Classification of Plenodomus lingam and Plenodomus biglobosus in Co-Occurring Samples Using Reflectance Spectroscopy

• Published in: Agriculture (Basel) Vol. 13; no. 12; p. 2228
• Main Authors: Wójtowicz, Andrzej, Piekarczyk, Jan, Wójtowicz, Marek, Jasiewicz, Jarosław, Królewicz, Sławomir, Starzycka-Korbas, Elżbieta
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2023
• Subjects: Cameras; Classification; Electromagnetic radiation; Feasibility studies; Fungi; fungi discrimination; Machine learning; Nondestructive testing; Oilseeds; pathogen discrimination; Pathogens; Plant species; Plenodomus biglobosus; Plenodomus lingam; Rape plants; Rapeseed; Reflectance; reflectance spectra; reflectance spectroscopy; Sampling methods; Software; Spectral classification; Spectral reflectance; spectral unmixing; Spectroscopy; Spectrum analysis; Wavelengths; United Kingdom > UK; United States > US
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture13122228

Under natural conditions, mixed infections are often observed when two or more species of plant pathogens are present on one host. Thus, the detection and characterization of co-occurring pest species is a challenge of great importance. In this study, we focused on the development of a spectral unmixing method for the discrimination of two fungi species, Plenodomus lingam and Plenodomus biglobosus, the pathogens of oilseed rape. Over 24 days, spectral reflectance measurements from Petri dishes inoculated with fungi were conducted. Four experimental combinations were used: the first two were pure fungal samples, while the other two were co-occurring fungal samples. The results of the study show the possibility of distinguishing, based on spectral characteristics, between P. lingam and P. biglobosus not only in pure but also in co-occurring samples. We observed the changes in the reflectance of electromagnetic radiation from the tested fungi over time and a strong correlation between the reflectance and changes in the areas of the mycelia on the Petri dishes. Moreover, the wavelengths most useful for spectral classification of the tested fungal mycelia were selected. Finally, a spectral unmixing model was proposed, which enables the estimation of the areas of two pathogens in co-occurring samples based on the spectral characteristics of the entire plate with an error smaller than 0.2. To our knowledge, the present study is the first report examining the use of reflectance spectroscopy methods for classifying pathogens on the same Petri dish. The study results indicate the feasibility of reflectance spectroscopy as a nondestructive sampling method for plant pathogen detection and classification.