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Soil salinity and the water crisis are imposing significant challenges to more than 100 countries as dominant factors of agricultural productivity decline. Given the rising trend of climate change and the need to increase agricultural production, it is crucial to execute appropriate management strategies in farmlands to address salinity and water deficiencies. Ground-based soil moisture and salinity sensors, as well as remote sensing technologies in satellites and unmanned aerial vehicles, which can be used for large-scale soil mapping with high accuracy, play a pivotal role in precision agriculture as advantageous soil condition monitoring instruments. Several barriers, such as expensive rates and a lack of systematic networks, may hinder or even adversely impact the progression of agricultural digitalization. As a result, integrating proximal equipment with remote sensing and Internet of things (IoT) capabilities has been shown to be a promising approach to improving soil monitoring reliability and efficiency. This chapter is an attempt to describe the pros and cons of various soil sensors, with the objective of promoting IoT technology in digital agriculture and smart farming.
Leibniz Institute for Agricultural Engineering and Bioeconomy, Germany
Siva K. Balasundram
Department of Agriculture Technology, Universiti Putra Malaysia, Malaysia
Abdullah Kaviani Rad
Department of Soil Science, School of Agriculture, Shiraz University, Iran
Muhammad Sultan
Department of Agricultural Engineering, Bahauddin Zakariya University, Pakistan
Ibrahim A. Hameed
Faculty of Information Technology and Electrical Engineering, Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), Norway
*Address all correspondence to: rshamshiri@atb-potsdam.de
1. Introduction
Drought and soil salinity are two of the world’s dominant abiotic stresses that severely restrict crop production, and it is expected that these challenges, along with accelerating climate change, will drive universal food insecurity [1]. In parallel, the 933 million people affected by the water crisis in 2016 are expected to increase to 1.693–2.373 billion people in 2050 [2] as a consequence of the global increasing population and an additional rise in water demand [3]. Despite the fact that agriculture receives more than 70% of water supplies [4], most governments lack precise irrigation water usage statistics [5]. Irrigation processes waste 25–30% of fresh water, resulting in a loss of $14 billion. Therefore, proper water management is critical [6, 7]. Otherwise, growers are compelled to use saline water for irrigation owing to water shortages that lead to soil salinity expanding [8]. Soil salinity is one of the most damaging agents to cropland in more than 100 countries [9, 10]. Salinity affects more than 25% of the world’s terrestrial lands and a third of the world’s irrigated fields [11]. The total area of saline soils is reported to be 1060.1 million hectares, with climate change driving this estimate to rise [12].
The factors that cause natural or primary salinity include parent materials and saline minerals in the soil. Anthropogenic factors, such as conventional irrigation techniques and weak drainage systems, cause secondary salinity [13]. Complications of the accumulation of excess soluble salts, specifically chloride sulfate [14] in the root zone of plants [15], include reducing plant growth, groundwater pollution, and diminishing soil fertility, ultimately degrading farmlands [11, 16]. High soil salinity decreases crop productivity, especially vegetables, which are extremely sensitive during the ontogeny stage. The salinity tolerance of most vegetables is low [17]. Castanheira et al. [18] observed that along with increasing the salinity of irrigation water to 5 ds.m−1, the average solute concentration in the root zone reaches a level higher than the corn tolerance. Moreover, high salinity negatively impacts the physicochemical and biological traits of soils, such as the diversity and abundance of microbes and animals [19], consequently leading to adverse consequences for farmers’ livelihoods, and the regional and national economy [20]. The financial loss caused by salinity-induced land degradation in 2013 was estimated at $441 per hectare, equivalent to $27 billion annually [21]. Hence, improper water management and subsequent salinization threaten the sustainability of agriculture [22]. Many investigations have been carried out to cope with the obstacles of water deficit and salinity. Irrigation water management strategies and drainage techniques as the most prevalent solutions [23, 24], specifically in arid and semiarid regions, can face numerous challenges such as high costs and inefficiency [11, 19, 25]. Notwithstanding investments in countering the salinity spread, farmers are still challenged by the consequences of soil salinity [26]. Food security is threatened whenever efficient management actions are not exerted to maintain agricultural production [27]. Figure 1 shows the salinity and water stress situations in various regions of the world.
Figure 1.
Map of global soil salinity and water stress status. Adapted from [28, 29].
