Distinguishing Healthy and Diseased Chestnuts via THz Spectroscopy and Unsupervised Learning

Abstract

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.