Agro-Nexus: An AI-Driven Digital Ecosystem for Crop Disease Detection and Mandi Analytics
(Also documented as: Agro-Nexus: AI-Powered Crop Disease Detection and Mandi Analytics System for Academic Records)
Agro-Nexus is a cutting-edge solution designed to transform traditional farming using Artificial Intelligence. This platform empowers farmers by providing instant disease diagnosis and real-time market insights to ensure better crop management and profitability.
- 🌐 Live Web App: View Agro-Nexus
- 📝 Research Paper (Zenodo DOI): https://doi.org/10.5281/zenodo.20033108
- 💻 GitHub Repository: [Public Access]
- 📱 Android App (APK): Download Production APK (Optimized Full-Screen Mobile Experience)
Here is a quick high-level technical preview of the Agro-Nexus digital ecosystem in action:
Video.mp4
📢 Note: Please watch the full system walkthrough above. A comprehensive deep-dive tutorial explaining every core module and architectural pattern is Coming Soon on YouTube!
- 🔬 Crop Doctor (AI Diagnosis): Powered by Google Gemini 1.5 Flash (Generative AI) multimodal vision engine. It accepts leaf images from any plant, tree, or cereal crop to identify pathogens with >90% precision and output instant organic/chemical solutions.
- 💬 AI Agro-Assistant: A Generative AI Large Language Model (LLM) powered conversational chat interface providing real-time expert advice on crop rotation and soil fertilizers.
- 📊 Live Mandi Analytics: Direct integration with the official Open Government Data (OGD) Platform India (Agmarknet) APIs for secure, real-time daily commodity price tracking and market trends.
- ☁️ Hyper-local Weather Intelligence: Real-time localization of weather parameters (Temperature, Humidity, Wind speed) using the Open-Meteo API to assist in automated irrigation planning.
- 📱 PWA & Android Accessibility: Optimized as a lightweight Progressive Web App (<15MB) featuring local data caching and an offline-capable user experience over rural 3G/4G networks.
- Core Framework: React.js (Powered by Vite for strict tree-shaking and production bundle optimization)
- Styling Engine: Tailwind CSS (Utility-first design pattern for ultra-lightweight UI footprint)
- Icons & Animation: Lucide React & Framer Motion (Ensuring dynamic and highly intuitive UX/UI layouts)
- API Orchestration: Axios HTTP Client (Configured with robust async error handling)
- Custom API Middleware: Python Flask Framework & Flask-CORS (Acts as a secure API gateway)
- Generative AI Processing: Google Gemini 1.5 Flash Architecture (Handles Base64 image streams for volatile, real-time vision diagnostics)
- User Session Management: Firebase Authentication (Secure OAuth 2.0 Google Sign-In)
- Data Persistence: Cloud Firestore (Schema-less NoSQL Document Store for secure real-time synchronization)
- Asset Storage: Firebase Cloud Storage Buckets (Served via high-speed Content Delivery Networks)
- Global Edge Hosting: Vercel Environment (Integrated with automated GitHub CI/CD pipelines)
This project is a collaborative undergraduate research effort:
- Irfaan Mansoori
- Jeet Lakhera
- Harshit Vishwakarma
- LinkedIn | [GitHub: Comming Soon]
This project is open-source and licensed under the MIT License. For deep architectural details and scientific validation, see the official academic reference in the LICENSE file.
If you utilize this source code, system architecture, or core research methodology in an academic work or project, please provide appropriate credit by citing our published paper.
Mansoori, I., Lakhera, J., & Vishwakarma, H. (2026). Agro-Nexus: An AI-Driven Digital Ecosystem for Crop Disease Detection and Mandi Analytics(1.0.0). Zenodo. DOI: [https://doi.org/10.5281/zenodo.20033108](https://doi.org/10.5281/zenodo.20033108)
Major Project | Bachelor of Technology (Computer Science & Engineering) Vindhya Institute of Technology and Science (VITS), Satna Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), Bhopal