Division of Water Resources Engineering, Department of Building and Environmental Technology, Lund University, Box 118, SE-221 00 Lund, Sweden.
Department of Hydraulics and Sanitation, São Carlos School of Engineering, University of São Paulo, CxP. 359, São Carlos 13566-590, Brazil.
Sensors (Basel). 2023 Feb 22;23(5):2451. doi: 10.3390/s23052451.
Automated soil moisture systems are commonly used in precision agriculture. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 tested under lab and field conditions. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor response in dry soil. During the second stage of testing, the sensors were coupled to a low-cost monitoring station and installed in the field. The sensors were capable of measuring daily and seasonal oscillations in soil moisture resulting from solar radiation and precipitation. The low-cost sensor performance was compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors provide single-point information with high reliability but at a high acquisition cost, while low-cost sensors can be acquired in larger numbers at a lower cost, allowing for more detailed spatial and temporal observations, but with medium accuracy. The use of SKU sensors is then indicated for short-term and limited-budget projects in which high accuracy of the collected data is not required.
自动化土壤湿度系统在精准农业中被广泛应用。使用低成本传感器,可以最大化空间延伸,但准确性可能会降低。本文通过在实验室和实地条件下测试电容式传感器 SKU:SEN0193,比较了低成本和商业土壤湿度传感器在成本和精度之间的权衡。除了单独校准外,还提出了两种简化的校准技术:基于所有 63 个传感器的通用校准,以及使用干燥土壤中传感器响应的单点校准。在测试的第二阶段,将传感器与低成本监测站耦合并安装在现场。传感器能够测量太阳能辐射和降水引起的土壤湿度的日变化和季节变化。基于五个变量:(1)成本、(2)精度、(3)合格劳动力需求、(4)样本量和(5)预期寿命,对低成本传感器的性能与商业传感器进行了比较。商业传感器提供单点信息,可靠性高,但获取成本高,而低成本传感器可以以较低的成本获取更多数量,从而实现更详细的空间和时间观测,但精度中等。因此,在不需要收集数据的高精度的短期和预算有限的项目中,可以使用 SKU 传感器。