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相关概念视频

Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

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Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
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Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
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相关实验视频

Updated: Jun 7, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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使用计算机视觉实时检测作物排列 - - 在农业机器人中的应用.

Md Nazmuzzaman Khan1, Adibuzzaman Rahi2, Veera P Rajendran3

  • 1Lead Research Scientist (Kroger), 84.51°, Cincinnati, OH, United States.

Frontiers in artificial intelligence
|November 14, 2024
PubMed
概括

这项研究引入了农业机器人的新作物排列检测算法,实时实现了90%以上的准确性. 该方法提高了自主导航,可靠地区分作物和杂草,即使在具有挑战性的条件下.

关键词:
农业机器人农业机器人农作物行检测检测 农作物行检测检测精准农业是精准的农业.实时应用程序实时应用程序没有监督的学习学习.

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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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科学领域:

  • 农业机器人农业机器人
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 农业中自主导航面临的挑战是由于作物图像 (天气,生长阶段) 的自然变化.
  • 实时处理对于农业机器人应用至关重要.

研究的目的:

  • 为自主农业机器人导航开发一个强大而高效的作物排列检测算法.
  • 为满足在可变场条件下低推理时间检测的需求.

主要方法:

  • 投影转换和基于颜色的细分,以隔离作物从背景.
  • 聚类算法来区分作物和杂草像素.
  • 强大的线路配件可用于精确的作物排列检测.

主要成果:

  • 在欧盟 (IOU) 上实现了0.73.3的整体交叉点.
  • 在具有显著杂草生长的场景中表现出高强度.
  • 在实时视频实验中显示了超过90%的检测准确度.

结论:

  • 拟议的算法是农业机器人实时自主导航的可行解决方案.
  • 提供高精度和低推断时间,最大限度地减少作物损坏.
  • 为未来精准农业的研究提供了基础.