Parivendan Sibi Chakravathy, Sailunaz Kashfia, Neethirajan Suresh
Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 4R2, Canada.
Faculty of Agriculture, Dalhousie University, Truro, NS B3H 4R2, Canada.
Animals (Basel). 2025 Jun 20;15(13):1835. doi: 10.3390/ani15131835.
This review critically analyzes recent advancements in dairy cow behavior recognition, highlighting novel methodological contributions through the integration of advanced artificial intelligence (AI) techniques such as transformer models and multi-view tracking with social network analysis (SNA). Such integration offers transformative opportunities for improving dairy cattle welfare, but current applications remain limited. We describe the transition from manual, observer-based assessments to automated, scalable methods using convolutional neural networks (CNNs), spatio-temporal models, and attention mechanisms. Although object detection models, including You Only Look Once (YOLO), EfficientDet, and sequence models, such as Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Long Short-Term Memory (convLSTM), have improved detection and classification, significant challenges remain, including occlusions, annotation bottlenecks, dataset diversity, and limited generalizability. Existing interaction inference methods rely heavily on distance-based approximations (i.e., assuming that proximity implies social interaction), lacking the semantic depth essential for comprehensive SNA. To address this, we propose innovative methodological intersections such as pose-aware SNA frameworks and multi-camera fusion techniques. Moreover, we explicitly discuss ethical challenges and data governance issues, emphasizing data transparency and animal welfare concerns within precision livestock contexts. We clarify how these methodological innovations directly impact practical farming by enhancing monitoring precision, herd management, and welfare outcomes. Ultimately, this synthesis advocates for strategic, empathetic, and ethically responsible precision dairy farming practices, significantly advancing both dairy cow welfare and operational effectiveness.
本综述批判性地分析了奶牛行为识别的最新进展，重点介绍了通过整合先进的人工智能（AI）技术（如变压器模型和多视图跟踪与社会网络分析（SNA））所做出的新颖方法贡献。这种整合为改善奶牛福利提供了变革性机遇，但目前的应用仍然有限。我们描述了从基于人工观察的评估向使用卷积神经网络（CNN）、时空模型和注意力机制的自动化、可扩展方法的转变。尽管目标检测模型（包括You Only Look Once（YOLO）、EfficientDet）和序列模型（如双向长短期记忆（BiLSTM）和卷积长短期记忆（convLSTM））提高了检测和分类能力，但仍然存在重大挑战，包括遮挡、注释瓶颈、数据集多样性和有限的通用性。现有的交互推理方法严重依赖基于距离的近似（即假设接近意味着社会互动），缺乏全面社会网络分析所需的语义深度。为解决这一问题，我们提出了创新的方法交叉点，如姿态感知社会网络分析框架和多摄像头融合技术。此外，我们明确讨论了伦理挑战和数据治理问题，强调了精准畜牧环境中的数据透明度和动物福利问题。我们阐明了这些方法创新如何通过提高监测精度、畜群管理和福利成果直接影响实际养殖。最终，本综述倡导采用具有战略意义、富有同理心且符合道德规范的精准奶牛养殖实践，显著提升奶牛福利和运营效率。
