Our system is a web-based tool designed to help farmers estimate rice phenology using drone imagery. Utilizing advanced machine learning algorithms, it analyzes multispectral images to monitor crop growth stages and analyze crop health, identify fields needing attention, and aid in yield optimization. The user-friendly interface ensures easy accessibility for farmers, enhancing crop management and contributing to agricultural efficiency.
This is a Next.js project bootstrapped with create-next-app.
First, run the development server: Install dependencies:
npm iRun the server:
npm run dev
# or
yarn dev
# or
pnpm devOpen http://localhost:3000 with your browser to see the result.
You can start editing the page by modifying pages/index.js. The page auto-updates as you edit the file.
API routes can be accessed on http://localhost:3000/api/hello. This endpoint can be edited in pages/api/hello.js.
The pages/api directory is mapped to /api/*. Files in this directory are treated as API routes instead of React pages.
This project uses next/font to automatically optimize and load Inter, a custom Google Font.
To learn more about Next.js, take a look at the following resources:
- Next.js Documentation - learn about Next.js features and API.
- Learn Next.js - an interactive Next.js tutorial.
You can check out the Next.js GitHub repository - your feedback and contributions are welcome!
The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.
Check out our Next.js deployment documentation for more details. "# rice-phenology" "# rice-phenology"