外文摘要：The quality of radish roots depends largely on its cultivar, production environment, and postharvest management along the supply chain. Quality monitoring of fresh products is of utmost importance during the postharvest period. The purpose of this study is to nondestructively estimate the weight of a radish using random forests based on color and shape information obtained from images, as well as volumetric information obtained by analyzing a point cloud obtained by combining multiple forms of shape information. The explanatory variables were color and shape information obtained through an image analysis of still images of radishes captured in a constructed photographic environment. The volume information was calculated from the bounding box and convex hull applied to the point cloud by combining the shape information obtained from the image analysis. We then applied random forests to relate the radish weight to the explanatory variables. The experimental results showed that the models using color, shape, or volume information all exhibited good performance with a Pearson's correlation coefficient (COR) >= 0.80, suggesting the potential of nondestructive monitoring of radish weight based on color, shape, and volume information. Specifically, the model using volume information showed very high performance, with a COR of 0.95 or higher.
外文关键词：3D reconstruction;Random forests;image analysis;volume;point cloud;weight estimation;quality monitoring;Raphanus sativus L. var. sativus;radish
作者：Fukuda, Shinji;Kamiwaki, Yuto
作者单位：Tokyo Univ Agr & Technol;Natl Agr & Food Res Org
期刊名称：HORTICULTURAE
期刊影响因子：0.0
出版年份：2024
出版刊次：10（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台