Early plant disease detection by Raman spectroscopy: An open-source software designed for the automation of preprocessing and analysis of spectral dataset

Abstract

This study introduces a reliable, non-coding software named qREAD-Raman, written in the JavaScript® language, for analyzing and interpreting Raman spectral information. It is designed with a focus on the early detection of diseases in tomato plants (S. lycopersicum) during the asymptomatic stage. The platform integrates a set of machine learning algorithms necessary for the preprocessing consisting of outlier removal, baseline correction, fluorescence removal, smoothing, and normalization. For classification, we applied a Consensus of five different classifiers: Multilayer Perceptron (MLP), Partial Least Squares-Discriminant Analysis (PLS-DA), Linear Discriminant Analysis (LDA), Long Short-Term Memory (LSTM), and K-nearest neighbors (kNN). The experiments were conducted on two bacterial diseases: bacterial canker of tomato induced by Clavibacter michiganesis subsp. michiganensis (Cmm), and the tomato vein-greening associated with Candidatus Liberibacter solanacearum (CLso), a non-culturable bacteria transmitted by Bactericera cockerelli insect. Binary models (Cmm-Healthy and CLso-Healthy) demonstrated excellent classification ability. Asymptomatic Cmm-infected plants were distinguished with an accuracy of 88–95 %, while CLso-infected plants showed an accuracy of 68–77 %. The three-class model (CLso-Cmm-Healthy) exhibited acceptable performance in differentiating between Cmm and CLso, with accuracy rates of 71–83% and 58–67%, respectively. The model's performance highlights differences in the relevant spectral regions associated with the biochemical changes induced by each studied disease. The qREAD-Raman software, implemented for the purpose of this research, was found to be a valuable and comprehensive tool that effectively differentiate diseased tomato plants during their asymptomatic stage.