Wireless Sensor Network Assisted Intelligent Drip Irrigation ...

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Wireless Sensor Network Assisted Intelligent DripIrrigation System for Water Conservation inAgricultureV PANDIYARAJU  ( [email protected] )

St Joseph's Institute of TechnologyP SHUNMUGA PERUMAL 

Vellore Institute of Technology: VIT UniversityV EZHIL ARASI 

Anna University ChennaiARPUTHARAJ KANNAN 

Vellore Institute of Technology: VIT University

Research Article

Keywords: Intelligent Drip irrigation System, Sensors, Soil Moisture, Soil Moisture Records, Intelligent SoilMoisture Sensor Unit, Agriculture Control Station.

Posted Date: July 6th, 2022

DOI: https://doi.org/10.21203/rs.3.rs-1249079/v1

License: This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License

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AbstractAgriculture is the backbone for most of the countries in the globe including our India. Crop Yieldin�uences a country's economy and common public. Water is the most important factor in agriculture,water requirements for different crops. Now availability of water is a big challenge everywhere because ofthe climate change, No proper rain fall. Now a day’s our country faces severe drought. In agriculture,Conventional Solutions to Fight the Drought: (1).Storing water in dams, lakes and ponds, (2.)Restorationof the dams, lakes and ponds, (3).Drip irrigation system. Moreover, the Conventional Solutions are(1).Highly in�uenced by rain fall.(2).A long time & continuous process.(3).The method of irrigating cropsby saving water by allowing water to drip slowly to the roots of plants either onto the soil surface ordirectly onto the root zone. Existing drip irrigation systems are not completely automated - Need moreinvolvement of farmers. Farmer’s history of knowledge is used to take decisions about “duration ofirrigation” and “quantity of irrigation”. These may not be accurate always which leads to underwater orover water irrigations - Nowadays farmers purchase water for irrigation. There is a huge gap betweenactivities of agriculture laboratories and actual �eld conditions. High-technologies and intelligent systemsare not integrated in full-�edge to agriculture in the perspective of drip irrigation. In this paper, we proposean Intelligent Drip Irrigation System (IDIS) for water conservation. The proposed system consists of twoimportant components namely an Intelligent Soil Moisture Sensor Unit (ISMSU) and an AgricultureControl Station (ACS). The ISMSU is used to measure the soil moisture value accurately indifferent rootlength of the crop dynamically using a newly proposed multi-depth soil moisture measurement algorithm.These soil moisture values help the farmers to conserve water, electricity and to avoid both excess andshortage of water supply in irrigation. Moreover, a new protocol called ISMSU communication protocolalso proposed in this paper to collect the soil moisture data from the network of ISMSUs with less batterypower consumption. This power conservation in ISMSUs is useful to prolong the lifetime of the sensorspresent in the network of ISMSUs. The ACS is responsible for collecting the data from sensor nodes andto process them accurately by using the data collection module for effective data collection. This data isuseful for agriculture scientists to guide the farmers through the farmer web portal, Short MessageService (SMS) and also to �nd the rules which are used for performing drip irrigation.

1 IntroductionThe word agriculture is derived from two Latin words ‘Ager’ and ‘Cultura.’ Ager means land or �eld andCultura means cultivation. Therefore, the term agriculture means cultivation of land. Moreover, Agricultureis the science and art of producing crops and livestock for economic purposes. Moreover, it is alsoreferred (Karuppannan et al. 2017) as the science of producing crops and livestock from the naturalresources of the earth. Agriculture supports the economic system of India by providing food and rawmaterials and also it provides employment opportunities to a very large proportion of population. Forexample, the foreign trade in India is closely associated with agriculture sector. The primary aim ofagriculture in India is to cause the land to produce more abundantly and at the same time, to protect itfrom deterioration and misuse. Indian agriculture has progressed a long way from an era of frequent

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droughts and vulnerability to food shortages to becoming a signi�cant exporter of agriculturalcommodities. This has become possible due to persistent efforts at harnessing the potential of land andwater resources for agricultural purposes.

