algorithm Based on YOLOv5 and DeepSORT Model

“…With the development of computer vision technology, an increasing number of computer vision algorithms are being applied in the field of precision agriculture, such as agricultural object detection [ 12 ], plant disease and pest recognition [ 13 ], animal behavior recognition [ 14 ], agricultural object segmentation [ 15 ], animal weight measurement [ 16 ], and agricultural object tracking [ 17 ]. These cases demonstrate the broad application prospects of using computer vision algorithms to solve agricultural problems.…”