A dual-constrained watershed algorithm for bean particle segmentation and sizing
Year: 2025
Type: article
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.
Authors: ZHUANG Licheng, GE Boang, HU Jun, SONG Yiheng, LIU Sheng
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7141778652
FWCI:
Cites:
Cited by:
Related to: