Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model
Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. To address thes...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/12/1315 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839655143321108480 |
|---|---|
| author | Zeqing Yang Jiahui Zhang Zhimeng Li Ning Hu Zhengpan Qi |
| author_facet | Zeqing Yang Jiahui Zhang Zhimeng Li Ning Hu Zhengpan Qi |
| author_sort | Zeqing Yang |
| collection | DOAJ |
| description | Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. To address these challenges, this study investigates a non-destructive approach integrating X-ray imaging and multi-criteria decision (MCD) theory for non-destructive internal defect detection in pears. Internal defects were identified by analyzing grayscale variations in X-ray images. The proposed method combines manual feature-based classifiers, including Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), with a deep convolutional neural network (DCNN) model within an MCD-based fusion framework. Experimental results demonstrated that the fused model achieved a detection accuracy of 97.1%, significantly outperforming individual classifiers. This approach effectively reduced misclassification caused by structural similarities in X-ray images. The study confirms the efficacy of X-ray imaging coupled with multi-classifier fusion for accurate and non-destructive internal quality evaluation of pears, offering practical value for fruit grading and post-harvest management in the pear industry. |
| format | Article |
| id | doaj-art-8c4aa9b15e4446ffa7da6d50e7e7c939 |
| institution | Matheson Library |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-8c4aa9b15e4446ffa7da6d50e7e7c9392025-06-25T13:19:52ZengMDPI AGAgriculture2077-04722025-06-011512131510.3390/agriculture15121315Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion ModelZeqing Yang0Jiahui Zhang1Zhimeng Li2Ning Hu3Zhengpan Qi4School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaPears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. To address these challenges, this study investigates a non-destructive approach integrating X-ray imaging and multi-criteria decision (MCD) theory for non-destructive internal defect detection in pears. Internal defects were identified by analyzing grayscale variations in X-ray images. The proposed method combines manual feature-based classifiers, including Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), with a deep convolutional neural network (DCNN) model within an MCD-based fusion framework. Experimental results demonstrated that the fused model achieved a detection accuracy of 97.1%, significantly outperforming individual classifiers. This approach effectively reduced misclassification caused by structural similarities in X-ray images. The study confirms the efficacy of X-ray imaging coupled with multi-classifier fusion for accurate and non-destructive internal quality evaluation of pears, offering practical value for fruit grading and post-harvest management in the pear industry.https://www.mdpi.com/2077-0472/15/12/1315X-raymulti-criteria decisiondeep convolutional neural networkinternal quality inspection |
| spellingShingle | Zeqing Yang Jiahui Zhang Zhimeng Li Ning Hu Zhengpan Qi Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model Agriculture X-ray multi-criteria decision deep convolutional neural network internal quality inspection |
| title | Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model |
| title_full | Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model |
| title_fullStr | Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model |
| title_full_unstemmed | Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model |
| title_short | Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model |
| title_sort | pears internal quality inspection based on x ray imaging and multi criteria decision fusion model |
| topic | X-ray multi-criteria decision deep convolutional neural network internal quality inspection |
| url | https://www.mdpi.com/2077-0472/15/12/1315 |
| work_keys_str_mv | AT zeqingyang pearsinternalqualityinspectionbasedonxrayimagingandmulticriteriadecisionfusionmodel AT jiahuizhang pearsinternalqualityinspectionbasedonxrayimagingandmulticriteriadecisionfusionmodel AT zhimengli pearsinternalqualityinspectionbasedonxrayimagingandmulticriteriadecisionfusionmodel AT ninghu pearsinternalqualityinspectionbasedonxrayimagingandmulticriteriadecisionfusionmodel AT zhengpanqi pearsinternalqualityinspectionbasedonxrayimagingandmulticriteriadecisionfusionmodel |