一种基于叶子图像自动标签算法和改进的YOLOv5模型的西红疾病识别方法
Jiaping Jing1, Shufei Li1, Chen Qiao1
1College of Information and Electrical Engineering, China Agricultural University, Beijing, China.
Journal of the science of food and agriculture
|June 16, 2023
概括
这项研究介绍了BC-YOLOv5,这是一种自动化方法,用于标记番茄叶的图像,以提高疾病识别的准确性. 这种方法简化了过程,提高了番茄生产的产量和质量.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 植物病理学 植物病理学
背景情况:
- 番茄生产在全球范围内具有重要意义,需要准确的疾病鉴定,以获得产量和质量.
- 卷积神经网络 (CNN) 对于疾病识别至关重要,但需要大量的手动图像注释.
- 手动注释是耗时和昂贵的,阻碍了研究效率.
研究的目的:
- 开发一种自动化的番茄叶图像标签算法,以简化疾病识别.
- 为了提高对各种番茄病的识别效应的准确性和平衡性.
- 通过深度学习改进现有的检测番茄病的方法.
主要方法:
- 建议的BC-YOLOv5方法用于番茄病的识别.
- 整合了一个自动番茄叶图像标签算法.
- 修改了YOLOv5部结构,采用加权双向特征金字塔网络.
- 集成了一个卷积块注意模块,并调整了检测层的输入通道.
主要成果:
- BC-YOLOv5实现了优异的图像注释通过率超过95%的西红叶.
- 与现有的番茄疾病识别模型相比,已经证明了更高的性能指数.
- 成功识别了健康的生长和九种疾病的西红叶.
结论:
- 在训练之前,BC-YOLOv5可以自动标记番茄叶的图像.
- 该方法准确地识别了九种常见的西红疾病,具有改进和平衡的识别能力.
- 为农业中番茄病的识别提供了可靠和有效的解决方案.
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