AgriNova AI: Smart Farming with Amazon Nova Pro & MCP

AgriNova AI uses Amazon Nova Pro, MCP, Mem0 and real-time APIs to create an intelligent farming assistant for agriculture workflows.

Thirumalaiboobathi B

Published May 25, 2026

Last modified Jun 11, 2026

Agriculture generates enormous amounts of information every day — weather updates, irrigation decisions, market prices, subsidies, crop planning, equipment availability, and government schemes.

However, most farmers still access these insights through fragmented sources.

Weather comes from one place. Market prices from another. Government schemes require manual searches. Irrigation decisions often depend on experience.

I wanted to explore whether an AI Agent could unify these workflows into a single intelligent assistant.

This led to the development of AgriNova AI Assistant — a smart farming assistant built using Amazon Nova Pro, Strands Agents SDK, MCP, Mem0, FAISS, real-time APIs, and structured agricultural datasets.

The project was developed as part of the AWS User Group Madurai – Nova Month Skill Sprint.

Problem Statement

Farmers frequently face challenges such as:

Difficulty identifying suitable crops
Limited visibility into market prices
Lack of awareness about subsidies and schemes
Water management uncertainty
Equipment access issues
Fragmented agricultural information

The objective of AgriNova AI was not simply to build another chatbot.

The objective was to create an agriculture intelligence system capable of combining:

Real-time data + agricultural knowledge + memory + decision support + AI workflows

System Architecture

AgriNova AI follows an agent-based architecture built on Strands Agents SDK.

This architecture enables AgriNova AI to combine both real-time intelligence and persistent agricultural knowledge.

Real-Time Agricultural Intelligence

AgriNova AI integrates live APIs for weather and market visibility.

Capabilities include:

Weather Intelligence

Farmers can request:

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What is the weather in Madurai?

The assistant retrieves:

Temperature
Humidity
Wind speed
Current conditions

Rainfall Prediction

Rainfall insights help support irrigation planning.

Example:

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Will it rain in Madurai today?

AgriNova evaluates rainfall probability and provides operational recommendations.

Example output:

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High rainfall probability.

Avoid irrigation and fertilizer spraying today.

Live Government Market Price Integration

Market uncertainty directly impacts farmer profitability and selling decisions.

AgriNova AI integrates Government APIs to fetch real-time crop market prices, allowing farmers to query prices based on crop and location.

Example:

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What is today's tomato price in Madurai?

Response:

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Commodity: Tomato

Markets in Madurai:

Usilampatti Market
Min Price: ₹2100
Max Price: ₹2500
Modal Price: ₹2300

Madurai Market
Min Price: ₹2200
Max Price: ₹2550
Modal Price: ₹2400

Melur Market
Min Price: ₹2150
Max Price: ₹2480
Modal Price: ₹2320

This transforms AgriNova AI from a simple assistant into a location-aware agricultural intelligence layer, helping farmers compare market prices across nearby markets and make informed selling decisions.

Agricultural Knowledge Layer

One major enhancement in AgriNova AI was moving from hardcoded logic to structured datasets.

The assistant now consumes agricultural knowledge stored as CSV and Excel datasets.

Datasets include:

Tamil Nadu Government Schemes
Central Government Schemes
Water Planning Knowledge
Equipment Subsidies
Equipment Rentals
Crop Calendars
Eligibility Rules

This approach improved maintainability and enabled domain expansion.

Government Scheme Discovery Engine

Farmers often miss available support programs because information remains fragmented.

AgriNova includes both Central and Tamil Nadu schemes.

Examples include:

Central:

PM-KISAN
PMFBY
PMKSY
MIDH
SMAM
eNAM
Soil Health Card

Tamil Nadu:

TN Micro Irrigation Scheme
Uzhavar Sandhai
Tamil Nadu Millet Mission
Coconut Development Programme
Collective Farming Programme

Example:

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Schemes for Coconut

Output:

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Central:
PMKSY
MIDH

Tamil Nadu:
Coconut Development Programme
Micro Irrigation Scheme

Water Intelligence Engine

Water availability strongly influences crop decisions.

AgriNova introduces a dedicated Water Management Engine supporting:

River irrigation
Well irrigation
Pond irrigation
Rainfed farming

Example:

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Crop:
Rice

Water Source:
Well irrigation

Availability:
Low

Output:

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Recommended Technique:

SRI Method

Water Saving Practices:

Alternate irrigation
Mulching

This enables context-aware agricultural recommendations.

