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相关概念视频

Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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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.
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相关实验视频

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一个可解释的视觉转换器与基于转移学习的有效干旱压力识别识别.

Aswini Kumar Patra1,2, Ankit Varshney3, Lingaraj Sahoo4

  • 1Department of Computer Science and Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli, India.

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概括
此摘要是机器生成的。

视觉变压器 (ViT) 通过使用空中图像,准确地检测土豆作物的干旱压力. 这种可解释的人工智能方法可以识别微妙的植物压力指标,从而能够及时作出作物管理决策.

关键词:
深度学习是一种深度学习.干旱造成的压力是干旱.机器学习是机器学习.压力现象类型的形成.支持矢量机器的支持矢量机器.视觉变压器 视觉变压器

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科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学
  • 植物科学 植物科学

背景情况:

  • 早期发现干旱压力对于尽量减少作物损失至关重要.
  • 非侵入性成像技术捕获细微的植物变化,对机器学习有价值.
  • 视觉转换器 (ViT) 为复杂的图像分析提供先进的功能提取.

研究的目的:

  • 开发一种可解释的深度学习管道,用于在土豆作物中检测干旱压力.
  • 利用ViTs分析空中图像以识别受干旱影响的植物.
  • 为农民提供可解释的解决方案,以改善作物管理.

主要方法:

  • 应用了两种基于ViT的方法:ViT-SVM协同作用和端到端的ViT分类.
  • 利用空中图像来检测土豆作物的干旱压力.
  • 采用注意力图来解释模型的可解释性,可视化压力特征.

主要成果:

  • 在使用ViT模型识别干旱压力方面取得了很高的准确性.
  • 证明了ViT在捕捉压力迹象的复杂空间关系方面的能力.
  • 注意地图成功地突出了空中图像中的关键干旱压力特征.

结论:

  • 拟议的可解释的ViT管道为干旱压力检测提供了一个强大的方法.
  • 该方法为与干旱压力相关的植物特征提供了宝贵的见解.
  • 这种可解释的AI解决方案支持有效的作物管理的知情决策.