Operation Dronagiri - Encode Nature

This startup is associated with the Technology Innovation Hub (TIH) on Positioning and Precision Technologies (PPT) at IIT Tirupati Navavishkar I-Hub Foundation (IITTNiF) through Operation Dronagiri, a flagship initiative of the Department of Science & Technology (DST) Geospatial Data Promotion and Development Committee (GDPDC) and the Geospatial Innovation Cell (GIC) under the National Geospatial Policy (NGP).

Operation Dronagiri demonstrates the power of geospatial technology in transforming Agriculture, Transportation & Infrastructure, and Livelihood & Skilling, aligning with the national vision of Viksit Bharat 2047.

The initiative is powered by the Geo-Intel Lab, the Geospatial Intelligence and Applications Laboratory of IITTNiF, which develops applications and platforms for public good and digital governance. The lab integrates GIS, Remote Sensing, GNSS, and Data Analytics to deliver scalable decision-support systems and hosts India's first Federated Geospatial Data Interface (GDI) node, enabling seamless data access and collaboration across the national geospatial ecosystem.

All products and platforms developed under this collaboration will be featured on the National Geo-Intel Portal

Project Overview

Problem Statement

Agricultural water management faces significant challenges due to unpredictable weather patterns and inefficient irrigation practices. Farmers need accurate, real-time data on soil moisture levels to optimize water usage and improve crop yields while conserving precious water resources.

Geospatial Innovation Accelerator (GIA)

IIT Tirupati

Pilot District

Implementation area to be announced

Solution Overview

Description

Our solution leverages advanced geospatial technologies including SAR (Synthetic Aperture Radar) satellite imagery, machine learning algorithms, and ground-truth validation to provide accurate soil moisture predictions. By integrating multi-temporal satellite data with field measurements, we develop predictive models that help farmers make informed irrigation decisions. The system analyzes soil conditions, weather patterns, and crop requirements to generate actionable insights delivered through a user-friendly decision support tool.

Geospatial Datasets - GDI

SAR satellite imagery for soil moisture estimation
Weather and climate data integration
Multi-temporal analysis of agricultural fields
Field segmentation and crop classification

Alignment with Operation Dronagiri Goals

Enhancing agricultural productivity through precision irrigation management
Promoting sustainable water resource management and conservation
Empowering farmers with data-driven decision-making tools

Milestones & Progress

Milestone 1: Data Collection & Processing

Week 1-8

Field campaigns finished with comprehensive data collection

Completed

Milestone 2: Algorithm Development

Week 9-12

ML field segmentation model completed with successful testing

Completed

Milestone 3: Model Validation

Week 13-18

SAR soil moisture model in progress
Validation and decision support tool under development

In Progress

Implementation Updates

Field visits, data collection, and validation activities

Field Data Collection

Ground truth measurements and sensor deployment

Field Campaign

Collaborative data gathering with agricultural experts

ML Segmentation Results

Automated field boundary detection and crop classification

Soil Moisture Validation

Comparison of satellite-derived and ground measurements

Current Pilot Status

Ongoing

Partnerships & Collaborations

IIT Tirupati Navavishkar I-Hub Foundation (IITTNiF)
Department of Science & Technology (DST)
Geo-Intel Lab
Local agricultural departments and farmer cooperatives

Team Members

Dr. Mohamed Musthafa

Project Lead & ML Engineer

Specializes in machine learning applications for remote sensing and geospatial data analysis. Leading the development of soil moisture prediction models and decision support systems.

Prem Mugundhan

Project Assistant

Expert in forest and agriculture field works, data collection, data collation and logistic planning. Assists the team in collection of ground truth points and data labellig

Impact & Outcomes

Water Conservation

Expected reduction in water usage through optimized irrigation scheduling, helping conserve precious water resources while maintaining crop health.

Increased Productivity

Improved crop yields through data-driven irrigation decisions, ensuring optimal soil moisture levels at critical growth stages.

Farmer Empowerment

Providing farmers with accessible, actionable insights to make informed decisions about irrigation timing and water allocation.

Sustainable Agriculture

Contributing to long-term agricultural sustainability by promoting efficient resource management and reducing environmental impact.

Metrics Being Tracked

Accuracy of soil moisture predictions
Water usage efficiency improvements
Crop yield variations across different irrigation strategies
Farmer adoption rates and satisfaction scores
Cost savings through optimized water usage

Future Plans

Model Validation

Complete model validation with new data from diverse agricultural regions and crop types
Decision Support System

Finalize the user-friendly decision support system with mobile and web interfaces for farmers
Final Report & Scaling

Deliver comprehensive final report and prepare for scaling to additional districts and states
Integration with GDI

Full integration with the National Geospatial Data Interface for broader accessibility

Sustainable Development Goals

This project contributes to the following UN Sustainable Development Goals:

Zero Hunger

Clean Water & Sanitation

Responsible Consumption

Climate Action

Soil Moisture Prediction & Irrigation Management