Open access peer-reviewed chapter

An IoT-based Immersive Approach to Sustainable Farming

Written By

Pratik Ghutke and Rahul Agrawal

Submitted: 07 May 2022 Reviewed: 17 May 2022 Published: 19 July 2022

DOI: 10.5772/intechopen.105449

From the Edited Volume

Irrigation and Drainage - Recent Advances

Edited by Muhammad Sultan and Fiaz Ahmad

Chapter metrics overview

124 Chapter Downloads

View Full Metrics


Despite the fact that the agricultural process is more data-driven, exact, and intelligent than ever before, the reality is that today’s agriculture industry is more data-driven, precise, and intelligent than ever before, regardless of public perception. Virtually every industry has been altered by the rapid expansion of Internet-of-Things (IoT)-based technologies, including “smart agriculture,” which has transitioned from statistical to quantitative methodologies. Such large advancements are upending conventional farming practises and offering new chances in the middle of numerous issues. A new paper looks at the promise of wireless sensors and the Internet of Things in agriculture, as well as the challenges that may occur when these technologies are integrated with traditional farming methods. Using Internet of Things (IoT) devices and communication protocols, wireless sensors utilized in agriculture applications are fully investigated. Sensors for soil preparation, crop status, irrigation, insect and pest detection, and other agricultural applications are on the list. From sowing to harvesting, packing, and transportation, this technique is explained. This article also discusses the use of unmanned aerial vehicles for agricultural monitoring and other useful purposes, such as crop yield optimization. When feasible, cutting-edge IoT-based agricultural ideas and systems are presented. Finally, we highlight present and future IoT trends in agriculture, as well as possible research challenges, based on this comprehensive analysis.


  • internet-of-things (IoTs)
  • smart agriculture
  • advanced agriculture practices
  • sensors

1. Introduction

Agriculture is the economic backbone of India. Since agriculture is our major source of sustenance, life would be impossible without it. The farmer needs to labour every day of the week to produce the harvest, which lowers his revenue, so he must look for alternative sources of food, especially since horticulture is becoming less popular. Mechanization is so critical in the rural cycle. Accordingly, this work proposed a framework so that livestock farmers could productively carry out their horticultural practices from remote locations while providing fewer ideal opportunities for farmland. In this framework, all the equipment works alone with the help of sources of information from sensors that are continuously checking the rural land, and ranchers can screen whether everything is going well or some activity should have been done. The entire cycle is controlled and monitored by a programmable regulator. Solar primarily based power is the maximum considerable source of electricity inside the whole рlаnts. Solar based electricity is not most effective а reaction to the contemporary electricity emergency, but additionally а sort of energy that is related to weather. The solar era is the era of efficient use of solar energy. Solar chargers (solar powers) are momently used to power street lights and water radiators.

1.1 Problem statement

The unеxресted drop in the price of solar chargers is increasing their use in various fields. This innovation is used in horticultural water supply systems. Inside the cutting-edge nation of energy emergency in India, а solar powered water system infrastructure can be an inexpensive option for farm animals’ farmers. it’s far а conductive to imparting green energy that releases strength when the underlying is estimated. Water device design is а dangerous water detection method for the vicinity or soil that bureаuсrасy the primary basis of our yield shape. Water for the most element should be made available within the fields or through pits. This framework will lessen the responsibility of the rancher and hold enough soil pleasant for better development. From that factor on, the development was viable in that it introduced а ranch water system into the field, which quickly killed units. This meсhаniсаl fin is the entire structure of an electric water system washed out of the field. In term of GSM, there are two important developments in the water supply system. “GSM” is an arbitrary and basic moderator or handler. Global System for Mobile Соmmuniсаtiоns (GSM) is а standard used to address infrastructure for mechanized data соlleсtiоn. Nowadays the agriculturist is focused on increasing output while keeping expenses down. This strategy necessitates an innovative approach to handle the problem and boost output and profitability while minimizing food production’s environmental impact.

1.2 Research gap

Precision agriculture in the cultivation field necessitates several ecological criteria that indirectly analyze the aforementioned issue. However, because the intra-field variability in sugar beet production and quality is unknown, it must be assessed in terms of soil qualities and microclimate conditions. The crop environment’s observed variability can therefore be managed by tailoring inputs to the places where they are needed. As a result, the precision agriculture model has been used and advanced in this study to track crop growth while taking into consideration field changes air temperature, soil moisture, soil type and other factors. Precision agriculture improves agricultural earnings and resource usage by reducing the use of traditional management practices.

1.3 Research objective

Precision farming has an impact on yield-based crop development depending on the soil type. Precision agriculture techniques have the potential to improve the Indian agricultural economy. The suggested research focuses on identifying limiting factors that have an impact on crop output. Providing precise water-based soil conditions predictions.

1.3.1 Irrigation system in India

In India, rural areas hold 18% of the US debt (GDP) and attract 49% of the workforce. According to records, 2022 of the military will be deployed in agricultural areas. 0205,000 siphons are continuously delivered to rural areas. Production of waste water devices, including siphon sets, uncontrolled water intake, unplanned water supply systems, water and energy savings, water loss and lack of expe.

Table 1 shows the effectiveness of different yields in India and the world. Unlike the general use of the world, India has а low yield potential. This is due to a decrease in the use of water in the water system as a result of the development of various misfortunes,

Cotton4.697.3611.4 (China)
Maize1.94.614.6 (Israel)
Oilseeds0.871.874.09 (Germany)
Onions10.717.846.8 (USA)
Paddy2.973.98.9 (Egypt)
Potato19.615.948.6 (Belgium)
Pulses0.590.94.6 (Netherland)
Soya0.952.343.58 (Turkey)
Sugarcane69631.17 (Zimbabwe)
Tomato16.526.861.9 (USA)
Wheat2.762.887.9 (Germany)

Table 1.

Crop productivity (MT/ha).

As shown in Figure 1. The repetition of dry season is increasing steadily, adversely affecting the usefulness of the crop. The dry season after that may be а repetition of the dry season, it is no shaggy dog story. The recurrence of drought in exceptional regions of India is proven in determine three. Streamlining water system productivity and electricity use is a vital take а look at inside the current water device state of affairs. Controlling using water sources is extremely fundamental to reap maximum core yield in higher energy use. Maintaining water device efficiency is extremely challenging. The effectiveness of а water device is the ratio of water utilized by the dust to the filth that is distributed through the water system. The effectiveness of the water device has to be cohesive, i.e., the water utilized by the filth and the contribution of water to the effluent via the water device ought to be same. If the performance of the water system is more prominent than the consent, the structure of the water device cannot satisfy the dirty water hobby. If the efficiency of the water unit is not as high as the clutch, this redundancy is supplied by using the water machine structure, which induces wastage of water.

Figure 1.

Availability of water for irrigation [1].

The water system’s structure is prone to large amounts of water catastrophe, reducing the effectiveness of the water system. What’s more, the water system productivity of the different structures is analyzed, given in Table 2.

Sources of lossesLeakagesEvaporationTotal
Canals & Branch Canals13.63.417.0
Water Courses in the Field16.04.020.0
Field application Losses13.23.316.5
Comparative Efficiency of Irrigation System
Surface 30–40%
Sprinkler 60–70%
Drip Irrigation 80–90%

Table 2.

Water losses in percentage in India.

Here in Figure 2, Experts are baffled by the frequency of droughts shown in official data. According to the agriculture ministry’s drought management section, Assam would experience a drought-like condition once every 15 years. However, the trend shows that the state has had three droughts in the last 9 years (including this year). Though Bihar and Uttar Pradesh are only vulnerable once every 5 years, the state has seen three drought-like circumstances in the last 5 years, while Uttar Pradesh has experienced two. The statistics for the southern states, particularly Karnataka and Andhra Pradesh, are considerably more mixed. While the states have had numerous droughts in the last 5 years, a study suggests that Karnataka is vulnerable once every 5 years and Andhra Pradesh twice every 5 years.

Figure 2.

Frequency of occurrence of drought in various parts of country.

1.3.2 Factors responsible for low productivity

Existence of Big Farmers: Even though India’s Zamindari system has been abolished, rural large farmers continue to play a shadow role. These large landowners control rent, tenure, tenancy rights, and other aspects of renters’ lives. As a result, the situation of tenants is deteriorating day by day. It is quite difficult to increase productivity using solely modern technologies in this type of tenure structure.

High Land-Man Ratio: Huge demographic pressures characterize Indian agriculture. According to the 2001 Census, over 72.2 percent of the entire population lived in rural areas, with agriculture employing nearly three-quarters of the total rural working population, or nearly 228 million employees (out of 310.7 million workers). Uneconomic land subdivisions occur as a result of population growth. All of these factors contribute to low production.

Rural Environment: In India, the rural social milieu is a major contributor to low productivity. Farmers in India are lethargic, illiterate, superstitious, have a primitive outlook, are conservative, unfit, and resistive to modern farming methods. Farmers’ marginal productivity in agriculture is zero, due to the family-based farming method. Credit and marketing facilities that are irregular and insufficient: According to Raj Krishna’s research, poor farmers are unable to effectively spend money in the land during the peak season of agriculture due to a lack of and insufficient availability of agricultural loans at a low rate of interest. Furthermore, crop marketing is regulated by intermediaries or touts. As a result of all of this, agricultural productivity was low. Modern technologies are lacking: In India, over 60% of cultivable land lacks irrigation facilities. In 2000–2001, only 75.14 million hectares (out of 87.94 million hectares) were irrigated. As a result, the green revolution’s ‘Package Program’ is ineffectual across the vast majority of India’s gross cultivated areas. Degradation of the Ecosystem: According to the Indian government, 329 million hectares (almost half of the country’s land) have already been degraded. This leads to a yield loss of 33 to 67%. Furthermore, 5% of the land has been ruined to the point where it can no longer be utilized.

Figure 3 shows that interest in this issue has grown over time as measured by the number of papers published every year. The smaller number of papers for 2019 is owing to the fact that the year was not quite through when the paper selection procedure was concluded. As a result, not all of the papers written in 2019 have been published.

Figure 3.

Annual number of articles presenting IoT irrigation systems that have been published [2].

