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Multimodal AI System for Decentralized and Inclusive Rural Technical Assistance

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Multimodal AI System for Decentralized and Inclusive Rural Technical Assistance

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DaniAlves Dec. 2, 2025
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Challenge: Open challenge

Industries

Agriculture & Food

Technologies

LLMs & NLP

Tags

AI

Description

The AgroTech Hub proposes an accessible Multimodal AI System (via WhatsApp/Offline) to provide inclusive rural technical assistance to smallholder farmers. The solution uses Computer Vision and fine-tuned LLMs to diagnose problems in 15 crops and offer contextualized recommendations, overcoming connectivity and language barriers. The project aims to reduce production losses and excessive agrochemical use, strengthening Deep Funding's BGI in the Agro sector.

Detailed Idea

Alignment with DF goals (BGI, Platform growth, community)

The core problem is the lack of inclusive technical assistance for over 500M smallholder farmers, as current AI solutions are inaccessible (requiring high-end tech/internet) and irrelevant (trained on Global North industrial data). This results in 30-40% production losses and 60% excess agrochemical use. The proposed solution is an accessible Multimodal AI System delivered via WhatsApp with offline capability. It uses Computer Vision (90%+ accuracy for 15 crops) and a fine-tuned LLM to provide contextualized recommendations based on local knowledge. This project strongly aligns with Deep Funding's BGI by promoting food security and sustainability. It drives Platform Growth by deploying open-source CV/LLM models on SingularityNET, attracting new users (farmers, NGOs) and developers. The participatory governance model ensures Community engagement and continuous improvement, fulfilling the AgroTech Hub's mission to connect agribusiness with decentralized innovation.

Problem description

Over 500M smallholder farmers lack technical assistance—rural extension reaches <15% globally. Existing AI solutions require advanced smartphones, stable internet, and exclude 40%+ who speak only local languages. Current agricultural AI is trained on industrial farming data from the Global North, ignoring traditional practices and resource constraints. Result: 30-40% production losses, 60% excess agrochemical use, rural youth exodus.

 
 
 
 
 

Proposed Solutions

Build accessible AI platform via WhatsApp with offline capability. Computer vision diagnoses 15 priority crops (90%+ accuracy). Fine-tuned LLM provides contextualized recommendations considering local resources, climate, and traditional knowledge. Participatory governance: farmers validate AI outputs, report real results, creating continuous improvement cycles. Open-source architecture (code, models, datasets). 

 
 
 
 
 

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