The detached leaf assay is a valuable method for studying plant-pathogen interactions, enabling the assessment of pathogenicity, plant resistance, and treatment effects. In this protocol, we outline how to set up a Phytophthora detached leaf assay and use non-expert machine learning tools to increase the reliability and throughput of the image analysis. Utilizing ilastik for pixel classification and Python scripts for segmentation, manual correction, and temporal linking, the pipeline provides objective and quantitative data over time. The protocol covers assay setup and image segmentation and outlines key considerations, providing a comprehensive guide for setting up and analyzing detached leaf assays. The very minimal material requirements and user-friendly software make this protocol accessible for all Phytophthora researchers.
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The detached leaf assay is a valuable method for studying plant-pathogen interactions, enabling the assessment of pathogenicity, plant resistance, and treatment effects. In this protocol, we outline how to set up a Phytophthora detached leaf assay and use non-expert machine learning tools to increase the reliability and throughput of the image analysis. Utilizing ilastik for pixel classification and Python scripts for segmentation, manual correction, and temporal linking, the pipeline provides objective and quantitative data over time.
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