Intelligent Decision-Making for Field Crop Production

To address the dynamic and uncertain challenges posed by climate variability, soil heterogeneity, water distribution, and other key factors in crop field production, intelligent decision support systems (IDSS) that integrate domain knowledge and multi-source data for irrigation, fertilization, pest and disease control, and field dynamic management, are of great importance in meeting modern agriculture’s demands for high precision, efficiency, and sustainability. By encompassing the development stages, typical practices, and technological pathways in China and developed countries, this paper summarizes the representative progress in Internet of Things (IoT), multimodal fusion, knowledge representation, reinforcement learning, reasoning, and practical applications in IDSS. Prominent research challenges include the lack of real-time or near-real-time sensor data, static domain knowledge, poor multimodal decision-making capability, weak cross-field generalization, and various implementation barriers, such as a vague definition of data governance, high costs of service infrastructure, and low user acceptance intention. To overcome these challenges, future research should prioritize the development of scalable, dynamic, robust, interpretable, and trustworthy multimodal IDSS, promote the formulation of standards, and establish an open platform for seamless model deployment, thereby facilitating the transformation from experience-driven to intelligence-driven agricultural production paradigms.