优化改进的YOLOv8用于在可持续农业中精确检测番茄叶病
Yue Shen1, Zhaofeng Yang1, Zohaib Khan1
1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
这项研究引入了一种改进的YOLOv8算法,用于精确检测番茄叶病,通过自动化,有针对性的农药应用和减少化学品使用来增强可持续农业.
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:
- 可持续农业需要有效的作物管理,以减少资源浪费和环境影响.
- 精确的农药应用对于有效管理番茄植物疾病至关重要.
研究的目的:
- 开发一种实时番茄叶检测方法,使用改进的YOLOv8算法进行精确的农药应用.
- 为了提高自动化作物管理系统的计算效率和检测准确性.
主要方法:
- 一个改进的YOLOv8算法集成深度分组卷积和AdamW优化器.
- 整合了SE_Block,以增强功能表示和注意力.
- 在1500张西红叶图像的多样化数据集上进行培训,有四个标签 (所有,绿色西红,唐尼菌,粉状菌).
主要成果:
- 改进的YOLOv8实现了更高的精度 (85.7%),回忆 (72.8%),mAP@0.5 (79.8%) 和F1得分 (78.6%).
- 观察到减少了计算复杂性和更快的推断速度.
- 在喷机器人LPE-260上成功部署,用于自动化,有针对性的农药应用.
结论:
- 增强的YOLOv8算法显著提高了检测患病西红叶的准确性和效率.
- 这项技术使得有针对性的农药应用成为可能,减少了化学品的使用和精密农业中的过度喷.
- 该系统展示了深度学习对于可扩展,可持续和资源高效的作物管理解决方案的潜力.
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