A Comprehensive Review of Big Data Intelligent Decision-Making Models for Smart Farms

作者：Chang Qin, Peiqin Zhao, Ying Qian, Guijun Yang, Xingyao Hao, Xin Mei, Xiaodong Yang, Jin He · 发表于：Agronomy · 年份：2025 · DOI：10.3390/agronomy15122898 · 被引用次数：5 · 研究领域：Smart Agriculture and AI、Greenhouse Technology and Climate Control、IoT and Edge/Fog Computing

Big data and artificial intelligence technologies are driving a paradigm shift in smart farming, yet intelligent decision-making faces critical bottlenecks. At the data level, challenges include fragmentation, high acquisition costs, and inadequate secure sharing; at the model level, issues involve regional heterogeneity, weak adaptability, and insufficient explainability. To address these, this paper systematically reviews global research to establish a theoretical framework spanning the entire production cycle. Regarding data governance, trends favor federated systems with unified metadata and layered storage, utilizing technologies like federated learning for secure lifecycle management. For decision-making, approaches are evolving from experience-based to data-driven intelligence. Pre-harvest planning now integrates mechanistic models and transfer learning for suitability and variety optimization. In-season management leverages deep reinforcement learning (DRL) and model predictive control (MPC) for precise regulation of seedlings, water, fertilizer, and pests. Post-harvest evaluation strategies utilize spatio-temporal deep learning architectures (e.g., Transformers or LSTMs) and intelligent optimization algorithms for yield prediction and machinery scheduling. Finally, a staged development pathway is proposed: prioritizing standardized data governance and foundation models in the short term; advancing federated learning and human–machine collaboration in the mid-term; an...