Agriculture is the backbone and important gift for human lives not only in India but also for all over theworld. However, majority of the cultivation lands in the globe suffer highly from shortage of irrigationwater. In such a scenario, drip irrigation techniques have been developed by researchers to reduce thewater consumption in dry areas. This is because there are no guidelines and systematic techniques toutilize the water and electricity in optimistic manner. Therefore, the overheads of farmers in conventionaldrip irrigation have become high and they have to manually visit and monitor the lands frequently.Moreover, many agricultural activities can be highly supported by e�cient decision making using datamining technologies. One of these activities is the regulation of the quantity of water in cultivated �eldsusing the rules derived from association rule mining. Moreover, wireless sensor networks have becomethe emerging technology in precision agriculture. Modern irrigation systems are built with smart sensornetworks for collecting �eld values for effectively watering the plants.

In the past, (Sakshi & Sonia Khajuria 2015) explained that agriculture is a natural enterprise where thenature’s various resources such as land, light, air, temperature and water encompassed into single primaryproductive unit called plants or trees. On the other hand animals, which are secondary productive units,feed on these primary units and produce various materials such as wool, egg, milk, meat, silk, etc.combined to meet the basic needs of the humans and also in the growth of the civilization. A croprequires certain amount of water at certain �xed intervals throughout its period of time for growth. Inagriculture, irrigation helps to grow the crops. Irrigation is the process of arti�cially supplying crops withwater. This technique is especially important in areas that receive little rain or irregular rainfall. Waterplays several vital roles in a plant's life. An understanding of the soil water and plant relationship isnecessary to understand the various water management principles during various climatic conditions. Inthe soil, there are different types of soil namely sandy, silty, clay, etc. Each type of soil has its ownadvantages and disadvantages. For example, the sandy soil has high drain capacity. However, thenutrients are swiftly carried away by the drain. On the other hand in the silty soil, the particles are smalland hence it can retain water for much longer period of time. However, this soil has poor water draincapability. Therefore, the soil must be good in all aspects such as drain nutrient holding and waterholding for e�cient agricultural practices. Hence, it is necessary to clearly understand the soil propertiesso that it is possible to control the water requirements for each type of the soil.

Irrigation is a method of transporting water to crops to maximize the amount of crops produced. Many ofthe irrigation systems in current use do not use the water in an e�cient way. However, they can improvethe e�ciency by the use of sensors for data collection and analysis. Moreover, wireless sensor networksare used in many applications like agricultural activities, natural disaster management systems, militaryapplications, forest �re detection systems, deep ocean navigations, industrial automations, health caresystems for elder people, smart home automation applications, etc. A smart irrigation system can be builtwith smart sensor networks for collecting �eld values and effectively watering the plants. Hence, a new

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sensor network assisted irrigation system has been developed in this research work to enhance thee�ciency of water usage. The main objective of this chapter is to conserve the water in dry areas byusing drip irrigation system with the help of multi_depth soil moisture sensor using wireless sensornetworks.

In this work, an Intelligent Drip Irrigation System (IDIS) has been designed and implemented. This systemconsists of two important components namely an Intelligent Soil Moisture Sensor Unit (ISMSU) and anAgriculture Control Station (ACS). The ISMSU is used to measure the soil moisture accurately using anewly proposed multi-depth soil moisture measurement algorithm. These soil moisture values help thefarmers to conserve water, electricity and to avoid both excess and shortage of water supply in irrigation.Moreover, a new protocol called ISMSU communication protocol has been proposed in this research workto collect the soil moisture data from the network of ISMSUs with less battery power consumption. Thispower conservation in ISMSUs is useful to prolong the lifetime of the sensors present in the network ofISMSUs. The ACS is responsible for collecting the data from sensor nodes and to process themaccurately by using the data collection module for effective data collection. This data is useful foragriculture scientists to guide the farmers through the farmer web portal, Short Message Service (SMS)and also to �nd the rules which are used for performing drip irrigation.

This paper is formulated as below: Section 2 discusses about the various drip irrigation techniques thatare proposed by various researchers in the past. The proposed intelligent drip irrigation system isexplained in detail in section 3. Section 4 demonstrated that the e�ciency of the system through variousexperiments that are conducted in this work. Section 5 gives conclusion on this work and also suggestsfew directions to proceed further in this direction.

