Authors

Abstract

A lightweight vision-based model, GENet, is proposed to overcome the limitations of conventional missing-seed detection systems, which are highly sensitive to seed characteristics and constrained by slow response and complex configuration. Deployed on a small precision seeder featuring an oblique hook-shaped spoon-type metering device, GENet integrates Ghost Modules, C3Ghost structures, and an ECA attention mechanism. Experiments demonstrate an mAP50–95 of 85.2%, accuracy of 99.9%, and 185 FPS inference speed on the Jetson AGX Xavier platform, while reducing model parameters by over 40%. Validation on the JPS-12 test bench confirms its robustness, providing an efficient solution for intelligent precision seeding.

Abstract in Chinese

为克服传统漏播检测系统易受种子特性影响、响应迟缓且结构复杂等问题，提出一种基于改进YOLOv11n架构的轻量化视觉模型GENet。该模型部署于配备倾斜钩勺式排种装置的小型精量播种机上，集成了Ghost模块、C3Ghost结构与ECA注意力机制。实验结果表明，在Jetson AGX Xavier平台上，GENet的mAP50–95达到85.2%，检测精度为99.9%，推理速度达185 FPS，参数量减少超过40%。在JPS-12试验台上的验证结果表明，该模型具有良好的鲁棒性，为智能精量播种提供了一种高效的解决方案。