najzdev/AmaneAI_Image_Processing_griculture_MATLAB

AmaneAI is an AI-driven IoT solution designed to optimize water usage in the Guelmim-Oued Noun region of Morocco. This repository contains the MATLAB core logic for processing drone aerial imagery to detect crop health and water stress. # AmaneAI: Drone-Based Precision Agriculture & Smart Irrigation 🛰️💧 AmaneAI is an AI-driven IoT solution designed to optimize water usage in the Guelmim-Oued Noun region of Morocco. This repository contains the MATLAB core logic for processing drone aerial imagery to detect crop health and water stress. ## 📸 Project Visuals ### Original Drone Capture High-resolution aerial view (Location: Agadir, Morocco). ### MATLAB Analysis Dashboard Automated Field Segmentation, Health Mapping (VARI), and Targeted Irrigation Map. ## 🚀 Features - **Land Classification:** Uses K-Means Clustering to segment crops, soil, and infrastructure. - **Health Mapping:** Implements the Visible Atmospherically Resistant Index (VARI) for standard RGB images. - **Water Stress Detection:** Identifies precise coordinates (X, Y) of dehydrated plants. - **Water Estimation:** Calculates the required liters of water based on the detected stressed area. - **Full-Stack Ready:** Exports analysis data to `amane_report.json` for integration with Web Dashboards (React/Node.js). ## 🛠️ Tech Stack - **Image Processing:** MATLAB - **Algorithms:** K-Means Clustering, Morphological Operations, VARI Indexing. - **Data Format:** JSON (for IoT/Web integration). ## 💻 How to Run 1. Place your aerial image in the root directory as `farm_drone.jpg`. 2. Open `analyze_crops.m` in MATLAB. 3. Run the script to generate the 6-panel dashboard and the JSON report. ## 🌍 Impact This project aims to reduce water waste by up to 40% by moving from traditional irrigation to **Data-Driven Targeted Irrigation**.