Abstract: With the continuous development of large-scale farming, changes in the agricultural labor structure, and the rapid advancement of smart farming, seeding operations have evolved from a conventional mechanical execution process into a key foundational link affecting crop establishment quality, field operational efficiency, and farm-level digital management capability. For large-scale smart farms, intelligent precision seeding requires not only high-quality seed metering, but also real-time sensing, dynamic regulation, remote operation and maintenance, and data traceability under complex field conditions. Focusing on monitoring and control technologies for intelligent precision seeding, this paper systematically reviews the basic architecture of intelligent precision seeding systems, key monitoring objects, and evaluation indices, with emphasis on recent advances in seed-metering monitoring, seed-spacing and seed-placement detection, seeding-depth monitoring, machine vision and multi-sensor fusion, control and compensation, as well as cloud-edge-end collaboration. The analysis shows that photoelectric, optical fiber, and infrared-based methods are relatively mature in engineering applications, but their stability is still limited under conditions of high speed, vibration, dust, and straw mulching. Capacitive, piezoelectric, near-infrared, laser-based, and machine vision methods exhibit advantages in detection accuracy and information richness, yet still face challenges in system complexity, edge-side deployment, and long-term robustness. Current studies also suffer from insufficient anti-interference capability of single sensors, weak coupling between monitoring and control links, inconsistent data interfaces between seeding equipment and smart farm platforms, and a lack of full closed-loop systems integrating sensing, diagnosis, compensation, and evaluation. In the future, intelligent precision seeding technology will develop toward multi-source heterogeneous information fusion, low-latency closed-loop control, standardized data sharing, and farm-level closed-loop quality evaluation.