The uninterrupted monitoring of soil moisture and salinity in agriculture is accepted in order to limit water and salinity crises. After sea level temperature, soil moisture as a significant climatic determinant is the second prominent factor influencing evapotranspiration, sensible surface heat, and latent heat flux, as well as water, carbon, and energy cycles on a global scale [30, 31]. Changes in soil moisture alter both agricultural and municipal soils [32]. This essential variable is employed in order to improve weather forecasting, rainfall estimation, drought monitoring, and landslide and flood prediction [31]. There are multiple methods to measure soil moisture, which is directly correlated with irrigation efficiency [33]. Indirect methods estimate soil moisture using a gravimetric, gamma-radiation probe, neutron probe, and porous blocks based on gravitational sampling or time-domain reflectometry (TDR) in a small soil bulk. Direct methods also evaluate soil moisture using weighted moisture in vitro [34, 35]. In most circumstances, soil moisture is not directly measurable; instead, it is measured indirectly through moisture-related characteristics [36]. TDR is extensively employed to identify the soil water content according to the connection between dielectric constant and moisture content [34]. However, a study in the USA ascertained that only 1.2 out of 10 farms use soil moisture sensors for irrigation planning. This quantity is lower globally due to a lack of systematic support, sensor inconsistency, and high costs, resulting in the rejection of these systems [37]. A thorough understanding of soil salinization processes is also required for long-term soil and water management [38], which employs conventional electrical conductivity (EC) sensors [39]. In addition to salinity, EC is an indicator of soil health and nutrient availability for plants [40]. Salinity sensors are designed according to three electromagnetic (EM) phenomena: (i) electrical resistance, (ii) electromagnetic induction, and (iii) reflectometry [41]. The most accurate commercial method of EC estimation is the application of electromagnetic induction, including four electrodes [42]. EM38 is a noninvasive soil electromagnetic induction sensor that can measure EC at 120 cm above the soil and assess the soil nutrient situation [43, 44]. Although soil salinity modeling in farmlands using EC sensors is crucial to assess crop yield and prevent productive soil loss [45], measuring apparent soil EC (ECa) is needed for calibration with the actual content of salts in the laboratory [46], which is not economically cost-effective.
The soil mapping of spatial and temporal variations in soil properties is presumably the most affordable and beneficial approach to front salinity and watering issues. In this regard, Mashimbye et al. [47] evaluated the role of hyperspectral or satellite data in soil mapping potential applications. Satellite technologies make it easier to measure salinity and moisture variables, and as a result, they can provide soil characteristic data instantly, quantitatively, and affordably [48]. For instance, the launching of Sentinel satellites upgraded free data access for users [49], including advanced facilities for earth monitoring [50]. Though the remote sensing of soil properties presents extensive coverage for spatial distribution, multispectral data have limited capabilities, such as low spatial resolution due to spectral and spatial division [35, 50]. A spatial description of soil salinity is essential for salinity management in agriculture [51]. On the other hand, conventional techniques for evaluating soil characteristics are costly and time-consuming [52] (Figure 2); the question of whether proximal sensors or aerial sensors are more efficient for controlling soil moisture and salinity levels in farmlands arises.
Figure 2.
Positive and negative attributes of proximal and aerial sensors.
Facing the growing demand for food and sustaining water resources needs irrigation optimization employing advanced technologies such as soil moisture sensors [53]. Technologies such as drip watering, proximal sensors, and remote controllers for water management have joined the farming sector owing to agricultural development and subsequently rising demand for freshwater [54]. Considering that implementing a systematic irrigation plan for farmers is practically complicated, digital instruments effectively assist in accurate irrigation planning [55]. Furthermore, the proximal platform can be used to evaluate plant health [56]. Recent advances in electromagnetic moisture sensor technologies have facilitated automatic irrigation scheduling [57], which enhances water-use efficiency. These sensors are divided into active and passive instruments, which are applied for crop yield assessment and watershed management in digital agriculture [58]. In another classification system, soil sensors can be divided into resistive or capacitive sensors. Resistance-based sensors are easy to use and inexpensive. However, error sources affect their accuracy and efficiency [59]. Jusoh et al. [60] reported that the resistive sensor operates defectively in sandy loam and clay loam soils owing to low bulk density and high organic matter.
