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Chili Pepper Object Detection Method Based on Improved YOLOv8n.

Na Ma1, Yulong Wu1, Yifan Bo1

  • 1College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

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Summary
This summary is machine-generated.

This study introduces an improved YOLOv8n model for accurate and fast chili pepper detection in natural settings, enhancing agricultural technology. The optimized model boosts detection performance while reducing computational load for intelligent harvesting.

Keywords:
YOLOv8ablation experimentchili pepperlightweightobject detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Agricultural Technology

Background:

  • Chili recognition in natural environments suffers from low accuracy and slow detection speeds.
  • Existing object detection models require optimization for agricultural applications.

Purpose of the Study:

  • To develop an improved chili pepper object detection method using an enhanced YOLOv8n model.
  • To optimize chili recognition accuracy and detection speed for intelligent harvesting.

Main Methods:

  • Evaluated multiple YOLO versions (YOLOv5n, YOLOv6n, YOLOv7-tiny, YOLOv8n, YOLOv9, YOLOv10) to select YOLOv8n as the baseline.
  • Improved YOLOv8n by replacing the backbone with HGNetV2, integrating the SEAM module, optimizing feature fusion with dilated reparam blocks, and using CARAFE upsampling.
  • Trained and evaluated the model on a custom chili dataset.

Main Results:

  • Achieved F0.5-score of 96.47%, mAP0.5 of 96.3%, and mAP0.5:0.95 of 79.4%, showing significant improvements.
  • Reduced parameter count by 29.5% and GFLOPs by 28.4%, resulting in a smaller model size of 4.6 MB.
  • Demonstrated enhanced feature extraction for occluded chili fruits and improved network feature fusion.

Conclusions:

  • The improved YOLOv8n model effectively enhances chili target detection accuracy and speed.
  • This method provides a strong technical foundation for intelligent chili harvesting systems.
  • The optimized model offers a balance between performance and computational efficiency.