卷积神经网络与XGBoost特征提取的融合,用于预测玉米中的多成分,使用近红外光谱学
Xin Zou1, Qiaoyun Wang2, Yinji Chen1
1College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China.
Food chemistry
|September 6, 2024
概括
一个新的XGBoost-CNN-TS-EN模型增强了近红外 (NIR) 谱学用于玉米和土壤分析. 这种方法准确地预测了多个成分,克服了用于改进农业应用的光谱数据挑战.
科学领域:
- 农业光谱学 农业光谱学
- 化学测量 化学测量 化学测量
- 在分析化学中的机器学习.
背景情况:
- 近红外 (NIR) 光谱对于农业分析至关重要,但从复杂的光谱数据中提取构成信息是具有挑战性的.
- 诸如宽吸收带和重叠信号等问题限制了玉米和土壤样本中化合物的直接定量分析.
研究的目的:
- 开发一种先进的模型,从原始NIR光谱数据中提取隐性特征.
- 提高定量模型的性能和准确性,用于预测玉米和土壤中的多种成分.
主要方法:
- 提出了一个一维的浅卷积神经网络 (CNN) 模型,与 eXtreme Gradient Boosting (XGBoost) 集成,用于特征提取.
- 利用XGBoost的叶节点功能,编码和重建以用于隐式特征提取.
- 引入了双参数的Swish (TSwish或TS) 激活功能和弹性网 (EN) 规范化,以提高CNN性能并防止过拟合.
主要成果:
- 开发的XGBoost-CNN-TS-EN模型实现了玉米成分的高确定系数 (R2):水分 (0.993),油 (0.991),蛋白质 (0.998) 和粉 (0.992).
- 该模型还准确地预测了土壤有机物质 (R2 = 0.992).
- 在公共NIR数据集上表现出卓越的稳定性,预测准确性和概括能力.
结论:
- XGBoost-CNN-TS-EN模型有效地解决了NIR光谱分析中的挑战.
- 这种方法显示了农业和环境样本中多种成分的精确定量分析的巨大潜力.
- 该模型的稳定性和高精度使其成为光谱应用的宝贵工具.
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