🌱 AI-Enabled Agro-Climate Intelligence System An intelligent agriculture decision support system that analyzes environmental data using Machine Learning and provides adaptive recommendations for irrigation, fertilizer application, and farm operations. This system integrates IoT concepts, Machine Learning models, cloud APIs, and a real-time dashboard to help farmers make data-driven decisions.
🚀 Live Deployment 🌐 Dashboard https://naveena23ece.github.io/agro-climate-ai/ ⚙️ API Backend https://agro-climate-ai-1.onrender.com 📘 API Documentation https://agro-climate-ai-1.onrender.com/docs
🧠 Project Motivation Agricultural decisions depend heavily on environmental conditions such as temperature, humidity, rainfall, and soil moisture. Farmers often rely on generalized weather forecasts, which do not represent farm-specific micro-climate conditions. This project aims to build an AI-driven climate intelligence platform that transforms raw environmental data into actionable farming recommendations.
System Architecture: Farm Sensors / Simulated Data ↓ FastAPI Backend (Cloud - Render) ↓ Machine Learning Layer(Random Forest + Isolation Forest) ↓ Decision Engine ↓ Firebase Realtime Database ↓ Interactive Web Dashboard (GitHub Pages)
🔧 Technologies Used Backend: -Python -FastAPI -Scikit-learn -Pandas -NumPy Machine Learning: -Random Forest Regression -Isolation Forest (Anomaly Detection) Cloud Services: -Render (API deployment) -Firebase Realtime Database Frontend: -HTML -CSS -JavaScript -Chart.js Deployment:GitHub Pages (Dashboard hosting)
⚙️ Machine Learning Models 🌿 Random Forest Regression Used for predicting future soil moisture levels based on environmental conditions. Features used: -Temperature -Humidity -Rainfall -Previous soil moisture levels
Advantages: -Handles nonlinear relationships -Works well with small datasets -Robust and stable predictions
⚠️ Isolation Forest Used for climate anomaly detection. Detects abnormal patterns such as: -Sudden temperature spikes -Rapid humidity drops -Unexpected soil moisture changes
🧠 Decision Engine The decision engine combines: -Current environmental conditions -ML prediction results -Anomaly detection output
to generate adaptive recommendations such as: -Skip irrigation -Delay fertilizer application -Monitor abnormal climate conditions
📊 Dashboard Features The web dashboard provides: -Real-time environmental monitoring -Soil moisture prediction results -Anomaly detection alerts -Climate trend visualization -Historical prediction analysis Charts are powered by Chart.js.
👩💻 Author Naveena N B.E Electronics and Communication Engineering AI / Cloud / Software Systems Enthusiast