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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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基于深度学习和计算机视觉的高精度自动化大豆表型特征提取.

Qi-Yuan Zhang1, Ke-Jun Fan1, Zhixi Tian2

  • 1College of Engineering, China Agricultural University, Beijing 100083, China.

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研究人员使用YOLOv8模型开发了自动化方法来分析大豆植物表型,包括和豆数. 一个新的中点坐标算法 (MCA) 有效地区分了树干和树枝,以进行精确的测量.

关键词:
实例细分 实例细分 实例细分现型的获取 现型的获取智能农业 智能农业大豆的表型大豆的表型

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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
  • 植物育种 植物育种

背景情况:

  • 自动化植物表型数据收集对于现代育种和智能农业至关重要.
  • 对大豆植物进行准确的表型鉴定对于作物改善和产量预测至关重要.

研究的目的:

  • 开发和评估用于细分大豆植物和量化表型特征的自动化方法.
  • 为了比较基于YOLOv8的不同植物和树识别模型的性能.
  • 引入一种新的算法,用于大豆植物中高效的茎和分枝分化.

主要方法:

  • 在受控的实验室环境中,利用了四种基于YOLOv8的模型来对成熟的大豆植物进行细分.
  • 实施了一种新的中点坐标算法 (MCA) 来区分主干与分支.
  • 用图像分析量化了和豆的数量,并计算了表型特征.

主要成果:

  • YOLOv8-Repvit模型实现了最佳的识别,和豆类的R2系数为0.96.
  • 根平均平方误差 (RMSE) 值为豆的2.89和豆类的6.90.
  • 与A*算法相比,MCA显示了较少的计算时间和空间复杂性.

结论:

  • 自动化的YOLOv8模型为大豆植物表型化提供了高效和准确的方法.
  • 中点坐标算法为植物结构分析提供了一个计算效率高的解决方案.
  • 这项研究为在现场采集成熟大豆植物的表型数据奠定了基础.