一个机器学习管道从葡萄种植数据预测Pinot Noir葡萄酒质量:开发和实施
Don Kulasiri1, Sarawoot Somin1, Samantha Kumara Pathirannahalage1
1Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Lincoln 7647, New Zealand.
Foods (Basel, Switzerland)
|October 16, 2024
概括
通过机器学习,可以在收获前预测Pinot Noir葡萄酒的质量. 这项研究开发了一条管道,将葡萄园特征和葡萄成分与专家评价的葡萄酒质量联系起来,为种植者提供早期质量评估.
科学领域:
- 葡萄种植和葡萄酒学 葡萄种植和葡萄酒学
- 农业机器学习 农业机器学习
- 数据科学在酒中的应用
背景情况:
- 葡萄酒的质量与葡萄的质量密切相关,受葡萄种植习惯和气候的影响.
- 对葡萄酒质量的专家评估是耗时和昂贵的.
- 在采摘前预测葡萄酒质量,可以积极管理葡萄的质量.
研究的目的:
- 研究用于预测Pinot Noir葡萄酒质量的机器学习.
- 开发一个从葡萄园到葡萄酒质量指数的预测管道.
- 将葡萄种植参数与葡萄和葡萄酒的成分以及专家评估的质量联系起来.
主要方法:
- 开发了一种机器学习管道,集成葡萄园数据,葡萄成分和葡萄酒化学分析.
- 利用专家的判断作为葡萄酒质量的黄金标准.
- 创建了一个基于Web的应用程序,用于产量和质量预测.
主要成果:
- 通过使用葡萄栽培和葡萄成分数据,成功预测了Pinot Noir葡萄酒的质量.
- 证明葡萄园的特点可以预测葡萄的产量.
- 建立了葡萄/葡萄酒组成和专家评估的质量之间的联系.
结论:
- 机器学习提供了一种可行的方法,可以在过程的早期预测葡萄酒的质量.
- 开发的管道为葡萄园所有者提供了宝贵的工具,以预测葡萄酒的质量.
- 早期预测可以为优化葡萄生产和葡萄酒质量做出战略决策.
关键词:
皮诺诺瓦尔葡萄酒 (Pinot Noir) 是一种葡萄酒.葡萄是葡萄,葡萄是葡萄.机器学习是机器学习.管道管道管道管道管道管道葡萄种植是葡萄种植的一种方式.葡萄酒质量 葡萄酒质量收益率 收益率 收益率 收益率更多相关视频
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