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Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors
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科学领域:

  • 农业工程 农业工程
  • 土壤科学 土壤科学
  • 数字农业 数字农业

背景情况:

  • 传统的土壤采样是劳动密集型,昂贵,并且提供有限的空间分辨率.
  • 开发准确的数字土壤地图需要整合各种不同分辨率的数据源.
  • 精准农业需要高分辨率的土壤数据,以优化作物管理.

研究的目的:

  • 开发和评估数据驱动的,现场近距离的多传感器方法,用于数字土壤绘图.
  • 调查高分辨率传感器数据与较粗的实验室测量之间的尺度不匹配的影响.
  • 为了比较四种数据整合方法用于杏仁园中的土壤预测建模.

主要方法:

  • 设计了一个新的数字土壤核心 (DSC) 探测器,具有七个不同的传感器 (摩擦,力,介电,电阻,图像,声学,光谱).
  • 收集在现场的DSC传感器数据和空间上同位置的土壤核心,用于实验室分析.
  • 应用了部分最小平方回归 (PLSR) 来比较土壤预测模型的四种数据整合方法 (A,B,C,D).

主要成果:

  • 方法C,将实验室数据分配到一个层内的所有传感器数据中,性能优于方法A和B.
  • 方法D直接将高密度传感器数据与作物反应联系起来,绕过了实验室成本,并被证明是最具成本效益的方法.
  • DSC系统在大约60秒内提供120厘米深度的毫米尺度垂直土壤数据.

结论:

  • 该DSC系统使得高密度的土壤在现场进行表征,以创建农业数字双胞胎.
  • 直接将多传感器土壤数据与作物表现 (方法D) 联系起来,为精准农业提供了一个具有成本效益的解决方案.
  • 这种方法通过提供可操作的土壤-作物关系见解,促进了工业规模的精密农业.