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React + Vite

This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.

Currently, two official plugins are available:

• @vitejs/plugin-react uses Babel (or oxc when used in rolldown-vite) for Fast Refresh
• @vitejs/plugin-react-swc uses SWC for Fast Refresh

React Compiler

The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see this documentation.

Expanding the ESLint configuration

If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the TS template for information on how to integrate TypeScript and typescript-eslint in your project.

Product Requirements Document (PRD)

1. Project Overview

Project Name: AI-Driven Agritech Assistant (Temporary Name) Tagline: Empowering farmers with personalized, data-driven crop insights through an accessible voice interface.

2. Problem Statement

Small-scale farmers often lack access to precision agriculture tools. They struggle with identifying crop health issues early, managing resources (like water and soil nutrients) efficiently, and receiving actionable, personalized advice. Existing solutions are often technically complex or inaccessible to those with lower digital literacy.

3. Target Audience

• Primary Users: Farmers in rural villages.
• Secondary Users: Agricultural extension workers or village administrators who might assist in data collection (e.g., operating drones).

4. Proposed Solution

A comprehensive, AI-powered system that combines an intuitive voice-based agent for farmers with a robust backend. The backend processes aerial drone footage and environmental data to provide personalized, field-specific insights directly to each farmer.

5. Key Features

5.1 Voice-Based Agent (Farmer Interface)

• Accessibility: A voice-first interface (UI details to be specified later) allowing farmers to interact with the system using natural language.
• Multilingual Support (Future): Support for local languages and dialects.
• Query Resolution: Farmers can ask questions about their personalized data, weather forecasts, or general agricultural best practices.

5.2 Drone Footage Processing & Analysis

• Field Mapping (Whose Crop is Whose): The backend analyzes top-down drone footage to map the village's agricultural land and demarcate individual farm boundaries.
• Crop Health Analysis: Computer vision algorithms assess the footage to evaluate crop health, detect early signs of disease, pest infestations, or nutrient deficiencies on a per-farmer basis.

5.3 Personalized Data Delivery

• Individual Insights: The system synthesizes the visual analysis and sends tailored advice and alerts to each specific farmer regarding their plot.

5.4 Environmental Data Integration

• Soil & Water Data: The system accepts episodic data inputs (weekly, monthly, or on-demand) regarding soil quality (e.g., pH, moisture, NPK levels) and water availability.
• Holistic Recommendations: The backend combines drone visual data with this ground-level environmental data to generate highly accurate and actionable farming recommendations.

6. Tech Stack Recommendations (To Be Finalized)

• Frontend / Interface: Voice Interface / Conversational AI (e.g., Dialogflow, OpenAI Realtime API). User Interface to be designed.
• Backend: Python (FastAPI/Django/Flask) or Node.js.
• Computer Vision: OpenCV, PyTorch, or TensorFlow for processing drone imagery (segmentation and health classification).
• Database: PostgreSQL/MongoDB with geospatial extensions (e.g., PostGIS) for mapping fields.

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