IDEA/APPROACH DETAILS. IT CRAKERS. @SIH Idea submission- Template. 

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Proposed Solution We aim to develop an AI-powered Digital Krishi Officer app to provide farmers with instant, personalized, and reliable agricultural support in Malayalam and other regional languages.

Key Features Multilingual & Multimodal: Voice, text, and image queries in local languages. Personalized & Context-aware: Tailored advice by crop, location, and season. Farmer-first: Affordable, offline mode for low network connectivity areas. Evidence-backed & Transparent: Recommendations cite trusted sources and show confidence scores. Smart Escalation: Escalates complex cases to agri officers with AI-prepared summaries. Continuous Learning: Improves with farmer feedback and expert corrections. Predictive Analytics + Market Linkage: Early warnings for pests/diseases plus weather and mandi price integration. Community Knowledge Sharing: Optional peer-to-peer Q&A platform for farmers to learn from each other.

Agricultural
Officer
Voice
(Escalsation
Text Input
Digital Krishi Officer
A I •powered
Text Input
Data
oo
Con tinuous Learning
predictive Analytics
(Pests/Diseases)

TECHNICAL	APPROACH. IT CRAKERS. SMART INDIA - HACKATHON 2025. 

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TECHNOLOGIES USED:
 Multilingual & multimodal: AssemblyAI (NLP), ResNet50, PaddleOCR Video crop analysis: Frame extraction algorithm Personalized advice: GPS + season-based recommendations Smart escalation: Dialogflow, Gemini AI summaries Problem classification: Artificial Neural Networks (ANN) Crop disease detection: Convolutional Neural Networks (CNN)
 App development: React, Node.js, Express, Google Cloud Data storage: MongoDB Security compliance: OWASP MASVS v2.0.0 Offline AI: TensorFlow Lite

INPUT LAYER
AssemblyAI
(Voice/NLP)
PROCESSING LAYER
INTELLIGENCE LAYER
ResNet50
(Text/ Image)
PaddleOCR
(Local Language OCR)
Video Data
Frame Extraction
Algorithm
Personalzed
Advice Generation
(GPS & Season-based)
Smart Escalation
(Dialogflow &
Gemni A1 Summiaries)
Artificial Neural Networks
(ANN) - Problem
Problem Classification
APPLICATION LAYER
Node is/ Express
Google Cloud
Mobile App
SECURITY LAYER
OWASP MASVS
v2.OO Compliance
Convolutional Neural
Network(CNN)
crop disease detection
DATA LAYER
MONGODB
(Secure Data Storage)

FEASIBILITY AND VIABILITY. Cost Structure & Revenue Streams. 

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There is a lack of quality datasets for regional crops and pests, so we will partner with agri universities and use farmer-uploaded images to continuously improve our models.

As the user base grows, models may slow down, so we will leverage cloud infrastructure and hybrid edge computing to ensure fast and reliable advisory even in remote areas.

Since farmers are price-sensitive, we will adopt a freemium model with free basic advisory, while advanced services are supported by government subsidies and CSR funding for wide-scale adoption.

Technology & Development Costs: Mobile app dev, AI/ML model training (OCR, NLP, crop detection), cloud hosting. Operations & Support Costs: Agri experts, data annotation, local language integration. Partnerships: Tie-ups with govt. schemes, agri universities, and NGOs. Revenue Streams: Govt. subsidies for farmer tech adoption. Subscription fees from Agri Co-ops/NGOs.

IMPACT AND BENEFITS. (D SIH SMART INDIA - HACKATHON 2024. 

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Bias Mitigation in AI Tools
 The platform eliminates bias by delivering data-backed, region-specific agricultural advice in local languages, ensuring that all farmers—regardless of location or literacy—receive equal support.

Farmer Empowerment The app is designed to be affordable, scalable, and usable in low-connectivity environments, making expert agricultural advice accessible to farmers who need it the most.

Best Fit Prediction
 AI offers personalized guidance tailored to each farmer's specific crop type, soil condition, location, and season, ensuring that the advice is not only relevant but also actionable and timely.

Continuous Learning & Adaptation The AI engine improves continuously through farmer feedback and expert input, making future responses more accurate, localized, and effective over time.

Eco-Friendly & Sustainable Practices PracticThe  app fosters sustainable farming by delivering precise, eco-friendly resource use advice and AI-driven disease detection. It empowers farmers with regional language guidance for climate-resilient, water-efficient practices

Inclusive Rural Access
 By supporting voice, text, and image inputs in multiple regional languages, the solution empowers low-literacy and non-English-speaking farmers, bridging the digital divide in rural communities.

RESEARCH	AND REFERENCES. Market Voice. ““Farmers don’t follow scientific practices due to lack of real-time support.” - Agri Officer. 

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““Farmers don’t follow scientific practices due to lack of real-time support.” - Agri Officer

“I struggle to get advice in my language.”- – Farmer, Kerala

““AI apps can reduce crop loss, but they must be easy to use.”  – Agri Expert

“““Most apps are hard for low-literate farmers.”  – Farmer Leader

Industry Outlook of farmers in India rely on traditional methods of getting advice (neighbors, local shops).

smartphone penetration in rural areas (2025).

farmers face crop losses due to lack of timely expert advice.

Difficulty accessing trusted crop advisory.

High workload handling repetitive farmer queries.

No timely alerts on pest/disease outbreaks.

Lack of AI tools to filter & prioritize cases.

Need smart escalation system for complex cases.

Research Sample: Interviews (Farmers: 30, Agri Officers: 10), Google Form Responses: 120

farmers want regional language support for advice.

feel AI-based crop & pest detection will save time and cost.

prefer voice or image-based query support.

FAO (2024): Digital Agriculture Report NITI Aayog (2023): Agri-Tech in India Study World Bank (2023): AI in Agriculture Insights AgFunder (2024): India Agri-Tech Market Report Primary Research: Farmer Interviews & Google Form Survey

IDEA/APPROACH DETAILS

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