增强的YOLOv8-ECCI算法用于在大豆中高精度检测紫斑病
Zhihua Deng1, Shuyao Ye1, Chunru Xiong1
1College of Computer Science and Engineering, Yangjiang Campus, Guangdong Ocean University, Yangjiang 529500, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
我们开发了YOLOv8-ECCI, 一种先进的算法, 这种方法显著提高了农业疾病检测的准确性和通用性.
科学领域:
- 农业科学
- 植物病理学
- 计算机视觉
背景情况:
- 在大豆中检测种子级疾病面临诸如小样本和遮等挑战.
- 现有的方法难以直接在种子上识别疾病.
研究的目的:
- 推出YOLOv8-ECCI,用于在种子上高精度识别大豆紫斑病的增强算法.
- 解决目前种子级疾病检测技术的局限性.
主要方法:
- 使用YOLOv8架构,并通过ECCI模块来提高性能.
- 进行了与基线YOLOv8n模型相比的YOLOv8-ECCI实验.
- 使用非洲野生动物数据集进行交叉数据集验证.
主要成果:
- 与YOLOv8n相比,YOLOv8- ECCI取得了显著的改进:精度为+5. 7%,回忆率为+6. 5%,mAP@0. 5%为+8. 0%和mAP@0. 5: 0. 95.
- 在单独的数据集上,表现优于传统方法,精度为+6.0%和+2.9% mAP@0.5.
- 有效地克服了种子级病理检测的挑战.
结论:
- YOLOv8-ECCI提供了一个强大而准确的解决方案,用于在种子层面检测大豆紫斑病.
- 增强的算法显示出在实地检测农业疾病和质量控制的巨大潜力.
- 验证的概括能力证实了模型在不同数据集中的可靠性.
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