1.3.3 Water management

There are various ways to distribute water irrigated agriculture is a type of agriculture that uses water as a source of input. The effectiveness of the various possibilities varies, in some circumstances, a specific technique for a given crop should be adopted. Irrigation can be done in a variety of ways, but they can all be categorized into the following groups: When it comes to how water is spread, we can examine the flood irrigation, (ii) spray irrigation, (iii) drip irrigation, and (iv) nebulizer irrigation. On the subject of sensing systems, we can discuss I unplanned irrigation, in which the amount of water is not calculated or estimated; (ii) planned irrigation, in which the water is supplied according to estimated demands over a year; and (iii) adhoc irrigation, in which the amount of water is estimated based on sensor readings. The great majority of the papers in this section propose to distribute water using pumps and valves in conjunction with sensors that assess ambient conditions in order to determine water needs. In this part, 83 of the 89 reviewed articles include detailed information about the planned irrigation system, while the remaining six just state that irrigation actuators are present (see Figure 4). There are 49 articles that just indicate that their system has motors/pumps (40 papers) or valves (nine papers) without providing any additional information. 19 of the studies that provide additional information use sprinklers (the most common irrigation technique) [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], eight utilize drip irrigation [22, 23, 24, 25, 26, 27, 28, 29, 30], two propose sprayers [22, 31], and the rest use highly specialized irrigation systems or it can be used on multiple systems [32]. Three papers [16, 18, 27] suggest using a fogging system in conjunction with the main irrigation system, whereas two papers [17, 28] suggest using fertigation in their systems.

Figure 4.

Number of papers that propose different irrigation systems [2].

1.3.4 Major applications

Every aspect of traditional agricultural methods can be significantly transformed by incorporating cutting-edge sensor and Internet-of-Things (IoT) technologies into farming practises. Smart agriculture has the potential to reach previously unimagined heights thanks to the current seamless integration of wireless sensors and the Internet of Things. By utilizing smart agriculture approaches, IoT can help improve answers to many traditional farming difficulties, such as drought response, yield optimization, land suitability, irrigation, and insect management. Figure 5 depicts a hierarchy of critical applications, services, and wireless sensors in smart agriculture applications. While key examples of how modern technology might aid in improving overall efficiency at various levels have previously been covered.

Figure 5.

A hierarchy of applications, services, and sensors exists for smart agriculture [33].

The Internet of Things (IoT) is beginning to affect a wide range of sectors and companies, spanning from manufacturing to health, communications, energy, and agriculture, in order to reduce inefficiencies and improve performance across all markets. When you think of the word, two words come to mind: [34, 35]. Current applications appear to be simply scraping the surface of IoT’s potential, with the full extent of its influence and applications still to be seen. Given the recent increase, we may expect IoT technology to play a vital role in a number of agricultural applications. This is due to the capabilities of the Internet of Things, which include basic communication infrastructure (used to connect smart objects to sensors, vehicles to user mobile devices via the Internet), as well as a variety of services such as local and remote data collection, cloud-based intelligent information analysis and decision making, user interfacing, and agriculture operation automation. Such people have the power to change agriculture, which is currently one of our economy’s least efficient sectors. Figure 6 displays the important technology drivers in smart agriculture, whereas Figure 7 depicts the major technological implementation roadblocks.

Figure 6.

Major challenges in technology implementation for smart agriculture [33].

Figure 7.

The agricultural industry’s key technological drivers [33].

1.3.5 Demand for water

The water demand of the irrigation system is determined by estimating the amount of water required for best crop output. The estimated crop evapotranspiration (ETc) is used to determine the water demand; however, estimating the ETc requires knowledge of the reference evapotranspiration (ET0). ET0 was defined by Dorenbos and Pruitt [36] as a result of the total amount of under ideal conditions, water evaporated from the soil and a large area of grass-covered ground transpired a large amount of water (vigorous development and unrestricted access to water). The Penman–Monteith equation is the most extensively utilized approach [37], was used to calculate ET0, as illustrated below [2].


The ET0 value is measured in millimeters per day, Rn is the net solar radiation incident on the crop surface, G is the soil heat rate (MJ/(m2day)), is the psychrometric constant (kPa/C), u2 is is the steam pressure slope, expressed in kPa/C. is the wind velocity recorded at a height of two metres, es is the saturated steam pressure, and ea. is the actual steam pressure, all in kPa. Agrometeorological stations measured all climate factors to determine ET0, which is dependent on wind speed, sun radiation, air temperature, and relative humidity.

ETc was calculated using ET0 and the crop coefficient as inputs (Kc). The type of crop, the climatic circumstances, the soil’s distinctive features, and the vegetative phase are all taken into account by Kc. The CNR (Chilean Irrigation Commission) bulletins [38, 39], as well as the paper headed “Reference Evapotranspiration, for the Determination of Water Demands for Agriculture in Chile” [40], tabulate values for each species and growth phase. Eq. (2) was used to compute crop evapotranspiration, ETc, where ET0 was changed based on the crop coefficient:


The crop coefficient is Kc, while ET0 is measured in millimeters per month (millimeters per month). The monthly net water demand was calculated using the Eq. (ND). ND is calculated using the difference between ETc and the crop’s effective rainfall (Pe). ND refers to the water required by the crop’s roots from the irrigation system.


