雪茄叶的水分含量预测 基于堆叠组合学习模型的空气固化过程
Zhuoran Xing1, Yaqi Shi2, Kai Zhang1
1College of Tobacco Science, National Tobacco Cultivation and Physiology and Biochemistry Research Center, Key Laboratory for Tobacco Cultivation of Tobacco Industry, Henan Agricultural University, Zhengzhou, China.
这项研究引入了一种使用图像分析和堆叠组合学习的非破坏性方法,以准确预测空气固化过程中雪茄叶的水分含量. 这种方法为农业应用中的实时监控提供了可行的解决方案.
科学领域:
- 农业工程 农业工程
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 准确的水分含量测定对于在空气固化过程中保持雪茄叶的质量至关重要.
- 传统的方法往往是主观的,破坏性的,对于实时监控是不切实际的.
研究的目的:
- 开发一种非破坏性的,基于图像的堆叠组合学习模型,用于预测雪茄叶的水分含量.
- 评估模型的性能,并确定关键的预测特征.
主要方法:
- 在整个空气固化过程中收集了雪茄叶的前后表面图像.
- 提取了颜色和纹理特征,应用过,并使用重法来选择模型.
- 构建了一个堆叠组合模型 (MLP,RF,GBDT基础学习者;LR元学习者) 并应用SHAP进行特征贡献分析.
主要成果:
- 堆叠组合模型实现了高预测准确性 (R测试=0.989),超过了传统模型.
- 在SHAP分析中,前面表面特征 (45.5%) 和叶子特征 (38.5%) 是最有影响力的.
- 关键预测因素包括播出时间 (AP),a*f,Gf和ASMf.
结论:
- 开发的模型提供了一个可行的,可扩展的解决方案,用于实时,非破坏性监测雪茄叶的水分含量.
- 为类似的农业和食品干燥应用提供有效的技术支持.
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