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State government · Precision agriculture & spatial forecasting

A state agriculture department unifies farmer services, spatial crop planning, and live soil analytics on one digital ecosystem

Fragmented farmer records, unpredictable terrain variations, and delayed crop reporting were limiting the reach of agricultural schemes. A Unified Digital Agriculture Ecosystem connected district e-Hubs, WebGIS crop suitability maps, and real-time IoT soil telemetry—enabling data-driven crop advisories and accurate seasonal yield forecasting across the state.

~85%faster delivery of targeted crop advisories and scheme benefits

The challenge

Managing agricultural policy and farmer support across diverse, hilly micro-climates presented major operational hurdles. Soil health records, farmer registries, seed distribution logs, and seasonal production estimates were maintained in isolated district files, making it difficult for state planners to view a consolidated picture in real time.

Without spatial visibility into localized soil nutrients, weather patterns, and land suitability, agricultural extension officers struggled to give timely, crop-specific advice to farmers. At the same time, state leadership lacked reliable predictive tools to forecast seasonal crop yields, anticipate pest or disease outbreaks, and measure the economic return of agricultural subsidies.

The approach

Sumato designed and deployed a Unified Digital Agriculture Ecosystem (UDAE) supported by localized District e-Hubs, combining Python-based spatial analytics, IoT field telemetry, and AI-assisted crop intelligence into a single state command portal.

Every farm plot, soil profile, and regional asset was layered onto an interactive WebGIS engine. By integrating field-level IoT soil sensors (measuring NPK, moisture, and temperature) with mobile data collection from district e-Hubs, the platform automatically generates localized crop suitability heatmaps, sends proactive plant disease and nutrient advisories, and models district-wise economic and yield forecasts before harvest.

  • WebGIS crop suitability & forecasting

    Python-driven spatial models combine soil nutrient layers, terrain data, and historical trends to recommend high-value crops and predict seasonal output.

  • IoT soil telemetry & AI crop advisory

    Real-time integration with field NPK and moisture sensors pairs with AI-assisted plant diagnostics to deliver early warnings and precise fertilizer recommendations.

  • Connected District e-Hubs

    Decentralized digital centers and offline-ready mobile apps verify farmer identities, track input distribution, and digitize field surveys across remote blocks.

The results

  • ~85%

    Faster verification and delivery of targeted farmer schemes & advisories

  • 90%+

    Accuracy in spatial crop suitability mapping and seasonal yield forecasting

  • 100%

    District coverage connected through unified e-Hubs and WebGIS dashboards

Figures reflect operational improvements across district e-Hub workflows, spatial planning, and advisory turnaround times post-implementation.

Solutions used

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