2 Related WorksMany research works have been carried out by the different researchers in various time periods in thedirection of agriculture (Pandiaraju et al 2016), networks and sensor based systems (Selvi et al 2018 and2019, Prabhu et al 2019, Thangramya et al 2019). Water conservation is an important role in agriculture.Therefore, many researchers carried out research for developing new techniques for water conservation.BM et al. (2014) conducted experiments on different seasons for sessional analysis based on dry seasonand wet season and also studied the effects of �ve water harvesting techniques. The moisture content ofthe soil in three season’s namely sowing, mid-season and after harvest at four different depths wasconsidered in their model and they proved that their method is useful to reduce the water consumptionand improves the production. Gutierrez et al. (2014) developed an algorithm for effective maintenance ofsoil temperature of plants and the algorithm was coded in the microcontroller. It used solar cell andcommunication link which was based on cellular internet interface. The authors carried out theexperiments for one hundred and thirty six days and from the experiment it is evident that their proposedirrigation method reduced the water usage up to 90% percent when compared to the methods used intraditional agriculture.

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Gajendran et al. (2017) explained about the e�ciency and delay of information gathering mechanism byusing distributed clustering mechanism. The algorithm calculates the threshold value based on thetransmission distance and this overall mechanism helps in building robust information deliverymechanism to the base station which reduces the packet loss. The drip irrigation method helps inreduction of water usage for crops when compared to traditional agricultural practices. Betts (2007)proposed an effective method for irrigation which is viewed from the surface and large scale processes.The experiments were conducted for all seasons such as winter, autumn, spring and summer. The mainadvantage of their model is the reduction in energy consumption. Berg et al. (2014) conducted a study onsoil moisture-atmosphere analysis using the temperature on the surface. They used the probabilitydistribution function for the features selected from the data set. Their model is useful to maintain the soilmoisture based on statistical analysis. Jaeger & Seneviratne (2011) made a study on precipitation andtemperature trends. They carried out simulations using a simulation model for temperature analysisclimatic conditions. They have found that the soil moisture depends on climatic condition andtemperature.

Mahmood et al. (2015) explained about some of the techniques involved in saving the irrigation waterwhich includes improvement in water course use of new technologies, and the study area is selected.Chen & Hu (2004) developed a soil hydrological model for analyzing the effects of the groundwater. Theycarried out their work by allowing exchange of water between the groundwater and unsaturated zone.They proved that soil moisture analysis is more important to be considered for effective waterconservation. Chiang & Tsai (2015) proposed a liquid level sensor transducer which uses ComplementaryMetal Oxide Semiconductor technology with power supply ranging from 3.5 to 5 volt. The transducerpresented in their proposed model converts the liquid level into pulse and it monitors the rate of thefrequency of the pulse liquid level to detect the rainfall. Abbas et al. (2014) discussed the use of wirelesssensor network for controlling the irrigation using their smart watering system. In their model, wirelesssoil moisture sensor was used to detect the level of water present in the soil. Navarro-Hellin et al. (2015)discussed their proposed architecture for e�cient water management using sensors for data gathering.Their model is useful in the area of agriculture based on sensors.

Grace et al. (2015) proposed a work based on wireless control system which was used to operate for thedrip irrigation without human interaction. The main advantage of their model was to receive informationabout rain. Gaddam et al. (2014) proposed a drought monitoring system to wireless networks to analyzethe weather and soil conditions in order to predict and identify drought conditions. Their model was ableto perform data collection and analysis for effective water conservation. A broadcast based protocol wasdeveloped by Shaikh et al. (2010) for crop irrigation control for water management in agriculture. A newfarmland irrigation infrastructure method in an essential way for water-saving agricultural �eld wasexplained by Li & Chen (2010). Their model is cost effective and also provides facility for water saving.Lee et al. (2010) explained about many different sensing technologies for specialty crop productionutilizing precision agriculture. Based on the observations, they also made suitable recommendation forcrop management.

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A comparison of three methods of soil water content determination namely Theta Probe, SpeedyMoisture Tester and Gravimetric soil water content were studied and discussed by Little & KM (1998).Their methods were able to accurately �nd out the water content in soil. Grewal et al. (1990) studied theapplicability of the readily measured Saturation Percentage (SP) as a possible index for estimating FieldCapacity (FC) and Wilting Point (WP) water contents of New Zealand soils. The authors used thesaturation percentage to provide a rapid estimate of the �eld capacity and wilting point water contents ofthese soils. Simulation analysis was carried out using the Regression analysis between FC and SP andprovided a comparative analysis. Mills (2000) discussed the issues in the water budget approach modelapplied to Ireland, which are the most important exchange cycles within the earth atmosphere system.The model measured the monthly values of meteorological variables namely precipitation, temperatureand sunshine hours and the land use details to calculate the water budget terms.

