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基于YOLOv11m的番茄叶病识别的超参数优化

Yong-Suk Lee^1,2, Maheshkumar Prakash Patil², Jeong Gyu Kim³

¹Department of Food Science and Technology/Institute of Food Science, Pukyong National University, Busan 48513, Republic of Korea.

Plants (Basel, Switzerland)

|March 17, 2025

概括

这项研究表明,YOLOv11m模型经过广泛的优化后,可以准确地识别番茄叶病. 这种自动化的疾病识别系统可以提高作物产量和农场管理效率.

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:

在番茄叶中自动识别疾病对于提高作物产量和农场管理至关重要.
现有的方法需要对各种番茄叶病进行高效准确的检测系统.

研究的目的:

评估YOLOv11的性能,用于自动识别番茄叶病.
优化YOLOv11模型以提高准确性和在农业中的实际应用.

主要方法:

在改进的番茄叶病数据集 (11类) 上微调所有可访问的YOLOv11版本.
选择YOLOv11m用于使用一次因素 (OFAT) 算法进行超参数优化.
执行随机搜索 (RS) 用100个配置进一步完善模型,导致C47模型.

主要成果:

优化的YOLOv11m (C47型号) 获得了0.99268的健身得分,0.99190的精度,0.99348的回忆,0.99262.5的mAP@.
在关键性能指标上,C47型号在最初的YOLOv11m型号上显示出显著的改进.
该模型在检测和识别10种不同的西红叶疾病和健康类中表现出高准确性.

结论:

优化的YOLOv11模型 (C47) 对于自动识别番茄叶病非常有效.
开发的系统适用于实际的农业应用,有助于高效的产量管理.
这项研究有助于在农业中推进人工智能,用于疾病检测和作物保护.

关键词:

这就是YOLOv11的意义.超参数优化超参数优化一个时间一个因素.随机搜索随机搜索随机搜索番茄叶病番茄叶病番茄叶病

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