Abstract: Due to high-speed operation, the production performance and indicators of rice plug tray precision seeders, such as tray qualification rate, over-sowing rate, and missed-sowing rate, are difficult to monitor in real time. In addition, failures such as air-suction pipe duct damage cause significant fluctuations in these indicators, which severely affect production continuity. To address these challenges, an intelligent solution integrating rice plug tray seeding recognition and counting with air-suction duct fault prediction was proposed. At the system architecture level, the overall high-performance monitoring framework for rice plug tray precision seeding was designed, in which the relationship between pneumatic suction holes and seeding qualification rate was analyzed, and the operating principles were systematically explained. Based on this design, an advanced detection approach was adopted, where the YOLOv8+LADH+NWD (You Only Look Once version 8 - Lightweight Asymmetric Dual-Head - Normalized Gaussian Wasserstein Distance) algorithm was employed to achieve seeding recognition in rice plug trays. By optimizing both the model architecture and the loss function, the algorithm’s recognition accuracy and counting efficiency were significantly enhanced under high-frequency operational conditions. Real-time statistical counting of production indicators such as tray qualification rate, over-sowing rate, and missed-sowing rate was achieved through the recognition results. Through statistical analysis of these indicators, it was observed that failures such as air-suction duct damage lead to considerable fluctuations in production performance. Such faults were identified as one of the primary factors influencing equipment stability and operational efficiency. To realize effective prediction of air-suction duct failures in rice plug tray precision seeders, a predictive model based on the Bi-Gate Convolutional Unit with Multi-Head Residual Self-Attention (BiGCU-MHResAtt) was proposed. This model was designed to address the issue of frequent failures that result in extensive short-term loss or absence of production data. By incorporating adaptive learning strategies and cross-condition robustness mechanisms, the model was further enhanced to ensure stability across diverse operational environments. By leveraging the integration of gated convolutional mechanisms with residual self-attention, robust temporal and contextual dependencies were captured, enabling accurate failure forecasting even in environments with noisy and incomplete datasets. On the basis of these methods, a high-performance monitoring system for rice plug tray precision seeding was developed. The proposed system was validated through comprehensive experimental studies, which confirmed the accuracy and reliability of both the recognition and prediction models. The system successfully enabled accurate recognition of rice plug tray seeding, efficient real-time counting of production indicators, and reliable fault prediction of air-suction ducts during high-speed operations. This achievement not only mitigated the risks associated with production instability but also contributed to the advancement of intelligent agricultural machinery. The presented research demonstrates the potential of integrating advanced computer vision algorithms with predictive modeling in agricultural machinery monitoring. By combining YOLOv8-based lightweight detection with BiGCU-MHResAtt-driven failure prediction, a holistic monitoring solution was achieved, capable of ensuring production stability in high-frequency seeding environments. The proposed framework therefore provides not only technical innovation but also a practical foundation for scaling intelligent seeding systems to large-scale agricultural production. The contribution is expected to provide significant support to the intelligentization of rice seeding machinery, promoting greater automation, reliability, and sustainability in modern agricultural practices.