Figueroa & pope (2017) presented three novel algorithms, namely Top Rule Pattern, pre-Validated TopRule Pattern and Series String Comparison algorithm for identifying the root system water consumptionfrom soil moisture sensor time series data. The algorithms were compared by the authors to an actualdeployed algorithm, Density Histogram Comparison (DHC) to effectively predict the water requirements ofthe future.

Leenhardt et al. (2012) presented a study on conceptual and operational framework are performed fordifferent water management purposes by the authors. Moreover, to identi�ed challenges and possiblesolutions to overcome the issues. They have used the case studies to highlight challenges and providedsolutions using arti�cial intelligence techniques.

Osroosh et al. (2016) conducted a comparison study of various scheduling algorithms for irrigation inwhich different parameters namely soil, water balance, evapotranspiration, time and temperaturethreshold are used for effective water irrigation and decision making. Stambouli et al. (2014) explainedthe issues in irrigation system using the data from Almudevar Irrigation District in Spain. In their model,data analysis was performed using �eld study. Four climate change scenarios have been considered andanalysed by Rolim et al. (2017) for providing suitable recommendation for crop rotation. Jimenez-Bello etal. (2015) discussed the new techniques for improvement of energy and water e�ciency. Moreover, theauthors used a modi�ed water intake for a time period and proposed an energy e�cient solution forwater management. Van der Kooij et al. (2013) explained the various drip irrigation protocols for effectivedesign of protocols.

Lorite et al. (2013) conducted a new study on irrigation performance and used Genil-Cabra IrrigationScheme for effective management of irrigation and monitoring. Casadesus et al. (2012) proposed a newalgorithm for automated irrigation, based on properties namely adoption, scheduled execution and fewexternal parameter monitoring techniques. Lopez-Mata et al. (2010) explained the various simulationmodels for analyzing the crop behaviour. Through the experiments conducted using these parameters,the authors proved that their model provides optimal irrigation rules and recommendation. Montazar etal. (2017) conducted a study on the economy of Subsurface Drip Irrigation (SDI) in the certain agricultural

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regions of California using technology management. They used a prediction model to provide effectivedecision. Kuppannan et al. (2017) explained the techniques used for the e�ciencies based on soilmoisture sensors. Their model suggested new techniques for enhancing the productivity.

Kumar et al. (2017) conducted a study and evaluation of the effect of drip lateral lengths in India. Theyhave proved that drip irrigation system and calculation of performance of parameters based on statisticalanalysis is useful to provide optimal water distribution. Serra et al. (2016) provided a study whichfocused on estimating the requirements of irrigation and the quantity of water which is actuallyconsumed by crop present in Mediterranean rural community by applying past data. Their model is ableto provide suitable suggestions for effective irrigations. Ahmad & Khan (2017) investigated about thewater-energy relation for the irrigation system. In their model, they developed a dynamic node-link modelfor the drip irrigation management.

Yang et al (2018) calibrated and also applied the model called Hybrid-Maize which is in a sub-humidHeilongjiang Province in Northeast China for estimating the irrigation needs for drip-irrigated maizeduring the various crop physiological development phases and also under the diverse agro-climaticconditions. Tobias et al (2018) have evaluated that the performance of Mobile Drip Irrigation (MDI) that isused for the maize production. Timothy and Robert (2019) introduced a new drip irrigation system whichis used for improving the e�ciency of the system in terms of the utilization of agronomic crops thatapply water from both irrigation and rain while increasing the seasonal requirements and alsoquantifying the proportion of rainfall that is applied by the crop for the particular rain season. Finally, theyhave proved that their methodology is best than others. Nouri et al (2019) designed two differentscenarios such as mulching for all crops and the mulching plus drip irrigation for all summer crops forevaluate the probability of blue water saving process on Upper Litani Basin by applying the agriculturalpractices that are available as alternative. Tipin et al (2019) aimed to characterize the salt movementswhich are occurred within the planted ridge due to the drip irrigation process of the amounts that arevarying and to identify the optimum soil matric potential threshold is applied for performing drip irrigationprocess in order for wolfberry to be successful production and also based on plant survival.

3 Intelligent Drip Irrigation System DesignThe main part of the Intelligent Drip Irrigation System (IDIS) is formed with wireless sensor networks,where each ISMSU is a sensor node. The networks of ISMSUs are deployed in the agriculture �eld in an�xed topology based on the requirements. The data collected by the sensor network are stored in aDatabase Centre (DBC) and are processed by the ACS. This section explains the different parts of theIDIS.

