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使用机器学习方法,基于现象学预测小麦的植物生物质和叶面积,使用机器学习方法

Biswabiplab Singh¹, Sudhir Kumar¹, Allimuthu Elangovan¹

¹Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India.

Frontiers in plant science

|July 14, 2023

概括

这项研究使用RGB和NIR图像与机器学习准确预测小麦生物质和叶面积. BLASSO模型在实验中显示出稳定和高性能,有助于作物改进.

科学领域:

植物现象学植物现象学
遗传学遗传学是一个
机器学习机器学习

背景情况:

现象学对于理解基因型-表型关系至关重要.
剖析复杂的植物特征,如生物质生产,需要对生长阶段进行详细的量化.
以前使用RGB图像的生物质预测模型面临着实验稳定性的挑战.

研究的目的:

评估使用多式RGB和NIR图像与机器学习模型的使用,以进行小麦生物质和叶面积的非侵入性预测.
在不同的实验条件下识别稳定和准确的预测模型.
通过识别基于图像的关键特征来剖析生物质积累的遗传基础.

主要方法:

记录了小麦生殖质的RGB和NIR图像以及重组杂交系 (RIL).
提取了77个基于图像的特征 (i-Traits),分为建筑和生理特征.
应用了16个机器学习模型来预测生物量 (新鲜重量,干重量) 和叶面积 (射击面积).

主要成果:

使用RGB和NIR图像,生物质和叶面积特征的预测准确率约为90%.
在连续两年的实验中,BLASSO模型显示出稳定和高性能.
对于BLASSO的R平方值,新鲜重量和发芽面积高达0.96,干重量高达0.93.

结论:

多式成像和机器学习提供准确和稳定的小麦生物质和叶面积的非侵入性估计.
BLASSO模型是生物质相关特征的强有力的预测器.
识别关键的i-Traits及其遗传基础可以促进作物改进,以提高生物质生产.

关键词:

在NIR图像中,可以看到NIR图像.在 RGB 图像中使用 RGB 图像.高通量表型化 (HTP) 是一种高通量表型化.我的特征是I-特征机器学习是机器学习.射击区域的射击区域是一个射击区域.小麦小麦小麦小麦小麦小麦小麦.

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