CNN-based weight estimation from point clouds obtained from walking breed sows

In the livestock industry, reducing the cost of raising animals while maintaining quality is of utmost importance. Growth management is crucial for livestock quality control, with weight being a primary focus. This study proposes a method for estimating the weight of a breed sow from point clouds using a convolutional neural network (CNN). The proposed method consists of two stages: first, 2D images are generated from point clouds of a walking breed sow; next, these images are used as input in a CNN model to estimate the weight. The point clouds are divided into two halves along a longitudinal plane. Then, distances are calculated between each element of the 128 × 64 rectangular mesh created on the aforementioned plane and the corresponding points in the divided point cloud. A 2D image is generated by varying the grayscale intensity according to the calculated distances. Three types of CNNs, namely, VGG16, DenseNet121, and EfficientNet B0, were employed to estimate the weight using the generated 2D images as input. The CNNs were trained using approximately 150,000 images of 71 pigs. To verify the effectiveness of the method, the trained networks were used to conduct weight estimation experiments on test cases. The results of the experiments demonstrate a high weight estimation accuracy, with an error rate as low as 1.35%.