Eeko AI Webapp

Introduction

The Eeko AI Webapp is a web-based application designed to leverage artificial intelligence for Agricultural Solutions leveraging NASA's LARC api key for data which was further used to generate agricultural Insights. Built on the Next.js framework, it enables efficient server-side rendering and static site generation. By utilizing APIs for machine learning and data handling, Eeko AI Webapp provides high-performance AI-driven functionalities catering to diverse user needs.

With its modular architecture, the application allows for easy customization and scalability, making it a valuable tool for developers and businesses seeking to integrate sophisticated AI capabilities seamlessly.

Features

• Next.js Framework: Efficient server-side rendering and static site generation, providing a solid foundation for high-performance web applications.
• API Integration: Integrates various APIs to manage complex data and AI tasks, ensuring smooth data processing.
• Object Detection with YOLOv5: Advanced object detection for real-time identification and classification.
• Disease and Insect Detection: Machine learning models for detecting diseases and insects, applicable in agriculture and environmental fields.
• Meta LLaMA 3.2 11B Integration: Incorporates a powerful language model to enhance natural language processing.
• Optimized Font Loading: Uses next/font to optimize the Geist font family for a modern UI.
• Comprehensive Documentation: Detailed resources and documentation for quick learning and contribution.
• Easy Deployment: Simplifies deployment with Vercel, allowing for quick, hassle-free application deployment.
• Community and Contribution: Encourages feedback and collaboration, fostering continuous improvement.

Getting Started

To run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 to see the result.

You can edit the page by modifying app/page.tsx, and the page will auto-update as you edit the file.

This project uses next/font to optimize and load the Geist font family from Vercel.

Learn More

For more information on Next.js, check out these resources:

• Next.js Documentation – Learn about Next.js features and APIs.
• Interactive Next.js Tutorial – Hands-on guide to mastering Next.js.

You can also explore the Next.js GitHub repository for feedback and contributions.

Deploy on Vercel

The easiest way to deploy a Next.js app is through the Vercel Platform. Check the Next.js deployment documentation for more details.

Contribution

Burhan

• Leadership: Led the project from conception to deployment, providing vision and guidance.
• Meta LLaMA Integration: Successfully integrated Meta LLaMA 3.2 11B, boosting the app's machine learning capabilities.
• Disease and Insect Detection: Pioneered detection functionalities, improving agricultural utility.
• Technical Problem Solving: Fixed deployment issues and errors, ensuring smooth operation.

Additional Contributions By Burhan:

• Commit: Smoke weed everyday
• Commit: Fixed deployment issues
• Commit: Fixed a few errors including removing unused packages

Ahsen Tahir

• Data Exploration: Analyzed complex data for LLM integration.
• Research and Implementation: Reviewed research papers and attempted Roboflow's weed detection model.
• YOLOv5 Fine-Tuning: Fine-tuned YOLOv5 with Mohib's assistance, using FastAPI for UI integration.