Soil moisture content measurement using E-IoT wireless, ad hoc local network and NB-IoT technology
Zoltan Kiss MSc, Export manager & Head of R&D, Csaba Kocsis Design engineer, Endrich Bauelemente Vertriebs
Previously we explained in detail how NeoCortec´s low-power wireless ad hoc neo.mesh network technology can be integrated into the Endrich IoT ecosystem. This design offers a possible solution for collecting data from physical networks consisting of many sensors and transmitting them to a cloud-based database. This scalable, long-life, independent, battery-powered local network of smart sensor nodes is also excellent for agricultural tasks. Instead of direct sensor-cloud communication, which assumes multiple Internet connections, a secure sub-gigahertz local network with a single connection to the world wide web is sufficient. This solution can be used at a much lower cost and with greater reliability in agricultural or horticultural applications in harsh weather conditions. In this article, we would like to talk about such an interesting possibility of use.
An interesting field of use
Soil moisture detection is one of the key elements of environmental monitoring and agricultural and horticultural IoT solutions. It involves measuring the amount of water in the top layer of soil, which directly affects plant growth, irrigation strategies and our water conservation efforts. A variety of methods are used to detect soil moisture, from traditional techniques such as gravimetric measurements to modern technologies such as capacitance sensing or TDR (TDR). Accurate soil moisture sensing helps optimize irrigation schedules, prevent over- or under-watering, promote sustainable farming, and ultimately increase agricultural productivity while minimizing water wastage.
Supplementing traditional sensors and control electronics with modern wireless communication units and integrating the IoT is a popular and interesting task, which really makes sense if we want to get an idea of the soil moisture conditions in large plantations using computational methods from the data of many sensors. During his business trip to South America this summer, the author spoke with representatives of several companies where the lack of coverage of cultivated areas with telecommunication services makes it impossible or uneconomical to use, for example, smart sensors with a direct sensor-cloud connection (GSM, SAT, etc.). In such cases, a solution may be to organize the soil moisture sensors into an ad hoc wireless network that uses renewable energy sources and low consumption modems, which can cover a large area, providing the network with a single connection to the Internet to promote cost-effectiveness. The used gateway (one piece) can be a device operating in a property with Internet connection on the edge of the area, a gateway with SAT connection, which is organically integrated into the smart sensor mesh network.
The E-IOT platform combined with a low-power ad hoc local area network
In this case, we call for a low-consumption ad hoc local sensor network solution, for example, in our case we can use the NeoCortec Neo.Mesh protocol presented earlier. A large number of smart sensors can be connected with ultra-low power consumption to a local, sub-Gigahertz wireless network, where a single data concentrator/gateway with an Internet connection takes care of delivering the data to the Cloud DB via the cellular network, for example using LTE-M or NB-IoT, even with satellite or wired connection.
This modular sensor network infrastructure offers multi-point-to-point communication to the cloud through the LPLAN-LPWAN/WAN gateway.
E-IoT wireless soil moisture transmitter
As an experiment, we created a sensor that works on the capacitive principle, which transmits a signal proportional to the moisture of the topmost layer of the soil through the neo.mesh network. As we discussed earlier, the "mesh" of sensors placed outdoors at a distance of up to a hundred meters from each other is suitable for covering relatively large areas due to the applicability of a large number (thousands) of nodes without the data being lost, since each sensor also acts as a repeater and finds the data at the same time the way to the target gateway. During the day, the integrated solar panel provides energy for the electronics of each node, while at night, the rechargeable battery ensures continuous operation.
This technology offers continuous monitoring capabilities, making it valuable in precision agricultural and horticultural operations. However, factors such as soil composition and temperature can affect the accuracy of capacitive soil moisture sensors, requiring proper calibration for reliable results. For this purpose, there is also a temperature sensor measuring the soil temperature at the tip of our sensor, whose signal is also sent. Since this device is a simple soil moisture detector, no other chemical and physical sensors were placed on it, so it cannot measure the characteristics of the soil composition and structure. This is perhaps not so important, because we only want to indicate the changes and form an idea of the need for irrigation.
Smart sensor operation
After describing the measurement principle, we would like to show how easy the NeoCortec module can be used in smart sensors. The integrated ARM M0+ microcontroller eliminates the need for additional microcontrollers in order to ensure the lowest power consumption. The available firmware enables the simultaneous sampling (ADC) of several analog voltage signals, so the resistance of the NTC thermistor for detecting the soil temperature, as well as the voltage appearing at the output of the capacitive moisture detector, can be measured at the same time. The data is transferred in 20-second cycles in a short 21-byte payload message to the neighboring module, which forwards it to the destination (Gateway) via the mesh network. It is also possible to connect the module’s internal HTU21 temperature and humidity sensor via the I2C bus, so that, for example, the ambient temperature and humidity value can also be sent to the database in addition to the soil-specific data.