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基于高通量表型和机器学习的大豆产量的估计

Xiuni Li1,2,3, Menggen Chen1,2,3, Shuyuan He1,2,3

  • 1College of Agronomy, Sichuan Agricultural University, Chengdu, China.

Frontiers in plant science
|June 21, 2024
PubMed
概括

这项研究表明,结合多角度RGB图像和机器学习,特别是渐变增强决策树 (GBDT),可以显著提高大豆产量预测的准确性. 这为加速大豆育种计划提供了宝贵的工具.

关键词:
在 RGB RGB RGB 里面.估计估计估计的估计.机器学习是机器学习.豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆收益率 收益率 收益率 收益率

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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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