Using integrated multischeme data for accurate beneficiary
An AI-powered system that uses integrated multi-scheme data to ensure accurate beneficiary identification, optimize subsidy targeting, and prevent duplication.
About Use Case
This system leverages cross-scheme agricultural datasets to enhance transparency and efficiency in benefit distribution. It enables farmer de-duplication, automates eligibility assessment, recommends suitable schemes based on farmer and crop profiles, and identifies excluded beneficiaries—ensuring fair, targeted, and inclusive access to agricultural support.
Suggestive Use Cases and Outcomes
• Farmer De-duplication & Identity Resolution: Identify unique farmers and eliminate
duplicate beneficiaries across schemes
• Smart Subsidy Eligibility Engine: Automatically determine farmer eligibility for schemes
based on crop, land, and activity
• Crop-Based Scheme Recommendation System: Recommend relevant schemes based on
crop type and farmer profile
• Farmer Exclusion Detection: Alert gaps where eligible farmers may be excluded from
benefits.
This dataset contains comprehensive information about various crops cultivated in India, including their names, scientific names, crop types, and categories.
A structured dataset capturing implementation details of agricultural infrastructure projects and agri-startups funded under the Rashtriya Krishi Vikas Yojana (RKVY). It includes data on state-wise activities, sectoral focus, financial allocations, and profiles of 1,524 startups supported with ?106.25 crore since 2019–20. Ideal for analyzing agricultural development, innovation trends, and policy impact.
The NFSM dataset is a collection of data on Demonstrations organized under the National Food Security Mission, which shows farmers new agricultural technologies and improved practices on their own fields to boost food grain output.
A structured dataset capturing micro-irrigation coverage under the PDMC scheme across 78 lakh hectares since 2015–16. It includes data on drip/sprinkler adoption, financial support, and water-saving interventions, enabling analysis of farm-level water-use efficiency and sustainable agriculture.
A structured dataset capturing horticulture development under the MIDH scheme, including crop area expansion, infrastructure creation, financial support, and beneficiary details. It supports analysis of horticulture growth, supply chains, and policy impact.
Crop Survey of Kodagu District of Karnataka For Kharif Season 2020-21
-
The dataset presents Hobli-wise crop survey details of Kodagu district, Karnataka for the Kharif season 2020–21, including crop types, extent, and survey metadata.