基于决策树的分类策略在植物电响应中检测外部化学刺激的比较
Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response
摘要：
植物通过产生电响应来监测其周围环境并控制其生理功能。在实验室条件下，我们通过将不同植物暴露于氯化钠（NaCl）、臭氧（O3）和硫酸（H2SO4）中来记录电信号。在对采集到的植物电信号进行滤波、去漂移等预处理后，提取了少量的统计特征。利用这些特征，结合不同的分类算法，我们使用基于决策树的多类分类策略来识别三种不同的外部化学刺激。我们在这里提出我们的探索，以获得最佳的一组排名功能和分类器的组合，可以从传入的植物电信号流中分离出特定的化学刺激。本文还报告了一个详尽的比较相似的特征分类使用过滤和原始植物信号，包含高频随机部分，也存在于其中的低频趋势，作为两种不同的情况下的特征提取。本文的工作为今后利用植物电信号监测和检测除NaCl、O3和H2SO4以外的其他环境刺激开辟了新的可能性。
英文摘要：
Plants monitor their surrounding environment and control their physiological functions by producing an electrical response. We recorded electrical signals from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3) and Sulfuric Acid (H2SO4) under laboratory conditions. After applying pre-processing techniques such as filtering and drift removal, we extracted few statistical features from the acquired plant electrical signals. Using these features, combined with different classification algorithms, we used a decision tree based multi-class classification strategy to identify the three different external chemical stimuli. We here present our exploration to obtain the optimum set of ranked feature and classifier combination that can separate a particular chemical stimulus from the incoming stream of plant electrical signals. The paper also reports an exhaustive comparison of similar feature based classification using the filtered and the raw plant signals, containing the high frequency stochastic part and also the low frequency trends present in it, as two different cases for feature extraction. The work, presented in this paper opens up new possibilities for using plant electrical signals to monitor and detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
