Full-condition monitoring and intelligent yield prediction and decision-making technology for wheat combine harvesters

This study addresses the problem of inconsistent and unstable sowing depth in conventional wheat planters. To solve this issue, a novel measurement and control strategy based on mulch quantity regulation is proposed, utilizing a dual-axis rotary planter. A mathematical model of sowing depth is established, and a sowing depth adjustment unit is designed. Meanwhile, a measurement and control system is developed and compared with a traditional PID controller. The adaptive fuzzy PID controller exhibits a rise time of 0.19 s and a stabilization time of 0.26 s, representing approximately 26.2% and 14.9% reductions, respectively, relative to the conventional PID controller. Simulation results further confirm the controller’s the effectiveness and adaptability. The system is then applied to a dual-axis rotary-tillage wheat planter, and field experiments are carried out to verify its performance. The proposed method enables precise and stable control of sowing depth, thereby improving the quality and consistency of wheat planting operations.

To address the difficulty of real-time detection of seed-filling performance in pneumatic suction seed metering devices under high-speed operation—where seed targets are tiny, prone to adhesion, and affected by motion blur—this paper proposes a lightweight online detection algorithm, YOLOv8n-MA. First, according to the seed adsorption characteristics of the suction holes, the detection targets are divided into three categories: none, one, and two. Second, based on YOLOv8n, the backbone network is replaced with MobileNetV1 to reduce computational cost, and an ACmix attention module is integrated into the Neck to enhance feature representation for the three suction-hole states. Finally, to meet the demand for low-latency inference on resource-constrained devices, the model is deployed on an edge computing controller to achieve real-time detection. Experimental results show that, compared with the original YOLOv8n, the parameters and FLOPs of YOLOv8n-MA are reduced by 34.4% and 59.8%, respectively, while the mean average precision (mAP) is improved by 2.0% to 96.8%, achieving a superior trade-off between accuracy and efficiency over other detection models of the same category, such as YOLOv5n, YOLOv9n, and YOLOv10n. In field tests, the detection accuracy reaches 95.02% at 12 km/h and 92.65% at 15 km/h. The proposed method provides effective technical support for the intelligent monitoring and control of precision seeding under high-speed operation.