Farm Equipment Ecosystem

Equipment accessibility remains a challenge for many farmers.

AgriNova introduces two additional capabilities:

Equipment Subsidy Discovery

Example:

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Subsidy for Tractor

Output:

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Central:
SMAM

State:
Farm Mechanization Programme

Equipment Rental Ecosystem

The assistant also supports rental discovery from:

Government centers
PPP providers
FPO ecosystems
Private operators

Example:

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Rental for Harvester

Crop Lifecycle Planning

AgriNova includes crop calendars to support operational planning.

Example:

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Crop Calendar for Rice

Output:

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Stage:
Vegetative

Duration:
20–60 days

Activity:
Fertilizer application

This provides structured guidance across cultivation phases.

Farm Economics Layer

Agriculture decisions are not only technical — they are economic.

AgriNova includes a Farm Profit Calculator.

Inputs:

Area
Yield
Fertilizer cost
Labor cost
Equipment cost
Market price

Output:

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Revenue:
₹50,000

Expenses:
₹22,000

Estimated Profit:
₹28,000

AWS Documentation MCP Integration

One of the most interesting learning experiences during development was exploring Model Context Protocol (MCP).

AgriNova integrates the AWS Documentation MCP Server.

Architecture:

This enables the assistant to dynamically access AWS documentation and extend responses using external context.

Technology Stack

Layer	Technology
Agent Framework	Strands Agents SDK
LLM	Amazon Nova Pro
Memory	Mem0
Vector Store	FAISS
MCP	AWS Documentation MCP
APIs	wttr.in + data.gov.in
Knowledge Layer	CSV + Excel
Language	Python

📊 AgriNova Evolution Journey

Version	Capability	Key Addition
V1	Weather Assistant	Real-time Weather API
V2	Agricultural Insights	Rainfall + Market API
V3	Memory	Mem0 + FAISS
V4	Government Intelligence	Scheme Engine
V5	Water Planning	Irrigation Intelligence
V6	Equipment Ecosystem	Subsidy + Rental
V7	Farm Economics	Profit Calculator
V8	Knowledge Layer	CSV + Excel datasets
V9	MCP Integration	AWS Documentation
V10	Agriculture Intelligence Platform	Unified workflow

Lessons Learned

AgriNova AI evolved significantly during development.

Major transitions included:

Hardcoded guidance → Structured datasets

Simple assistant → Agriculture intelligence system

Static responses → Real-time decision support

The project provided practical experience in:

AI Agents
Memory systems
MCP integration
Real-time intelligence
Domain datasets
Agricultural workflows

Future Roadmap

Planned improvements include:

Crop disease detection
Voice interactions in Tamil
Agriculture RAG workflows
Satellite intelligence
Farmer profile memory
Image-based crop analysis

🌾 Final Thoughts

AgriNova AI started as a simple farming assistant and gradually evolved into an agriculture intelligence platform integrating AI Agents, memory, MCP, real-time APIs, and structured agricultural knowledge.

The biggest takeaway from this journey was to start simple, iterate continuously, and gradually introduce complexity through tools, datasets, and workflows.

This project provided practical experience in Generative AI, AI Agents, memory systems, MCP integration, and agriculture-focused intelligence workflows.

GitHub:https://github.com/Thirumalaiboobathi/builders-skill-sprint-ai-agent/tree/main/Challenge-5

Built as part of the AWS User Group Madurai – Nova Month Skill Sprint 2026 🌾🚜

Thank you for taking the time to read my article! If you found it helpful, feel free to like, share, and drop your thoughts in the comments; I’d love to hear from you.

# amazon-nova # agents # amazon-bedrock-agents # amazon-bedrock # ai-engineering

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AgriNova AI: Smart Farming with Amazon Nova Pro & MCP

Thirumalaiboobathi B

Problem Statement

System Architecture

Real-Time Agricultural Intelligence

Weather Intelligence

Rainfall Prediction

Live Government Market Price Integration

Agricultural Knowledge Layer

Government Scheme Discovery Engine

Water Intelligence Engine

Farm Equipment Ecosystem

Equipment Subsidy Discovery

Equipment Rental Ecosystem

Crop Lifecycle Planning

Farm Economics Layer

AWS Documentation MCP Integration

Technology Stack

📊 AgriNova Evolution Journey

Lessons Learned

Future Roadmap

🌾 Final Thoughts