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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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利用光谱,结构和纹理特征来估计麦地表生物质,使用无人机基于多光谱数据和机器学习.

Rakshya Dhakal1, Maitiniyazi Maimaitijiang2, Jiyul Chang3

  • 1Plant Breeding Graduate Program, University of Florida, Gainesville, FL 32608, USA.

Sensors (Basel, Switzerland)
|December 23, 2023
PubMed
概括

无人机与机器学习相结合,通过整合光谱,结构和纹理数据,改善了麦生物质估计. 这种先进的表型增强了作物育种效率.

关键词:
无人机无人机无人机是什么?生物质的生物质是生物质.机器学习是机器学习.多光谱图像处理技术麦 麦是一种麦.远程传感是一种遥感技术.

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科学领域:

  • 农业科学 农业科学
  • 遥感 遥感 遥感 遥感
  • 植物育种 植物育种

背景情况:

  • 精确的地表生物质监测对于植物育种至关重要,但传统方法是劳动密集型和昂贵的.
  • 无人驾驶飞行器 (UAV) 为田间地块提供了一个快速,非破坏性的表型化解决方案.
  • 现有的植被指数 (VI) 方法主要使用光谱数据,忽视了3D树冠结构和空间关系.

研究的目的:

  • 探索无人机多光谱图像衍生的光谱,结构和纹理特征与机器学习的整合,以准确估计麦生物质.
  • 评估树冠结构和纹理特征以及光谱特征的重要性.
  • 为了比较不同机器学习算法用于生物质估计的预测性能.

主要方法:

  • 无人机多谱图像采集了2020年和2021年在两个地点和多个生长阶段的六种麦基因型.
  • 从图像中提取了图片层面的天花板光谱,结构和纹理特征.
  • 部分最小平方回归 (PLSR),支向量回归 (SVR) 和随机森林回归 (RFR) 用于估计生物量.

主要成果:

  • 树冠结构和纹理特征被确定为麦生物质估计的重要指标,补充光谱数据.
  • 与使用单个特征类型相比,结合光谱,结构和纹理特征显著提高了生物质估计的准确性.
  • 机器学习算法表现出强大的预测能力,随机森林回归 (RFR) 获得了最高的准确性 (R2 = 0.926,RMSE% = 15.97%).

结论:

  • 基于无人机的多功能融合与机器学习提供了一个有前途的方法,用于准确地表生物质估计在麦育种苗圃.
  • 这种综合方法可以通过先进的表型识别显著提高麦育种计划的效率.
  • 这些发现支持采用基于无人机的表型鉴定来加强作物管理实践.