An Integrated Perception‐to‐Navigation Framework for Autonomous Robots in Corn Fields Using RO‐YOLO‐Based Root–Stalk Detection

This is the authors' abstract. We don't add key points for this paper.

To address the poor performance of navigation line extraction for agricultural robots in complex open‐field corn ( Zea mays L.) environments during the 6–8 leaf growth stage, this study proposes an accurate and efficient navigation baseline extraction method based on an improved YOLO11n model, specifically designed for complex corn field conditions. First, an improved corn root–stalk detection model, termed RO‐YOLO, is proposed (YOLO is short for You Only Look Once). By incorporating the EBlock, a low‐resolution self‐attention module and a multifeature fusion module into the YOLO11n framework, the detection accuracy of the model is significantly enhanced. Next, corn root–stalk positions are precisely localised based on the bounding boxes generated by the model, and representative feature points of corn crop rows are extracted. Subsequently, a feature point screening strategy based on coarse lateral position partitioning and precise longitudinal near‐field filtering is proposed to select valid feature points, and the navigation baseline is fitted using the random sample consensus (RANSAC) algorithm. Finally, the navigation line is extracted by solving the angle bisector. Experimental results demonstrate that the improved RO‐YOLO achieves a precision, recall and AP@0.5 (average precision at an intersection over union [IoU] threshold of 0.5) of 91.4%, 88.3% and 92.7%, respectively, for corn root–stalk detection. Moreover, the proposed navigation line extraction algorithm for the corn 6–8 leaf stage attains an average fitting time of only 51.6 ms and an accuracy of up to 92.0%, ensuring both high precision and real‐time performance of navigation line extraction.