This repository contains a Fiji macro designed to evaluate leaf damage caused by salt stress treatment, a critical part of my PhD research. The macro processes images of leaves to determine the total leaf area and the damaged (senesced) area, outputting the data in a tabular format. This data can be further analyzed and visualized using tools like R or Python.
To date,with some modification in the paremeters, this macro can also be used for evaluating Leaf Senescence Index (LSI), Greenness Index (GI) and Leaf Area Index (LAI) etc.
- Standalone Macro: Process a single photo of a leaf.
- Batch Processing Macro: Process a list of files, each containing a single photo of a leaf.
- R Visualization Script: Visualize the proportion of damaged (senesced) areas.
An example of the analyzed figure.
- Fiji (ImageJ): Make sure you have Fiji installed. You can download it here.
- R: For data visualization, R needs to be installed. You can download it here.
- Photos should be taken in a consistent environment (e.g., same background). It is highly recommended to use a dedicated camera instead of a phone to ensure unified image quality.
- In the example above, three paper with an area of 1
$cm^2$ were used as controls (white, blue and red). This approach is recommended because it allows for precise area calculation and reduces bias associated with manual photography.
TBC.
- Standalone Macro:
- Open Fiji.
- Load the standalone macro.
- Open a single leaf photo.
- Run the macro to evaluate the total and damaged areas.
- Batch Processing Macro:
- Open Fiji.
- Load the batch processing macro.
- Provide a directory of leaf photos.
- Run the macro to process all photos and generate a table of results.
- R Visualization Script:
- Open R or RStudio.
- Load the provided R script.
- Import the table generated by the macro.
- Run the script to visualize the proportion of damaged (senesced) areas.
The output from the macro includes:
- Total leaf area
- Damaged (senesced) area
- Proportion of damaged area
This data is saved in a table format, which can be used for further analysis and visualization.
If you encounter any issues or have suggestions for improvement, feel free to open an issue or submit a pull request.