Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms

Abstract

The hyperspectral imaging technique was used for the non-destructive detection of tomato spotted wilt virus (TSWV) infection in tobacco at an early stage. Spectra ranging from 400 to 1000 nm with 128 bands from inoculated and healthy tobacco plants were analyzed by using three wavelength selection methods (successive projections algorithm (SPA), boosted regression tree (BRT), and genetic algorithm (GA)), and four machine learning (ML) techniques (boosted regression tree (BRT), support vector machine (SVM), random forest (RF), and classification and regression tress (CART)). The results indicated that the models built by the BRT algorithm using the wavelengths selected by SPA as the input variables obtained the best outcome for the 10-fold cross-validation with the mean overall accuracy of 85.2% and area under receiver operating curve (AUC) of 0.932. The band selection results and variable contribution analysis in BRT modeling jointly showed that the near-infrared (NIR) spectral region is informative and important for the differentiation of infected and healthy tobacco leaves. Different stages of post-inoculation were split according to the molecular identification and visual observation. The classification results at different stages indicated that the hyperspectral imaging data combined with ML methods and wavelength selection algorithms can be used for the early detection of TSWV in tobacco, both at the presymptomatic stage and during the period before the systematic infection can be detected by the molecular identification approach.