**2. Current status of irrigation water application methods**

In the past 50 years, irrigation has undergone tremendous changes driven by increased drought indices. From 1970 to 1985, automatic control of irrigation systems emerged in the United States of America as water scarcity became prevalent [11]. As a result, researchers were interested in developing ways of optimizing irrigation to achieve maximum yields. The advent of the Internet in 1989 spiked another interest in internet-based control of irrigation systems like data storage on the web became possible. And over the years, wireless sensor networks, sensors for monitoring and control, have since been developed to facilitate precision irrigation today. **Figure 1** depicts the evolution of irrigation from 1970 to 2020.

**Figure 1.** *Trends in irrigation since 1970s.*

*Smart Irrigation for Climate Change Adaptation and Improved Food Security DOI: http://dx.doi.org/10.5772/intechopen.106628*

**Figure 2.** *Irrigation methods [12].*

Eisenhauer and Martin [12] classified irrigation methods, as shown in **Figure 2**, ranging from micro-irrigation (surface drip irrigation, sub-surface drip irrigation, micro-spray tube irrigation, bubbler irrigation), sprinkler irrigation (solid-set system, portable sprinkler system, rain gun system, centre pivot system), surface irrigation (flood irrigation, furrow irrigation, check irrigation, border irrigation). Over the years, research has helped develop several tools and techniques for various production systems and ecologies that help save water and improve water productivity in agriculture. The advent of the Internet of Things (IoT) and wireless sensor networks has led to developing tools like soil, weather, and plant sensors that have improved monitoring that informs data-driven irrigation scheduling [13]. As depicted in **Figure 3**, the water use efficiency in these irrigation systems increases with the level of automation employed in the system.

#### **2.1 Micro-irrigation**

Water is delivered at low pressure through emitters by a distribution system located on the soil surface, beneath the surface, or suspended above the ground in micro-irrigation systems [12]. Irrigation water droplets are directed to the plant root zone via emitters, sprayers, or porous pipes, where it then infiltrates by gravity or capillary rise. The applicator's design reduces the water pressure within the delivery lines, resulting in a low discharge.

Micro-irrigation has received a lot of attention from agriculturalists, especially for high-value crops like vegetables, fruits, and nut trees. When determining whether to invest in a costly micro-irrigation system, a producer must consider whether the increase in crop production will be sufficient to pay for the system. The other concern is, can the system be built to filter the irrigation water to avoid the emitters from clogging? Another crucial choice is selecting emitters from the broad array available are suited for the specified function. Solving these difficulties will give an excellent irrigation system for decades. Micro-irrigation is defined by water being applied: at low rates, over longer durations, near the root zone of the plants, and at constant flow rate.

#### **Figure 3.**

*Water use efficiency and level of automation Adapted from [14].*

Precision water application with micro-irrigation systems has been reported to save up to 35–65% more water than standard flood irrigation systems, with a commensurate increase in the production of crops. Scholars [15–19] have all found consistent results confirming water savings and increased yields with micro-irrigation devices.

#### **2.2 Sprinkler irrigation**

Sprinkler irrigation is a system where water is uniformly applied over the crop canopy or soil surface identical to rainfall. With a sprinkler irrigation system, water is pumped and conveyed through high-density polyethylene (HDPE) pipes eliminating water losses through seepage and evaporation as in the case of surface canals under surface irrigation. Compared to conventional flood irrigation, sprinkler irrigation is more efficient, with irrigation efficiency of up to 80–90%. The performance of a sprinkler irrigation system solely depends on the design and selection of sprinklers. In irrigation design, it is recommended to select a sprinkler whose application rate is lower than the soil infiltration rate to prevent surface ponding and runoff.

The implementation of intelligent sprinkler irrigation systems involves high control precision, high intelligence, good dependability, simple operation, wired or wireless sensor network technology, and crop water demand data collection devices [20]. Fuzzy control logic, neural networks, and expert systems and machine learning, control technologies can be built for sprinkler irrigation [21]. Furthermore, intelligent precision irrigation systems are being built with remote transmission, monitoring, decision, and control functions. Therefore, it is vital to build automatic sprinkler

irrigation equipment, encompassing flow meters, solenoid valves, precision control equipment, and robotic equipment.