3.1 Intelligent Soil Moisture Sensor Unit DesignIn the proposed work, soil moisture of the cultivation land is measured at different depths based on theroot length of the plants over different time periods. The proposed Intelligent Soil Moisture Sensor Unit

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(ISMSU) which is used in the proposed IDIS is shown in Figure 1.

This intelligent sensor unit has multiple soil moisture units namely U1, U2, U3 with multiple depths d1, d2

and d3, respectively. The proposed design can be extended to support many levels of depths based on therequirements. This intelligent sensor unit is capable of activating individual sensor unit or combination ofsensor units based on the commands received from the control station at any point of time. Thiscon�gurable nature of the sensor unit adds higher �exibility in soil measurement of the land based onagricultural scientist’s recommendation or farmer’s choice. The procedure to con�gure the sensor unit isgiven in the algorithm for ISMSU con�guration. Table 1 shows the list of symbols used in ISMSUcon�guration algorithm.

Table : 1 List of symbols used in ISMSU con�guration algorithm

SYMBOL DEFINITION

DBC Database Centre

ISMSU Intelligent Soil Moisture Sensor Unit

ID_DBC Identi�cation number of DBC

ID_ISMSU Identi�cation number of target ISMSU

ID_SELF Identi�cation number of the receiving ISMSU

DEPTH Depth to be con�gured in the sensor units of ISMSU

di Depth of the sensor unit (d1, d2, d3)

Ui Sensor unit (U1, U2, U3)

Algorithm for ISMSU Con�guration

Input : Data received from DBC

Output : Selection of sensor unit(s)

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For power saving, generally the transmission unit of all ISMSUs are turned off. Timers are used to wakeup the transmission units of ISMSU for data transmission in well planned timing set by the ACS.Similarly, each ISMSU measures the soil moisture in de�ned intervals with timers. Moreover, each ISMSUis equipped with memory for storing the Soil Moisture Records (SMR). The sensor unit’s depth andnumber of records to be collected at each session are dynamically con�gurable by the ACS. The multi-depth soil moisture measurement algorithm is explained in this thesis and the symbols used in thisalgorithm are given in Table 2.

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Table 2List of symbols used in multi-depth soil moisture measurement algorithm

SYMBOL DEFINITION

TRIGGER_TIMER Triggers activated by Timers of ISMSU

NO_OF_RECORDS Number of records about soil moisture to be collected

SMR Soil moisture record

SUi Sensor Unit (SU1, SU2, SU3)

di Depth of the sensor unit (d1, d2, d3)

FILE File used to store the soil moisture records

PIN_VALUE Soil moisture value of the pin connecting the sensor unit of ISMSU

TIME_ISMSU Current time of the ISMSU

ID_ISMSU Identi�cation number of the ISMSU

Algorithm for Multi-Depth Soil Moisture Measurement

Input : TRIGGER_TIMER

Output : FILE_SMR

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3.2 Data Collection CentreThe proposed Intelligent Drip Irrigation System uses have a data center which is controlled by the controlunit. The control unit receives input from ISMSU and ACS and it stores the data in two databases namelysoil moisture database and Global Positioning System (GPS) databases. GPS receiver collects the GPSdata and it stores them in the GPS database. The architecture of the proposed Data Collection Centre(DCC) is shown in the Figure 2.

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In this research work, a special communication protocol called DBC_ISMSU_COMM is proposed toestablish the communication channel between the ISMSU and DBC without collisions. The ISMSUs aredeployed in the cultivating area in well known locations. The DCC is directed from Agriculture ControlStation (ACS) to reach the agriculture sites in well de�ned time intervals.

The working principle of DBC_ISMSU_COMM protocol is explained in the algorithm forDBC_ISMSU_COMM Protocol. Table 3shows the list of symbols used in the DBC_ISMSU_COMMalgorithm. Interrupts are raised in ISMSUs using timers in de�ned intervals to wake up the transmissionsystem for transferring the soil moisture data to the DBC. The arrival of DBC and wakeup timing ofISMSUs are well synchronized. After landing in the reception-location, the DBC sends the alive signalDBC_ALIVE to con�rm its arrival. If an ISMSU hear the alive signal, it starts the data transmission mode. Ifan ISMSU fails to receive the DBC_ALIVE signal within a maximum period TIME_OUT_ALIVE, it will turnoff its transmission system. This mechanism reduces the unnecessary power consumption in the sensorunits.  

