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首页> 外文期刊>Journal of the ASABE >Application of Chlorophyll a Fluorescence in Analysis and Detection of Bacterial Wilt in Tomato Plants
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Application of Chlorophyll a Fluorescence in Analysis and Detection of Bacterial Wilt in Tomato Plants

机译：叶绿素A荧光在分析和检测番茄植物中的细菌枯萎病中

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摘要

Bacterial wilt seriously threatens global tomato yield. Timely and accurately identification of plants infected with bacterial wilt is crucial to the implementation of disease management practices, but such detection methods are lacking. In this study, chlorophyll a fluorescence (ChlF) was used in the analysis and detection of tomato bacterial wilt. ChlF induction curves were collected from the leaves of control and infected plants after different days-post-inoculation (DPI), and eight JlP-test parameters most relevant to tomato bacterial wilt were selectedfrom 22 JlP-test parameters through statistical analysis. A novel detection model, multidimensional multiclass genetic programming with multidimensional populations extreme learning machine (M3GP-ELM), was developed to identify tomato plants infected with bacterial wilt based on the selected JlP-test parameters. The M3GP-ELM model used a genetic programming algorithm to perform linear and/or nonlinear transformations on the selected eight variables and then used the classification accuracy of ELM as a fitness function to evaluate the performance of the transformed variables. The results of the experiment indicated that the differences in the ChlF induction curves and the eight selected JlP-testparameters between the infected group and the control group became more obvious with increased time after inoculation. Compared with partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and ELM, the M3GP-ELM model achievedthe best detection performance with an overall accuracy of88.42% and an accuracy of82.83% at the early stage (1 to 5 DPI). Therefore, ChlF technology combined with M3GP-ELM has the potential to detect tomato bacterial wilt.
机译：细菌枯萎会严重威胁全球番茄产量。及时，准确地鉴定感染细菌枯萎病的植物对于实施疾病管理实践至关重要，但是缺乏这种检测方法。在这项研究中，叶绿素A荧光（CHLF）用于分析和检测番茄细菌枯萎病。不同的天post接种（DPI）后，从对照和感染植物的叶子中收集了CHLF诱导曲线，并通过统计分析从22个JLP测试参数中选择了八个与番茄细菌枯萎病最相关的JLP检测参数。开发了一种新型的检测模型，即具有多维种群极限学习机（M3GP-ELM）的多维多类遗传编程，以鉴定基于所选JLP检测参数感染细菌枯萎病的番茄植物。 M3GP-ELM模型使用遗传编程算法对所选八个变量进行线性和/或非线性转换，然后将ELM的分类精度作为健身函数来评估转化变量的性能。实验的结果表明，随着感染组和对照组之间的八个CHLF诱导曲线和八个选定的JLP-TestParameters的差异随着接种后的时间而增加。与局部最小二乘判别分析（PLS-DA），支持向量机（SVM）和ELM相比，M3GP-ELM模型以88.42％的总体准确度达到了最佳检测性能，并且早期的准确度为82.83％。阶段（1至5 dpi）。因此，CHLF技术与M3GP-ELM相结合具有检测番茄细菌枯萎病的潜力。

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  • 来源
    《Journal of the ASABE》 |2022年第2期|347-356|共10页
  • 作者

    Xin Wang; Wei Yang; Yu Yang;

  • 作者单位

    Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, Jiangsu, China;

    Jiangsu Key Laboratory for Eco-Agricultural Biotechnology around Hongze Lake, School of Life Science, Huaiyin Normal University, Huai'an Jiangsu, China;

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