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种子蛋白质含量估计使用基板顶部高光谱成像和注意力卷积神经网络模型

Imran Said¹, Vasit Sagan^1,2,3, Kyle T Peterson⁴

¹Department of Computer Science, Saint Louis University, Saint Louis, MO 63104, USA.

Sensors (Basel, Switzerland)

|January 25, 2025

概括

超光谱成像和机器学习准确地预测小麦种子蛋白质. 卷积神经网络 (CNN) 在小麦育种计划中显示出对自动化蛋白质估计的前景.

科学领域:

农业科学农业科学
频谱学是一种光谱学.
机器学习机器学习

背景情况:

小麦种子蛋白度是全球粮食安全和动物料的关键质量特征.
确定小麦蛋白质含量的准确和快速方法对于育种计划和质量控制至关重要.
传统的蛋白质分析方法往往耗时且具有破坏性.

研究的目的:

开发和评估可靠的高光谱成像方法来预测小麦种子蛋白质度.
将卷积神经网络 (CNN) 的性能与传统的机器学习模型进行蛋白质估计.
评估CNN分类对分类小麦蛋白质水平在育种应用中的有用性.

主要方法:

在可见,近红外 (VNIR) 和短波红外 (SWIR) 区域采用了基板上的高光谱成像.
计算机视觉辅助图像联合注册被用于对齐VNIR和SWIR光谱数据.
用注意力机制的CNN,随机森林 (RF) 和支持矢量机 (SVM) 回归模型被用于分析.

主要成果:

使用注意力机制的CNN在蛋白质含量预测方面达到0.70 (腹部) 和0.65 (背部) 的R2值.
射频模型在直接蛋白质含量预测方面表现优于CNN,达到0.77.2的R2.
在CNN分类中,有效区分了低,中,高蛋白度,R2为0.82.2.

结论:

超光谱成像与机器学习相结合,为小麦蛋白质分析提供了强大的,非破坏性的方法.
麦芽蛋白估计自动化有很大的潜力,特别是用于繁殖中的分类任务.
这些技术可以推进精密育种,优化种子分类,并指导有针对性的农业投入.

关键词:

3D CNN 建模 3D CNN 建模细心的模型,注意的模型.超光谱成像技术的使用.机器学习是机器学习.种子组成估计种子组成估计

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