The model species Tetranychus urticae produces important plant injury and economic losses in the field. The current accepted method for the quantification of the spider mite damage in Arabidopsis whole rosettes is time consuming and entails a bottleneck for large-scale studies such as mutant screening or quantitative genetic analyses. Here, we describe an improved version of the existing method by designing an automatic protocol. The accuracy, precision, reproducibility and concordance of the new enhanced approach are validated in two Arabidopsis accessions with opposite damage phenotypes. Results are compared to the currently available manual method. Image acquisition experiments revealed that the automatic settings plus 10 values of brightness and the black background are the optimal conditions for a specific recognition of spider mite damage by software programs. Among the different tested methods, the Ilastik-Fiji tandem based on machine learning was the best procedure able to quantify the damage maintaining the differential range of damage between accessions. In addition, the Ilastik-Fiji tandem method showed the lowest variability within a set of conditions and the highest stability under different lighting or background surroundings. Bland-Altman concordance results pointed out a negative value for Ilastik-Fiji, which implies a minor estimation of the damage when compared to the manual standard method. The novel approach using Ilastik and Fiji programs entails a great improvement for the quantification of the specific spider mite damage in Arabidopsis whole rosettes. The automation of the proposed method based on interactive machine learning eliminates the subjectivity and inter-rater-variability of the previous manual protocol. Besides, this method offers a robust tool for time saving and to avoid the damage overestimation observed with other methods.

中文翻译：

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节省时间保持可靠性：一种量化拟南芥整个莲座丛中二点叶螨损害的新方法。


模式物种二斑叶螨在田间造成严重的植物伤害和经济损失。目前公认的量化拟南芥整个莲座丛中叶螨损害的方法非常耗时，并且给突变体筛选或定量遗传分析等大规模研究带来了瓶颈。在这里，我们通过设计自动协议来描述现有方法的改进版本。新增强方法的准确性、精密度、重现性和一致性在两个具有相反损伤表型的拟南芥种质中得到了验证。将结果与当前可用的手动方法进行比较。图像采集实验表明，自动设置加上10个亮度值和黑色背景是软件程序特定识别红蜘蛛损害的最佳条件。在不同的测试方法中，基于机器学习的 Ilastik-Fiji tandem 是能够量化损害并保持种质之间损害差异范围的最佳程序。此外，Ilastik-Fiji 串联方法在一组条件下表现出最低的变异性，在不同的照明或背景环境下表现出最高的稳定性。 Bland-Altman 一致性结果指出 Ilastik-Fiji 的值为负，这意味着与手动标准方法相比，对损害的估计较小。使用 Ilastik 和 Fiji 程序的新方法极大地改进了拟南芥整个莲座丛中特定叶螨损害的量化。所提出的基于交互式机器学习的方法的自动化消除了先前手动协议的主观性和评估者之间的可变性。 此外，该方法提供了一个强大的工具，可以节省时间并避免使用其他方法观察到的损坏高估。