/execute: AI Genesis

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We are a team of AI, drone, and energy experts building a smart system to monitorsolar_panels join

GenisiusDz

Keen_Eclipsehq9n

Here is a **long description** for your project idea: **Solar Panel Monitoring System with Drone Simulation, CNN-Based Defect Detection, and YOLOv9 Dataset Integration via Web Interface**.

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## 🌞 Long Description: Solar Panel Monitoring with AI and Drones

This project presents a simulated **intelligent solar panel monitoring system** that integrates **drone-based inspection**, **deep learning-based defect detection**, and a **web-based visualization interface**. The goal is to demonstrate how modern AI techniques can automate and enhance the monitoring of solar panel infrastructure to ensure optimal performance and early fault detection.

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### 🚁 1. Drone Simulation and Image Capture

The system includes a simulated **DroneAPI** which emulates:
- **Takeoff and landing operations**
- **Autonomous navigation** to predefined solar panel locations
- **Capture of RGB images** representing visual data
- **Capture of thermal images** mimicking heat distribution across panels

This simulation framework can easily be adapted to real drone APIs such as DJI or Parrot for real-world deployment.

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### 🧠 2. Defect Detection using Deep Learning (CNN)

To analyze the condition of the solar panels:
- A **Convolutional Neural Network (CNN)** is used to classify RGB images into **three categories**:
  - `Normal`
  - `Crack`
  - `Dirt`

The CNN is designed to be lightweight and fast, ideal for real-time edge deployment or drone-based processing.

Thermal images are further analyzed for **hot spots**, which may indicate malfunctioning cells or overheating, using threshold-based anomaly detection.

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### 📊 3. Automated Report Generation

After inspecting all panels:
- The system generates a visual report using `matplotlib` which includes:
  - A histogram showing the distribution of detected defects
  - A bar chart indicating the number of hot spots per panel
- The report and all captured images are saved in the `static/` folder and can be accessed from the web interfac

detction the solar when damage whith a drone simulate

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