Abstract: To address the difficulty of timely and accurate identification of missed seeding and multiple seeding during precision cotton sowing in Xinjiang, this study takes the seed pickup process of a toothed-disc precision seed metering device as the research object. Focusing on the complex imaging conditions inside the enclosed chamber, including the small scale of cotton seed targets, weak texture features, obvious motion blur, fluctuating illumination, and strong background reflection interference, an improved YOLOv10-based cotton seed pickup state recognition model, YOLOv10-LSEF, is proposed. First, an image acquisition system for the seed pickup state of the toothed-disc precision seed metering device was established, and the seed-retaining region of the pickup spoon after seed cleaning was selected as the detection site. Cotton seed pickup videos were collected under different rotational speeds and lighting conditions, and a cotton seed pickup state dataset containing three categories—normal seeding, missed seeding, and multiple seeding—was constructed, providing a data foundation for model training, performance evaluation, and robustness verification under multiple operating conditions. Second, to address the loss of edge, contour, and local structural information of small-scale cotton seeds during the downsampling process, a lightweight feature extraction network was adopted, and a Spatial Pixel Downsampling Convolution (SPDConv) module was introduced into the shallow layers of the backbone network to enhance the preservation of small-object details and alleviate the degradation of shallow spatial information caused by motion blur, thereby improving subsequent feature representation. To overcome the instability of cross-scale feature fusion caused by reflections, shadows, and uneven illumination inside the enclosed chamber, an Enhanced Multi-scale Bidirectional Spatial Feature Pyramid Network (EMBSFPN) was constructed. A Dynamic Gated Bi-directional Feature Pyramid Network (DG-BiFPN) module was further incorporated into the key cross-scale fusion paths to adaptively regulate the contributions of features at different scales, suppress unstable high-level semantic responses and background noise interference, and improve the stability and discriminative consistency of multi-scale fused features. Meanwhile, a Spatial Pyramid Pooling Fast with Large-Kernel Spatial Attention (SPPF-LSKA) module was introduced to enlarge the effective receptive field through large-kernel spatial attention, strengthen the response to cotton seed target regions, and further enhance the model's feature representation capability under complex backgrounds. In the detection stage, a Re-parameterized Shared Convolutional Detector (RSCD) head was employed to reduce redundant computations in the detection head, while the WIoU loss function was used to optimize the bounding box regression process, thereby reducing model complexity while maintaining detection accuracy and improving training stability and deployment adaptability on edge devices.Experimental results show that YOLOv10-LSEF achieved a precision of 96.4%, a recall of 97.3%, and an mAP@0.5 of 97.9% on the cotton seed pickup state video dataset, with an inference speed of 131.0 frames/s. The model contains only 1.95 million parameters and requires 5.4 GFLOPs of computation. Compared with YOLOv10 and representative object detection models such as Faster R-CNN, RT-DETR, YOLOv8n, and YOLOv11n, the proposed model achieved a better balance among detection accuracy, inference speed, and lightweight design. Under operating conditions with rotational speeds of 15, 25, and 35 r/min, YOLOv10-LSEF maintained stable detection performance, exhibiting less performance degradation under motion blur caused by higher rotational speeds. Under different illumination conditions, including natural light, LED supplementary lighting, and low-light environments, the model also demonstrated strong adaptability to lighting variations. Edge deployment experiments showed that YOLOv10-LSEF achieved a detection precision of 95.7% and a frame rate of 17.1 frames/s on the Raspberry Pi 5 platform, indicating its preliminary real-time inference capability and deployment feasibility on resource-constrained embedded platforms. The results demonstrate that the proposed YOLOv10-LSEF can effectively improve the accuracy, robustness, and real-time performance of cotton seed pickup state recognition within the enclosed chamber of a toothed-disc precision seed metering device, providing technical support for the online monitoring and intelligent regulation of missed seeding and multiple seeding during precision cotton sowing.