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Traitly is an open-source Python tool for automated, high-throughput fruit phenotyping from digital images. Using computer vision methods, it quantifies morphological, symmetry, and color traits across both internal structures and the external appearance of the fruit.

It supports both single-image analysis and batch processing workflows, making it easy to handle large image datasets with just a few lines of code, which is especially useful in plant breeding programs and research.

What can Traitly do?

Traitly processes fruit images to measure:

• Fruit morphology: Area, perimeter, circularity, dimensions, and aspect ratio
• Locule anatomy: Locule number, size distribution, and spatial arrangement
• Pericarp structure: Thickness, uniformity (CV), and surface irregularity (lobedness)
• Color phenotypes: Multi-channel analysis (RGB, HSV, Lab) across different fruit regions

Usage

Traitly can be used from Python, the command line (CLI), or as a web application (Shiny App). For more details:

• Input image specifications
• Traitly architecture
• Python quickstart
• CLI and Shiny App
• Results overview
• Try our interactive demo onlineˎˊ˗

Publications & Presentations

Posters related to Traitly can be found in this folder:

• Posters ★ˎˊ˗
• Presentations ★ˎˊ˗

These materials provide additional methodological details and results from research derived from our package.

Citation

We are working on a manuscript describing this software and its applications, expected to be submitted in Spring–Summer 2026. In the meantime, if you use Traitly in your research, please cite it as:

Torres-Meraz, M. A., Lopez-Moreno, H., & Zalapa, J. (2026). Traitly: A Python Toolkit for High-Throughput Fruit Phenotyping. Zenodo. https://doi.org/10.5281/zenodo.18738366

Contact

For questions or comments about the project, feel free to reach out to:

• ma.torresmeraz@gmail.com
• torresmeraz@wisc.edu

We are open to collaborations, including adding new traits, and creating tutorials or workflows for specific crops or plant tissues.

Contributions

Inspired by All Contributors, we recognize all kinds of contributions, not just code:

Contributor	Role
María Meraz	💻 🚧 📓 ✅ 🐛 📖 ⚠️ 💬 👀 🌍
Héctor López	📖 📓 ✅ 🤔 🐛 🔣 🌍
Juan Zalapa	🔣
Álvaro Guerrero	🐛

Acknowledgements ♡

We thank the developers of OpenCV, Ultralytics, EasyOCR, NumPy, Pandas, Matplotlib, and Shiny, as well as all open-source libraries that made this project possible.