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.

中文翻译：

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基于监督自组织图和荧光动力学的人工神经网络模型诊断番茄诱导抗性状态

本研究的目的是开发三个有监督的自组织图 (SOM) 模型，用于在应用抗性诱导剂后自动识别植物的系统抗性状态。病理系统尖孢镰刀菌f. sp。番茄使用（FORL）+番茄。据报道，无机防御诱导剂 Acibenzolar-S-methyl (benzo-[1,2,3]-thiadiazole-7-carbothioic acid-S-methyl ester, ASM) 可诱导番茄防御基因的表达激活植物的防御机制。SCI 的 FluorPen FP 100-MAX-LM 手持式荧光计用于评估番茄植物中诱导抗性的荧光动力学响应。为了实现抗性诱导的识别，使用三种监督 SOM 模型，即 SKN、XY-F 和 CPANN，对荧光动力学数据进行分类，以确定番茄植株的诱导抗性情况。为了实现这一点，开发了对应于考茨基曲线的荧光计变量的荧光动力学曲线的参数化。SKN 是受监督最好的 SOM，达到 97。22% 到 100% 的准确度。基因表达数据用于确认监督 SOM 的准确性。