基于改进的YOLOv8s的作物害虫识别和实时监测系统设计
Qiang Gao1,2, Chongchong Shi1,2, Yu Ji2,3
1School of Information Engineering, Xi'an University, Xi'an 710065, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究通过添加轻量级的注意力机制和功能增强来增强YOLOv8s作物害虫检测模型. 改进的模型显示了对实时害虫监测的更高的准确性和效率.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 由于现有的YOLOv8模型的准确性和适应性,农作物害虫检测面临着挑战.
- 有效和准确的害虫识别对于作物保护和产量优化至关重要.
研究的目的:
- 提高YOLOv8s模型用于作物病虫害检测的检测准确度和部署适应性.
- 开发基于优化YOLOv8s模型的实时害虫监测系统.
主要方法:
- 将轻量级的注意力机制和功能增强模块集成到YOLOv8s架构中.
- 在自建害虫数据集和IP102数据集上评估了改进的模型.
- 开发了一个实时害虫监测系统,利用增强的模型.
主要成果:
- 改进的YOLOv8s模型在两个数据集上都实现了更高的平均精度 (mAP) (例如,自建数据集上的+0.6% mAP0.5和+0.8% mAP0.5-0.95).
- 模型参数从1110万减少到1020万,推断速度略有增加 (249.76 FPS与225.38 FPS).
- 在各种状态下对害虫的识别精度很高,在IP102数据集上表现优于原始YOLOv8s模型 (例如,精度+2.6%).
结论:
- 提议的改进有效地提高了YOLOv8在作物害虫检测方面的性能.
- 优化的模型为准确和实时的作物害虫识别系统提供了有价值的参考.
- 该研究强调了轻量级注意力和功能增强在农业AI应用中的潜力.
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