Table 3List of symbols used in DBC_ISMSU_COMM algorithm

Symbol De�nition

TIMER_ON Timer triggers the transmission unit of ISMSU in regular interval

SMP Soil moisture packet

DBC Database Centre

ISMSU Intelligent soil moisture sensor unit

REC_LOC Reception-location

DBC_ALIVE Alive signal sent by DBC to nearby ISMSUs

TIME_OUT_ALIVE Maximum timeout period that each ISMSU waits for DBC_ALIVE

DATA_TRANSFER_MODE Data transfer mode, in which each ISMSU prepares to send the SMR toDBC

RTR Request to receive signal

ID_ISMSU Identi�cation number of the ISMSU

CTS Clear to send signal

TIME_OUT_ACK Maximum timeout period that the DBC waits for CTS

DBSM Soil moisture database

Algorithm for DBC_ISMSU_COMM Protocol

Input : TIMER_ON

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Output : SMPs stored in DBSM of DBC

Step 1

Begin

Step 2

land DBC in REC_LOC

Step 3

send DBC_ALIVE from DBC to ISMSUs

Step 4

if (DBC_ALIVE received before TIME_OUT_ALIVE) then

Step 5

activate DATA_TRANSFER_MODE in ISMSUs

Step 6

goto step 9

Step 7

else

Step 8

End

Step 9: DBC sends RTR with ID_ISMSUi ( i = 1,2,3,…,n)

Step 10

ISMSUi sends CTS with ID_ISMSUi

Step 11

if (CTS received with ID_ISMSUi before TIME_OUT_ACK) then

Step 12

while (!EOF)

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Step 13

DBC receives SMP from ISMSU and stores in DBSM

Step 14

end while

Step 15

end if

Step 16

repeat step 9 to 15 for remaining ISMSUs

Step 17

End

After collecting the soil moisture records, the DBC sends all information to the ACS. The soil moisturerecords are processed by the ACS and used for scienti�c analysis as explained in the next section.

3.3 Agriculture Control StationThe information is sent from sensor nodes to the agriculture control station in speci�c intervals about thesoil moisture data from the network of ISMSUs deployed in the �eld. The ACS retrieves the data from thesoil moisture database (DBSM). The functional architecture of the ACS is shown in the Figure 4.

The ACS consists of Farmer Database (DBF) which is used to store the details about farmers, their landsand the unique identi�cation number of the ISMSUs deployed in their lands. The VegetationPreprocessing Unit (VP-Unit) present in the ACS preprocess the raw data retrieved from the DBSM. Thisunit extracts the date, time, identi�cation number of ISMSUs, soil moisture value at different depths, thebattery status of ISMSUs and directs these data to the classi�cation unit. The pre-processed data isfurther classi�ed using the rule based classi�cation algorithm by the classi�cation unit and the classi�edresults are stored in the classi�ed database (DBCLASS). The information about the battery condition helpsthe farmers to recharge or replace the battery of ISMSUs if necessary.

The soil moisture at a particular depth alone is not a deciding factor for watering the plants, since thewater holding capacity of each layer in the �eld are highly dependent on the adjacent layers. If the bottomsoil layer is too dry, then the water supplied at the upper layer will be drained soon. Hence in the real timeenvironment, the soil moisture content to be maintained in a particular depth is highly dependent on thesoil moisture content of the upper and bottom layers. The proposed scienti�c processing unit accessesthe soil moisture data from the classi�ed database and analyzes it for calculating the actual quantity ofwater to be supplied in the plant's root in different Crop Period (CP). Algorithm 4 shows the procedure to

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calculate the Actual Soil Moisture Required (ASMR) in different crop periods and the list of symbols usedin this algorithm is given in Table 4.

