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RT-DETR-土壤Cuc:黄瓜发芽的检测方法在土壤基础环境中

Zhengjun Li1, Yijie Wu1, Haoyu Jiang1

  • 1College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China.

Frontiers in plant science
|September 6, 2024
PubMed
概括

这项研究介绍了RT-DETR-SoilCuc,这是一种轻量级的深度学习模型,用于准确地检测土壤中的黄瓜种子发芽. 它显著改进了现有的方法,减少了手工劳动和帮助新品种的选择.

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其他国家/地区 RT-DETRR黄瓜发芽的发芽时间发芽速度的发芽速度是多少盐的耐受性 盐的耐受性基于土壤的环境土壤环境.

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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
  • 植物育种 植物育种

背景情况:

  • 目前用于种子发芽检测的深度学习模型在复杂的土壤环境中表现不佳.
  • 传统的手工方法是劳动密集的,耗时的,容易出现错误,特别是在土壤耕种中.

研究的目的:

  • 开发一种准确和有效的方法来检测土壤环境中的黄瓜种子发芽.
  • 在现实世界农业条件下解决现有技术的局限性.
  • 为了促进新的黄瓜品种的选择和育种.

主要方法:

  • 开发了一种种子发芽表型化系统,用于在盐应激下以土壤为基础的黄瓜发芽环境.
  • 构建了一个黄瓜发芽数据集,并设计了一个轻量级的实时检测模型,RT-DETR-SoilCuc,基于实时检测转换器 (RT-DETR).
  • 通过在线图像增强,Adown downsampling,通用化高效轻量级网络骨干,在线卷积重新参数化和规范化高斯瓦斯斯坦距离损失来增强模型.

主要成果:

  • 与RT-DETR-R18.18相比,RT-DETR-SoilCuc模型实现了显著的轻量化,参数减少了61.2%,FLOP减少了61%,重量大小减少了56.5%.
  • 实现了高检测精度,mAP@0.5的98.2%,精度为97.4%,回忆率为96.9%.
  • 在相似尺寸的You Only Look Once模型中表现出卓越的性能,并在土壤干扰中检测胚胎根目标时得到验证的准确性.

结论:

  • 在复杂的土壤环境中,RT-DETR-SoilCuc模型提供了一个高度准确和高效的解决方案,用于在复杂的土壤环境中检测黄瓜发芽.
  • 这项技术显著减少了监测发芽的手工工作量,并有助于有效选择和育种新的黄瓜品种.
  • 该模型对土壤背景干扰的强度为农业研究和实践提供了宝贵的工具.