research received no external funding Institutional Review Board Statement: Not applicable. This study did not involve humans or animals. Informed Consent Statement: Not applicable. This study did not involve humans. Data Availability Statement: Complete source code, documentation, and example datasets are publicly available at https://github.com/rexionmars/icev-remote-sensing under MIT license. The platform can be deployed locally or accessed via hosted instance for testing purposes. Sentinel-2 satellite imagery used in this study was obtained from the Copernicus Open Access Hub (https://browser.dataspace.copernicus.eu/) and is freely available. Acknowledgments: The authors thank the iCEV I
Open resource ↗https://github.com/rexionmars/icev-remote-sensing · icev-remote-sensing · pdf-layout-page:11 lines:1-70Unverified paper record
An Open-Source Web Platform for Sentinel-2 Multispectral Analysis in Smallholder Agriculture: Design, Implementation and Validation
12 Nov 2025 · 10.20944/preprints202511.0787.v1
Abstract
Precision agriculture technologies based on satellite remote sensing remain largely inaccessible to smallholder farmers in developing countries due to technical complexity, cost barriers, and infrastructure demands. This study presents the design and implementation of an open-source, web-based platform for processing Sentinel-2 Level-2A imagery tailored to the specific needs of family farming systems. The platform integrates a FastAPI backend for geospatial data processing with a Next.js frontend providing simplified tools for spectral index computation (NDVI, EVI, SAVI, NDWI, NDBI), crop classification using supervised and unsupervised machine learning, and interactive 2D/3D visualization. A laboratory module implements thirteen digital image processing techniques—including Gaussian filtering, edge detection, morphological operations, and thresholding—for educational and comparative analysis. The browser-based system eliminates installation requirements and automates key workflows such as coordinate reprojection, JP2 band extraction, and statistical evaluation. Validation using ground-truth data from coffee and soybean fields in the Brazilian Cerrado achieved classification accuracies above 85% and correlation coefficients exceeding 0.90 for biomass estimation based on NDVI-derived metrics. The platform contributes to the democratization of remote sensing technologies and enhances accessibility of precision agriculture tools for smallholder farmers.
Plant phenotyping relevance
植物圃場の衛星画像を処理し、NDVI等からバイオマスを推定するオープンソース基盤の設計・実装・検証が中心であり、植物形質推定ワークフローとして収録対象。
titleAn Open-Source Web Platform for Sentinel-2 Multispectral Analysis in Smallholder Agriculture: Design, Implementation and Validation
abstractThis study presents the design and implementation of an open-source, web-based platform for processing Sentinel-2 Level-2A imagery
abstractcorrelation coefficients exceeding 0.90 for biomass estimation based on NDVI-derived metrics
Code and data availability
The paper's Data Availability Statement explicitly deposits the authors' complete source code, documentation, and example datasets for the Sentinel-2 phenotyping/analysis platform on a public GitHub repository under MIT license. Sentinel-2 imagery is from the public Copernicus browser, but that is a generic data source
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