The Problem

Plant ionomics measures the elemental composition of plant tissues, and the data comes out wide and multivariate. Done by hand, the analysis is slow, easy to get wrong, and rarely consistent from one study to the next. The field needs a workflow that applies the right statistics the same way every time and produces results ready to publish.

The Approach

Ionomica takes raw ionomics data through a single automated pipeline:

• Data validation and quality control
• Statistical modeling (mixed models, generalized additive models, multivariate analysis, machine learning)
• Automated visualization generation
• Reproducible research documentation

It follows established practice in agronomy and statistics, so the analysis stays rigorous while taking a fraction of the manual effort.

Technical Implementation

• Python-based statistical pipeline
• Automated machine learning model selection and hyperparameter tuning
• User interface and automated reporting with Marimo

Current Status

Currently used in agricultural research projects. Ongoing development focuses on expanding statistical methods and improving integration with additional data sources.