SpotCard: an optical mark recognition tool to improve field data collection speed and accuracy

Area of Science:

• Botany
• Ecology
• Bioinformatics

Background:

• Manual data recording for plant photography is time-consuming and prone to errors.
• Field researchers often struggle with integrating image capture and data logging.
• Existing tools lack integrated image annotation and data capture capabilities.

Purpose of the Study:

• To develop an automated tool for capturing and organizing data alongside plant photographs.
• To streamline the process of field data collection for botanical research.
• To provide researchers with an efficient method for annotating and analyzing plant images.

Main Methods:

• Developed SpotCard, a two-macro ImageJ/Fiji tool.
• Created a template for reusable annotation cards for plant photography.
• Implemented macros for human- and machine-readable annotation encoding and automated data extraction.

Main Results:

• SpotCard accurately reads annotations from images at high speed.
• The tool automatically extracts annotations, date, time, and GPS coordinates into a CSV file.
• Integrated scale bar and coordinate location facilitate automated floral trait measurements.

Conclusions:

• SpotCard significantly enhances the speed and accuracy of field data collection compared to manual methods.
• The tool is user-friendly, requiring no ImageJ macro coding knowledge.
• SpotCard is an open-source, accessible solution for researchers photographing plants in the field.