The United States Department of Agriculture’s Natural Resources Conservation Service (NRCS) developed a method for calculating Pe based on real rainfall [36]. It was estimated in this study using the monthly average of actual rainfall data from the national agroclimatic network (Agromet) [41]. Because any irrigation system water losses must be compensated, the irrigation system must provide more water than the net water demand (ND). Certain security elements were also implemented to ensure that the crop received at least the ND. The effects of deep percolation and surface runoff are factored into the drip irrigation system’s application effectiveness (Ea), which was determined to be 90% efficient. Two further elements that influence water demand are the washing requirement (RL) and the coverage coefficient (Kr). The minimal amount of percolation water required to maintain a constant soil salinity and avoid an increase in salinity that could stymie crop development is known as RL. Water does not require to be applied to the entire anticipated surface of the crop when Kr is used. The value of Kr is determined to be less than or equal to unity. Equation shows the water requirement [2].


The irrigation schedule specifies how frequently and for how long water must be provided to the crops. The irrigation frequency interval and volume of water provided are determined by the amount of water kept in the root zone of the crops and how quickly it is consumed. The soil texture, soil structure (water percolation), effective root zone depth, crop type, and crop growth stage all influence irrigation frequency [3]. For high-frequency watering requirements, a short interval is defined (one, two, or more days). The goal is to maintain a consistent soil humidity [4]. The annual irrigation schedule, which detailed the frequency of irrigation for each month, was presented by an irrigation consultant. The daily irrigation demand (RID) in liters was calculated using Eq. (5) once the irrigation calendar was defined.


where Di is the number of irrigation days each month and Ac is the number of hectares of land covered by the crops. Ac was calculated using Eq. (6), which took into consideration the surface of each plant frame (PF) as well as the quantity of plants (Nplants)


The length of time (ti) for which an irrigation system may run in order to provide enough water to meet the needs of the crops was determined using Eq. (7).


where qe is the volume flow rate supplied by the emitters in liters per hour, and Ne is the number of emitters [2].

1.3.6 Irrigation System’s electricity demand

Drip irrigation, a water-saving irrigation technique that distributes water to crops through a pressurized network of valves, pipelines, and emitters, is one of the most widely used irrigation systems. The irrigation system pump is chosen based on the irrigation system head (necessary pumping pressure) and the amount of water that the crops demand. The irrigation system head takes into account the elevation head, the pressure drop due to friction in the pipes and singularities (i.e., valves), and the required working pressure by the emitters. The pump’s electrical demand remains constant since the pumps in this study deliver a constant volume flow rate. Other research [5, 6, 42] suggested using a variable speed pump to modify water flow in response to changes in solar radiation, allowing for an enhanced irrigation regime. A control system that aligns water supply with solar radiation could aid energy optimization [7], particularly in off-grid environments; however, this option is not explored in this study. The optimal design achieves the lowest overall cost, which includes the operational cost (electricity cost) of pumping, which lowers as the pressure drop decreases, as well as the capital cost of the irrigation system. Pipe friction and singularity losses in valves and fittings are used to compute the pressure drop. The pressure reduces as the pipe diameter increases, cutting the operational cost; nevertheless, the capital cost climbs in lockstep. Reduced pressure loss can also be helped by selecting the right emitters and filters.

The pumping system was designed to manage the worst-case scenario, which occurred during the peak water demand month. The operational characteristics of the pump were determined using the pump characteristic curve, which was produced during an experimental standard test. The pump characteristic curve provided information on the system head (H), pump efficiency (), and electrical power required by the pump (Wp) as a function of the pumped volume flow rate of water (Q). Eq. (8) was used to calculate Wp:


where ηp denotes the efficiency of a mechanical pump and m denotes the efficiency of an electric motor. In general, ηp was between 90 and 95 percent, and m was between 45 and 65 percent. Some high-efficiency pumps can attain up to 85 percent total pump efficiency (ηp ηm). Eq. (9) was used to compute the daily electricity requirement (Ed) once Wp was estimated (from Eq. (8):


1.3.7 Solar PV system design

Irrigation water demand is frequently seasonal, throughout the year, the PV system generates electricity. The solar PV system should be able to deliver the electricity required by the irrigation system in order to ensure a uniform distribution of the volume flow rate of water required by all crops. Reliable data on solar resources is required for proper PV system design. In this investigation, the Solar Explorer, an online tool developed by Chile’s Ministry of Energy [10], was used. The Solar Explorer’s solar PV model is used in this study. It’s based on a Sandia National Laboratories model, which is outlined in the reference. Solar radiation data for each unique location was also collected using the Solar Explorer, which was then incorporated in the solar PV model. The reference goes into great detail on the radiation database and its accuracy. Internet of Things Research: Key Technology and Applications In this paper, the author discusses the importance of IoT and RFID. With proper administration and dependable transmission, IOT can connect all items anywhere, at any time. There are several strata to be found.

  1. Layer of Access: Data is transferred from the sensing layer to the network layer via this layer.

  2. Network Layer: To pool the knowledge resources of the network.

  3. Layer of Middleware: To deal with real-time data processing.

  4. Layer of Application: To combine the functions of the bottom system. In this work, RFID technology is utilized to distinguish enemy aircraft by machine. RFID devices can also be used for inventory control, transportation, high security, and high irresponsibility. One of RFID’s most essential elements is antenna technology.


