🤖 AI Summary
This study addresses the fragmented and irreproducible nature of existing hyperspectral data processing pipelines in agriculture, which lack unified open-source tools to support plant phenotyping research. To bridge this gap, the authors present the first end-to-end, open-source Python library for leaf-level hyperspectral preprocessing. The library integrates raw ENVI data calibration, leaf detection and cropping based on vegetation indices such as NDVI, CIRedEdge, and GCI, data augmentation, and spectral visualization capabilities. It supports both command-line execution and programmatic import as a library module. By standardizing the preprocessing workflow, this work significantly enhances consistency and efficiency in hyperspectral data handling for plant phenotyping, enabling reproducible and scalable analyses.
📝 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