To address the high-throughput real-time detection requirements in industrial seed sorting scenarios, this study proposes an innovative solution coupling a lightweight detection algorithm with an industrial control system. By optimizing and integrating the YOLOv11-S architecture with the MobileNetV4 depth-wise separable convolution backbone, introducing the Focus operation for 4x downsampling via slicing concatenation without increasing computation, and embedding a mixed local channel attention mechanism, an industrially applicable model, Anomalous Seed Detection-YOLO(ASD-YOLO), with a parameter size of only 9.5 MB, was constructed. This model achieves a mean average precision (mAP) of 96.5 % while reaching a maximum processing capability of 62 FPS on a single device. Simultaneously, by incorporating algorithms such as a feedback error correction mechanism developed in conjunction with an industrial-grade pulse coordination control mechanism, the system achieves stable end-to-end latency control at the 35 ms level in a pepper seed anomaly detection production line environment. It supports continuous 24-h stable operation at a throughput of 10,000 seeds/min, with a relative error controlled to 3.3 mm. Based on the detection results, a fuzzy grading algorithm was developed to categorize the seed quality into five levels using membership functions. This provides a quantitative basis for refined storage management and differentiated processing, achieving a statistically significant 16.2 % reduction in the misjudgment rate compared with traditional grading methods. By constructing an “artificial intelligent algorithm-pulse coordination-protocol coupling” trinity architecture, the proposed model establishes a universal methodological framework for lightweight model deployment in agricultural intelligent manufacturing scenarios, offering a scalable standardized solution for seed quality control.
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