Deep learning-based detection and control system for real-time uniform of straw coverage in no-tillage precision planters

• Developed a system to detect and regulate uniform straw coverage in real time. • Improved YOLOv11 with StarsNet backbone and SegNeXt in the C3k2 structure. • Segmented scattered straw and extracted centroids for deviation measurement. • Achieved 95.1 mAP and over 80% pass rate for spreading width in field trials. • System supports intelligent, sustainable straw return-to-field and no-till seeding. Returning straw to fields is a fundamental agricultural practice that enhances soil structure, mitigates erosion, improves fertility and productivity, and supports subsequent crop cultivation. To address environmental imbalances, inconsistent growth, and pest, disease, and weed proliferation caused by the uneven distribution of existing straw return machinery, this study introduces a uniform straw cover detection and control system utilizing machine vision and deep learning segmentation. An optimized YOLOv11s-seg network was developed to extract geometric and positional data from scattered straw. Using an upper computer, the system calculates the straw center deviation, coverage ratio, and preset differences. Simultaneously, a uniform coverage control system was designed around a single-chip microcomputer and a hydraulic system. Utilizing visual inspection data, the system regulates the straw scattering angle and width in real time. The optimized segmentation model demonstrates exceptional performance, achieving an average accuracy of 96.7% for chaff detection. Field tests indicated that, across various operational speeds, the qualified scattering width rate exceeds 80% following system optimization. Furthermore, the average scattering unevenness surpassed the national standard requirement of 20%, confirming highly effective closed-loop control. The enhanced YOLOv11s-seg model satisfies the rigorous accuracy and efficiency demands for real-time field detection. This vision-based control system accurately regulates straw spreading direction and width, significantly improving uniformity and operational efficiency. These findings provide essential technical support for the automation and intelligent development of straw shredding and returning machinery.