MVOS_HSI: A Python Library for Preprocessing Agricultural Crop Hyperspectral Data

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

Plant phenotyping relevance

葉レベルHSIの校正・葉検出・切り出しを含む再現可能な植物表現型解析用ソフトウェアであり、手法が中心。

Code and data availability

This is a software paper describing MVOS_HSI, the authors' open-source Python library for hyperspectral plant-phenotyping preprocessing (calibration, leaf segmentation/clipping, augmentation, spectral plotting). The authors' code is explicitly and publicly available on GitHub at the allowed URL, making it a paper-pheny