Simultaneous non-destructive detection of watercore and bruising in ‘Fuji’ apples via X-Ray computed tomography: Dual-threshold segmentation and machine learning classification

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成果类型：

期刊论文

作者：

Haitao Fu;Menglong Ma;Yixiao Wang;Ziwei Wang;Jun Wang;...

通讯作者：

Jiahua Wang<&wdkj&>Ming Zhang

作者机构：

[Haitao Fu; Menglong Ma; Yixiao Wang; Xiaodan Liu; Huang Dai; Jiahua Wang] College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, Hubei 430023, China

[Ming Zhang] School of Food and Health, Beijing Technology & Business University, Beijing 100048, China

[Ziwei Wang] Wuhan Rayson School, Wuhan, Hubei 430048, China

[Jun Wang] Hubei Key Laboratory of Resource Utilization and Quality Control of Characteristic Crops, Hubei Engineering University, Xiaogan, Hubei 432000, China

School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China

通讯机构：

[Jiahua Wang] C

[Ming Zhang] S

School of Food and Health, Beijing Technology & Business University, Beijing 100048, China<&wdkj&>College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, Hubei 430023, China

语种：

英文

关键词：

X-ray computer tomography;Apple;Watercore;Bruise;Machine learning;Image processing

期刊：

Postharvest Biology and Technology

ISSN：

0925-5214

年：

2026

卷：

231

页码：

113878

DOI：

10.1016/j.postharvbio.2025.113878

机构署名：

本校为第一且通讯机构

院系归属：

食品科学与工程学院

摘要：

In markets such as China and Japan, watercore and bruising are decisive factors determining the commercial value of premium-grade 'Fuji' apples. This study presents a non-destructive approach integrating X-ray computed tomography (CT) with image processing and machine learning (ML) to simultaneously detect watercore and bruising, and grade watercore severity. A threshold-based segmentation protocol (grayscale value, GSV > 437 for watercore; GSV < 0 for bruising) enabled precise 3D reconstruction of affected tissues. Key morphological and statis...

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Simultaneous non-destructive detection of watercore and bruising in ‘Fuji’ apples via X-Ray computed tomography: Dual-threshold segmentation and machine learning classification

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