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Corn stalk diameter estimation using deep learning
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DOI：10.1007/s11119-026-10430-w.png)
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Abstract
En 中文
Purpose Accurate measurement of corn stalk diameter is important for assessing plant robustness, lodging resistance, and harvest performance, but automated measurement across changing crop conditions remains challenging. This study evaluated whether a ground-based stereo-vision system could reliably estimate stalk diameter throughout the growing season, from mid-summer through senescence. Methods A stereo-vision pipeline using dual AR0234 global-shutter cameras was deployed on an autonomous ground robot. YOLOv8 provided stalk localization and pose correction, BoT-SORT enabled multi-frame tracking, and U-Net segmentation with an edge-mask strategy extracted stalk boundaries under partial occlusion. Repeated per-frame width estimates were filtered and converted to physical dimensions using disparity-based calibration. Vision estimates were compared with perpendicular caliper measurements. Results Under mid-summer conditions with minimal leaf interference, the system achieved a mean absolute error (MAE) of 1.1–1.5 mm and r² of 0.90. During late-season senescence, leaf-sheath expansion and occlusion caused systematic diameter overestimation and reduced accuracy. Applying a seasonally derived offset of approximately 3.8 mm reduced MAE to approximately 1.3 mm, although correlation remained modest (r² ≈ 0.41). Independent human measurements also exhibited variability, with inter-rater r² ≈ 0.77 and mean disagreement of approximately 1.0 mm. Conclusion Stereo vision can provide accurate, non-contact corn stalk diameter measurements under favorable canopy conditions and remains viable across the crop lifecycle. However, robust late-season phenotyping will require improved modeling of leaf sheaths and occlusions together with more reliable ground-truth measurement procedures.
Keywords:
Corn Stalk
Detection
Tracking
Computer Vision
Segmentation
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