🌿🛩️ Automated Olive Tree Detection & Counting from Drone Footage Using AI 🌍🌳

Описание: Excited to share one of my recent precision agriculture projects, an AI-based system for detecting and counting olive trees from aerial drone videos, designed to support smarter, data-driven decision-making for farmers, agronomists, and land management systems.
🎯 Project Objective:
Enable fast, accurate, and scalable olive tree detection & counting from aerial footage to assist in:
Yield estimation
Orchard monitoring
Land-use optimization
Tree health analysis & planning
🧠 Key Features:
✅ Accurate detection of olive trees using a custom-trained YOLOv11 instance segmentation model
✅ Real-time object detection from drone videos
✅ Total tree count displayed as overlay
✅ Scalable across large orchards with varying lighting and tree density
✅ Post-processing pipeline for geotagging, if required (GIS-ready)
🛠️ Tools & Technologies Used:
🌐 Python & OpenCV
🧠 YOLOv11 (Ultralytics)
🎮 Aerial video processing
🌿 Labeling & model training with custom dataset
📦 Numpy, Matplotlib (for results visualization)
🚀 Impact:
This project empowers smart agriculture by reducing manual surveying time, improving accuracy in tree inventory management, and laying the groundwork for deeper insights such as canopy health, growth tracking, and automated irrigation planning.
🌱 This is a step toward the future of AI in agriculture, where drones, computer vision, and deep learning work together to sustainably scale food production while preserving labor and environmental resources.