Objectives
To detect wheat leaf diseases using AI and camera input for early treatment. To monitor real-time environmental factors (temperature, humidity, soil moisture, UV) using IoT sensors. To provide instant alerts and smart recommendations to help farmers make timely decisions.
Socio-Economic Benefit
Higher yield Lower crop loss Cost-effective farming Farmer empowerment Improved food security Efficient resource use Early disease control Smart decision-making
Methodologies
Sensor-Based Monitoring: Used DHT22, soil moisture, and UV sensors to collect real-time environmental data. Image-Based Disease Detection: Applied deep learning (CNN) on wheat leaf images for accurate disease classification. IoT Integration: Connected NodeMCU with sensors to send data wirelessly to the cloud. Mobile App Development: Developed a Flutter app for live data display, disease results, and alerts. Real-Time Notifications: Used Firebase for sending instant alerts and storing live sensor data.
Outcome
Accurate disease detection Real-time crop monitoring Instant farmer alerts Improved crop health Increased productivity Reduced crop losses Efficient resource usage Data-driven decisions
Project Team Members
| Registration# | Name |
|---|---|
| Cu-2079-2021 | |
| Cu-2471-2021 | |
| Cu-2102-2021 |
PROJECT GALLERY
Smart Agriculture Monitoring System
18 Jun, 2025 03:04 pm