2. Common architecture designs for IoT irrigation systems in agriculture

This section will provide an overview of the most popular architectures for these systems. In IoT irrigation management solutions, multi-agent architectures are widespread [43, 44]. These structures provide a distinction between the numerous components that make up its structure. The difference is typically made depending on the architectural strata in which the elements are housed. For example, nodes higher in the hierarchy may act as a broker for nodes lower in the hierarchy [43]. The most of of designs are divided into layers or functional blocks that represent the main tasks that must be done [45]. These blocks or layers are considered generic and are found in the majority of IoT irrigation management system architectural designs. The essential components of these architectures are devices, connectivity, services, administration, applications, and security. IoT systems are made up of devices that are put in a specific location and may perform activities including detection, monitoring, control, and action. In order to convey the essential data, the devices must have interfaces that allow them to communicate with other devices. The information gathered by various sensors will be treated as a whole, and the results will be applied to various actuators. The data collected and the actions taken must then be sent between the devices. The use of communication protocols is required for this task. In the majority of circumstances, different communication protocols are used on the same IoT system in order for it to work together. Services may be required to complete tasks such as device discovery, device control, and data analysis. The user can interact with the system using the programmes. The user will be able to see data acquired through monitoring as well as data extracted once it has been processed utilizing the applications. On numerous occasions, the user can execute actions that he considers relevant to the scenario presented by the data, and the actions can also be performed automatically.

Finally, the security of the system may be considered. The three layers of IoT architecture have typically been believed to be perception, network, and application. After several research, an intermediary layer was built between the network and application levels. In cloud and fog computing environments, this layer, also known as the service layer, is used to store and process data. For the past few years, authors such as Ferrández-Pastor [46] have proposed a new architecture based on four layers: objects, edge, communication, and cloud. In their current architectural proposals, the authors use the edge layer to locate critical apps and perform basic control activities. According to [46], cloud (internet/intranet) can also include Web services, data storage, HMI interfaces, or analytic applications. An illustration of the architecture models is shown in Figure 9. These designs in Sensors 2020, 20, x 34 of 48, include devices, communications, services, administration, applications, and security. IoT systems are made up of devices that are put in a specific location and may perform activities including detection, monitoring, control, and action. To transfer the essential data, the devices must have interfaces that allow them to communicate with other devices. The information gathered by numerous sensors will be processed in general, and the results will be applied to various actuators. The observed data as well as the response actions must then be sent between the devices. Communication protocols are required for this task. In the majority of circumstances, different communication protocols are used on the same IoT system in order for it to work together. Services may be required to complete tasks like device discovery, device control, or data analysis. The programs enable the user to interact with the system. The user will be able to visualize information collected through monitoring as well as information taken from data after it has been processed using the applications. On numerous occasions, the user can take actions that he considers important to the scenario presented by the data, and these actions can also be taken automatically. Finally, assess the system’s security. Traditionally, the three layers of IoT architecture have been thought to be perception, network, and application. Following several research, an intermediary layer between the network and application layers was built. In cloud and fog computing environments, this layer, also called the service layer, is used to store and process data. For the past several years, authors like Ferrández-Pastor [46] have proposed a new architecture that is built on four layers: objects, edge, communication, and cloud. In these current architectural methods, the authors employ the edge layer to locate critical apps and perform basic control operations. According to [46], cloud (internet/intranet) can also include Web services, data storage, HMI interfaces, or analytic applications. Figure 8 shows a representation of the architecture.

Figure 8.

Evolution of the layered model in IoT architecture [2].

Both 3-layered [43, 47] and 5-layered [48] designs are accessible in the assessed IoT systems for irrigation. The sensor nodes and actuators are usually found in the lowest layer. The middle layer has a gateway and is concerned with data transport. Finally, the third layer is often responsible for data storage and analysis. Cloud services, databases, and applications are common examples of third layers. The Internet of Underground Things [33] is considering an innovative approach to IoT deployments for precision agriculture. In-situ sensing, wireless communication in underground environments, and the interaction between architectural features like sensors, machinery, and the cloud are all identified as functions by the authors. In the case of IoUT, sensors are implanted underground. Wireless communication between above-ground and beneath devices was examined by the researchers. The route loss link between above ground and subterranean devices achieved −80 dBm over a distance of 50 metres. The distance between underground devices for −80 dBm was roughly 10 m. The authors also explore the impact of soil moisture on route loss.

2.1 Recommendations for putting a smart agriculture irrigation system in place

In this section, the researcher has presented an architecture suggestion for an IoT irrigation system. To ensure the optimal functioning of the IoT irrigation system for precision agriculture, the architecture should provide interoperability, scalability, security, availability, and robustness. Following a thorough analysis of other researchers’ work, we have divided our architecture concept into four tiers, as shown in Figure 9, which we refer to as devices, communication, services, and applications. Furthermore, the communication and services levels should solve management and security concerns at the same time.

Figure 9.

Architecture proposal for an IoT irrigation system for agriculture [2].

