Non-Invasive Pig Weight Prediction using SAM-Enhanced Deep Learning and Depth Imaging

Non-invasive pig weight prediction is crucial for efficient pork production and animal welfare monitoring. This study introduces a novel approach combining Segment Anything Model (SAM)-enhanced image preprocessing with deep learning techniques for accurate pig weight estimation using depth images. We compare traditional statistical methods with advanced deep learning models, focusing on minimizing preprocessing requirements and improving accuracy. Our custom image preprocessing pipeline, leveraging SAM for precise segmentation, enables efficient feature extraction and data cleaning. We evaluate various models, including Convolutional Neural Networks (CNNs), Multi-input CNNs, ResNets, and XceptionNet, against statistical methods such as linear regression, random forest, and support vector regression. Results demonstrate that deep learning models, particularly XceptionNet, significantly outperform statistical approaches. XceptionNet achieves a mean absolute error of 2.82 kg and a mean absolute percentage error of 7.42%, compared to the best statistical model (support vector regression) with 4.51 kg and 15.56%, respectively. This study highlights the potential of integrating state-of-the-art segmentation techniques like SAM with deep learning for enhanced accuracy in livestock monitoring, paving the way for real-time, non-invasive weight prediction systems in modern pig farming.