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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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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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基于改进的YOLOv8模型的小麦种子检测和计数方法

Na Ma1, Yaxin Su1, Lexin Yang1

  • 1College of Information Science and Engineering, Shanxi Agricultural University, Taigu District, Jinzhong 030801, China.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
概括
此摘要是机器生成的。

一个新的YOLOv8-HD模型显著提高了小麦种子检测的准确性和速度,即使在具有挑战性的种子凝聚和杂质的情况下. 这种轻量级模型的平均精度比YOLOv8高16.8%,有助于农业应用.

关键词:
这就是YOLOv8的意义.注意力机制注意力机制轻量级的轻量级的轻量级的轻量级的小麦种子检测检测小麦种子检测

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科学领域:

  • 农业工程 农业工程
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 准确的小麦种子计数对于农业应用至关重要,例如计算千粒重量和作物育种.
  • 现有的方法在种子积累,粘附和封闭方面扎,导致精度和速度低.
  • 为种子计数器开发高效的嵌入式系统需要优化检测模型.

研究的目的:

  • 提出一种轻量级的实时小麦种子检测模型 (YOLOv8-HD),以提高计数精度和速度.
  • 为了应对聚类,粘附和封闭的小麦种子在检测中所带来的挑战.
  • 为开发嵌入式种子计数平台提供技术支持.

主要方法:

  • 在YOLOv8检测头中引入了共享的卷积层,以实现轻量化设计和提高速度.
  • 集成视觉变压器与可变形的注意力进入脊柱的C2f模块,以增强特征提取.
  • 基于YOLOv8架构开发了YOLOv8-HD模型.

主要成果:

  • 在杂质堆叠的场景中,YOLOv8-HD实现了77.6%的mAP,比YOLOv8.1提高了9.1%.
  • 总的来说,mAP达到99.3%,与YOLOv8.8相比增加了16.8%.
  • 模型大小减少到6.35 MB (YOLOv8的4/5),GFLOPs减少了16%,推断时间为2.86 ms (在GPU上).

结论:

  • 与主流网络相比,YOLOv8-HD在小麦种子检测方面表现出卓越的性能,在准确性,速度和模型大小方面表现出卓越的性能.
  • 该模型在各种场景中高效地检测小麦种子,包括具有挑战性的条件,如严重的粘附.
  • YOLOv8-HD为开发先进的种子计数仪器提供了有效的技术支持.