ADQ-YOLOv8m:在复杂的环境中精确检测甘病的模型
Zhaowen Li1,2, Jihong Sun3, Ying Yang4
1College of Big Data, Yunnan Agricultural University, Kunming, China.
Frontiers in plant science
|October 24, 2025
概括
一个新的ADQ-YOLOv8m模型精确地识别了甘疾病,优于现有的方法. 这种先进的模型显示出强大的通用化,用于智能栽培和疾病控制.
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:
- 目前的甘疾病鉴定方法在范围和适用性方面有限.
- 需要在甘种植中采用更精确,更多功能的疾病检测系统.
研究的目的:
- 开发一个增强的物体检测模型,ADQ-YOLOv8m,用于准确识别甘疾病.
- 改善特征表征,解决甘疾病检测中的阶级不平衡问题.
主要方法:
- 修改了YOLOv8m框架,配有动态头部,用于增强功能表示.
- 整合了ATSS动态标签分配和QFocalLoss来处理类不平衡.
- 利用视觉分析和跨场景适应性测试进行全面评估.
主要成果:
- 与其他九种物体检测模型相比,ADQ-YOLOv8m实现了卓越的性能.
- 关键性能指标包括86.90%的精度,85.40%的回忆,90.00%的mAP50和86.00%的F1得分.
- 该模型展示了出色的多目标处理和强大的泛化能力.
结论:
- ADQ-YOLOv8m模型提供了强大的和准确的甘疾病检测,即使在复杂的场景.
- 它为智能甘种植和有效的疾病管理策略提供了强有力的支持.
- 该模型的概括能力使其适合于不同类别不平衡的农业应用.
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