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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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使用深度学习模型检测在中发现的常见类虫.

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科学领域:

  • 农业科学 农业科学
  • 昆虫学 昆虫学是一门学科.
  • 计算机科学 计算机科学

背景情况:

  • 生产面临诸如甘虫 (SCA) 等害虫的挑战,需要有效的管理策略.
  • 目前对SCA及其自然敌人的现场侦察是劳动密集型和昂贵的,影响了综合性害虫管理 (IPM).
  • 自然的敌人,主要是类动物,对于SCA的生物控制至关重要,但它们的检测效率低下.

研究的目的:

  • 开发和训练机器学习模型,用于自动检测和分类中关键的类类.
  • 建立深度学习软件,通过促进自然敌人的识别来增强IPM.

主要方法:

  • 训练对象检测模型,包括基于快速区域的卷积神经网络 (快速R-CNN) 与特征金字塔网络 (FPN),YOLOv5和YOLOv7.
  • 利用来自 iNaturalist 项目的 coccinellid 图像数据集进行模型培训和评估.
  • 使用标准物体检测指标,如平均精度 (AP) 和AP@0.50.0,评估模型性能.

主要成果:

  • YOLOv7模型在检测和分类菌体方面表现出卓越的性能,AP@0.50达到97.3%,AP达到74.6%.
  • 更快的R-CNN-FPN,YOLOv5和YOLOv7模型被成功训练,以识别在中发现的七种常见的类.
  • 开发的模型为在农业环境中自动检测昆虫提供了基础.

结论:

  • 自动化深度学习软件提供了一个可行的解决方案,可以有效地检测田中的自然敌人.
  • YOLOv7模型显示,在的综合性害虫防治中,其实际应用具有显著的希望.
  • 这项技术可以通过促进自然捕食者的保护来减少对化学杀虫剂的依赖.