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SMART INDIA HACKATHON 2025
• Problem Statement ID – SIH25030
• Problem Statement Title- Al-Based Crop
Recommendation for Farmers
• Theme- Agriculture, FoodTech & Rural
Development
• PS Category- Software
• Team ID- VITSIH0483
• Team Name- Ctrl+V
AgriSage: The Smart Farming Companion
1. Idea Summary: AgriSage is a simple mobile application that acts as a farmer's smart
companion. It analyzes local soil, weather, and market data to provide clear, practical
advice on which crops to grow, when to plant, and how to care for them. The goal is to
boost income while promoting long-term farm health.
2. Key Features:
• Smart Crop Choices: Recommends the top 3 crops based on a mix of profit, yield, and
sustainability.
• Yield & Income Forecasts: Provides realistic estimates for harvest size and potential
earnings.
• Multilingual Chat & Voice: Designed for all farmers, regardless of literacy, with easy-to-use
voice and text commands in local languages.
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TECHNICALAPPROACH
1. Tech Stack:
• Frontend: React Native
• Backend: FastAPI with Python
• Database: PostgreSQL & SQLite
• AI Model: TensorFlow/PyTorch
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2. Implementation Flow:
FEASIBILITY AND VIABILITY
1. Feasibility:
• The project is highly realistic, using
proven web technologies and publicly
available data.
• A pilot program will be initiated for local
data collection and model refinement.
• A freemium model ensures economic
viability, keeping core services free for all
farmers.
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IMPACT AND BENEFITS
1. Impact on Farmers:
• Higher Profitability: Increased yields and optimized resource use.
• Reduced Risk: Mitigates risks from weather and poor crop selection.
• Empowerment: Access to data-driven, scientific farming knowledge.
2. Broader Benefits:
• Economic: Boosts agricultural GDP and farmer income.
• Social: Enhances food security and improves rural livelihoods.
• Environmental: Promotes sustainable agriculture and preserves soil health.
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RESEARCH AND REFERENCES
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1. Primary Data Sources:
• ISRO Bhuvan Platform APIs
• SoilGrids Global Soil Information
• Agmarknet (Government Market Data)
2. Key Research Areas:
• AI in Precision Agriculture
• Machine Learning for Crop Recommendation
• NLP for Low-Resource Indian Languages