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相关实验视频

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Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
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基于多尺度特征融合的番茄叶病检测方法

Xiangrui Meng1, Cong Chen2, Wenxue Dong3

  • 1School of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu 611730, China.

Plants (Basel, Switzerland)
|October 29, 2025
PubMed
概括

这项研究引入了一种增强的YOLOv11n模型,用于准确检测番茄叶病. 改进的框架在复杂的现场条件下表现更好,有助于精准农业.

关键词:
在C2CU模块中.在 CAFM 融合模块中.有效的MSF模块这就是YOLO11n.番茄叶病 番茄叶病 番茄叶病

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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:

  • 番茄的产量和质量受到早期和准确的叶病检测的影响.
  • 手动诊断方法是劳动密集型的,容易产生主观偏见,需要自动化解决方案.
  • 现有的自动化方法在复杂的环境条件下难以准确.

研究的目的:

  • 开发基于YOLOv11n的增强检测框架,以更好地识别番茄叶病.
  • 解决现有疾病检测方法在现实世界农业环境中的局限性.
  • 提高自动化疾病监测系统的稳定性和实用性.

主要方法:

  • 开发了一个增强的YOLOv11n模型,结合了EfficientMSF模块用于多尺度特征提取.
  • 集成了一个C2CU模块以改善全球上下文表示,以及一个CAFMFusion模块以实现高效的功能融合.
  • 该模型在一个包含9个番茄叶类别 (八种疾病和健康样本) 的自建数据集上进行了训练和评估.

主要成果:

  • 拟议的模型实现了平均召回率为71.0%,mAP@0.5为76.5%,mAP@0.5-0.95为60.5%.
  • 与基线YOLOv11n相比,表现的改善为RECALL的3.4%,mAP@0.5的1.3%,mAP@0.5-0.95.5%的2.0%.
  • 值得注意的是,具有挑战性的叶模类的mAP@0.5提高了3.4%.

结论:

  • 增强的YOLOv11n框架在复杂的现场条件下表现出强大的稳定性和实际适用性.
  • 开发的模型为智能番茄疾病监测提供了有效的解决方案.
  • 这种方法通过准确和早期发现疾病来支持精确农业管理.