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 sample IDs and analysis metadata for later review.

It standardizes acquisition and measurement steps, reduces operator subjectivity, and preserves the settings + outputs so analyses 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 consistent 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 remains available for further testing after imaging.

Vibe measures configured visual characteristics such as size, color, broken grains, and visible foreign material. The results may support internal comparison and visual screening, but they do not constitute an official USDA, FGIS, ISO, or Codex grade. Any standards-based determination must follow the applicable official method.

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.

Rapid, high-throughput analysis supports large sample volumes. Batch processing with barcode scanning and automated data entry makes large trials practical.