Answers for research teams evaluating Vibe

Vibe is a reproducible imaging and analysis workflow for grains and seeds, built for research labs that need consistent, publishable measurements.

It automates high-resolution imaging of large grain/seed samples, runs segmentation and measurement pipelines, and exports structured outputs for analysis and reporting.

Labs typically get per-kernel measurements (size/morphology, color, texture features), defect indicators, summary statistics, and exportable tables with traceable run metadata.

It standardizes acquisition and measurement steps, reduces operator subjectivity, and preserves the settings + outputs so runs can be repeated and compared across teams and seasons.

Yes. The goal is clean, structured exports (tables + metadata) so downstream analysis can be repeated and figures can be regenerated without manual intervention.

The primary focus is grain and seed phenotyping, but the system design supports extending to other sample types where consistent imaging and measurement are required.

University research labs, breeding programs, grain quality labs, seed companies, and food R&D teams that need objective and comparable measurements.

Usually: a short demo, alignment on the imaging protocol and required outputs, then a limited pilot on a representative sample set before scaling usage.

A curated collection of publications, workflows, and real research examples that show how Vibe is used in practice, and help teams benchmark methods and cite prior work.

Yes. The QM3i uses optical imaging - the sample is not altered during analysis. The same grain can be used for downstream milling, moisture testing, or DNA extraction after imaging.

The system measures the same parameters used in official grading (broken %, foreign material, color) with traceable settings. Labs typically validate against official grade standards as part of their SOPs.

Yes. Per-kernel measurements and raw images are exportable for training or validating machine learning models, and the structured CSV/Parquet format is compatible with standard data science workflows.

Under 1 minute per 40 g sample means hundreds of accessions can be processed per session. Batch processing with barcode scanning and automated data entry makes large trials practical.