Apple Detection in Orchards

This project was implemented using models like YOLO and FRCNN to address challenges in agricultural automation by enabling efficient and precise fruit detection.

What is YOLO (You Only Look Once)?

YOLO is a state-of-the-art real-time object detection algorithm. It processes the entire image in one go and predicts bounding boxes and class probabilities directly from the image pixels. YOLO is fast and efficient, making it suitable for applications like real-time fruit detection in agriculture.

• Single-stage detector that provides high-speed predictions
• Divides the image into grids and predicts bounding boxes for each grid cell
• Achieves great balance between accuracy and speed

What is FRCNN (Faster R-CNN)?

Faster R-CNN is a two-stage object detection model that improves upon previous versions like R-CNN and Fast R-CNN. It uses a Region Proposal Network (RPN) to generate regions of interest, significantly improving detection speed while maintaining accuracy.

• Two-stage detector for higher accuracy
• Uses a Region Proposal Network (RPN) to generate candidate object proposals
• Highly accurate but slower compared to YOLO

Results

The lower accuracy is due to the dataset used and its ground truth. The dataset had no ground truth bounding boxes for apples fallen on the floor, resulting in many false positives and reduced precision.