Design and experiment of miss-seeding detection and preparatory seed scraper-belt compensation mechanism based on improved YOLOv5s for potato seed-metering devices

This study addresses the issue of miss seeding in spoon-chain potato seed-metering devices, which impacts planting efficiency and quality, by proposing a miss-seeding detection and compensation system based on an improved YOLOv5s model integrated with a preparatory seed scraper-belt compensation mechanism. The enhanced model incorporates the Convolutional Block Attention Module (CBAM) and Soft Non-Maximum Suppression (Soft-NMS), achieving a mean average precision (<i>mAP</i>) of 99.40% in complex field environments. The system combines visual recognition with mechanical compensation. Experimental results demonstrate that at operating speeds of 0.2-0.4 m/s, the original miss-seeding rate of 5.28%-9.40% is reduced to 0.70%-1.68%, with a reseeding success rate of 82.14%-86.67% and a preparatory seed reseeding success rate exceeding 96%. The study validates the system's efficiency and reliability under medium-low speeds, with slight performance degradation at higher speeds due to vibrations. This solution offers an intelligent upgrade path for traditional potato seed-metering devices and advances precision agriculture technologies.