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相关概念视频

Survival Tree01:19

Survival Tree

84
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
84

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Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response
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时间序列衰老根识别改进的变压器

Hui Tang1, Xue Cheng1, Qiushi Yu1

  • 1College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China.

Plant phenomics (Washington, D.C.)
|April 17, 2024
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概括

这项研究引入了一种新的深度学习方法,用于在现场提取棉花根衰老特征. SegFormer-UN模型准确地识别了根衰老,为作物研究提供了快速而非破坏性的方法.

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科学领域:

  • 植物科学 植物科学
  • 计算机视觉 计算机视觉
  • 农业技术 农业技术

背景情况:

  • 植物根对于营养和水的吸收至关重要,具有与功能相关的表型特征.
  • 使用深度学习对根衰老特征进行高吞吐量,现场提取仍然是一个未被充分探索的领域.

研究的目的:

  • 开发和评估一种基于变压器神经网络的新技术,用于检索棉花的 in situ 根衰老特性.
  • 将拟议方法的性能与现有的深度学习和图像处理算法进行比较,用于棉花根衰老提取.

主要方法:

  • 利用高分辨率的现场根图像,具有不同的衰老水平.
  • 使用 SegFormer-UN,一个变压器神经网络模型,用于棉花根系统的语义细分.
  • 将SegFormer-UN与用于根系细分的一般卷积神经网络进行比较.

主要成果:

  • 对于根衰老特征的提取,SegFormer-UN实现了最佳的评估指标 (mIoU: 81.52%,mRecall: 86.87%,mPrecision: 90.98%,mF1: 88.81%).
  • 该模型在细分根系连接方面表现出卓越的准确性.
  • SegFormer-UN算法处理图像的速度很快 (大约为1分钟). 4分钟/图像) 的参数数量为581万,超过其他两个深度学习算法.

结论:

  • SegFormer-UN模型提供了一种快速,非破坏性和准确的方法,用于从实地图像中识别棉花根中的衰老.
  • 这种技术为高效的作物衰老研究和表型化提供了重要的方法支持.