#### **2.3 Surface irrigation**

Surface irrigation is the oldest irrigation application method in the world. It involves the application of water over the surface of the land to supply moisture to the plant. Surface Irrigation includes furrow, border, basin and, check irrigation. Surface irrigation requires less pressure than sprinkler or micro-irrigation systems. Under surface irrigation, irrigation water is applied at the inlet end, and the water subsequently flows to the downstream end. A part of the water infiltrates as it progresses over the field. Water is frequently applied by gated pipelines, siphons, or gates. Surface irrigation may be an effective application technique provided the soils and fields are well adapted to this approach. But, it may be exceedingly inefficient if the soils and other elements are not carefully addressed while constructing and administering the system. The soil infiltration rate is very crucial in the proper functioning of surface irrigation systems. If the soil's infiltration rate is excessively high, the depth of water that infiltrates at the entrance will be significantly bigger than the downstream end. The land slope and its regularity also considerably effect surface irrigation. Slopes that are overly steep create undue runoff and erosion. Acceptable slopes are usually less than 2%. The regularity of the slope is also crucial so that water does not gather in depressions on the surface.

To improve smart irrigation scheduling in surface irrigation systems, Supervisory Control and Data Acquisition (SCADA) software has been deployed in surface irrigation systems to improve the programming, monitoring and operation of an entire scheme from a central point [22]. Composed of field equipment, programmable logic controller (PLCs) and/or remote terminal units (RTUs) communication networks, SCADA host software and, third party systems, these components are connected to minimize human intervention but also ensure convenient operation and delivery of irrigation water with just a click of a button. SCADA systems provide real-time monitoring, remote supervisory or automatic control, troubleshooting, and automatic data reporting and archiving capabilities [22].

#### **3. Smart irrigation scheduling approaches**

Irrigation scheduling is a systematic process of determining when and how much to irrigate. This depends on various factors, including daily crop water requirement, the effective root zone, and the available soil moisture. Irrigation scheduling can be done using one or all of the following approaches: Plant-based, soil-based, and weather-based irrigation scheduling approaches. Each of these is shown in **Figure 4**.

#### **3.1 Plant-based irrigation scheduling**

Plant-based irrigation scheduling is based on the physiological and phenological status of the plant [16]. The physiological condition depicts the water stress level, which is estimated from canopy temperature depression relative to air temperature measured by infrared thermometry. The calculation of the cumulative stress degree days and crop water stress index can be used for scheduling irrigation. Phenological stages can also be used to determine when to irrigate. For example, in wheat

**Figure 4.**

*Smart irrigation scheduling approaches. (a) soil sensors for irrigation scheduling, (b) plant sensor for detecting sap flow in plant stems, (c) ATMOS 41 for weather monitoring.*

cultivation, crown root initiation (CRI), tillering, jointing, flowering, and the grainfilling stage are critical stages of growth that need irrigation [16]. Failure to supply irrigation water at these critical stages of growth leads to low yields as water stress becomes severe. The cumulative effect of water stress is determined with this method, making it effective as a water stress indicator. This helps to capture the moisture reduction in the soil through evapotranspiration [23]. Direct and indirect measurement techniques are used to determine plant water status. Direct plant water stress detection methods include using sap flow sensors, xylem sensors, leaf sensors, and others. On the other hand, indirect methods involve thermal sensing, near-infrared spectroscopy, and aerial imagery [24].

Several authors have used plant-based approaches for irrigation scheduling [23, 25–27]. For example, King et al. [27] used data-driven models for canopy temperature-based irrigation scheduling of sugar beet and winegrape. The datadriven models developed by the authors estimated reference temperatures enabling automatic calculation of the crop water stress index for crop water stress assessment. Similarly, Meeks et al. [25] used leaf water potential monitoring system for irrigation scheduling of winter rye cover crop. The authors reported significant water savings and an improvement in crop yields.

#### **3.2 Soil moisture-based irrigation scheduling**

Soil moisture-based irrigation scheduling involves determining the soil moisture status within the root zone, and knowing the permanent wilting point [28]. Soil moisture measurements are compared to moisture thresholds to trigger irrigation. Soil moisture monitoring is done by time-domain transmission sensors, neutron probes, capacitance sensors, or granular matrix sensors. Soil moisture-based irrigation scheduling allows variable rate irrigation scheduling due to its ability to measure spatiotemporal variability in the field.

The use of soil moisture-based irrigation scheduling has been reported in the literature. For example, Pramanik et al. [28] developed an automated basin irrigation system based on soil moisture sensors for irrigation scheduling. The authors highlighted that the ideal position of sensors for shutting the system would be at 37.5 cm depth put at 25% length from the intake in larger soil moisture deficit situations and at 7.5 cm depth set at 75% length in low moisture deficit conditions. Consequently, the irrigation application efficiency was enhanced up to 86.6% using automation.