As efficient machines, capacitive soil moisture sensors are affordable for reducing water costs and wastage and computerized scheduling of irrigation [57, 61]. Capacitive probes and electronic TDR soil moisture sensors with in situ measurement have easy use, high accuracy, and fast data retrieval that are extensively used to monitor soil moisture changes in fields and predict drought, particularly in arid and semiarid lands. Furthermore, these instruments are applied for hydrological flux calculations, modeling runoff infiltration, and calibration of remote sensing data. However, precisely estimating moisture content is not convenient due to the spatial diversity of soils and the spatiotemporal heterogeneity of soil water content at high depths [36]. It is further challenging to measure moisture using discrete and wire-based instruments in fields with high vegetation diversity and different hydrological properties, which cause numerous obstacles in analysis and control systems, specifically at broad geographical scales [55, 62]. Since some sensors retrieve various data from a farm, it is not possible to automatically turn on or off the federal irrigation system. Moreover, many users have reported fractures of the watermark rod during dipping or separating it from the soil (Figure 3). The low accuracy of some sensors, which have a high moisture detection limit and detect the soil as dry, directly challenged farmers. Therefore, there is a possibility of flooding the root zone and loss of plants in the event of inadequate knowledge of farmers. Hence, growers’ propensity to purchase sensors decreases. The cost of sensors determines their resistance and precision in heterogeneous ambient conditions [63]. A flawless calibration process is necessary to optimize the sensor’s accuracy. In order to improve the accuracy of the soil moisture sensor, Gonzalez-Teruel et al. [64] calibrated it on three different types of soil. According to Radi et al. [65], the soil moisture sensor SKU:SEN0193 is a low-cost commercial sensor that must be calibrated before being used on farms. Figure 4 shows the calibration process of a soil moisture sensor. Since different raw materials are used to make sensors, low-cost sensors have low resistance to adverse environmental conditions such as sunlight, strong winds, and wild animals. Therefore, it is challenging to achieve integrated systems on farms owing to the natural obstacles. A proximal network is high-priced due to the need for periodic servicing of sensor portions [66], which increases costs for producers. Given that experimental determination of soil moisture is a fundamental characteristic of agricultural operations [66, 67], cost-effective analysis of soil volumetric water content (VWC) is an important strategy for promoting sustainable agriculture through the use of computerized machines and Internet of things (IoT) development, particularly for smallholder farmers [68].
Figure 3.
Instances of different soil moisture sensor probes that are used for digital farming applications.
Figure 4.
Calibration of soil moisture sensor for different types of soil. Source: SunBot.de.
Significant advances have been made in technologies for assessing, mapping, and spatiotemporal monitoring of salinity on a field, regional, and national scale [10]. Generally, there are five methods for estimating salinity on a farm: (1) observing salts on the soil surface, (2) estimating EC in saturated soil extracts, (3) measuring in situ electrical resistance, (4) determining in situ EC by TDR, and (5) noninvasive EC measurement using EM sensors [69]. The EM38 sensor is one of the most popular sensors in agriculture and consists of a receiver and a transmitter coil with a distance of 1 meter from each other, which are connected at the opposite end of a nonconducting rod, which measures salinity and other soil properties such as nutrient level and clay bulk [70]. This sensor is comfortable to use, and users can interpret its data after processing obtained images [71]. Slavich et al. [72] and Guo et al. [73] used EM38 data to determine soil salinity and barley tolerance to salinity and for digital soil mapping of spatiotemporal salinity changes. Hammam and Mohamed [74] mapped the spatial pattern of soil salinity in the East Nile Delta using geographic information system (GIS) and inverse distance weighting (IDW) techniques. Ding and Yu [75] reported that the obtained EC data from the EM38 sensor were significantly correlated with the experimental soil analysis in the laboratory. Guo et al. [76] recognized a significant correspondence between actual soil EC and sensor data (r > 0.9) by employing EM38 proximal technology. Additionally, EM38 is beneficial for prompt soil assessment before planting operations (Figure 5) [77]. Despite the speedy operation of this sensor, its vulnerability to metals and electromagnetic noise sources, such as power cables, can generate fluctuations in data registration [78]. The framework of the soil moisture proximal measuring procedure is outlined in Figure 6, along with three models of soil sampler robots.
Figure 5.
Instances of different portable pH and EC meters used for measuring soil salinity.
Figure 6.
Summary of the soil moisture measurement process by the proximal sensor and models of soil sampler robots. Adapted from [79, 80, 81, 82].
