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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

Updated: Jun 10, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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集成自动标签框架,以增强深度学习模型,以使用UAS图像计算玉米植物.

Sushma Katari1, Sandeep Venkatesh2, Christopher Stewart3

  • 1Department of Food, Agricultural, and Biological Engineering, Ohio State University, 590 Woody Hayes Dr, Columbus, OH 43210, USA.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
概括

使用深度学习模型和自动图像注释框架的自动植物计数显著提高了玉米识别的准确性. 这种方法简化了数据生成,以实现高效的作物管理和产量预测.

关键词:
在UAS,UAS就是UAS.自动标签是自动标签.农作物排列 农作物排列工厂支架数量计数 工厂支架数量计数

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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
  • 机器学习 机器学习

背景情况:

  • 准确的植物计数对于作物管理至关重要,影响产量和质量评估.
  • 传统的方法是劳动密集型的,当前的自动化系统通常需要大量的手动数据标签.

研究的目的:

  • 通过集成自动图像注释框架来开发一个强大的玉米计数模型.
  • 通过无人机系统 (UAS) 图像和深度学习 (DL) 来提高植物计数的准确性和效率.

主要方法:

  • 收集了玉米在V2-V4生长阶段的高空间分辨率UAS图像.
  • 通过提取玉米行和应用图像增强,开发了一种自动化的图像注释过程.
  • 通过使用自动注释图像训练了四个DL模型 (InceptionV3,VGG16,VGG19,Vision Transformer).

主要成果:

  • 自动注释在识别玉米植物方面取得了80%的准确性.
  • 在DL型号中,VGG16表现优越,F1得分为0.955.
  • 与地面真相数据相比,VGG16模型实现了0.94的R2和9.95的RMSE.

结论:

  • 集成的自动图像注释框架显著提高了DL模型培训用于植物计数的可扩展性和一致性.
  • 这种方法简化了准确的玉米计数模型的开发和部署,有利于大规模的农业数据管理.