Use Case



Discover PlantStat, the innovative package for robust statistical analysis and AutoML in plant science. Developed by LPBPS in Kharkiv, Ukraine, it streamlines data processing and supports plant physiology and biochemistry research.


PlantStat is an innovative package designed for the fast and convenient statistical processing of experimental data, tailored specifically for the needs of plant science research. Developed by the Laboratory of Physiology and Biochemistry of Plant Stress (LPBPS) in Kharkiv, Ukraine, PlantStat integrates simple AutoML algorithms for classification and regression, making it an invaluable tool for researchers and specialists in the field of plant physiology and biochemistry.

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Current Status:

PlantStat is currently under development, and its functionality is limited. As the package is in its early stages, users may encounter bugs and errors. The functions have been tested on a small set of real data, and ongoing updates will expand the number of functions and address any issues.

Key Features:

  1. Statistical Processing:
    • A set of functions for robust statistical analysis of experimental data.
    • Streamlines data processing tasks, allowing researchers to focus on interpretation and application.
  2. AutoML Algorithms:
    • Incorporates simple AutoML algorithms for both classification and regression tasks.
    • Designed to automate the machine learning process, making it accessible for users with limited programming expertise.
  3. User-Friendly Interface:
    • Easy-to-use functions and intuitive interface ensure a smooth user experience.
    • Developed with non-professional developers in mind, aiming to support a wide range of users from plant science fields.
  4. Community-Driven Development:
    • Published on GitHub and Kaggle to facilitate community engagement and feedback.
    • Continuous improvements based on user feedback and contributions from the community.

Future Development:

  • Function Expansion: Plans to add more statistical and machine learning functions to broaden the package’s capabilities.
  • Error Fixes: Ongoing efforts to fix bugs and improve reliability based on user feedback and further testing.
  • Optimization: Enhancements to existing methods and approaches to ensure they meet the needs of plant science researchers effectively.


  • Plant Physiology and Biochemistry: Supports research in plant stress physiology, enabling detailed analysis and interpretation of experimental data.
  • Experimental Data Processing: Facilitates the statistical processing of diverse experimental datasets, streamlining research workflows.
  • Educational Tool: Serves as a valuable resource for students and educators in plant sciences, providing practical tools for data analysis and machine learning.

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