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A spontaneous keypoints connection algorithm for leafy plants skeletonization and phenotypes extraction
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DOI：10.3389/fpls.2025.1641255.png)
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Abstract
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IntroductionLeaf phenotypes are key indicators of plant growth status. Existing deep learning–based leaf skeletonization typically requires extensive manual labeling; long training; and predefined keypoints; which limits scalability. We developed a training-free and label-free approach that connects spontaneously detected keypoints to generate leaf skeletons for leafy plants.MethodsThe method comprises random seed-point generation and adaptive keypoint connection. For plants with random leaf morphology; we determine a threshold for the angle difference among any three consecutive adjacent points and iteratively identify keypoints within circular search neighborhoods to trace leaf skeletons. For plants with regular leaf morphology; we fit the skeleton trajectory by minimizing curvature. We validated the approach on vertical and front-view images of orchids (covering random and regular morphological cases) and extracted five phenotypic parameters from the resulting skeletons. Generalization was further assessed on a maize image dataset.ResultsOn orchid images; the proposed approach achieved an average curvature fitting error of 0.12 and an average leaf recall of 92%. Five orchid phenotypic parameters were accurately derived from the skeletons. The method also showed effective skeletonization on maize; indicating cross-species applicability.DiscussionBy eliminating manual labels and training; this approach reduces annotation effort and computational overhead while enabling precise geometric phenotype calculation from skeleton-based keypoints. Its effectiveness on both randomly distributed and regularly shaped leafy plants suggests suitability for high-throughput plant phenotyping workflows.
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