小丝虫的生理状态识别模型基于改进的YOLOv5模型
Pu Liu1, Xingrui He1, Kai Zhao2
1Sichuan University of Science & Engineering, Yibin, China.
Science progress
|November 14, 2024
概括
这项研究开发了一种改进的YOLOv5模型,以准确识别小丝虫的生理状态,帮助有效的丝虫繁殖. 改进后的模型实现了92.2%的准确性,改善了农村振兴工作.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 丝虫繁殖对农村经济至关重要,但由于死亡率高,在幼虫早期阶段面临挑战.
- 准确识别丝虫的生理状态对于及时的农民干预和提高育种效率至关重要.
研究的目的:
- 开发和验证一个增强的YOLOv5模型,以准确和快速识别小丝虫的年龄和休眠状态.
- 提高丝虫繁殖效率,并通过先进的AI技术支持农村振兴.
主要方法:
- 训练了一个神经网络,使用手动标记的数据来根据身体特征和身体长度识别丝虫年龄.
- 实现了改进的YOLOv5架构,具有C3-SE注意力机制和Focal-EIoU损失功能.
- 通过分析丝虫的中心点坐标与设定的值的运动来评估休眠期.
主要成果:
- 改进后的YOLOv5模型的平均准确率为92.2%,比原始模型提高了2.29%.
- 显示精度增加了0.3%,回忆率提高了3.4%,每秒幅提高了7.7%.
- 该模型有效地确定了丝虫的年龄和休眠期,这对于监测脆弱的早期发育阶段至关重要.
结论:
- 改进的YOLOv5模型为监测小丝虫生理状态提供了准确有效的解决方案.
- 这项技术有可能显著提高丝虫的繁殖实践,并为农业发展做出贡献.
- 注意力机制的整合和改进的损失功能推动了计算机视觉在鱼养殖中的应用.
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