zunaid1626/AgroGuide

"AgroGuide is a smart agriculture dashboard built with Python and Streamlit. It translates complex weather and soil data into simple, actionable daily tasks for low-resource farmers. Bypassing expensive sensors, it uses historical rain data to estimate moisture and features an AI crop disease scanner, live Mandi prices, and offline SMS alerts." 🌍 **Live App:** Click here to view AgroGuide # 🌾 AgroGuide: Smart Farmer Advisory System ## 📌 The Problem Modern agricultural tech often overwhelms farmers with raw data (e.g., "Soil moisture is 15%") without providing clear directions. Furthermore, many existing solutions require high-end smartphones and expensive sensor hardware, creating a massive technical barrier for low-resource farmers. ## 🚀 Our Solution AgroGuide is a "Zero-Learning Curve" advisory dashboard built to translate complex environmental data into simple, color-coded daily tasks. ### ✨ Key Features * **Actionable Intelligence:** Instead of showing graphs, the system calculates variables (like days since last rain and local temperature) to give definitive commands (e.g., "Irrigate for 2 hours today"). * **Low-Resource Logic:** Bypasses the need for expensive IoT soil sensors by calculating estimated moisture via historical rain data and weather APIs. * **Offline Accessibility (SMS Gateway):** Features a simulated SMS push notification system, ensuring farmers with basic 2G feature phones still receive their daily action plans. * **Visual AI Diagnostics:** A UI-ready crop disease scanner module for instant pest identification and chemical prescription. * **Market Integration:** Live tracking of local Mandi prices to ensure maximum profit margins. ## 🛠️ Tech Stack * **Frontend/Backend:** Python, Streamlit * **Architecture:** Session-state routed multi-page application * **UI/UX:** Custom CSS styling for high-contrast readability and native light/dark mode support. ## 💻 How to Run Locally 1. Clone the repository: `git clone github.com` 2. Navigate to the directory: `cd AgroGuide` 3. Install dependencies: `pip install -r requirements.txt` 4. Run the app: `streamlit run app.py`