Distinguishing Healthy and Diseased Chestnuts via THz Spectroscopy and Unsupervised Learning
Classifying chestnuts as healthy or diseased remains a complex challenge in quality assessment. In our study, we use THz imaging to determine accurately the health status of chestnuts. Through innovative spectroscopic analysis, we explore the potential of three distinct unsupervised data analysis te...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2024/19/epjconf_eosam2024_15002.pdf |
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| _version_ | 1850198167482531840 |
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| author | Martinez Anna Di Sarno Valentina Maddaloni Pasquale Pagliarulo Vito Paparo Domenico Paturzo Melania Rocco Alessandra Ruocco Michelina |
| author_facet | Martinez Anna Di Sarno Valentina Maddaloni Pasquale Pagliarulo Vito Paparo Domenico Paturzo Melania Rocco Alessandra Ruocco Michelina |
| author_sort | Martinez Anna |
| collection | DOAJ |
| description | Classifying chestnuts as healthy or diseased remains a complex challenge in quality assessment. In our study, we use THz imaging to determine accurately the health status of chestnuts. Through innovative spectroscopic analysis, we explore the potential of three distinct unsupervised data analysis techniques: Principal Component Analysis (PCA), K-Means Clustering (KMC), and Agglomerative Clustering (AC). Compared to traditional analysis methods, our findings unveil the remarkable ability of these methods to differentiate between healthy, diseased and in an intermediate state chestnuts, even when concealed beneath the peel. This research not only advances our understanding of quality control in chestnut production but also highlights the potential of THz imaging in agricultural applications. |
| format | Article |
| id | doaj-art-8edbb1a80a6e4a659dd595b253d7d063 |
| institution | OA Journals |
| issn | 2100-014X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-8edbb1a80a6e4a659dd595b253d7d0632025-08-20T02:12:57ZengEDP SciencesEPJ Web of Conferences2100-014X2024-01-013091500210.1051/epjconf/202430915002epjconf_eosam2024_15002Distinguishing Healthy and Diseased Chestnuts via THz Spectroscopy and Unsupervised LearningMartinez Anna0Di Sarno Valentina1Maddaloni Pasquale2Pagliarulo Vito3Paparo Domenico4Paturzo Melania5Rocco Alessandra6Ruocco Michelina7Scuola Superiore Meridionale, Università di Napoli “Federico II”Istituto Nazionale di Ottica INO-CNR, Consiglio Nazionale delle RicercheIstituto Nazionale di Ottica INO-CNR, Consiglio Nazionale delle RicercheISASI, Institute of Applied Sciences and Intelligent Systems, Consiglio Nazionale delle RicercheISASI, Institute of Applied Sciences and Intelligent Systems, Consiglio Nazionale delle RicercheISASI, Institute of Applied Sciences and Intelligent Systems, Consiglio Nazionale delle RicercheIstituto Nazionale di Ottica INO-CNR, Consiglio Nazionale delle RicercheIPSP, Istituto per la Protezione Sostenibile delle Piante, Consiglio Nazionale delle RicercheClassifying chestnuts as healthy or diseased remains a complex challenge in quality assessment. In our study, we use THz imaging to determine accurately the health status of chestnuts. Through innovative spectroscopic analysis, we explore the potential of three distinct unsupervised data analysis techniques: Principal Component Analysis (PCA), K-Means Clustering (KMC), and Agglomerative Clustering (AC). Compared to traditional analysis methods, our findings unveil the remarkable ability of these methods to differentiate between healthy, diseased and in an intermediate state chestnuts, even when concealed beneath the peel. This research not only advances our understanding of quality control in chestnut production but also highlights the potential of THz imaging in agricultural applications.https://www.epj-conferences.org/articles/epjconf/pdf/2024/19/epjconf_eosam2024_15002.pdf |
| spellingShingle | Martinez Anna Di Sarno Valentina Maddaloni Pasquale Pagliarulo Vito Paparo Domenico Paturzo Melania Rocco Alessandra Ruocco Michelina Distinguishing Healthy and Diseased Chestnuts via THz Spectroscopy and Unsupervised Learning EPJ Web of Conferences |
| title | Distinguishing Healthy and Diseased Chestnuts via THz Spectroscopy and Unsupervised Learning |
| title_full | Distinguishing Healthy and Diseased Chestnuts via THz Spectroscopy and Unsupervised Learning |
| title_fullStr | Distinguishing Healthy and Diseased Chestnuts via THz Spectroscopy and Unsupervised Learning |
| title_full_unstemmed | Distinguishing Healthy and Diseased Chestnuts via THz Spectroscopy and Unsupervised Learning |
| title_short | Distinguishing Healthy and Diseased Chestnuts via THz Spectroscopy and Unsupervised Learning |
| title_sort | distinguishing healthy and diseased chestnuts via thz spectroscopy and unsupervised learning |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2024/19/epjconf_eosam2024_15002.pdf |
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