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Pumpkin Detection & Tracking System
A real-time computer vision system that detects and tracks pumpkins using a YOLO12 custom model. Ideal for smart farming, field analysis, and agricultural robotics.
Project Overview
PumpkinTrack AI is a computer vision solution designed to detect and track pumpkins in real-time using a custom-trained YOLO12 model. The system efficiently identifies pumpkins in video feeds, tracks them across frames, and visually labels them with bounding boxes and unique IDs, supporting applications in precision agriculture, smart farming, and harvest automation.
Key Features
Pumpkin Detection
- 🎃 Accurately identifies pumpkins using a YOLO12 custom model (best.pt).
Object Tracking
- 🆔 Maintains consistent IDs for each pumpkin across video frames.
Visual Feedback
- 📦 Draws bounding boxes, labels, and object centers for easy visualization.
Mouse Coordinate Debugger
- 🖱️ Logs real-time cursor positions for calibration and analysis.
Video or Webcam Input
- 🎥 Easily adaptable to any video source, including field footage or live camera.
Technology Stack
- 🐍 Programming Language: Python
- 🎥 Computer Vision: OpenCV
- 🧠 Object Detection: Ultralytics YOLO12
- 🖥️ Visualization & Annotation: cvzone (optional)
Use Cases
- 🌱 Smart Agriculture – Monitor pumpkin growth in real-time.
- 🤖 Automated Harvesting – Guide robotic arms or harvesters.
- 📊 Yield Estimation – Track pumpkin count over a field.
- 🔬 Research & Development – Study size, distribution, or ripeness patterns.
Conclusion
PumpkinTrack AI provides a fast, efficient, and customizable framework for detecting and tracking pumpkins. By combining YOLO12 and OpenCV’s real-time video capabilities, it empowers farmers, researchers, and agricultural engineers to automate and optimize pumpkin analysis effectively.