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**. --- ## 🌞 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. --- ### 🚁 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. --- ### 🧠 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. --- ### 📊 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