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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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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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相关实验视频

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使用遥感数据和机器学习算法在花生育种计划中的产量预测.

N Ace Pugh1, Andrew Young1, Manisha Ojha2

  • 1United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States.

Frontiers in plant science
|March 6, 2024
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概括

使用无人飞行器 (UAV) 和机器学习的高吞吐量表型化准确地预测了花生产量. 这些先进的方法通过识别高性能基因型来提高作物育种效率.

关键词:
人工智能的人工智能是人工智能.农作物产量 农作物产量增长曲线的增长曲线机器学习是机器学习.花生花生花生花生花生植物育种 植物育种远程传感是一种遥感技术.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:

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

背景情况:

  • 花生是全球重要的粮食作物,需要在育种方面取得进展,以增加遗传收益.
  • 通过遥感直接估计花生产量具有挑战性,需要使用地表特征的间接方法.
  • 高通量表型是加速作物改进的关键.

研究的目的:

  • 开发和评估机器学习模型,以使用无人机衍生的表型数据预测花生产量.
  • 评估随机森林和 eXtreme Gradient Boosting (XGBoost) 算法在花生产量估计中的有效性.
  • 证明这些模型在提高花生育种计划效率方面的实用性.

主要方法:

  • 无人驾驶飞行器 (UAV) 用于花生表面特征的高通量表型化.
  • 从无人机图像中构建了多时代生长曲线 (天花板覆盖,高度).
  • 来自生长曲线的潜在现象型为预测产量提供了随机森林和XGBoost模型的信息.

主要成果:

  • 随机森林模型实现了花生产量的高预测精度 (R2 = 0.93).
  • 极端梯度提升 (XGBoost) 模型也证明了有效的收益率预测 (R2 = 0.88).
  • 这两种模型都被证明是对基因型的分类有价值的,有助于在育种管道中的选择过程.

结论:

  • 机器学习模型,特别是随机森林和XGBoost,显示出预测花生产量的巨大潜力.
  • 基于无人机的表型化与机器学习相结合,可以大大提高花生育种计划的效率.
  • 这些方法有助于识别优秀的基因型和过表现不佳的基因型.