A YOLOv8n-based hybrid approach for detecting and metrically estimating the length of chopped maize stalks

Abstract Efficient, precise measurement of chopped maize-stalk length is vital for biomass processing and sustainable residue use. Yet fragmented segments—with irregular shapes, curvature, overlap, and low contrast—challenge lightweight detectors and degrade length estimation. We propose a lightweight, two-stage hybrid framework: an optimized YOLOv8n first localizes stalk and calibration regions, and a calibrated geometric module then refines these ROIs to segment, skeletonize, and convert pixel arc length to millimeters. The detector integrates RepELAN for richer spatial features, Coordinate Attention for direction-aware sensitivity, an LSCD head for robust multi-scale prediction, and WIoU for precise box regression. On our dataset, the optimized YOLOv8n attains 96.90% detection accuracy and surpasses representative lightweight baselines. The ROI-based measurement stage applies contour enhancement and skeletonization, followed by washer-based pixel-to-length conversion, yielding a mean absolute error of 2.16 mm (3.63% relative error). Together, this integrated detection–measurement pipeline offers a practical solution for automated residue management, improving straw-utilization efficiency and supporting sustainable agricultural production.