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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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Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis
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一种用于检测小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小小

Suwan Wang1, Jianqing Zhao2,3, Yucheng Cai1,3

  • 1National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.

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|January 30, 2024
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概括

这项研究引入了一种新方法,用于使用本地注释和改进的深度学习模型准确计算小麦苗. 该方法提高了检测准确度,这对于小麦产量预测至关重要.

关键词:
当地注释 地方注释无人驾驶飞行器 (UAV) 的图像小麦幼苗检测检测 小麦幼苗检测检测这是一个YOLO YOLO.

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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:

  • 准确的小麦种苗计数对于种群规模评估和产量预测至关重要.
  • 使用无人机 (UAV) 图像的深度学习方法越来越多地用于小麦幼苗的检测.
  • 当前方法的挑战包括幼苗的小尺寸,不同的姿势,以及由于全球注释造成的背景土壤干扰.

研究的目的:

  • 开发一种改进的小麦幼苗检测方法,解决全球注释的局限性.
  • 为了提高从无人机图像中计数小麦苗的准确性.

主要方法:

  • 提出了一种使用本地注释而不是全球注释的小麦苗木检测方法.
  • 改进了检测模型,在YOLOv5头部内加入了一个空间到深度传输模块和一个微尺度检测层.
  • 优化了模型,以更好地提取小规模的特征,并减轻叶子封闭等问题.

主要成果:

  • 拟议的方法实现了90.1%的检测精度.
  • 超过现有的最先进的小麦苗木检测方法.
  • 在减少因苗木大小和封闭引起的错误方面已证明有效.

结论:

  • 当地注释和模型优化显著提高了小麦苗的检测准确性.
  • 开发的方法为未来的小麦苗木检测和产量预测研究提供了宝贵的参考.
  • 这种方法解决了自动化农业监测的关键挑战.