菜 (Spinacia oleracea L.) 在室内受控环境中使用农业4.0的生长模式
Cesar Isaza1, Angel Mario Aleman-Trejo1, Cristian Felipe Ramirez-Gutierrez1
1Cuerpo Académico de Tecnologías de la Información y Comunicación Aplicada, Universidad Politécnica de Querétaro, Carretera Estatal 420 SN, El Marqués 76240, Querétaro, Mexico.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
这项研究开发了一个封闭式农业系统,使用数据科学和机器学习精确模拟菜的生长. 多项式回归准确地预测了菜叶和茎的发展,优化了受控环境农业.
科学领域:
- 控制的环境 农业 农业
- 植物科学 植物科学
- 计算建模计算建模
背景情况:
- 对于像菜这样的营养丰富作物的日益增长的需求需要先进的农业解决方案.
- 目前的农业系统需要改进的计算模型来准确预测植物生长.
- 在城市农业中优化蔬菜生长解决了粮食安全和资源效率问题.
研究的目的:
- 开发一个封闭的农业系统,用于种植和模拟菜 (Spinacia oleracea L.).
- 应用数据科学,机器学习和数学建模来准确预测植物生长.
- 评估计算模型在优化控制环境农业方面的有效性.
主要方法:
- 建造了一个带有LED照明,自动灌和环境控制的封闭生长系统.
- 收集了60天的温度,湿度,基板湿度和光谱数据.
- 利用多项式回归模型,根据收集的数据预测菜的生长模式.
主要成果:
- 多项式回归模型在预测菜生长方面表现出有效性.
- 叶子长度预测实现了0.158.8的最小平均平方误差 (MSE).
- 叶子宽度和茎长度模型显示高精度与低MSE值,表明可靠的可预测性.
结论:
- 开发的封闭式农业系统和建模方法对于菜种植是有效的.
- 计算模型,特别是多项式回归,可以在受控环境中准确预测植物生长情况.
- 这项研究有助于优化资源利用,并通过先进的农业技术加强粮食安全.
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