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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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松病检测算法基于改进的YOLOv5检测算法.

Zengjie Du1,2,3, Sifei Wu1,2,3, Qingqing Wen4

  • 1College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, China.

Frontiers in plant science
|May 3, 2024
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概括
此摘要是机器生成的。

早期发现松病 (PWD) 对森林管理至关重要. 一个新的轻量级深度学习模型,YOLOv5L-s-SimAM-ASFF,使用无人机图像实时准确识别PWD感染的树木.

关键词:
这就是SIMAM-ASFF.这是YOLOv5的.深度学习是一种深度学习.松病是松病的一种疾病.无人驾驶飞行器是一种无人驾驶飞行器.

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科学领域:

  • 林业科学 林业科学
  • 遥感技术 遥感技术 遥感技术
  • 人工智能的人工智能

背景情况:

  • 松病 (PWD) 是一个严重的森林威胁,由于高传染性和致命性.
  • 有效的PWD管理依赖于及时检测和隔离受感染的树木.
  • 目前使用无人机图像进行PWD检测的深度学习方法通常需要广泛的专业知识,并且缺乏效率.

研究的目的:

  • 开发一种高度准确,轻量级和高效的深度学习模型,用于实时检测受PWD感染的松树.
  • 提高基于无人机的遥感用于森林疾病监测的实际应用.

主要方法:

  • 在YOLOv5架构中集成ShuffleNetV2,SimAM注意模块和ASFF用于功能融合,创建YOLOv5L-s-SimAM-ASFF模型.
  • 使用无人机 (UAV) RGB图像在自然条件下检测生病的松树.
  • 使用诸如平均平均精度 (mAP) 和每秒 (FPS) 等指标评估模型性能.

主要成果:

  • 拟议的YOLOv5L-s-SimAM-ASFF模型在PWD检测方面实现了95.64%的mAP和91.28%的召回率.
  • 该模型显示了95.70 FPS的高处理速度,从而实现实时检测.
  • 与原始YOLOv5-Lite相比,模型尺寸和参数数量大大减少,提高了效率.

结论:

  • YOLOv5L-s-SimAM-ASFF模型非常适合实时,准确和轻量级检测PWD感染的树木.
  • 该模型为精确定位和定量受感染树木提供了有价值的工具,有助于森林管理.
  • 开发的模型为有效的PWD管理和根除策略提供了实际指导.