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Overview
Overview
AgriLens is being developed through an iterative research and development process that combines farmer interviews, agricultural datasets, machine learning models, and field validation. Rather than building a solution based solely on assumptions, our approach focuses on understanding real agricultural decision-making and continuously refining the system through evidence and feedback.
The development process is organized into four major areas: user research, data infrastructure, machine learning development, and field validation. Each stage contributes to improving both the technical accuracy and practical usefulness of the platform.
User Research
User Research
Our development process began with direct conversations with farmers across multiple regions to understand how agricultural decisions are made in practice. These discussions explored crop selection, weather-related risks, market considerations, information sources, and the challenges farmers face when interpreting available data.
Key findings from initial interviews indicated that weather uncertainty, market volatility, and access to actionable advisories are among the most significant challenges affecting agricultural decision-making. These insights continue to guide the design and priorities of the AgriLens platform.
Data and Machine Learning Development
Data and Machine Learning Development
The AgriLens decision engine is being developed using structured agricultural datasets covering climate patterns, soil characteristics, crop requirements, and market conditions. Historical and weekly data are being organized into machine-learning-ready formats to support predictive modeling and risk assessment.
Current development focuses on building models capable of evaluating crop suitability and risk exposure based on multiple interacting factors. As additional data is collected, model weights and parameters will be refined using data-driven approaches to improve recommendation quality and reliability.
Current Research Areas
Current Research Areas
Farmer Decision-Making Behaviour
Crop Risk Assessment Models
Climate & Weather Integration
Market Signal Analysis
Agricultural Advisory Systems
Voice-Based Accessibility
Current Pilot Region
Current Pilot Region
Vellore, Tamil Nadu
Current Development Status
Current Development Status
✓ Farmer Interviews
✓ Initial Dataset Collection
✓ Prototype UI Development
✓ Baseline Prediction Engine
⏳ ML Model Training
⏳ Advisory Integration
⏳ Tamil Language Support
⏳ Field Validation
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