Optimal sensors number and distances is crucial to obtain representative estimates of soil water availability at field-scale. Precision irrigation scheduling is based on actual site-specific real-time crop water requirements or measurements which can be logged and used to trigger irrigation when a threshold soil water content is reached. The need to increase water sustainability in agriculture has led to investments in precision irrigation techniques as well as a growing interest in phenotyping root traits for drought tolerance.
This work provides a framework for the replication and upgrading of a customized low cost platform, consistent with the open source approach whereby sharing information on equipment design and software facilitates the adoption and continuous improvement of existing technologies. Data recorded on the card were automatically sent to a remote server allowing repeated field-data quality checks.
The overall model (pooled soil data) fitted the data very well (R 2 = 0.89) showing a high stability, being able to generate very similar RMSEs during training and validation (RMSE training = 2.63 RMSE validation = 2.61). Empirical calibration curves were subjected to cross-validation (leave-one-out method), and normalized root mean square error (NRMSE) were respectively 0.09 for the overall model, 0.09 for the sandy soil, 0.07 for the clay loam and 0.08 for the sandy loam. Low cost high-frequency dielectric probes were used in the platform and lab tested on three non-saline soils (ECe1: 2.5 < 0.1 mS/cm). The system is based on an open-source ARDUINO microcontroller-board, programmed in a simple integrated development environment (IDE). The objective of this work was to design a low cost “open hardware” platform for multi-sensor measurements including water content at different depths, air and soil temperatures. The cost of instrumentation, however, limits measurement frequency and number of sensors.
Monitoring soil water content at high spatio-temporal resolution and coupled to other sensor data is crucial for applications oriented towards water sustainability in agriculture, such as precision irrigation or phenotyping root traits for drought tolerance.