Rao Shreya, 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.
Sensors (Basel). 2025 Aug 8;25(16):4899. doi: 10.3390/s25164899.
Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010-2025, this systematic review reveals how DT architectures for dairy cattle are conceptualized, validated, and deployed. We introduce a novel five-dimensional classification framework-spanning application domain, modeling paradigms, computational topology, validation protocols, and implementation maturity-to provide a coherent comparative lens across diverse DT implementations. Hybrid edge-cloud architectures emerge as optimal solutions, with lightweight CNN-LSTM models embedded in collar or rumen-bolus microcontrollers achieving over 90% accuracy in recognizing feeding and rumination behaviors. Simultaneously, remote cloud systems harness mechanistic fermentation simulations and multi-objective genetic algorithms to optimize feed composition, minimize greenhouse gas emissions, and balance amino acid nutrition. Field-tested prototypes indicate significant agronomic benefits, including 15-20% enhancements in feed conversion efficiency and water use reductions of up to 40%. Nevertheless, critical challenges remain: effectively fusing heterogeneous sensor data amid high barn noise, ensuring millisecond-level synchronization across unreliable rural networks, and rigorously verifying AI-generated nutritional recommendations across varying genotypes, lactation phases, and climates. Overcoming these gaps necessitates integrating explainable AI with biologically grounded digestion models, federated learning protocols for data privacy, and standardized PRISMA-based validation approaches. The distilled implementation roadmap offers actionable guidelines for sensor selection, middleware integration, and model lifecycle management, enabling proactive rather than reactive dairy management-an essential leap toward climate-smart, welfare-oriented, and economically resilient dairy farming.
具备传感器的数字孪生（DTs）正在重塑精准奶牛营养，它通过将实时牛舍遥测数据与云端的先进生物物理模拟无缝集成来实现这一点。通过对2010年至2025年期间122项同行评审研究的深入分析，本系统综述揭示了奶牛数字孪生架构是如何被概念化、验证和部署的。我们引入了一个新颖的五维分类框架，涵盖应用领域、建模范式、计算拓扑、验证协议和实施成熟度，以便为各种数字孪生实现提供一个连贯的比较视角。混合边缘云架构成为最优解决方案，嵌入项圈或瘤胃 bolus 微控制器中的轻量级CNN-LSTM模型在识别采食和反刍行为方面的准确率超过90%。同时，远程云系统利用机械发酵模拟和多目标遗传算法来优化饲料组成，最大限度减少温室气体排放，并平衡氨基酸营养。经过实地测试的原型显示出显著的农艺效益，包括饲料转化效率提高15%-20%以及用水量减少高达40%。然而，关键挑战依然存在：在牛舍高噪音环境下有效融合异构传感器数据，确保在不可靠的农村网络中实现毫秒级同步，以及在不同基因型、泌乳阶段和气候条件下严格验证人工智能生成的营养建议。克服这些差距需要将可解释人工智能与基于生物学的消化模型、用于数据隐私的联邦学习协议以及基于PRISMA的标准化验证方法相结合。提炼出的实施路线图为传感器选择、中间件集成和模型生命周期管理提供了可操作的指导方针，实现主动而非被动的奶牛管理，这是朝着气候智能、注重福利和经济韧性强的奶牛养殖迈出的关键一步。
