Mass and volume estimation of diverse kimchi cabbage forms using RGB-D vision and machine learning

• Published in: Postharvest biology and technology 2024-12, Vol.218, p.113130, Article 113130
• Main Authors: Yang, Hae-Il, Min, Sung-Gi, Yang, Ji-Hee, Eun, Jong-Bang, Chung, Young-Bae
• Format: Article
• Language: English
• Subjects: Automated grading; cabbage; Colorimetric feature; computer vision; Depth sensing; kimchi; Predictive modeling; Quality control; vision
• ISSN: 0925-5214
• DOI: 10.1016/j.postharvbio.2024.113130

This study introduces a custom-built RGB-D-based machine vision system designed to accurately estimate the mass and volume of whole kimchi cabbage (WC) and longitudinally cut kimchi cabbage (LCC). Given the pivotal role of kimchi cabbage (KC) in both Asian and Western diets, accurate post-harvest assessment of its mass and volume is critical for quality control, sorting, and pricing. Conventional manual measurements and visual estimations are laborious and inaccurate. Our research leveraged RGB-D data to refine machine learning models and enhance the extraction and analysis of 2D, 3D, and colorimetric features for a more reliable estimation approach. The results demonstrate that integrating 3D and colorimetric features markedly improves the estimation accuracy, with notable success in mass estimation for LCC (R² = 0.913, ratio of performance to deviation (RPD) = 3.38) and robust volume predictions for both cabbage types (R² > 0.90, RPD > 3). However, challenges such as potential over-exclusion of outer leaves in LCC and the need for more advanced WC mass estimation techniques have been identified. Future work will focus on refining the feature extraction methods and assessing various imaging environments to enhance the precision of mass and volume predictions across different forms of KC. •Novel RGB-D-based system accurately estimates mass and volume of kimchi cabbage.•Integration of 3D and colorimetric features enhances estimation accuracy.•Significant success in mass estimation for longitudinally cut cabbage (R² = 0.913).•Robust volume predictions achieved for both whole and cut cabbage (R² > 0.90).•Study addresses critical need for reliable post-harvest assessment methods.