Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics
Xanthoula Eirini Pantazi,
Anastasia L. Lagopodi,
Afroditi Alexandra Tamouridou,
Nathalie Nephelie Kamou,
Ioannis Giannakis,
Georgios Lagiotis,
Evangelia Stavridou,
Panagiotis Madesis,
Georgios Tziotzios,
Konstantinos Dolaptsis,
Dimitrios Moshou
Affiliations
Xanthoula Eirini Pantazi
Laboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Anastasia L. Lagopodi
Laboratory of Plant Pathology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Afroditi Alexandra Tamouridou
Laboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Nathalie Nephelie Kamou
Laboratory of Plant Pathology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Ioannis Giannakis
Laboratory of Plant Pathology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Georgios Lagiotis
Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece
Evangelia Stavridou
Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece
Panagiotis Madesis
Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece
Georgios Tziotzios
Laboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Konstantinos Dolaptsis
Laboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Dimitrios Moshou
Laboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
The aim of this study was to develop three supervised self-organizing map (SOM) models for the automatic recognition of a systemic resistance state in plants after application of a resistance inducer. The pathosystem Fusarium oxysporum f. sp. radicis-lycopersici (FORL) + tomato was used. The inorganic, defense inducer, Acibenzolar-S-methyl (benzo-[1,2,3]-thiadiazole-7-carbothioic acid-S-methyl ester, ASM), reported to induce expression of defense genes in tomato, was applied to activate the defense mechanisms in the plant. A handheld fluorometer, FluorPen FP 100-MAX-LM by SCI, was used to assess the fluorescence kinetics response of the induced resistance in tomato plants. To achieve recognition of resistance induction, three models of supervised SOMs, namely SKN, XY-F, and CPANN, were used to classify fluorescence kinetics data, in order to determine the induced resistance condition in tomato plants. To achieve this, a parameterization of fluorescence kinetics curves was developed corresponding to fluorometer variables of the Kautsky Curves. SKN was the best supervised SOM, achieving 97.22% to 100% accuracy. Gene expression data were used to confirm the accuracy of the supervised SOMs.
