Chapter 29 — MedAlert AI
Based on: SIH Problem related to AI-driven Crop Disease and Environmental Monitoring System
Skills Required
- Artificial Intelligence & Machine Learning
- Image Processing
- Environmental Data Analysis
- Python programming (TensorFlow, PyTorch, OpenCV)
- IoT sensor data integration
- Cloud computing and data pipelines
Project Description
MedAlert AI is an AI-driven monitoring platform designed for early detection of crop diseases using image analysis and environmental data. The system integrates real-time sensor data with image processing models to predict disease outbreaks, enabling timely interventions and reducing crop loss. MedAlert AI provides a user-friendly dashboard with alerts, notifications, and actionable insights for farmers and agricultural agencies.
Tech Stack
- Python for AI model development and data processing
- TensorFlow or PyTorch for deep learning models
- OpenCV for image preprocessing
- MQTT and other protocols for IoT sensor data collection
- Cloud platforms (AWS/GCP/Azure) for hosting and scalability
- React or Angular for dashboard UI
Week-wise Roadmap
Week 1 — Requirement Analysis and Data Collection
- Define project goals, data sources (images, weather, soil conditions).
- Collect datasets of crop images with annotated disease labels.
- Setup environment for model training and data ingestion.
- Deliverable: Project specification, collected datasets.
Week 2 — Image Preprocessing & Augmentation
- Implement image normalization, resizing, augmentation techniques.
- Prepare datasets for training and validation.
- Deliverable: Preprocessed dataset ready for model training.
Week 3 — AI Model Development
- Develop CNN-based classification models for disease identification.
- Train, validate, and tune hyperparameters.
- Deliverable: Initial trained model with benchmark metrics.
Week 4 — Environmental Sensor Integration
- Integrate IoT sensor data streams for temperature, humidity, soil moisture.
- Develop fusion models combining image and sensor data for better accuracy.
- Deliverable: Sensor data ingestion and fusion model prototype.
Week 5 — Alerting & Notification System
- Build real-time alert system based on disease prediction confidence.
- Implement SMS/email/push notifications for stakeholders.
- Deliverable: Functional alerting system.
Week 6 — Dashboard Development
- Develop frontend dashboard to display disease maps, sensor readings, and alerts.
- Implement filtering and historical data visualization.
- Deliverable: Interactive user dashboard.
Week 7 — Testing & Field Validation
- Test model accuracy with real-world samples and sensor data.
- Gather user feedback and refine models and UI.
- Deliverable: Validation reports and improved system.
Week 8 — Deployment & Documentation
- Deploy the system on cloud infrastructure with monitoring.
- Prepare documentation and user guides.
- Deliverable: Production-ready MedAlert AI platform.
Testing & Deliverables
- Validate accuracy on diverse crop types and varying conditions.
- Measure latency and reliability of real-time alerts.
- Deliver source code, trained models, deployment scripts, and demo videos.
MedAlert AI empowers precision agriculture through AI-powered disease detection and environmental monitoring, improving crop health and yield.