Detección temprana y discriminación de enfermedades fúngicas en plantas usando espectroscopía in situ

Abstract

After infection, a plant develops symptoms that appear in different parts of plants; however, at moment in which these symptoms are visible, the plant can already be affected negatively. In addition, plants that remain asymptomatic are pathogens reservoirs, since they can remain infected for most of their development cycle, becoming a source of contamination for entire crop. After the symptoms onset, disease is verified using detection techniques, such as ELISA, Polymerase Chain Reaction, Immunofluorescence, Flow Cytometry, Fluorescence in situ and, Gaseous Metabolite Profiles, among others. However, despite the availability of these techniques, a diseases early detection system based on spectrometry techniques can help to reduce losses caused in crops and prevent a greater spread of disease, with more speed, sensitivity, selectivity and without requiring the samples destruction required for analysis. The aim of this study is to evaluate early detection of plants diseases caused by fungal infections using in situ reflectance spectroscopy. To achieve this, reflectance spectra were measured from leaves of S. lycopersicum infected with a fungus pathogenic strains at various times of pathogenesis before the symptoms of the disease were visible. Additionally, physiological analyzes were performed and were related to reflectance spectra of the infected and healthy plants in different infection periods; also, were developed disease prediction models based on Vis/NIR reflectance data before the visual expression of the symptoms using different multivariate statistical tools. In this study it was possible to characterize the spectral variation in leaves of S. lycopersicum L. infected with F. oxysporum during the incubation period. It was also possible to identify the relevant specific wavelengths in the range of 380-1000 nm that can be used as spectral signatures for the detection and discrimination of vascular wilt in S. lycopersicum. We watch that inoculated tomato plants increased their reflectance in the visible range (Vis) and decreased slowly in the near infrared range (NIRs) measured during incubation, showing marked differences with plants subjected to water stress in the VIS/NIR. Additionally, three ranges were found in the spectrum related to infection by F. oxysporum (510nm-520nm, 650nm-670nm and 700-750nm). Linear discriminant models on spectral reflectance data were able to differentiate between tomatos varieties inoculated with F. oxysporum from healthy ones with accuracies higher than 70% 9 days after inoculation (only with three explanatory variables). Additionally, it was possible to characterize and relate the spectral variance in leaves of S. lycopersicum infected with F. oxysporum with the physiological variation and pathogen concentration in tomato plants during the asymptomatic period of vascular wilt. Photosynthetic parameters derived from gaseous exchange analyzes in the tomato leaves correlated related with four bands in the visible range (Vis). Additionally, five specific bands also correlated highly correlated with the increase of F. oxysporum conidia concentration measured at root: 448-523nm, 624-696nm, 740-960nm, 973-976nm and 992-995nm. These wavelengths allowed classifying correctly 100% the plants inoculated with F. oxysporum of plants subjected to hydric stress and controls in the disease asymptomatic period. Finally, it was possible to develop logistic regression models to predict infection by F. oxysporum in plants, obtaining accuracies and areas under the curve greater than 0.9 for one of the tomato varieties evaluated. The results of this study will contribute to a better understanding of the optical properties of the plant during the development of fungal diseases. These methods will be applicable in development of precision crops, specifically in crop protection, differentiation, quantification, and disease early detection of plant; in addition to, the developed models which can be used as a basic input in the design of technological tools that allow the plant disease detection in real time