A dual-constrained watershed algorithm for bean particle segmentation and sizing

Abstract

Accurate measurement of bean particle size is essential for automated grading and quality control in agricultural processing. However, existing image segmentation methods often suffer from low efficiency, over-segmentation, and high computational cost. We proposed a distance-gradient dual constrained watershed algorithm for precise segmentation and measurement of bean particles. The method integrated distance transform-based seed extraction with gradient-constrained flooding, effectively suppressing noise-induced region fragmentation and improving the separation of adherent particles. An experimental platform was constructed using an industrial camera and an image-processing pipeline to evaluate performance. Compared with the conventional watershed algorithm, the proposed method improves segmentation accuracy by 7.2% and reduces the mean particle size error by 27.8% (0.13 mm, representing a relative error of 2.4%). Validation on three soybean varieties confirmed the robustness and generalizability of the approach. The results indicated that the proposed algorithm provided an efficient and accurate technique for agricultural particle size analysis, offering potential for integration into practical low-cost inspection systems.

Keywords

distance-gradient dual constraint watershed algorithm / machine vision inspection system / particle size sorting / precision agriculture / metrology

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