| Issue |
EPJ Web Conf.
Volume 309, 2024
EOS Annual Meeting (EOSAM 2024)
|
|
|---|---|---|
| Article Number | 15002 | |
| Number of page(s) | 2 | |
| Section | Focused Sessions (FS) 5- Machine-Learning for Optics and Photonic Computing for AI | |
| DOI | https://doi.org/10.1051/epjconf/202430915002 | |
| Published online | 31 October 2024 | |
https://doi.org/10.1051/epjconf/202430915002
Distinguishing Healthy and Diseased Chestnuts via THz Spectroscopy and Unsupervised Learning
1 Scuola Superiore Meridionale, Università di Napoli “Federico II”, Napoli, Italy
2 Istituto Nazionale di Ottica INO-CNR, Consiglio Nazionale delle Ricerche, Pozzuoli, Italy
3 ISASI, Institute of Applied Sciences and Intelligent Systems, Consiglio Nazionale delle Ricerche, Pozzuoli, Italy
4 IPSP, Istituto per la Protezione Sostenibile delle Piante, Consiglio Nazionale delle Ricerche, Portici, Italy
* Corresponding author: anna.martinez@unina.it
Published online: 31 October 2024
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.
© The Authors, published by EDP Sciences, 2024
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