The integrated wireless sensor network (WSN) is designed to measure soil salinity and support automated irrigation systems [83]. With the WSN, numerous facilities are provided such as remote monitoring of soil fertility, crop water situation, and assistance to the irrigation system with reasonable costs, low energy consumption, and extended life [84, 85]. In a study by Sui and Baggard [86], WSN sensors automatically recorded soil condition data over the Internet every minute. The combination of WSN with the GIS in a study by Zhang et al. [87] proposed a soil moisture distribution map for accurate irrigation control. This system improves irrigation efficiency by decreasing freshwater loss and watering costs [88]. The precision of the data retrieved by the WSN depends on the system’s capability to hold the input voltage constant and the dependability of the calibration curves [89]. Though the WSN with fast data retrieval capability is a promising strategy in precision agriculture, barriers such as soil and canopy interference can affect data validity [79].
Monitoring soil conditions with remote sensing systems is a new approach that enhances productivity in digital agriculture [90]. Through the development of unmanned aerial vehicle (UAV) technologies [91, 92], it is now possible to retrieve soil property data with high resolution and low cost for mapping. UAVs reliably transfer soil characteristic data to computers, thereby playing an important role in precision agriculture [93]. When compared to satellites, UAVs have superior control and high spatial resolution [94]. Hu et al. [95] reported that UAVs using 62 hyperspectral bands afforded more reliable data for soil salinity prediction models than satellites, making UAVs a valuable machine for small-scale soil mapping. UAVs are also useful for assessing soil moisture in heterogeneous landscapes [96]. In addition to soil moisture, multispectral images of UAVs can be applied to map the distribution of water stress in crops (Figure 7) [98]. Although UAVs play a prominent role in precision agriculture, further attempts should be made to derive from data processing techniques and vegetation calibration in the future [99]. Moreover, UAVs face other challenges, such as limited flight time and stabilization, so future studies should concentrate on addressing these problems [100].
Figure 7.
The measurement process of soil moisture using UAVs. Adapted from [97].
3.2 Satellite-based remote sensing
High spatial resolution is necessary for analyzing soil moisture [101]. Thereby, satellites are the principal instruments for characterization and monitoring soil moisture with an accuracy of approximately 5 cm [102]. Ahlmer et al. [103] demonstrated that using satellite data enhances the reliability of flood forecasting. The microwave brightness temperature is sensitive to soil moisture content due to water affecting the dielectric constant [104]. In recent years, digital agriculture has made enormous progress in estimating soil moisture by applying microwave sensors. In contrast, advancements have been restrained owing to heterogeneities between satellite data resolution and hydrological scales, vegetation, and low microwave infiltration [105]. Satellite sensors are potentially designed to monitor vast regions; however, their spatial resolution depends on the microwave frequency, antenna size, and elevation. Most passive radiometers have a spatial resolution of 10 km, which is inapplicable for hydrological aims. Although microwave remote sensing drives many algorithms for calculating large-scale soil moisture, their low resolution is not appropriate for small scale [106]. Presently, the passive microwave retrieved resolution of soil moisture is about 25 km [107], and the low spatial resolution outputs, unreliable rainfall, and evaporation-transpiration data can make it challenging to estimate irrigation water demand [5]. Moreover, soil moisture data may not be available regularly. The spatial distribution of soil moisture is a prerequisite for agricultural and ecological management, while retrieving soil moisture data in heterogeneous landscapes is a significant challenge [108]. Heterogeneous landscapes generate irregularities in moisture measurements [109]. Consequently, merging surface reflectance data and auxiliary geospatial data can accurately estimate soil moisture, supporting precision agriculture strategies efficiently. Table 1 summarizes some investigations that measured soil moisture using a combination of proximal and satellite data.
Satellite
Application
Location
Result
Reference
ASCAT
Estimating soil moisture
Arizona (USA)
The geostatistical approach is beneficial to estimate soil moisture for network cells without data from satellite imagery.
Applying meteorological data to missing pixels of the satellite can enhance the accuracy of estimation and afford a comprehensive map of soil moisture in broad regions.
Some studies on the merged application of ground-based and satellite sensors to estimate soil moisture.
