您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Fast detection of the early decay in oranges using visible-LED structured- illumination imaging combined with spiral phase transform and feature-based classification model

文献类型： 外文期刊

作者： Cai, Zhonglei 1 ; Sun, Chanjun 2 ; Zhang, Yizhi 3 ; Shi, Ruiyao 3 ; Zhang, Junyi 1 ; Zhang, Hailiang 4 ; Li, Jiangbo 3 ;

作者机构： 1.Shihezi Univ, Coll Mech & Elect Engn, Shihezi 832003, Xinjiang, Peoples R China

2.Jiangsu Univ, Jiangsu Prov & Educ Minist Cosponsored Synergist I, Zhenjiang 212013, Jiangsu, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

4.East China Jiaotong Univ, Coll Elect & Automat Engn, Nanchang 330013, Peoples R China

5.Natl Engn Res Ctr Informat Technol Agr, Beijing 100089, Peoples R China

关键词： oranges; early decay detection; structured-illumination imaging; spiral phase transform; classification model

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.2； 五年影响因子：2.5 ）

ISSN： 1934-6344

年卷期： 2024 年 17 卷 3 期

页码：

收录情况： SCI

摘要： The early decay of citrus can cause economic and serious food safety issues. The early decayed area has no obvious visual characteristics, making effective detection of this damage very difficult for the citrus industry. This study constructed a new detection system based on visible-light emitting diode (LED) structured-illumination imaging and proposed an effective methodology combined with a spiral phase transform (SPT) algorithm for the early detection of decayed oranges. Each sample obtained three phase-shifting pattern images with phase shifts of -2 pi/3, 0, and 2 pi/3 at a spatial frequency of 0.25 cycles/mm. Three strategies (i.e., the conventional three-phase-shifting method, 2-phase SPT, and 1-phase SPT) were used to demodulate the original patterned images to recover the direct component (DC) and amplitude component (AC) images. The partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) classification models were established based on the texture features of DC, AC, and RT (i.e. the ratio of AC to DC) images. Then, the random frog (RF) algorithm was used to simplify the optimal full-featured model. Finally, the LS-SVM model constructed using 7 texture features from the RT image obtained an average classification accuracy of 95.1% for all tested samples. This study indicates that the proposed structured-illumination imaging technique combined with 2-phase SPT and feature-based classification model can achieve the fast identification of early decayed oranges.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台