soil-spectroscopy 开源项目推荐（15 个）

l-ramirez-lopez /prospectr

R package: Misc. Functions for Processing and Sample Selection of Spectroscopic Data

l-ramirez-lopez /resemble

resemble is an R package for similarity-based modelling and local learning in spectroscopy. It provides tools for dissimilarity computation, nearest-neighbour search, memory-based learning, and spectral library optimisation (methods designed for large, heterogeneous spectral datasets where global models underperform)

eupassarinho /soil-carbon-and-spectroscopy-with-PLS-and-LASSO

R scripts for predicting soil organic carbon using soil spectral library from visible, near-infrared and shortwave-infrared (VNIR) and middle-infrared (MIR) using LASSO and PLS regression methods and the target-oriented cross-validation strategy.

franckalbinet /soilspecdata

Load soil spectral data, such as the Open Soil Spectral Library (OSSL) and Thermo OMNIC .spa files, as spectra and soil properties ready for machine learning

franckalbinet /mirzai

Prediction of Exchangeable Potassium in Soil through Mid-Infrared Spectroscopy and Deep Learning: from Prediction to Explainability, Albinet et al., 2022

luyaz999 /SOGM

Soil VIS-NIR reflectance spectra simulation based on generative model. PROSAIL model is integrated..

henningte /irpeat

Functions to analyse mid infrared spectra of peat samples

chinilin /Spectra

Comparing different data preprocessing methods to predict soil organic carbon content on soil spectra features

franckalbinet /soilspectfm

Scikit-learn transformers for preprocessing soil infrared spectra

vah-khosravi /Soil_properties_spectral_modelling

R implementation of a Vis-NIR soil spectroscopy workflow for predicting soil properties using Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Cubist, Random Forest (RF), Support Vector Regression (SVM), Memory-Based Learning (MBL), Artificial Neural Networks (ANN) and Extreme Gradient Boosting (XGBoost) algorithms

Abdelmalek05 /soil-texture-from-spectra

Predicting sand, silt and clay from VisNIR soil spectra. Shows that the choice of validation costs 5× more accuracy than the choice of sensor.

abstratovcm /pruned-soil-fm

Code and precomputed results for "Unconditional Flow Matching With Classifier-Based Pruning for Distribution-Aligned Soil Spectral Synthesis" (IEEE GRSL 2026). Generates synthetic LUCAS 2015 topsoil data with flow matching, pruned by a classifier to align distributions.

franckalbinet /soilspecdl

Practical deep learning for soil infrared spectroscopy, built on the fastai framework.

eniolapeter6356 /synthetic-soil-spectra-wgan-gp

Generative deep learning (cWGAN-GP) for vis-NIR soil spectral library augmentation. MSc thesis code.

HugoMachadoRodrigues /autoSpectra

Soil spectral modelling, visualization and prediction · soilVAE + OSSL v1.2 · VisNIR & MIR