The first layer is the Device layer, which includes all of the devices that will perform detection, monitoring, control, and action functions. There would be four types of nodes in total. The water quality would be checked at the water monitoring node to verify if it was suitable for crop irrigation. The soil monitoring node would monitor soil moisture, temperature, and other parameters, which would contribute in the irrigation schedule decision-making process. The weather monitoring node would measure air temperature and humidity, precipitation, luminosity, radiation, and wind parameters to facilitate decision-making. Finally, the decision-making process’s operations would be carried out by the actuator nodes. The second layer is the communication layer, which has three blocks. The Hop-to-Hop communication block allows for the design of data link layer technologies as well as frame transmission with device layer data. In order to reach far-flung sites, frames will be transmitted from this block to the network communication block. The routing function may be assumed in this block in mesh networks, such as 802.15.4 networks. The end-to-end communication block is responsible for delivering the capabilities of the TCP/IP model’s transport and application layers when communication spans various network contexts. Finally, the network communication block is responsible for network communication (routing), hop-to-hop communication at end-to-end blocks using IPv4 and IPv6 addresses, as well as ID resolution. It will also be in charge of overseeing service quality. The following layer is the services layer, which consists of three blocks. The services section includes IoT services as well as the ability to discover and search for them. Users are assigned services by the organization block based on their needs or available resources. Finally, in IoT-related business environments, service block modeling and execution will be triggered by application execution. Management and security are two elements that work on both the communication and service tiers. The management block is built using the fault, configuration, accounting, performance, and security (FCAPS) idea and architecture. This model represents the ISO Telecommunications Management Network [33]. The security block, which consists of four blocks, ensures the security and privacy of the systems. User and service authentication are handled by the authentication block. The authorization block is in charge of access control policies. Furthermore, access control decisions will be made based on access control regulations. To provide secure peer-to-peer communication, the key exchange & management block is used. Finally, the trust & reputation block is responsible for scoring the user and evaluating the level of trust in the service. The final layer is the application layer. It allows customers to interact with IoT technologies. This layer allows users to receive alarms, see acquired data in real time, and trigger actuators or actions that have not been configured automatically.

2.2 India’s IoT farming challenges

  • Inadequate knowledge about the local climate.

  • There aren’t enough sales of distribution data sources to go around.

  • Inadequate ICT infrastructure and illiteracy in the use of technology.

  • Farmers are under-informed on the advantages of smart farming.

  • Machinery for the workplace is expensive. There is a need for more manual labour. Keep a written record of all you have done.

  • a scarcity of market research competence and a research centre.

  • Changes brought on by the weather.

  • Agriculture is attracting the attention of young and educated individuals who have no desire to work in the industry.

2.3 Limitations

  • Agriculture is a phenomenon that is completely dependent on nature, and man can forecast or regulate nature, such as rain, drought, daylight convenience, and pest management, among other things. As a result, IOT systems are used in agriculture on a regular basis.

  • Smart agriculture is constantly looking for ease on the internet. The rural areas of developing countries were unable to meet these needs. Furthermore, the internet is sluggish.

  • Fault detectors or data processing engines can lead to erroneous decisions, resulting in waste of water, fertilizers, and other resources.

  • Smart farming, which is mostly based on instrumentation, necessitates that farmer understand and learn how to use technology. This could be the most difficult obstacle to overcome in implementing smart agriculture frameworks on a large scale across countries.

  • It conjointly has some problems that ought to be half tracked properly to achieve the total good thing about it.


3. Conclusion

Water management is crucial in locations where water is scarce. This has an influence on agriculture, as agriculture consumes a substantial amount of water. Water management approaches are being studied in light of growing concerns about global warming in order it is necessary to ensure that water is available for agricultural production and consumption. As a result, the number of studies on irrigation water saving has grown over time. In this paper, we present a summary of the current state of the art in IoT irrigation systems for agricultural. When it comes to deciding irrigation, soil, and weather water quality, we have discovered out what parameters are most strictly observed. The most widely utilized IoT and WSN crop irrigation nodes, as well as the most widely used wireless technologies, were also identified. The most current breakthroughs in the use of IoT technologies for crop management and irrigation were also presented. In addition, a four-layer crop irrigation management system has been developed. Based on the proposed architecture, we are creating a smart irrigation system that analyses water quality before irrigation.

As a result, we are expanding the system that can monitor crops in fields where humans cannot provide protection. We’re setting up a system in the field to keep track of valuable crops and ensure that all climatic requirements are met. In this place, we provide this type of system. As a result, this effective and dependable technology aids in agricultural monitoring. Aside from its core objective, the system makes a substantial contribution to global warming reduction. In a roundabout approach, plants’ normal instincts are impeded. Plants can also be protected from fire using this method. Crop destruction is reduced as a result. As a result, the ecological balance is maintained. The research develops both an automatic watering system and a field monitoring system. The results of this study would catapult farming to another developmental level.


