The soil mate app provides a simple and convenient way to collect soil data at sample locations in the field. The Soil Mate app is targeted across multiple industries, including agriculture, environmental science, geology, and mining. The current version of the app collects soil texture data.

Soil Sample and Soil Profile Datasets: an Open Compilation

Large-scale digital mapping of soil organic carbon content by using machine learning algorithms

A simple and light CNN-based regression model for soil parameters estimation from hyperspectral images.

Repository of Jupyter notebooks aimed at learning how to use Python to retrieve data from Google Earth Engine

Open datasets for crop modeling describing climate and soil in France.

🌱A machine learning web and mobile application for soil type classification for plant decision making

SWAT+ model input data preparation helper

APIs for soil quality, types and other factors in Germany

Soil Data Access Scripts

The scripts contained in this repository relate directly to the work conducted by the Tree Root Microbiome Project (TRMP) led by Dr Steve Wakelin.

Code for analysis of soils, crops, and nutrition along a distance-to-forest gradient in Ethiopia

Application for Soil health identification. Air health identification. Upcoming feature for agriculture, land Mining and other research purpose through using machine learning and artificial Intelligence and culminating into a vast application for computer vision also.

MASSAI multi-agent simulation of sustainable agricultural intensification

simple tool to find intersections

SCMA work now in https://github.com/ANZSoilData/def-au-scma

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

Reproducible Research Compendium for "Improving Models to Predict Holocellulose and Klason Lignin Contents for Peat Soil Organic Matter with Mid-Infrared Spectra" and "The need to reinterpret Hodgkins et al. (2018)".

Ongoing research on several projects..

Published original data and R code from “Above- and belowground plant pathogens along elevational gradients: patterns and potential mechanisms”