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

doi:10.3390/s22165970

Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics

Sensors (Basel). 2022 Aug 10;22(16):5970. doi: 10.3390/s22165970.

Affiliations

¹ Laboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
² Laboratory of Plant Pathology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
³ Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece.
⁴ Laboratory of Molecular Biology of Plants, School of Agricultural Sciences, University of Thessaly, 38221 Volos, 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.

Keywords: artificial intelligence; clustering; data mining; gene expression; plant protection.

MeSH terms

Fluorescence
Fusarium*
Kinetics
Neural Networks, Computer
Plant Diseases / genetics
Solanum lycopersicum* / genetics
Solanum lycopersicum* / metabolism

Grants and funding

This research was co-financed by Greece and the European Union (European Social Fund—ESF) through the Operational Program «Human Resources Development, Education and Lifelong Learning 2014–2020» in the context of the project “Monitoring of defensive reactions in the tomato, with the help of Artificial Neural Networks, after application of defense inducers” (MIS 5047890).