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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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相关实验视频

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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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深度学习用于果叶病的识别:一个视觉变形的视角.

Md Arban Hossain1, Saadman Sakib1, Hasan Muhammad Abdullah2

  • 1Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh.

Heliyon
|September 16, 2024
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概括

视觉转换器 (ViT) 在识别果叶疾病方面表现出很高的准确性,表现优于流行的卷积神经网络 (CNN). 智能农业的这一进步通过移动应用程序提供了更快的培训和实时疾病检测.

关键词:
深度学习是一种深度学习.植物疾病 植物疾病智能农业是一个智能农业.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
  • 机器学习 机器学习

背景情况:

  • 机器学习,特别是使用卷积神经网络 (CNN) 的深度学习,在智能农业中越来越多地用于早期检测植物疾病.
  • 视觉转换器 (ViTs) 已经成为图像分类的强大工具,经常超过传统的CNN性能.
  • 农业中ViT的应用是一个正在发展的领域.

研究的目的:

  • 评估视觉转换器 (ViTs) 识别果叶病的性能.
  • 在这个农业应用中,将ViT性能与已建立的CNN模型进行比较.
  • 引入一个优化的ViT模型,以提高疾病识别的准确性和效率.

主要方法:

  • 使用预训练的数据效率图像转换器 (DeiT) 架构,优化用于果叶疾病识别.
  • 与几个流行的CNN架构 (SqueezeNet,ShuffleNet,EfficientNet,DenseNet121,MobileNet) 进行了优化ViT模型的性能比较.
  • 开发了一个集成ViT模型的移动应用程序,用于实时诊断疾病.

主要成果:

  • 优化的ViT模型在识别果叶病时达到99.75%的高精度.
  • 与评估的CNN模型相比,ViTs表现出更高的性能.
  • 视觉转换器表现出较短的训练时间,比CNN需要更少的时代才能达到最佳结果.

结论:

  • 视觉变压器代表了在智能农业中识别果叶病的高效和高效的方法.
  • 开发的ViT模型和移动应用程序为实时疾病管理提供了一个有希望的解决方案.
  • 在农业中进一步采用ViT可以显著提高作物监测和疾病检测能力.