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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: May 24, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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基于智能手机的葡萄检测和葡萄园管理框架,使用无人机训练的人工智能.

Sergio Vélez1,2, Mar Ariza-Sentís2, Mario Triviño3

  • 1JRU Drone Technology, Department of Architectural Constructions and I.C.T., University of Burgos, Burgos, 09001, Spain.

Heliyon
|March 3, 2025
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概括

这项研究引入了一个AI框架,使用无人机数据和智能手机图像进行准确的葡萄束检测,使葡萄种植监测成为农民可访问和有效的产品. 该系统集成对象检测和细分,实现高精度和可靠性.

关键词:
数字农业 数字农业精准农业 精准农业 精准农业实时检测检测实时检测.葡萄园管理管理的葡萄园管理.这是一个YOLO YOLO.收益率映射 收益率映射 收益率映射

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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科学领域:

  • 农业技术 农业技术
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 葡萄种植需要有效地识别葡萄束,以评估产量和质量.
  • 传统的方法是劳动密集型的,而先进的无人机系统可能无法为农民提供.
  • 智能手机为农业数据收集提供了一个广泛可访问的平台.

研究的目的:

  • 开发一种可访问和准确的基于人工智能的系统,用于在葡萄园中自动检测葡萄束.
  • 将无人机数据与智能手机成像集成,以进行强大的模型训练和部署.
  • 为农民创建一个实用,负担得起和可扩展的解决方案来监测葡萄产量.

主要方法:

  • 开发了一个结合对象检测 (YOLO) 和像素级别细分 (X-Decoder) 的AI管道.
  • 无人机 (UAV) 视频被用于初始模型训练和细分.
  • 经过训练的模型被应用于普通智能手机 (小米Poco X3 Pro,iPhone X) 拍摄的图像中.
  • 创建了一个Web应用程序,以促进系统与移动技术的集成.

主要成果:

  • 人工智能系统实现了高检测准确度,精度为0.92,回忆率为0.735,F1得分为0.82.
  • 该模型表现出强度,人工智能检测到的葡萄束与基本真相有很强的相关性 (R2 = 0.84).
  • 综合方法在效率和适应性方面超过了传统和纯粹基于无人机的方法.

结论:

  • 结合用于培训的无人机数据和用于应用的智能手机成像,为葡萄种植监测提供了一个实用且可扩展的解决方案.
  • 开发的AI框架使先进的葡萄束检测能够通过随时可用的技术向农民提供.
  • 这种方法大大减少了葡萄园产量评估所需的时间和精力,提高了农业实践.