Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System
One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/5262294 |
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| _version_ | 1832565602283159552 |
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| author | V. Hemamalini S. Rajarajeswari S. Nachiyappan M. Sambath T. Devi Bhupesh Kumar Singh Abhishek Raghuvanshi |
| author_facet | V. Hemamalini S. Rajarajeswari S. Nachiyappan M. Sambath T. Devi Bhupesh Kumar Singh Abhishek Raghuvanshi |
| author_sort | V. Hemamalini |
| collection | DOAJ |
| description | One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of fruits and other foods has increased significantly. This is primarily because these technologies make significant advancements in areas where the human eye falls short. This means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. This article discusses how to check and assess food using picture segmentation and machine learning. It is capable of classifying fruits and determining whether a piece of fruit is rotten. To begin, Gaussian elimination is used to remove noise from images. Then, photos are subjected to histogram equalization in order to improve their quality. Segmentation of the image is carried out using the K-means clustering technique. Then, fruit photos are classified using machine learning methods such as KNN, SVM, and C4.5. These algorithms determine if a fruit is damaged or not. |
| format | Article |
| id | doaj-art-ad90ad339d924f54b5eb40cde9a8269f |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-ad90ad339d924f54b5eb40cde9a8269f2025-02-03T01:07:11ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/5262294Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based SystemV. Hemamalini0S. Rajarajeswari1S. Nachiyappan2M. Sambath3T. Devi4Bhupesh Kumar Singh5Abhishek Raghuvanshi6School of ComputingSchool of Computer Science and EngineeringSchool of Computer Science and EngineeringSchool of Computer Science and EngineeringDepartment of Computer Science & EngineeringArba Minch UniversityMahakal Institute of TechnologyOne of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of fruits and other foods has increased significantly. This is primarily because these technologies make significant advancements in areas where the human eye falls short. This means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. This article discusses how to check and assess food using picture segmentation and machine learning. It is capable of classifying fruits and determining whether a piece of fruit is rotten. To begin, Gaussian elimination is used to remove noise from images. Then, photos are subjected to histogram equalization in order to improve their quality. Segmentation of the image is carried out using the K-means clustering technique. Then, fruit photos are classified using machine learning methods such as KNN, SVM, and C4.5. These algorithms determine if a fruit is damaged or not.http://dx.doi.org/10.1155/2022/5262294 |
| spellingShingle | V. Hemamalini S. Rajarajeswari S. Nachiyappan M. Sambath T. Devi Bhupesh Kumar Singh Abhishek Raghuvanshi Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System Journal of Food Quality |
| title | Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System |
| title_full | Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System |
| title_fullStr | Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System |
| title_full_unstemmed | Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System |
| title_short | Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System |
| title_sort | food quality inspection and grading using efficient image segmentation and machine learning based system |
| url | http://dx.doi.org/10.1155/2022/5262294 |
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