Advances in geospatial technologies like remote sensing and geographical positioning systems have made it possible to determine soil moisture from space over large land areas. Satellites in space are able to predict soil moisture by taking images *Smart Irrigation for Climate Change Adaptation and Improved Food Security DOI: http://dx.doi.org/10.5772/intechopen.106628*

and using inbuilt algorithms to assess the soil moisture deficit. This is then used to inform irrigation scheduling. Recently, Kisekka et al. [29] compared in-situ soil moisture measurements and remotely sensed measurements. The authors concluded that remotely sensed soil moisture presents an effective means of soil-moisture-based irrigation scheduling in large agricultural fields.

#### **3.3 Weather-based irrigation scheduling**

Weather-based irrigation scheduling involves the use of weather sensors to monitor and measure the parameters that affect evapotranspiration. Automatic weather stations with temperature, humidity, wind speed, rainfall, and air pressure sensors are installed in the field to collect field data around the plant. The data from the weather sensors are then used to estimate water demand using evapotranspiration models. The Penman-Monteith evapotranspiration model is used to determine the daily water demand [30]. Irrigation is scheduled after a pre-determined amount of evapotranspiration has occurred and this threshold varies with soil type, crop type, and stage of growth.

#### **4. Enabling technologies for smart irrigation**

#### **4.1 Communication technologies**

#### *4.1.1 Wireless sensor networks (WSN)*

The advent of the industrial revolution and recent advances in electronics and wireless communications have led to the development of smart sensors with low power and cost solutions [31]. WSN provides a high spatio-temporal resolution for monitoring soil and crop parameters via wirelessly-connected sensor nodes installed across the field [32]. **Table 1** presents some of the common wireless sensor communication technologies used in smart irrigation.

WSNs allow the surveillance of plants and soil and may enhance production, efficiency, and profitability. Effective monitoring and communication helps to reduce risks due to climate aberrations, water shortages, insect infestation, and other factors unfriendly to agricultural growth and development. WSNs are helping to attain improved reaction times owing to real-time sensing and communication in agricultural contexts. There are numerous techniques for irrigation scheduling utilizing wireless sensors. Depending on the threshold levels of temperature and soil water content, the gateway permits automatic activation of the irrigation system. However, some of the sensors, modules, and valves that are commercially available for installation in an irrigation network are very sophisticated and costly to be implemented for managing stationary irrigation systems reference.

#### *4.1.2 Internet of Things (IoT)*

The IoT technology is the evolutionary phase of the Internet that builds a global infrastructure uniting devices and people [46]. IoT has its origins in numerous previous technologies: ubiquitous information systems, sensor networks, and embedded computers. Adelodun et al. [47] identified IoT as an interesting paradigm with seamless integration of smart capabilities into physical devices for context-oriented services. It


#### **Table 1.**

*Most utilized communication technologies in smart irrigation.*

has previously been effectively utilized in agricultural systems to monitor and manage environmental variables [48–51]. The Internet of Things provides a platform for precision farming, digitally integrating several soil sensing devices and, context-aware sensors, custom devices. Data analytical implementations enhance farmers' ability to resolve intricate agricultural issues such as soil preparation, water feed estimation, yield prediction, etc., throughout the whole growing and harvesting cycle. Several mechanistic irrigation scheduling systems have been presented employing most key farmed land parameters-soil moisture content and climatic data to predict the amount of water at certain time intervals. Campos et al. [51] built an IoT framework (**Figure 5**) to provide services for smart irrigation, such as data monitoring, pre-processing, fusion, synchronizing, storing, and irrigation management augmented by predicting soil moisture.

*Smart Irrigation for Climate Change Adaptation and Improved Food Security DOI: http://dx.doi.org/10.5772/intechopen.106628*

**Figure 5.** *IoT system for smart irrigation Adapted from [51].*

#### **4.2 Decision support systems**

A decision support system for smart irrigation provides a framework for incorporating various tools and techniques for site-specific irrigation decisions. Commercial precision irrigation systems will thrive with improvement in robust and optimal decision support systems [52]. Decision support systems for irrigation scheduling/control can be categorized into two, namely: open-loop irrigation and closed-loop irrigation [14, 20].