Remote sensing data can be applied to map surface soil salinity in broad regions [39], and the Landsat satellite has made it attainable to study soil salinity at different scales [118]. Wu et al. [119] reported that the overall accuracy of Landsat in soil salinity detection from 1973 to 2006 was approximately 90.2%. Combining proximal instruments with remote sensing systems is advantageous in precision evaluating soil salinity [120]. Bouaziz et al. [121] extracted 18 indicators from MODIS Terra data to improve salinity prediction patterns in northeastern Brazil and recognized a moderate correlation between EC and spectral indices. However, the limitations of using remote sensing data to map salt-affected areas include salt spatial distribution, temporal changes, and vegetation interference [122]. Moreover, it is challenging to estimate soil salinity through single-factor models [123]. Although remote sensing has numerous advantages over conventional proximal systems for mapping and predicting soil salinity [124], it is possible to determine the spatial variability of soil EC by local proximal sensor EM38 connected to GPS [125]. Casterad et al. [126] applied a combination of soil experiment data, proximal sensors, and satellites to investigate how soil salinity develops and distributes. Corwin [127] used proximal sensors and remote imaging to assess soil salinity at different scales; furthermore, Douaoui et al. [128] demonstrated that the regression-kriging approach combines remote sensing systems and ground network monitoring stations, thereby providing well-defined spatial and temporal monitoring of soil salinity. Eldeiry and Garcia [129] similarly reported that the modified kriging model presents the most reliable estimate of soil salinity by combining satellite and proximal data.
In a study by Fourati et al. [52], ordinary kriging with an average of 1.83 squares and a standard error of 0.018 had the most reliable performance for identifying and classifying saline soils. In an investigation by Fan et al. [130], the partial least squares regression model was applied to retrieve soil salinity from multispectral sensors, allowing salinity mapping with low cost and significant accuracy. Yahiaoui et al. [131] analyzed the topographic characteristics of the study area using Landsat 7 satellite data; accordingly, they created a multiple linear regression based on height and an adjusted soil salinity index that could predict soil salinity by 45%. Soil salinity modeling by satellite and proximal data in central Iraq revealed that models could reliably forecast salinity with 82.57% precision [119]. Therefore, modeling spatial soil salinity changes based on remote sensing data regression analysis is an economical, simple, and promising approach [132].
The precision agriculture approach employs new technologies to optimize farming inputs and ameliorate agricultural systems [133]. As one of the newest Internet-based technologies to have joined the agricultural sector, IoT is a type of intelligent sensor with software based on a web connection, applied to proposed purposes on farms. It drives modern agriculture toward the automatization of manual operations [134, 135], and its architecture is shown in Figure 8. The Wi-Fi module forwards the soil parameter data assembled by the sensors to the controller and processor [136]. Growers can inspect soil moisture, temperature, and pH data on an Android mobile phone using IoT technology [137]. Automated irrigation can also minimize human mediation [138] as an incentive to save more water [139]. Yamin et al. [140] demonstrated that a digital soil test kit connected to the IoT system could be used to dynamically evaluate changes in soil elements. Moreover, IoT can help optimally control greenhouse conditions [141]. Shamshiri et al. [142] applied a systematic approach to automatically retrieving and processing greenhouse condition data in order to enhance tomato yield. Divyavani and Rao [143] could receive moisture sensor data using the Android mobile phone. Payero et al. [144] controlled soil moisture in a field through a mobile-based IoT system. The WSN system proposed by Shylaja and Veena [85] dispatched soil fertility circumstances to the mobile phone that are beneficial for fertilizer recommendation.
Figure 8.
Deployment of hybrid data logger with Wi-Fi connectivity for IoT monitoring of soil moisture in berry fields. Source: SunBot.de.
Figure 9 demonstrates a solar-powered hybrid (Wi-Fi, LoRa, data logger) soil moisture and salinity sensors that were deployed in commercial berry fields in Germany. This device benefits from an onboard memory module for logging the measurements before transmitting the data via Wi-Fi and LoRa. It should be noted that due to the rising salinity trend caused by climate change, these devices are required for the precision monitoring of soil salinity in small and large scales [128]. Evaluating salinity-affected zones combats global climate change and prevents water resource loss [145]. Soil mapping is crucial for determining positional salinity levels and promoting appropriate management strategies for saline land restoration [146]. Therefore, combining remote sensing systems and EM38 sensors has provided an accurate soil salinity assessment approach, which is necessary to prevent further land salinization [76]. Future studies should concentrate on advancing remote sensing technologies for soil properties and the integration of salinity maps [147]. The measurement of soil moisture is critical in predicting drought and warning of natural disasters. Recently, many attempts have been made to address the development of soil moisture measurement facilities [148]. Launching advanced satellites promotes new innovative research approaches and encourages the development of new systematic empirical techniques for measuring soil moisture [149]. Non-cost-effectiveness plus inaccessibility to soil characteristics is one of the most significant constraints of precision agriculture [150]. Future soil moisture sensors should have high precision, low cost, and nondestructive features. Prospective research should also include the creation of specialized sensors for specific situations [33]. Using soil probes is critical for the most efficient and cost-effective use of water and chemical fertilizers; thus, numerous experiments on soil health indicators, such as water-holding capacity, salinity, temperature, pH, and soluble gas concentrations, are carried out [151]. However, high costs and the complex protection of sensors prevent the development of digital farming technologies, especially in rural regions [152].
