Harnessing Deep Learning and 3D Computer Vision for Enhanced Cattle Management in Precision Livestock Farming

Abstract

Precision Livestock Farming (PLF) leverages data-driven, automated monitoring systems to enhance animal productivity, welfare, and traceability. Traditional methods—ear tagging, manual weighing, and visual inspection—are labor-intensive, invasive, and error-prone. This paper explores how Deep Learning (DL) and 3D Computer Vision (CV) technologies together enable non-invasive, automated cattle identification and body-measurement estimation. Techniques such as ResNet-50 and YOLOv4 are employed for breed recognition, while Mask R-CNN and stereo vision systems estimate volumetric weight with high precision. Integrating these approaches provides a humane, scalable, and cost-effective solution for next-generation livestock management.

Introduction

The livestock industry is rapidly adopting Precision Livestock Farming (PLF) to meet growing demands for food security, animal welfare, and sustainability. PLF leverages sensors, computer vision (CV), and artificial intelligence (AI) to enable real-time, contactless monitoring of animal health, identification, and weight, overcoming limitations of traditional methods like ear tagging, RFID, and manual weighing.

This study proposes an AI-driven, non-invasive livestock management system that integrates deep learning and 3D vision for two main tasks:

1. Biometric identification of cattle using deep learning models that analyze unique features such as muzzle prints and facial patterns.

2. 3D morphometric analysis for accurate estimation of live body weight and body condition through depth-sensing and volumetric modeling.

The framework consists of four modules:

• Data acquisition using RGB-D cameras (e.g., Intel RealSense) for both color and depth data.

• Pre-processing via noise reduction and background subtraction.

• Feature extraction and detection using ResNet-50 (for breed recognition) and YOLOv4 (for real-time detection of anatomical features).

• 3D reconstruction with Mask R-CNN to create point clouds and estimate body volume and weight using regression or neural models.

The models were trained with augmented datasets to enhance robustness under diverse conditions and deployed on edge computing devices (e.g., NVIDIA Jetson Nano) for real-time inference.

Results:

• Breed classification accuracy: 95.6%

• Muzzle-based identification accuracy: 93%

• Weight estimation error: within 4%

• Real-time detection speed: 32 FPS
  The 3D-based approach improved weight prediction accuracy by 9.8% compared to 2D methods.

A cloud-integrated dashboard (AWS + Plotly + Flask) visualizes live analytics, showing 2D/3D overlays, growth trends, and health indicators through intuitive heatmaps.

Conclusion

This research presents a comprehensive framework for automated cattle monitoring and management by integrating Deep Learning and 3D Computer Vision technologies. The combined use of ResNet-50, YOLOv4, and Mask R-CNN architectures enables accurate breed classification, biometric identification, and live weight estimation, all achieved through non-invasive and automated methods. Experimental results confirm that the integration of 3D data with deep neural networks not only enhances accuracy but also provides stability under diverse environmental conditions.

References

[1] Awad A. I., Hassaballah M., “Bag-of-Visual-Words Model for Cattle Muzzle Print Identification,” Computers and Electronics in Agriculture, 2019. [2] Gupta S. et al., “Automated Cattle Breed Classification Using YOLOv4 and Transfer Learning,” IEEE Access, 2022. [3] Li Y., Zhao F., Zhou J., “3D Cattle Morphometry Using Mask R-CNN and Depth Cameras,” Sensors, 2023. [4] Wang J., Zhou L., “Applications of 3D Computer Vision in Precision Livestock Farming,” Frontiers in Animal Science, 2023. [5] He K., Zhang X., Ren S., Sun J., “Deep Residual Learning for Image Recognition (ResNet),” CVPR, 2016. [6] Redmon J., Farhadi A., “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020. [7] He K., Gkioxari G., Dollár P., Girshick R., “Mask R-CNN,” IEEE ICCV, 2017. [8] AWS IoT Greengrass Documentation. Available: https://aws.amazon.com/greengrass [9] Plotly Graphing Library. Available: https://plotly.com [10] Flask Framework Documentation. Available: https://flask.palletsprojects.com

Copyright

Copyright © 2025 Vibhuti Shelke, Shrushti Kachare, Aishwarya Deshmukh, Pratiksha Survase. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.