茶叶疾病的分类和识别基于改进的YOLOv7模型的MobileNeXtt
Yuxin Xia1, Wenxia Yuan2, Shihao Zhang1
1College of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming, 650201, China.
Scientific reports
|May 23, 2024
概括
一个改进的YOLOv7模型通过使用MobileNeXt和注意力机制提高了茶病识别的准确性和速度. 这种轻量级模型提供了更高的精度和更快的检测,以有效地监测茶叶植物.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确和快速地识别茶叶病对作物管理至关重要.
- 现有的方法经常受到低准确度和缓慢响应时间的影响.
- 需要自动化系统来支持茶叶病的监测和预防.
研究的目的:
- 开发一个改进的轻量级YOLOv7模型,用于加强茶叶疾病的分类和识别.
- 提高茶叶植物疾病检测的准确性和速度.
- 为了减少识别模型的计算负载和参数数量.
主要方法:
- 在YOLOv7架构中使用MobileNeXt作为轻量级的骨干网络.
- 集成了一种双层路由注意力机制,以改善特征提取.
- 采用SIoU损失函数来提高识别精度,特别是在复杂的背景中.
主要成果:
- 获得的精度为93.5%,召回率为89.9%,平均精度为92.1%.
- 与原始模型相比,显著改进,精度提高了4.5%,回忆率提高了1.9%,平均精度提高了2.6%.
- 模型体积减少了24.69M,总参数减少了12.88M,检测速度增加了24.41/秒.
结论:
- 增强的轻量级YOLOv7模型提供了高效和准确的茶病识别.
- 拟议的模型提供了较低的计算要求和更快的检测速度.
- 这一进步为实用的茶病监测和预防系统提供了坚实的基础.
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