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Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8s
Shujin Qiu1,2, Jian Gao1,2, Mengyao Han1,2
1College of Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Sensors (Basel, Switzerland)
|January 11, 2025
Summary
This study introduces an improved YOLOv8s model for accurate sorghum spike detection, addressing challenges like high density and occlusion. The enhanced model significantly boosts precision and recall in field conditions.
Area of Science:
- Agricultural Engineering
- Computer Vision
- Machine Learning
Background:
- Sorghum spike detection is challenging due to high planting density, similar colors, and occlusion.
- Existing methods suffer from low accuracy, high false detection, and missed detection rates.
Purpose of the Study:
- To develop an improved sorghum spike detection method to overcome current limitations.
- To enhance the accuracy and reliability of sorghum spike identification in natural field environments.
Main Methods:
- An improved YOLOv8s model (YOLOv8s-Gold-LSKA) was developed by integrating the Gold feature pyramid module and refining the SPPF module with the LSKA attention mechanism.
- A Focal-EIOU loss function was employed to address class imbalance and expedite model convergence.
Main Results:
- The YOLOv8s-Gold-LSKA model achieved a precision of 90.72%, recall of 76.81%, mAP of 85.86%, and F1-score of 81.19%.
- The improved model demonstrated superior detection performance compared to YOLOv5s, SSD, and YOLOv8.
Conclusions:
- The proposed method significantly enhances sorghum spike detection accuracy in natural field settings.
- This advancement provides technical support for accurate sorghum yield estimation and intelligent harvesting equipment.
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