基于机器学习的分析,对水培养大豆的营养和水吸收进行了分析
Sambandh Bhusan Dhal1, Shikhadri Mahanta2, Janie McClurkin Moore3
1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA.
Scientific reports
|October 17, 2024
概括
这项研究通过分析营养素对吸水的影响来优化水培大豆的生长. 机器学习模型确定了关键营养素,增强了可持续农业实践.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 可持续农业 可持续农业
背景情况:
- 水栽培为传统农业提供了一个可持续的替代方案.
- 在水培系统优化中,数据驱动的方法缺乏.
- 营养管理对于水培作物产量和资源效率至关重要.
研究的目的:
- 开发基于数据的模型,用于预测水培养大豆的吸水量.
- 为了确定影响不同水培媒介水吸收的关键营养素.
- 优化营养度,以提高可持续农业的用水效率.
主要方法:
- 大豆是用水耕种种植的,,和的度各不相同.
- 进行了营养成分分析和统计分析,包括线性插值.
- 使用特征选择管道 (chi-squared,线性回归与RFE,ExtraTreesClassifier) 和非参数回归模型 (随机森林,支向量回归) 来预测水吸收.
- 为了解释模型预测,使用了夏普利添加式扩展 (SHAP).
主要成果:
- 随机森林回归器在霍格兰+和霍格兰+介质 (MSE分数分别为24.55和8.23) 中实现了对水吸收的强有力的预测性能.
- 支持向量回归在Hoagland+介质 (MSE 4.37) 中表现优越.
- SHAP分析阐明了个别营养素对水吸收预测的具体贡献.
结论:
- 这项研究为优化水培养营养溶液提供了重要的数据驱动洞察力.
- 这些发现支持开发更有效,更可持续的水培农业系统.
- 根据营养资料准确预测水的吸收,可以显著改善农业的资源管理.
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