Figure 9.
Wireless monitoring of soil moisture with solar-powered modular sensors.
For the purpose of downloading data from multiple sensors, a standalone software application shown in Figure 10 was developed by Adaptive AgroTech to interface with the sensors’ controllers via multiple serial COM ports as well as to execute commands and set custom configurations. The software also provides users with other features such as downloading log files of the sensor performance (i.e., battery and clock status, or historical parameters) or uploading the stored data to a cloud server. In addition, users can set labels to each node for simultaneously reading and writing log files from multiple devices and store the data on local memory cards. The Adaptive AgroTech Port Logger was developed in C# programming language environment and the Microsoft dot Net Core technology and can be operated on Microsoft Windows, Apple macOS, and Linux operating system. It should be noted that the MS-DotNet is a free open-source software for cross-platform development that supports various languages, such as C#, C++, and VB.NET. These features have provided a cost-effective and flexible solution for the future improvement of the Port logger. To have the best result and optimum performance, the software uses multithreading technology to execute parallel routines such as listening to multiports and executing more than one task at a time. Each thread defines a unique flow of control. As soon as the port logger engages in complicated and time-consuming parallel operations, it automatically sets different execution paths or threads, with each thread performing a particular task.
Figure 10.
Adaptive AgroTech Port Logger software for simultaneously downloading data from multiple sensor nodes under windows and Linux operating system.
For the purpose of a visual comparison between air temperature, soil temperature, and soil surface moisture, sample data from the hybrid data logger shown in Figure 9 that were collected every 10 minutes for 13 days in March 2021 are plotted in Figure 11. These plots validate the sensitivity of the sensor for the continuous monitoring of agricultural field and for planning precision irrigation practices in arid areas. The measurements of the hybrid data logger can be used as the feedback data for a decision support system or controller that activates the irrigation pumps based on air and soil temperature, soil moisture, hours of the day, and other field parameters. It can be seen from the plots of Figure 11 that during early morning hours, soil surface experiences more moisture (due to the morning dew) in the entire 13 days of the experiments compared to the mid-day hours. It can also be seen that the hybrid datalogger did not miss a single measurement during the experiments, even when the air temperature was below the freezing point.
Figure 11.
Plots of air temperature, soil temperature, and soil surface moisture during 13 days of experiment for performance evaluation of an adaptive AgroTech hybrid data logger.
As two global challenges without national borders, soil salinity and the water crisis endanger sustainable agricultural production through decreasing farmland productivity and crop yield [153, 154]. These principal abiotic stresses significantly restrict crop productivity by inhibiting metabolic activities and disturbing the ionic balance. Water deficits caused by osmotic stress severely reduce the crop yield, which drives considerable economic losses for farmers. Hence, monitoring their changes in farmlands using sensors is crucial due to the significant regional or national financial loss caused by drought and salinity. Despite soil moisture and salinity probes effectively measuring soil parameters, inefficient performance in broad fields plus the high cost and low accuracy have accelerated the application of new remote sensing technologies. Satellites and UAVs have the possibility of monitoring these variables on a broad scale. However, low spatial resolution, difficulty of use, the need for technological operators, and lengthy data processing make them unpopular with farmers, particularly in rural regions. In addition to remote sensing, IoT technology combines sensor systems and web-based software that transfers soil moisture and salinity data to a computer or mobile phone. While precision agriculture is gradually developing new technologies in farmlands, more extensive investigations are needed to address the challenges of agricultural digitalization.
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Written By
Redmond R. Shamshiri, Siva K. Balasundram, Abdullah Kaviani Rad, Muhammad Sultan and Ibrahim A. Hameed
Submitted: 06 February 2022Reviewed: 23 February 2022Published: 22 June 2022
Open access peer-reviewed chapter
