🖼️ Image Detection and Classification with YOLOv11

📌 Description

This project consists of two main programs:

1. Count: Detects oil palm trees and draws sequential bounding boxes on each tree.
2. Classify: Detects apples and classifies them into red, yellow, or green categories, then crops and saves each as separate images.

⚙️ Features

✅ Count Program

• Detects oil palm trees.
• Draws bounding boxes labeled with sequential numbers.
• Outputs a single image with all trees detected and numbered.

✅ Classify Program

• Detects apples by color (red, yellow, green).
• Crops each apple and saves it as a separate image.
• Outputs images like: red_1.jpg, yellow_1.jpg, etc.

📂 Requirements

• Python 3.11+
• YOLOv11
• OpenCV
• Roboflow dataset

🧪 Dataset

• Palm Tree Dataset
• Apple Dataset

🧠 Training

Training is done via Jupyter notebooks provided in the repo:

• Count.ipynb for palm detection
• Classify.ipynb for apple classification

🚀 Usage

Count Program

You can run a prediction to detect palm trees and generate an image where each tree is boxed and labeled with a number.

Output Example:

Classify Program

This program identifies apples by their color, crops each one, and saves them as individual images in a folder.

Output Examples:

🍎 Red Apple	🍋 Yellow Apple	🍏 Green Apple

📎 Summary

This project uses YOLOv11 to detect and classify palm trees and apples, providing accurate image annotations and color-based apple cropping for further analysis or dataset generation.