一个处理农田高光谱数据的开源工具库，让植物表型研究更可复现。
MVOS_HSI: A Python Library for Preprocessing Agricultural Crop Hyperspectral Data
- 整合从校准到叶片分割的全流程，支持多种植被指数自动识别叶片
- 提供数据增强功能，用于机器学习训练，提升模型泛化能力
- 既可作为库导入，也可命令行运行，适合植物表型与遥感研究者
高光谱成像（HSI）能非破坏性地研究植物性状。通过每像素捕捉数百个窄波段，揭示标准相机无法捕捉的植物生化和胁迫信息。然而，数据处理常面临挑战：许多实验室仍依赖松散组织的专用MATLAB或Python脚本，导致流程难共享、结果难复现。MVOS_HSI是一个开源Python库，提供叶级高光谱数据的端到端处理工作流。软件从校准原始ENVI文件开始，基于多种植被指数（NDVI、CIRedEdge、GCI）实现叶片自动检测与裁剪，并包含数据增强工具以生成机器学习训练所需的多样性变化，以及可视化光谱曲线的实用函数。该工具可作为可导入库使用，也可直接通过命令行调用。代码与文档已公开于GitHub。通过将常见任务整合为单一模块，MVOS_HSI助力植物表型研究实现一致且可复现的结果。
原文摘要 · Abstract (English)
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
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