  1. 1. Carvin D, Philippe O, Pascal B. Managing the upcoming ubiquitous computing. In: 2012 8th International Conference on Network and Service Management (CNSM) and 2012 Workshop on Systems Virtualiztion Management (SVM). International Federation for Information Processing; 2012. pp. 276-280
  2. 2. Fernández D, Sánchez P, Álvarez B, López JA, Iborra A. TRIoT: A proposal for deploying Teleo-reactive nodes for IoT systems. In: Proceedings of the International Conference on Practical Applications of Agents and Multi-Agent Systems, Porto, Portugal. MDPI; 21–23 June 2017
  3. 3. Fathima N, Ahammed A, Banu R, Parameshachari BD, Naik NM. Optimized neighbor discovery in internet of things (IoT). In: Proceedings of International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IGI Global Publisher; 2017. pp. 1-5
  4. 4. Doorenbos J, Pruitt WO. Guidelines for Predicting Crop. Water Requirements; FAO Irrigation and Drainage Paper. Vol. 24. Rome, Italy: FAO; 1977
  5. 5. Allen RG, Pereira LS, Raes D, Smith M. Evapotranspiración del Cultivo Guías Para la Determinación de los Requerimientos de Agua de los Cultivos. Rome, Italy: FAO; 2006. pp. 1-322
  6. 6. CNR. Boletín N_6: Coeficiente de Cultivo. Santiago, Chile: CNR; 2010. pp. 1-7
  7. 7. CNR. Boletín N_2: Programación de Riego Usando Estaciones Meteorológicas Automáticas. Santiago, Chile: CNR; 2010. pp. 1-14
  8. 8. Melillán, C. Evapotranspiración de Referencia. Para la Determinación de las Demandas de Riego en Chile. 2018. Available from: (Accessed: May 27, 2018)
  9. 9. Agromet Red Agroclimática Nacional. Available from: Accessed: July 1, 2017
  10. 10. Gonzalez-Andujar JL et al. SIMCE: An expert system for seedling weed identification in cereals. Computers and Electronics in Agriculture. 2006;54(2):115-123
  11. 11. Andri P, Siti MW, Azhari. Expert system model for identification pests and diseases of Forest tree plantations. International Journal of Advance Soft Computing and its Applications. 2017;9(2):204-216
  12. 12. Evangelos A et al. Integrating RFIDs and smart objects into unified internet of things architecture. Advances in Internet of Things. 2011;1:8
  13. 13. Chen XY, Zhi-Gang J. Research on key technology and applications for the internet of things. Physics Procedia. 2011;33:561-566
  14. 14. Rubeena MM, Denny J, Gokilavani M. Recent survey on IOT application: Smart agriculture. International Journal of Innovative Research in Advanced Engineering. 2019;6:MYAE10081. DOI: 10.26562/IJIRAE.2019.MYAE10081
  15. 15. Anand N, Er VP. Smart farming: IOT based smart sensors agriculture stick for live temperature and moisture monitoring using Arduino, Cloud Computing & Solar Technology. International Journal of Engineering Research & Technology. 2016
  16. 16. Maras V, Popovic T, Gajinov S, Mugosa M. Optimal irrigation as a tool of precision agriculture. In: 2019 8th Mediterranean Conference on Embedded Computing (MECO): IJERT. 2019. pp. 1-4
  17. 17. Algeeb A, Albagul A, Asseni A, Khalifa O, Jomah OS. Design and fabrication of an intelligent irrigation control system. Latest Trends on Systems.
  18. 18. Balakrishna K, Rao M, Kumar YHS. A WSN application to optimize the irrigation for horticulture crops in real-time using climatic parameters. Journal of Advance Research in Dynamical and Control Systems. 2018;10:199-207
  19. 19. Baoanh TN, Tan S-L. Real-time operating Systems for Small Microcontrollers. IEEE Micro. 2009;29:30-45
  20. 20. Arumugam SS, Ganeshmurthi M, Annadurai R, Ravichandran V. Internet of things based smart agriculture. International Journal of Advnaces in Computer and Electronics Engineering. 2018;33:8-17
  21. 21. Boonchieng E, Chieochan O, Saokaew A. Smart farm: Applying the use of node MCU, IOT, NETPIE and LINE API for a lingzhi mushroom farm in Thailand. IEICE Transactions on Communications. 2018;101:16-23
  22. 22. Talpur MSH, Shaikh MH, Talpur HS, et al. Relevance of internet of things in animal stocks chain Management in Pakistan's perspectives. International Journal of Information and Education Technology. 2012;2(1):29-32
  23. 23. Rawal S. IoT based smart irrigation system. International Journal of Computers and Applications. 2017;159:7-11
  24. 24. Guo T, Zhong W. Design and implementation of the span greenhouse agriculture internet of things system. In: Proceedings of the 2015 International Conference on Fluid Power and Mechatronics, Harbin, China. Research Gate; 5–7 August 2015
  25. 25. Khattab A, Abdelgawad A, Yelmarthi K. Design and implementation of a cloud-based IoT scheme for precision agriculture. In: Proceedings of the 28th International Conference on Microelectronics, Giza, Egypt. Research Gate; 17–20 December 2016
  26. 26. Nawandar NK, Satpute VR. IoT based low cost and intelligent module for smart irrigation system. Computers and Electronics in Agriculture. 2019;162:979-990
  27. 27. Barkunan SR, Bhanumathi V, Sethuram J. Smart sensor for automatic drip irrigation system for paddy cultivation. Computers and Electrical Engineering. 2019;73:180-193
