💻 Software & Environment Setup
Agriculture & Food Technology
The computational toolchain and environment used throughout Applied Machine Learning for Agriculture and Environmental Data.
Applied machine learning training IndiaAgriculture workshopApplied machine learning for PhD studentsApplied machine learning training course for researchersApplied machine learning training NigeriaGoal workshop
# Recommended conda environmentconda create -n dstc_env python=3.11 -yconda activate dstc_envpip install rasterio geopandas scikit-learn xarrayGoogle Earth Engine
Remote-sensing crop analysis
Remote-sensing crop analysis
QGIS
Field mapping & spatial analysis
Field mapping & spatial analysis
R (agricolae)
Agronomic trial statistics
Agronomic trial statistics
scikit-learn
Yield & disease prediction models
Yield & disease prediction models
Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.
Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.
Earn government-registered certification in Applied Machine Learning for Agriculture and Environmental Data
e-Certificate and e-Marksheet issued on successful completion.
