A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness
The integration of a non-invasive microwave imaging system with a machine learning algorithm could improve food quality and food safety. In this paper, a S- and C-band microwave imaging system that utilizes DAS (Delay and Sum) beamforming with an automated high-frequency switching network is built t...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10856151/ |
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| author | Zachary M. Choffin Lingyan Kong Yu Gan Nathan Jeong |
| author_facet | Zachary M. Choffin Lingyan Kong Yu Gan Nathan Jeong |
| author_sort | Zachary M. Choffin |
| collection | DOAJ |
| description | The integration of a non-invasive microwave imaging system with a machine learning algorithm could improve food quality and food safety. In this paper, a S- and C-band microwave imaging system that utilizes DAS (Delay and Sum) beamforming with an automated high-frequency switching network is built to scan watermelons and determine their ripeness. A total of 288 images were collected from eight different watermelons varying the height and angle of capture. A convolutional neural network (CNN) was employed to assess the ripeness level, which was determined by analyzing the Brix sugar content. The results show 86% accuracy for ripeness classification in three fold cross validation. This novel approach demonstrates the potential of combining microwave imaging with machine learning for non-destructive food quality assessment, offering a scalable and reliable tool for real-time evaluation of fruit ripeness and quality. |
| format | Article |
| id | doaj-art-53231cc23da445628ab44a5695246bfe |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-53231cc23da445628ab44a5695246bfe2025-02-05T00:00:56ZengIEEEIEEE Access2169-35362025-01-0113214132142110.1109/ACCESS.2025.353580410856151A CNN-Based Microwave Imaging System for Detecting Watermelon RipenessZachary M. Choffin0https://orcid.org/0000-0002-0395-6230Lingyan Kong1https://orcid.org/0000-0002-4775-0226Yu Gan2https://orcid.org/0000-0003-3409-3412Nathan Jeong3https://orcid.org/0000-0003-4610-2208Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, USADepartment of Human Nutrition and Hospitality Management, The University of Alabama, Tuscaloosa, AL, USADepartment of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ, USADepartment of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, USAThe integration of a non-invasive microwave imaging system with a machine learning algorithm could improve food quality and food safety. In this paper, a S- and C-band microwave imaging system that utilizes DAS (Delay and Sum) beamforming with an automated high-frequency switching network is built to scan watermelons and determine their ripeness. A total of 288 images were collected from eight different watermelons varying the height and angle of capture. A convolutional neural network (CNN) was employed to assess the ripeness level, which was determined by analyzing the Brix sugar content. The results show 86% accuracy for ripeness classification in three fold cross validation. This novel approach demonstrates the potential of combining microwave imaging with machine learning for non-destructive food quality assessment, offering a scalable and reliable tool for real-time evaluation of fruit ripeness and quality.https://ieeexplore.ieee.org/document/10856151/Microwave imaging systemconvolutional neural networkwatermelon ripenessfood qualityBrix sugar content |
| spellingShingle | Zachary M. Choffin Lingyan Kong Yu Gan Nathan Jeong A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness IEEE Access Microwave imaging system convolutional neural network watermelon ripeness food quality Brix sugar content |
| title | A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness |
| title_full | A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness |
| title_fullStr | A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness |
| title_full_unstemmed | A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness |
| title_short | A CNN-Based Microwave Imaging System for Detecting Watermelon Ripeness |
| title_sort | cnn based microwave imaging system for detecting watermelon ripeness |
| topic | Microwave imaging system convolutional neural network watermelon ripeness food quality Brix sugar content |
| url | https://ieeexplore.ieee.org/document/10856151/ |
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