MVOS_HSI: A Python Library for Preprocessing Agricultural Crop Hyperspectral Data

TL;DR

MVOS_HSI is an open-source Python library that streamlines the preprocessing of hyperspectral plant data, enhancing reproducibility and facilitating plant phenotyping research.

Contribution

It provides an integrated, easy-to-use workflow for hyperspectral data processing, including calibration, leaf detection, data augmentation, and visualization.

Findings

Simplifies hyperspectral data preprocessing for plant research.

Enables reproducible workflows through an open-source package.

Includes tools for data augmentation and spectral visualization.

Abstract

Hyperspectral imaging (HSI) allows researchers to study plant traits non-destructively. By capturing hundreds of narrow spectral bands per pixel, it reveals details about plant biochemistry and stress that standard cameras miss. However, processing this data is often challenging. Many labs still rely on loosely organized collections of lab-specific MATLAB or Python scripts, which makes workflows difficult to share and results difficult to reproduce. MVOS_HSI is an open-source Python library that provides an end-to-end workflow for processing leaf-level HSI data. The software handles everything from calibrating raw ENVI files to detecting and clipping individual leaves based on multiple vegetation indices (NDVI, CIRedEdge and GCI). It also includes tools for data augmentation to create training-time variations for machine learning and utilities to visualize spectral profiles. MVOS_HSI can be used as an importable Python library or run directly from the command line. The code and documentation are available on GitHub. By consolidating these common tasks into a single package, MVOS_HSI helps researchers produce consistent and reproducible results in plant phenotyping