AI-Powered UAV for Precision Crop Monitoring and Yield Optimization

This project demonstrates a low-cost AI-enabled UAV perception prototype using an ESP32-CAM module.
The system simulates a UAV perception pipeline capable of detecting anomalies and sending alerts to a monitoring server.

The prototype models how AI-powered UAV systems can monitor agricultural environments and provide real-time alerts.

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Project Overview

Modern UAV systems rely on artificial intelligence to convert sensor data into useful insights.
This project implements a prototype perception node that captures images, performs simulated anomaly detection, and sends alerts via Wi-Fi to a webhook server.

The system demonstrates the Sense → Analyze → Alert pipeline used in UAV perception systems.

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System Architecture

Block Diagram

UAV System Architecture

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Hardware Components

• ESP32-CAM (OV2640 camera module)
• FTDI USB-to-TTL adapter
• Regulated 5V power supply
• Laptop or local server for webhook receiver

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Software Stack

• Arduino IDE (ESP32 firmware development)
• Python Flask server for webhook receiver
• Wi-Fi communication using HTTP POST
• JSON alert messaging

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System Workflow

1. ESP32-CAM captures image frames
2. Detection logic analyzes sensor input
3. An anomaly trigger generates an alert
4. JSON payload is sent to a webhook server via Wi-Fi
5. Flask server receives and logs the alert

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Repository Structure

ai-uav-crop-monitoring
│
├── firmware
│   └── esp32_cam_alert_system.ino
│
├── server
│   └── webhook_receiver.py
│
├── diagrams
│   ├── block_diagram.png
│   └── system_architecture.png
│
├── report
│   └── AI-Powered_UAV_for_Precision_Crop_Monitoring.pdf
│
└── README.md

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Key Features

• Low-cost UAV perception prototype
• ESP32-CAM based sensing system
• Real-time alert communication using Wi-Fi
• Flask webhook receiver for event logging
• Modular design for TinyML integration

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Future Improvements

• Integrate TinyML models for real object detection
• Add SLAM / VIO modules for UAV localization
• Replace Wi-Fi with LoRa or LTE telemetry
• Integrate with PX4 flight controller

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Author

Shaik Naved Ahmed
B.Tech – Computer Science & Artificial Intelligence
SR University, Warangal

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References

• YOLOv3: An Incremental Improvement – Joseph Redmon
• ESP32-CAM Technical Documentation
• PX4 Autopilot Documentation
• LSD-SLAM Research Paper