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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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RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

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相关实验视频

Updated: Jun 11, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

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用云雾计算处理智能农业应用的图像处理.

Dušan Marković1, Zoran Stamenković2,3, Borislav Đorđević4

  • 1Faculty of Agronomy in Čačak, University of Kragujevac, Cara Dušana 34, 32102 Čačak, Serbia.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了智能农业的深度学习模型,优化边缘设备上的图像分类. 这有助于提早发现问题和资源管理,提高作物监测的效率和降低成本.

关键词:
农业应用程序 农业应用程序云雾计算是一种云雾计算.深度学习是一种深度学习.图像的分类图像的分类.

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科学领域:

  • 农业技术 农业技术
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 产生了大量数据,需要先进的分析解决方案,特别是用于农作物监测的智能农业.
  • 持续监测作物生长有助于及时干预疾病,杂草和害虫控制,提高农业生产力和可持续性.
  • 使用卷积神经网络 (CNN) 的图像分析为增强智能农业决策系统提供了巨大的潜力.

研究的目的:

  • 开发一个深度学习模型,用于对资源有限的Fog计算设备进行优化的图像分类.
  • 通过高效的图像处理,实现早期问题检测和优化智能农业的资源管理.
  • 通过利用边缘计算和雾计算来减少农业运营成本和人工劳动,用于数据处理.

主要方法:

  • 实现一个Fog计算架构,连接云和边缘设备进行数据处理.
  • 开发和优化用于图像分类的深度学习模型,适用于硬件有限的设备.
  • 在现场可编程门阵列 (FPGA) 上测试西红疾病分类模型,以评估性能权衡.

主要成果:

  • 拟议的解决方案有效地将数据处理卸载到Edge和Fog设备上,提高了系统的响应能力和可靠性.
  • 实现了数据传输和存储成本的显著降低.
  • 针对FPGA执行的优化模型显示,对番茄病分类的测试准确性 (0.83%) 的最小下降,保持高性能 (95.46%).

结论:

  • 开发的深度学习模型可以适应在资源有限的Fog计算设备上实现,从而增强智能农业应用.
  • 在边缘优化图像处理可以显著提高系统效率,降低成本,提高整体可靠性和安全性.
  • 该方法证明了平衡模型大小和精度的可行方法,这对于在农业边缘环境中部署AI至关重要.