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Updated: Aug 30, 2025
High-Throughput Identification of Resistance to Pseudomonas syringae pv. Tomato in Tomato using Seedling Flood Assay
Published on: March 10, 2020
Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised
Xanthoula Eirini Pantazi1, Anastasia L Lagopodi2, Afroditi Alexandra Tamouridou1
1Laboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Abstract:
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.
