This work aimed to verify the existence of patterns on the electrophysiological systemic responses of tomato plants inoculated with a pathogenic fungus in an environment with controlled light and temperature. Electrical signalling was measured before and after inoculation in the same plants, and data were analysed with time series techniques and approximate multi-scale entropy (ApEn). Machine learning algorithms were utilised in order to classify data before and after infection throughout the five days of experiments. The obtained results have shown that it is possible to distinguish differences in the plant’s electrome activity before and after the fungus inoculation. In some cases, we have found scale invariance quantified by the power law decay in the distribution histogram. We also found a higher degree of internal organisation quantified by ApEn. The results of the classification algorithms achieved higher accuracy of infection detection at the initial stage of pathogen recognition by the plant. Besides, this study showed evidence that long-distance electrical signalling is likely involved in the plant-pathogen interaction, since signals were obtained in the stem and the inoculum applied on the plant leaves. This might be useful for the early detection of plant infections.

这项工作旨在验证在受光和温度控制的环境中，接种病原性真菌的番茄植株在电生理系统反应中是否存在模式。在相同植物中接种前后测量电信号传导，并使用时间序列技术和近似多尺度熵（ApEn）分析数据。在整个五天的实验中，均使用机器学习算法对感染前后的数据进行分类。获得的结果表明，可以区分真菌接种之前和之后植物的电音活性的差异。在某些情况下，我们发现在分布直方图中通过幂律衰减量化的尺度不变性。我们还发现由ApEn量化的内部组织程度更高。分类算法的结果在植物病原体识别的初始阶段实现了更高的感染检测精度。此外，这项研究表明证据表明，长距离电信号可能与植物-病原体的相互作用有关，因为信号是在茎中获得的，接种物在植物叶片上获得。这对于早期检测植物感染可能有用。因为在茎中获得了信号，并且接种物已在植物叶片上使用。这对于早期检测植物感染可能有用。因为在茎中获得了信号，并且接种物已在植物叶片上使用。这对于早期检测植物感染可能有用。