Saving time maintaining reliability: a new method for quantification of Tetranychus urticae damage in Arabidopsis whole
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METHODOLOGY ARTICLE
Open Access
Saving time maintaining reliability: a new method for quantification of Tetranychus urticae damage in Arabidopsis whole rosettes Dairon Ojeda-Martinez1, Manuel Martinez1,2, Isabel Diaz1,2 and M. Estrella Santamaria1*
Abstract Background: 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. Results: 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. Conclusions: 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. Keywords: Arabidopsis thaliana, Assess, Chlorotic spots, CompuEye, Ilastik, Fiji, Photoshop, Plant damage quantification, Tetranychus urticae, Machine learning
* Correspondence: [email protected] 1 Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid – Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Madrid, Spain Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article ar
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