A Novel Damage Inspection Method Using Fluorescence Imaging Combined with Machine Learning Algorithms Applied to Green Bell Pepper
Fluorescence imaging has emerged as a powerful tool for detecting surface damage in fruits, yet its application to vegetables such as green bell peppers remains underexplored. This study investigates the fluorescent characteristics of minor mechanical damage, specifically 5 × 5 mm cuts in the exocar...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/10/12/1336 |
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| author | Danial Fatchurrahman Noelia Castillejo Maulidia Hilaili Lucia Russo Ayoub Fathi-Najafabadi Anisur Rahman |
| author_facet | Danial Fatchurrahman Noelia Castillejo Maulidia Hilaili Lucia Russo Ayoub Fathi-Najafabadi Anisur Rahman |
| author_sort | Danial Fatchurrahman |
| collection | DOAJ |
| description | Fluorescence imaging has emerged as a powerful tool for detecting surface damage in fruits, yet its application to vegetables such as green bell peppers remains underexplored. This study investigates the fluorescent characteristics of minor mechanical damage, specifically 5 × 5 mm cuts in the exocarp of green bell peppers, which conventional digital imaging techniques fail to classify accurately. Chlorophyll fluorescence imaging was combined with machine learning algorithms—including logistic regression (LR), artificial neural networks (ANN), random forests (RF), k-nearest neighbors (kNN), and the support vector machine (SVM) to classify damaged and sound fruit. The machine learning models demonstrated a high classification accuracy, with calibration and prediction accuracies exceeding 0.86 and 0.96, respectively, across all algorithms. These results underscore the potential of fluorescence imaging as a non-invasive, rapid, and cheaper method for assessing mechanical damage in green bell peppers, offering valuable applications in quality control and postharvest management. |
| format | Article |
| id | doaj-art-3ff3476f5866483b99d6fa762d9791e9 |
| institution | OA Journals |
| issn | 2311-7524 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj-art-3ff3476f5866483b99d6fa762d9791e92025-08-20T02:00:23ZengMDPI AGHorticulturae2311-75242024-12-011012133610.3390/horticulturae10121336A Novel Damage Inspection Method Using Fluorescence Imaging Combined with Machine Learning Algorithms Applied to Green Bell PepperDanial Fatchurrahman0Noelia Castillejo1Maulidia Hilaili2Lucia Russo3Ayoub Fathi-Najafabadi4Anisur Rahman5Dipartimento di Scienze Agrarie, Alimenti, Risorse Naturali e Ingegneria (DAFNE), Università di Foggia, Via Napoli 25, 71122 Foggia, ItalyDipartimento di Scienze Agrarie, Alimenti, Risorse Naturali e Ingegneria (DAFNE), Università di Foggia, Via Napoli 25, 71122 Foggia, ItalyLaboratory of Bio-Sensing Engineering, Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, JapanDipartimento di Scienze Agrarie, Alimenti, Risorse Naturali e Ingegneria (DAFNE), Università di Foggia, Via Napoli 25, 71122 Foggia, ItalyDipartimento di Scienze Agrarie, Alimenti, Risorse Naturali e Ingegneria (DAFNE), Università di Foggia, Via Napoli 25, 71122 Foggia, ItalyDepartment of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh 2202, BangladeshFluorescence imaging has emerged as a powerful tool for detecting surface damage in fruits, yet its application to vegetables such as green bell peppers remains underexplored. This study investigates the fluorescent characteristics of minor mechanical damage, specifically 5 × 5 mm cuts in the exocarp of green bell peppers, which conventional digital imaging techniques fail to classify accurately. Chlorophyll fluorescence imaging was combined with machine learning algorithms—including logistic regression (LR), artificial neural networks (ANN), random forests (RF), k-nearest neighbors (kNN), and the support vector machine (SVM) to classify damaged and sound fruit. The machine learning models demonstrated a high classification accuracy, with calibration and prediction accuracies exceeding 0.86 and 0.96, respectively, across all algorithms. These results underscore the potential of fluorescence imaging as a non-invasive, rapid, and cheaper method for assessing mechanical damage in green bell peppers, offering valuable applications in quality control and postharvest management.https://www.mdpi.com/2311-7524/10/12/1336<i>Capsicum annuum</i> L.early detectiondefectivesortingfruit quality |
| spellingShingle | Danial Fatchurrahman Noelia Castillejo Maulidia Hilaili Lucia Russo Ayoub Fathi-Najafabadi Anisur Rahman A Novel Damage Inspection Method Using Fluorescence Imaging Combined with Machine Learning Algorithms Applied to Green Bell Pepper Horticulturae <i>Capsicum annuum</i> L. early detection defective sorting fruit quality |
| title | A Novel Damage Inspection Method Using Fluorescence Imaging Combined with Machine Learning Algorithms Applied to Green Bell Pepper |
| title_full | A Novel Damage Inspection Method Using Fluorescence Imaging Combined with Machine Learning Algorithms Applied to Green Bell Pepper |
| title_fullStr | A Novel Damage Inspection Method Using Fluorescence Imaging Combined with Machine Learning Algorithms Applied to Green Bell Pepper |
| title_full_unstemmed | A Novel Damage Inspection Method Using Fluorescence Imaging Combined with Machine Learning Algorithms Applied to Green Bell Pepper |
| title_short | A Novel Damage Inspection Method Using Fluorescence Imaging Combined with Machine Learning Algorithms Applied to Green Bell Pepper |
| title_sort | novel damage inspection method using fluorescence imaging combined with machine learning algorithms applied to green bell pepper |
| topic | <i>Capsicum annuum</i> L. early detection defective sorting fruit quality |
| url | https://www.mdpi.com/2311-7524/10/12/1336 |
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