*2.4.2 Type of soil moisture sensors: Frequency domain reflectometry*

Frequency domain reflectometry (FDR) sensors estimate soil water level using the dielectric properties of soil, which are highly dependent on moisture content. The


#### **Table 2.**

*Soil matric potential (SMP) for 30%, 50%, and 70% of soil water depletion for different soil types [4].*


**Table 3.**

*Example of dielectric permittivity.*

changes in the dielectric permittivity correlate with changes in the circuit frequency, which also correlates with soil moisture level. As soil moisture content increases, the dielectric permittivity increases. **Table 3** shows an example of dielectric permittivity for different materials.

Common FDR sensors are the EC-5 and 10-HS manufactured by Meter Group (Pullman, WA, USA), which provide the value as volumetric water content (cm<sup>3</sup> / cm3 ). This sensor comes with factory calibration, which should be validated with the site-specific soil condition. A previous study described the correction equations of soil moisture sensors for different types of soils to improve the accuracy of the reading [5]. Most of the cases, the sensors provide the trends of moisture level changes, which allows understanding of how the water flows in soils. This information is helpful to determine the field capacity of the field and evaluate the irrigation practice, such as under- or over-irrigation.

#### *2.4.3 Soil moisture sensor: Placement and installation*

There are several considerations when installing soil moisture sensors. Sensors should be installed between plants in a representative area within a row. For the drip irrigation system, ensure to install the sensors in the wetting zone, as shown in **Figure 2**. The figure clearly shows the wetting zone in the soil. For center pivot irrigation systems, avoid placing the soil moisture sensors close to a wheel track or lane edge. Additionally, sensor locations that are not representative of most of the field, such as the top of the hill, low areas, and the edge of the field should be avoided. The placement of the soil depth of the sensors is important. The user should be considered based on the crop's root depth (**Figure 3**). For example, the corn root

**Figure 2.** *Demonstrated wetting zone under the drip irrigation system using a blue dye (Benton Harbor, MI, USA).*

**Figure 3.** *Installed FDR soil moisture sensors in a blueberry field (west olive, MI, USA).*

system typically grows up to 36-inch soil depth. The recommended soil moisture placements are 6-, 18-, 24-, and 36-inch depths. Typical effective root zone moisture extraction depths for crops are described in **Table 4** [6]. It is important to monitor these effective moisture extraction soil depths to improve the accuracy and precision of irrigation scheduling. Soil moisture sensors measure only a small volume of the soil surrounding sensor. Therefore, the sensor installation technique is critical in obtaining accurate readings.


#### **Table 4.**

*Effective root zone water extraction depth in unrestricted soils [6].*

#### **2.5 Plant-based irrigation scheduling method**

A common method of plant-based irrigation scheduling is using an sap flow sensor. Sap flow is the measurement of the water, nutrients, hormones, and anything else in the water that flows through the stem of a plant. The sensors use a heater and thermocouples to measure the amount of heat carried by the sap. This can then be converted to sap flow in units of grams per hour. Once the sensors are installed and the parameters are set, the system will record and calculate the sap flow, which can be downloaded from the system at any time. The sap flow sensor has been previously used in woody plants or other herbaceous plants [7, 8]. A common sap flow sensor is Dynagage Flow 32-1 K Sap Flow system, manufactured by Dynamax (Houston, TX). **Figure 4** shows the installed Flow 32-1 K Sap Flow system in a potato field (Lakeview, MI). An example of sap flow sensor data is shown in **Figure 5**. The result shows that the transpiration started at 9:30 am and stopped at 8:00 pm. Based on the total amount of water used by the plant, irrigation scheduling can be developed to maintain adequate soil moisture levels for plant growth. This approach is similar to weather-based irrigation scheduling methods, but uses directly measured values of water uptake from the plant.

#### **2.6 IoT (internets of things) sensor technology**

Agricultural technology industry is moving toward Agriculture 4.0, which includes the internet of things (IoT) and the utilization of big data to improve practices and efficiencies. Many microcontroller systems, such as Arduino and ESP 32, can be used

*Irrigation Scheduling Methods: Overview and Recent Advances DOI: http://dx.doi.org/10.5772/intechopen.107386*

**Figure 4.** *Installed Dynagage flow 32-1 K sap flow system in a potato field (Lakeview, MI, USA).*

**Figure 5.** *SAP flow data result from a potato field (Lakeview, MI, USA).*

in agricultural fields. Analog or digital soil moisture sensors can be connected to a microcontroller system to measure soil conditions. In addition to the soil conditions, other irrigation information, including water pressure, energy usage, irrigation system uniformity, and environmental conditions, can be measured using a microcontroller system. Many microcontroller systems allow sending data to a web server using Wi-Fi, cellular, or long range radio (LoRa) network system. The advantages of the remote monitoring system are that it allows monitoring the performance of data loggers and sensors and detecting any problems without having to visit the field. It also allows farmers to make timely farm management decisions. Michigan State University team

has developed LOCOMOS (IoT-based Low-Cost Sensor Monitoring System), which measures soil and environmental conditions in irrigated fields, including multiple depths soil moisture levels, leaf wetness duration, temperature, humidity, soil temperature, and precipitation. The data is collected every 15 minutes and sent to the LOCOMOS IoT cloud web service. Then the data output is displayed on the IoT dashboard website and a smartphone app. IoT-based irrigation management has been very positive to most farmers as they can see soil moisture status in realtime without visiting the field.

#### **2.7 Smartphone APP-based irrigation scheduling**

Numbers of smartphone apps are available for irrigation scheduling. A large number of irrigation scheduling decision support tools have been developed in recent decades, many of them available via mobile apps. For example, water irrigation scheduling for efficient application (WISE) was developed by Colorado State University as an irrigation scheduling mobile app that uses evapotranspiration data and the water balance method [9]. WISE collects weather data from Colorado Agricultural Meteorological Network (CoAgMet) and Northern Colorado WATER Conservation District (NCWCD) weather stations. Cotton SmartIrrigation App (Cotton App), developed by the University of Georgia and the University of Florida, is an evapotranspiration-based irrigation scheduling tool that estimates root zone. Soil water deficits based upon weather data, soil parameters, crop phenology, crop coefficients, and irrigation rates [10]. In addition to cotton, SmartIrrigation offers separate apps for vegetables, soybean, turf, avocado, strawberry, citrus, and blueberry [11]. The apps obtain meteorological data from Florida Automated Weather Network (FAWN) and Georgia Automated Environmental Monitoring Network (GAEMN). While these and other similar systems improve the ability to operationally monitor crop water needs, they primarily weather data-based and do not take in situ soil moisture observations or data into account. Within growing season, observations of soil moisture can be extremely useful in monitoring crop water usage and plant available water in the rooting zone. LCOOMOS-APP is developed by Michigan State University's Irrigation Team, which is an easy-to-use smartphone app that accounts for in-field sensor data for determining irrigation management decisions. LOCOMOS-APP connects with a IoT cloud web server, which collects sensor data from each infield LOCOMOS station. LOCOMOS-APP requires username and password to log-in. Once a user signs in, the app displays all registered devices to the specific user. When the user clicks a device, it will show available soil water (%) and recommended irrigation amount (in). Available soil water (%) value can help farmers when to irrigate. Recommended irrigation amount (in) value can help farmers how much to irrigate. Additionally, the app shows daily disease severity value (DSV) and cumulative DSV, and precipitation data. Cumulative DSV values can help farmers to apply fungicide in a timely manner. Overall, the advantage of the smartphone app-based irrigation scheduling tool is that it is an easy-to-use scheduling tool for farmers. This may increase the adoption of irrigation scheduling tools.
