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Early bruising detection of 'Korla' pears by low-cost visible-LED structured-illumination reflectance imaging and feature-based classification models

文献类型： 外文期刊

作者： Mei, Mengwen 1 ; Cai, Zhonglei 1 ; Zhang, Xinran 1 ; Sun, Chanjun 2 ; Zhang, Junyi 1 ; Peng, Huijie 1 ; Li, Jiangbo 5 ; Shi, Ruiyao 5 ; Zhang, Wei 7 ;

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

2.Jiangsu Univ, Jiangsu Prov & Educ Minist, Synergist Innovat Ctr Modern Agr Equipment, Zhenjiang, Peoples R China

3.Xinjiang Prod & Construct Corps Key Lab Modern Agr, Shihezi, Peoples R China

4.Minist Educ, Engn Res Ctr Prod Mechanizat OasisCharacterist Cas, Shihezi, Peoples R China

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

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

7.Anhui Univ Finance & Econ, Dept Comp Technol & Sci, Bengbu, Peoples R China

关键词： pears; early bruise detection; classification; machine learning; visible LED structured illumination

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.6； 五年影响因子：6.8 ）

ISSN： 1664-462X

年卷期： 2023 年 14 卷

页码：

收录情况： SCI

摘要： IntroductionNondestructive detection of thin-skinned fruit bruising is one of the main challenges in the automated grading of post-harvest fruit. The structured-illumination reflectance imaging (SIRI) is an emerging optical technique with the potential for detection of bruises.MethodsThis study presented the pioneering application of low-cost visible-LED SIRI for detecting early subcutaneous bruises in 'Korla' pears. Three types of bruising degrees (mild, moderate and severe) and ten sets of spatial frequencies (50, 100, 150, 200, 250, 300, 350, 400, 450 and 500 cycles m-1) were analyzed. By evaluation of contrast index (CI) values, 150 cycles m-1 was determined as the optimal spatial frequency. The sinusoidal pattern images were demodulated to get the DC, AC, and RT images without any stripe information. Based on AC and RT images, texture features were extracted and the LS-SVM, PLS-DA and KNN classification models combined the optimized features were developed for the detection of 'Korla' pears with varying degrees of bruising.Results and discussionIt was found that RT images consistently outperformed AC images regardless of type of model, and LS-SVM model exhibited the highest detection accuracy and stability. Across mild, moderate, severe and mixed bruises, the LS-SVM model with RT images achieved classification accuracies of 98.6%, 98.9%, 98.5%, and 98.8%, respectively. This study showed that visible-LED SIRI technique could effectively detect early bruising of 'Korla' pears, providing a valuable reference for using low-cost visible LED SIRI to detect fruit damage.

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