  28. 28. Parameswaram G, Sivaprasath K. Arduino based smart drip irrigation system using internet of things. International Journal of Engineering and Computer Science. 2016;6:5518-5521
  29. 29. Kumar A, Surendra A, Mohan H, Valliappan KM, Kirthika N. Internet of things based smart irrigation using regression algorithm. In: Proceedings of the 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India. IJESC; 6–7 July 2017
  30. 30. Kodali RK, Jain V, Karagwal S. IoT based smart greenhouse. In: Proceedings of the 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Agra, India. IEEE; 21–23 December 2016
  31. 31. Abidin SAHZ, Inrahim SN. Web-based monitoring of an automated fertigation system: An IoT application. In: 2015 Proceedings of the IEEE 12th Malaysia International Conference on Communications, Kuching, Malaysia. 23–25 November, 2015. pp. 1-5
  32. 32. García L, Parra L, Jimenez JM, Lloret J, Lorenz P. IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT Systems for Irrigation in precision agriculture. Surveys of Sensor Networks and Sensor Systems Deployments Sensors. 2020;20(4):1042. DOI: 10.3390/s20041042
  33. 33. Babu MSP, Murty NVR, Narayana SVNL. A web-based tomato crop expert information system based on artificial intelligence and machine learning algorithms. International Journal of Computer Science and Information Technologies. 2010;1(1):6-15
  34. 34. Daskalakis SN, Goussetis G, Assimonis SD, Tenzeris MM, Georgiadis A. A uW backscatter-morse_leaf sensor for low-power agricultural wireless sensor networks. IEEE Sensors Journal. 2018;18:7889-7898
  35. 35. Jain S, Vani KS. A survey of the automated irrigation systems and the proposal to make the irrigation system intelligent. International Journal of Computational Science and Engineering. 2018;6:357-360
  36. 36. Karimi H, Navid H, Besharati B, Behfar H, Eskandari I. A practical approach to the comparative design of non-contact sensing techniques for seed sow rate detection. Computers and Electronics in Agriculture. 2017;142:165-172
  37. 37. Mohanraj I, Ashokumar K, Naren J. Field monitoring and automation using IOT in agriculture domain. In: 6th International Conference on Advances in Computing & Communications, ICACC 2016, 6–8 September 2016. Vol. 93. Cochin, India: Procedia Computer Science; 2016. pp. 931-939
  38. 38. Mizunuma M, Katoh T, Hata SI. Applying IT to farm fields a wireless LAN. Special Feature. 2003;1:56-60
  39. 39. Balakrishna K. Fusion approach-based horticulture plant diseases identification using image processing. In: Chakraborty S, Mali K, editors. Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities. Hershey, PA: IGI Global; 2020. pp. 119-132
  40. 40. Balakrishna K, Rao M. Tomato plant leaves disease classification using KNN and PNN. International Journal of Computer Vision and Image Processing (IJCVIP). 2019;9(1):51-63
  41. 41. Balakrishna K. WSN-based information dissemination for optimizing irrigation through prescriptive farming. International Journal of Agricultural and Environmental Information Systems (IJAEIS). 2020;11(3):14
  42. 42. Zampieri M, Carmona García G, Dentener F, Krishna Gumma M, Salamon P, Seguini L, et al. Surface freshwater limitation explains worst rice production anomaly in India in 2002. Remote Sensing. 2018;10:244
  43. 43. González-Briones A, Castellanos-Garzón JA, Mezquita Martín Y, Prieto J, Corchado JM. A framework for knowledge discovery from wireless sensor networks in rural environments: A crop irrigation systems case study. Wireless Communications and Mobile Computing. 2018;2018:6089280
  44. 44. Sebastian S, Ray PP. Development of IoT invasive architecture for complying with health of home. In: Proceedings of the International Conference on Computing and Communication Systems (I3CS), Shillong, India. Wireless Communications and Mobile Computing; 9–10 April 2015
  45. 45. Ferrández-Pastor FJ, García-Chamizo JM, Nieto-Hidalgo M, Mora-Pascual J, Mora-Martínez J. Developing ubiquitous sensor network platformusing internet of things: Application in precision agriculture. Sensors. 2016;16:16-1141
  46. 46. Merezeanu D, Vasilescu G, Dobrescu R. Context-aware control platform for sensor network integration in IoT and cloud. Studies in Information and Control. 2016;25:489-498
  47. 47. Robles T, Alcarria R, Martín D, Morales A, Navarro M, Calero R, et al. An internet of things-based model for smart water management. In: Proceedings of the 28th International Conference on Advanced Information Networking and Applications Workshops, Victoria, BC, Canada. 13–16 May 2014
  48. 48. Ayaz M, Ammad-Uddin M, Sharif Mansour Z, Aggoune E-HM. Internet-of-things (IoT)-based smart agriculture: Toward making the fields talk, special section on new technologies for smart farming 4.0 research challenges and opportunities. IEEE Access. 2019;7:129551-129583

Written By

Pratik Ghutke and Rahul Agrawal

Submitted: 07 May 2022 Reviewed: 17 May 2022 Published: 19 July 2022