MVOS_HSI: A Python Library for Preprocessing Agricultural Crop Hyperspectral Data
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917405928718336 |
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| author | Aggarwal, Rishik Joshi, Krisha Yadav, Pappu Kumar Qin, Jianwei Burks, Thomas F. Kim, Moon S. |
| author_facet | Aggarwal, Rishik Joshi, Krisha Yadav, Pappu Kumar Qin, Jianwei Burks, Thomas F. Kim, Moon S. |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07656 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MVOS_HSI: A Python Library for Preprocessing Agricultural Crop Hyperspectral Data Aggarwal, Rishik Joshi, Krisha Yadav, Pappu Kumar Qin, Jianwei Burks, Thomas F. Kim, Moon S. Software Engineering Computer Vision and Pattern Recognition 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 |
| title | MVOS_HSI: A Python Library for Preprocessing Agricultural Crop Hyperspectral Data |
| topic | Software Engineering Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.07656 |