Pruthvi
Soil & Field Intelligence
Ground-level inputs — soil health, nutrient profiles, moisture levels, and field productivity metrics — captured through mobile labs and on-site diagnostics.
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Research · Datasets · AI Intelligence
Research & Intelligence
One destination for everything CIDSA ships for agricultural researchers — field-validated datasets, multi-season analytics, and a dedicated AI-intelligence workspace that pins seven peer-reviewed analytical models to LaTeX-rendered governing equations. Built with and for universities, ICAR institutions, and independent PhD/MSc researchers.
From Field to Research
CIDSA bridges the gap between field-level data collection and research-grade intelligence. Every data point collected through our platform contributes to a growing repository of validated agricultural knowledge.
Unlike synthetic or simulation-based datasets, our data comes directly from real farms, real seasons, and real farmers — with rigorous quality assurance at every step.
Sitting on top of that data fabric, the AI Intelligence tab below lets you query seven peer-reviewed analytical models (RUSLE, NDVI, AHP, SOC, Penman-Monteith, SAR moisture, yield forecasting) with your location, crop, and season — and receive a structured scientific brief in seconds.
Research Focus Areas
Four interconnected domains that define our research infrastructure — each feeding data into the AI intelligence layer below.
Ground-Truth Datasets
High-quality, labeled datasets collected directly from fields with rigorous validation protocols.
- Multi-spectral crop imagery with ground-truth labels
- Soil nutrient profiles across agro-climatic zones
- Pest and disease occurrence records
- Yield measurements with input correlations
- Weather-integrated crop performance data
Multi-Season Analytics
Longitudinal studies tracking crop performance, climate impacts, and intervention outcomes across seasons.
- Season-over-season yield comparisons
- Climate variability impact assessments
- Input efficiency trending analysis
- Crop rotation outcome studies
- Regional benchmarking datasets
Model Validation
Infrastructure for testing and validating AI models against real-world agricultural conditions.
- Standardized validation protocols
- Regional model calibration data
- Performance benchmarking frameworks
- Edge case documentation
- Model comparison studies
Climate & Sustainability
Research on climate-resilient agriculture, carbon sequestration, and sustainable farming practices.
- Carbon footprint assessments
- Water use efficiency studies
- Climate adaptation strategies
- Sustainable practice evaluations
- Environmental impact monitoring
AI Intelligence · Powered by Field Experience
Research Intelligence Workspace
The Research Intelligence Workspace is the analytical core of the CIDSA ecosystem, designed to transform field data into structured, decision-ready intelligence. Built on scientifically validated models and enriched with real-world agricultural inputs, this workspace enables institutions, agronomists, and agri-enterprises to move from observation to precision action.
Configure analyses by selecting location, crop type, season, and time range to receive a comprehensive research brief — model summary, governing equations, data pipelines, interpretation layers, assumptions, and limitations. The result is not just data, but a complete scientific narrative that supports confident decision-making.
Modular Intelligence Stack
The workspace integrates seamlessly with CIDSA's five Panchabhuta modules — soil, water, environment, data, and execution — to deliver a closed-loop intelligence system from data acquisition through field execution.
Jala
Water Intelligence
Irrigation patterns, water-stress indicators, and hydrological insights essential for crop optimisation across surface, sprinkler, and drip systems.
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Vayu
Environmental Intelligence
Real-time and historical weather data, micro-climate conditions, and environmental risk factors aligned to local agro-ecological zones.
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Akasha
Data & Cloud Intelligence
The central data layer — integrating satellite data, historical datasets, and processing pipelines that power large-scale analytics.
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Agni
Execution & Operations
Translates insights into actionable workflows — enabling implementation, monitoring, and continuous improvement on the ground.
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Transparent, interpretable analytical models
The workspace ships seven analytical models covering vegetation growth, soil moisture dynamics, nutrient behaviour, pest and disease indicators, crop growth stages, and yield optimisation. Each model is designed to be transparent and interpretable — users understand not just the outcome, but the reasoning behind it.
Beyond analysis, the workspace provides historical context, scenario comparison, and validation frameworks — making it equally powerful for immediate decision-making, long-term planning, research, and policy development.
Secure & collaborative
Available to authenticated CIDSA and LMS users — for institutional research, large-scale farming operations, or precision advisory services. A consistent advantage that turns complex data into clear, actionable intelligence.
Available analytical models
Each card pins to a peer-reviewed governing equation. Click to supply your inputs.
Sign in to run analyses
The Research Intelligence workspace is available to authenticated CIDSA or LMS users. Sign in to query the seven analytical models with your own inputs.
Sign inAvailable Datasets
Access curated, validated datasets for your research. All data includes provenance tracking, quality metrics, and usage documentation.
Beneficiaries
Designed to benefit a wide spectrum of stakeholders across the agricultural ecosystem, including:
Crop Health Imagery
500K+ images
Universities & Agriculture Colleges
Access datasets for student projects and faculty research
Soil Profiles
50K+ samples
ICAR Institutions
Collaborate on national research priorities
Yield Records
100K+ records
PhD/MSc Researchers
Ground-truth data for thesis work and publications
Weather Integration
10+ years
Agri-Tech Startups
Training data for AI model development
Pest Occurrence
30K+ records
Farmer Groups & Collective
Farmers collaborate to improve productivity together.
Market Prices
5+ years
Partner with CIDSA Research
Joint research projects, dataset-access agreements, co-authored publications, or full institutional MoUs — we work with universities, ICAR institutions, FPOs, and agri-tech startups across the Indian research ecosystem. Tell us about your project and we'll propose the fastest way to get started.