Evaluating Growth of Colletotrichum species by Near infrared (NIR) hyperspectral imaging

Abstract

This work focuses on the evaluation of growth characteristics of two kinds of Colletotrichum species, i.e., Colletotrichum truncatum and Colletotrichum gloeosporioides, by near infrared (NIR) hyperspectral imaging. The hyperspectral images of the two fungi growing on potato ager medium were recorded daily for 6 days. The average spectra of each fungi were extracted, and the reflectance of the average spectra preliminarily indicated the growth phases of fungi. Principal component analysis (PCA) and support vector machine classifier (SVM) were applied on the full spectral range. Two groups of optimal PCs (PC1-5 for C. truncatum; and PC1-2, PC4 and PC6 for C. gloeosporioides) were respectively selected by Wilks-λ criterion to build the PCA-SVM classification models. The identification accuracies were 90.83% and 94.17% for C. truncatum and C. gloeosporioides, respectively. To simplify the prediction models, competitive adaptive reweighted sampling (CARS) was employed to choose optimal wavelengths. Total twelve (471.8, 597.4, 777.6, 790.2, 792.8, 795.3, 861.8, 882.5, 892.9, 895.5, 898.1, 963.6 nm) and ten (516.3, 523.4, 563.8, 571.0, 747.4, 802.9, 825.9, 828.4, 831.0, 856.7 nm) wavelengths were selected for the two Colletotrichum species. Corresponding SVM models build by those wavelengths could identify fungal growth days with the accuracies of 97.50%. Results indicate that NIR hyperspectral imaging is a powerful tool to evaluate the growth characteristics of C. truncatum and C. gloeosporioides.