💻 Software & Environment Setup
Agriculture & Food Technology
The computational toolchain and environment used throughout Weather-Aware Smart Irrigation Scheduling: From Rule Engines to Explainable ML.
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# 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 Weather-Aware Smart Irrigation Scheduling: From Rule Engines to Explainable ML
e-Certificate and e-Marksheet issued on successful completion.
