Domain / Agriculture Intelligence

AI agriculture stress and hydrology intelligence for Vidisha and Raisen

Combine surface temperature, evapotranspiration, soil moisture, cropping pattern signals, and hydrological predictions for district-scale agriculture risk screening.

Open Modelling Panel

Decision Outputs

Surface temperature and heat-stress indicators

Reference evapotranspiration and irrigation demand

Soil moisture and crop stress signal

Cropping pattern interpretation for Kharif/Rabi/Zaid

Hydrology-coupled runoff and field-risk summary

Workflow

Step 1

Select Vidisha, Raisen, or combined district AOI.

Step 2

Choose season, crop group, irrigation scenario, and data-source families.

Step 3

Fetch online climate/weather sources where available and fall back to transparent demo indicators.

Step 4

Generate agriculture and hydrology risk bands with recommendations.

GeoAI Toolchain

Recommended AI and geospatial tools for turning this model into a production-grade Nita AI module.

GeoAI / OpenGEOAI

Geospatial AI model training, segmentation, object detection, inference, and imagery workflows.

Optional runtime integration through geoai-py; the site reports availability when installed. Open docs

Google Earth Engine

Cloud-scale public geospatial datasets, satellite imagery, population rasters, and time-series analysis.

Optional live mode after Earth Engine authentication. Open docs

Leaflet + Leaflet Draw

Interactive AOI drawing, map inspection, layer controls, and user-facing spatial workflows.

Enabled in the browser UI. Open docs

Chart.js

Time-series, indicator, and demographic charts for decision dashboards.

Enabled in the browser UI. Open docs