基于视觉和传感技术的Tieguanyin Oolong茶的发酵度预测模型
Yuyan Huang1, Jian Zhao1, Chengxu Zheng1
1Institute of Digital Agriculture, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China.
Foods (Basel, Switzerland)
|April 15, 2025
概括
这项研究开发了机器学习模型来监测乌龙茶发酵过程. 数据融合和优化显著提高了自动茶叶生产的准确性.
科学领域:
- 食品科学与技术 食品科学与技术
- 农业工程 农业工程
- 机器学习应用 机器学习应用
背景情况:
- 乌龙茶发酵质量控制依赖于主观的感官评估.
- 有效的茶叶生产需要客观的实时监测方法.
研究的目的:
- 开发和优化机器学习模型,用于评估Tieguanyin乌龙茶发酵度.
- 为了评估单一功能,多源功能融合和算法优化的有效性.
主要方法:
- 使用传感器收集水损失,气味,颜色和质感的数据.
- 应用支持向量回归 (SVR),随机森林 (RF) 和长短期记忆 (LSTM) 模型.
- 利用数据融合技术和Sparrow搜索算法 (SSA) 来进行优化.
主要成果:
- 单一功能型号实现了高达0.959.9的R2值.
- 数据融合模型显著提高了性能 (R2升至0.991).
- 优化SSA模型,特别是Fusion-SSA-LSTM,显示出最好的准确性 (R2高达0.994).
结论:
- 多源功能融合增强了乌龙茶发酵评估.
- 搜索算法优化进一步提高了模型精度.
- 开发的模型允许在线实时监控自动化Tieguanyin乌龙茶生产.
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