Project Title: Smart Farming with IoT

Objective:

To enhance agricultural productivity and sustainability by integrating IoT sensors and data analytics to monitor crop health, soil conditions, weather, and equipment—enabling precision farming and real-time decision-making.

Key Components:

IoT Sensor Integration:

Deploys sensors to collect data on soil moisture, temperature, humidity, pH levels, light intensity, and crop growth.

Devices like drones, automated irrigation systems, and GPS-equipped machinery are also connected.

Data Collection & Transmission:

Uses wireless networks (e.g., LoRaWAN, Wi-Fi, GSM) to transmit sensor data to cloud servers or edge devices.

Data collected in real-time or at defined intervals for storage and analysis.

Data Preprocessing:

Cleans, normalizes, and interpolates raw sensor data.

Handles missing values, outliers, and sensor noise.

Predictive Analytics & ML Models:

Crop yield prediction using regression models.

Disease/pest detection using image classification or anomaly detection.

Irrigation scheduling with time series forecasting and environmental modeling.

Automation & Control:

Automatically controls irrigation, fertilization, or ventilation systems based on predictive insights.

Implements threshold-based alerts and actuator commands.

Dashboard & Visualization:

Real-time dashboards for farmers showing actionable insights like:

Optimal watering times

Pest risk alerts

Growth trends and productivity maps

Sustainability & Resource Optimization:

Minimizes water and fertilizer usage.

Promotes eco-friendly farming through targeted interventions and reduced waste.

Outcomes:

Increased crop productivity and resource efficiency.

Reduced manual monitoring and improved decision-making.

Promotes sustainable agriculture through tech-enabled farming.

Scalable solution for small and large-scale farms.