GiNA, an Efficient and High-Throughput Software for Horticultural Phenotyping.

Area of Science:

• Horticultural science
• Plant phenotyping
• Image analysis

Background:

• Traditional trait phenotyping is labor-intensive, costly, and prone to bias, hindering crop research.
• Existing high-throughput phenotyping platforms are often expensive, complex, and limited to major crops.

Purpose of the Study:

• To introduce GiNA, an open-source, multiplatform software for accessible high-throughput phenotyping of horticultural traits.
• To provide a simple, free tool for measuring morphological and color parameters in fruits, vegetables, and seeds.

Main Methods:

• GiNA software utilizes conventional digital camera images for analysis.
• It processes up to 11 horticultural traits including length, width, area, volume, and RGB color.
• Validation involved cross-validation with manual measurements and comparison to standard industry methodologies.

Main Results:

• GiNA demonstrated high consistency across different lighting and camera setups, indicating reliability.
• Cross-validation for length and width in cranberries showed high accuracy (0.97 and 0.92).
• Color estimates from GiNA images predicted total anthocyanin content with accuracies above 0.83.

Conclusions:

• GiNA offers a scalable, user-friendly, and affordable solution for massive phenotypic data acquisition.
• The software overcomes limitations of traditional methods and existing platforms for diverse crop research.
• GiNA enables reliable and reproducible high-throughput phenotyping for horticultural applications.