{"name": "PARACUDA-NG", "package_name": "paracuda_ng", "description": "Spectral machine learning: build, validate and apply regression models for spectroscopy and multispectral / hyperspectral imagery, integrated with QGIS raster layers.", "about": "PARACUDA-NG (PARAmetric CUbe Data Analysis, Next Generation) is a spectral machine-learning toolkit for spectroscopy and multispectral / hyperspectral remote sensing, embedded in QGIS as a native, dockable panel. A 7-step wizard (Data, Configuration, Preprocess, Model, Validate, Execution, Apply) takes you from loading spectral data through spectral resampling to a selectable sensor grid (Sentinel-2, Landsat 5/7/8/9, EnMAP, EMIT, PRISMA, DESIS, PlanetScope, VENuS and more), preprocessing (smoothing, continuum removal, derivatives, absorbance, baseline correction), and training or comparing regression models (PLS-R, SVM, Ridge, Lasso, MLR, Elastic Net, Huber, Gradient Boosting, Gaussian Process, Random Forest, XGBoost) with optional Optuna hyperparameter tuning and cross-validation. A trained model can be applied directly to a loaded QGIS raster layer, writing the prediction back as a new layer with a matching colour map, or to an unknown tabular dataset. Additional tools include compositional (CLR/ALR/ILR) modelling for parts that sum to 100 percent, a label-permutation and spectral-mixing integrity suite, cross-sensor spectral harmonisation (transfer functions), and a data converter for arbitrary instrument layouts. PARACUDA-NG needs a few Python packages QGIS does not ship (scikit-learn, joblib, optuna, xgboost, rasterio, spectral); it installs them into QGIS's Python on first run, or via Tools then Install Python dependencies.", "homepage": "https://paracuda-ng.github.io/", "repository": "https://github.com/sharadgupta27/paracuda-qgis", "tracker": "https://github.com/sharadgupta27/paracuda-qgis/issues", "author": "Sharad Kumar Gupta", "tags": ["raster", "soil", "remote sensing", "regression", "multispectral", "random forest", "machine learning", "sentinel-2", "hyperspectral", "enmap", "prisma", "emit", "resampling", "xgboost", "chemometrics", "spectroscopy", "pls"], "downloads": 16, "latest_version": "1.0.2", "versions": [{"version": "1.0.2", "experimental": false, "qgis_min": "3.30.0", "qgis_max": "4.99.0", "downloads": 16, "uploaded_by": "sharadgupta27", "upload_datetime": "2026-08-11T04:48:39.278722"}]}