  Table 4

List of symbols used in Actual Soil Moisture Required Calculation algorithmSymbol De�nition

CP Crop period

SMd1, SMd2, SMd3 Soil moisture values in depth d1, d2, d2 respectively

ESM Expected soil moisture value

ASMR Actual soil moisture required

dx Depth at which the sensor measures the soil moisture

Algorithm for Actual Soil Moisture Required (ASMR) Calculation

Input : CP, SMd1, SMd2, SMd3

Output : ASMR at depth (dx)

Step 1

Begin

Step 2

if (CP<=5 days) then

Step 3

if (SMd1 <= SMd2 && SMd2 <= SMd3) then

Step 4

ASMR = ESM

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Step 5

else if (SMd1 > SMd2 && SMd2 > SMd3) then

Step 6

ASMR = ESM + SMd1

Step 7

end if

Step 8

end if

Step 9

if (CP>5 days and CP <=10 days) then

Step 10

if (SMd2 >= SMd1) then

Step 11

ASMR1= ESM + SMd2;

Step 12

else if (SMd2 < SMd1) then

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Step 13

ASMR1 = ESM;

Step 14

end if

Step 15

if (SMd2 > SMd3) then

Step 16

ASMR= ASMR1 + SMd3;

Step 17

else (SMd2 < SMd3) then

Step 18

ASMR = ASMR1;

Step 19

end if

Step 20

end if

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Step 21

if (CP>10 days and CP <=15 days) then

Step 22

if (SMd3 > SMd2 && SMd2 > SMd1) then

Step 23

ASMR= ESMR + SMd2 + SMd2;

Step 24

else (SMd3 < SMd2 && SMd2 < SMd1) then

Step 25

ASMR= ESMR;

Step 26

end if

Step 27

end if

Step 28

End

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The scienti�c processing unit calculates the ASMR at a particular depth in a particular crop period basedon the available soil moisture content at different depths. The conventional static measurement methodcalculates the quantity of water required based on the soil moisture content at a �xed depth, which leadsto inaccuracy. The dynamic nature of the proposed algorithm accurately estimates the quantity of waterand duration of water supply for the crops. This information is stored as the knowledge in the AgricultureKnowledge Database (DBAK). The farmers can login to the farmer’s web portal with registered usernameand password to receive the recommendations given by the agriculture scientists. Theserecommendations help farmers to avoid both over watering and shortage irrigation of plants and alsoincrease the crop yield. The ACS has a push service unit that pushes the recommendations to the farmersvia Short Message Service (SMS) in emergency scenario.

As a whole, the proposed system helps both the scientists and farmers to frequently examine thedynamic nature of soil moistures in the cultivation �elds without frequently visiting the lands withmanual interventions. Based on the dynamic nature of the lands, the scientists can guide the farmersabout the duration and quantity of water supply via farmer web portal and SMS. The proposed approachsigni�cantly increases the accuracy of soil moisture measurement with the help of multi-depth sensorsand hence conserves the water in dry areas. This water conservation extends the water availability, wherewater is a scarce resource. The proposed system also conserves the battery energy in ISMSUs and saveselectricity.

Finally, a set of rules are used in the decision making process which are listed below,

Rule:1

if (CP<=5 days) then

if (SMd1 <= SMd2 && SMd2 <= SMd3) then

ASMR = ESM

else if (SMd1 > SMd2 && SMd2 > SMd3) then

ASMR = ESM + SMd1

end if

Rule:2

if (CP>5 days and CP <=10 days) then

if (SMd2 >= SMd1) then

ASMR1= ESM + SMd2;

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else if (SMd2 < SMd1) then

ASMR1 = ESM;

end if

if (SMd2 > SMd3) then

ASMR= ASMR1 + SMd3;

else (SMd2 < SMd3) then

ASMR = ASMR1;

end if

Rule:4

if (CP>10 days and CP <=15 days) then

if (SMd3 > SMd2 && SMd2 > SMd1) then

ASMR= ESMR + SMd2 + SMd2;

else (SMd3 < SMd2 && SMd2 < SMd1) then

ASMR= ESMR;

end if

end if

4 Experimental SetupIn this research experiment, the ISMSU is built on Intel Galileo board, where soil moisture sensors (FC-28)are interfaced with the Analog-to-Digital Conversion (ADC) pins of the board to measure the soil moisture.Three FC-28 sensors are placed in the �eld at three different depths (10cm, 20cm, and 30cm) andinterfaced with each ISMSU. Also Bluetooth (HC-05) is interfaced with each ISMSU. In this experiment,sensor nodes are communicating with ISMSU using Bluetooth protocol. Figure 5 shows the componentsused in this experiment.

In a well planned scheduling, the ISMSU measures the soil moisture values at 10cm, 20cm, 30cm depths,date and time of measurement, battery status and writes to the Secure Digital (SD) card. The sensornodes collect data from the �eld in well de�ned schedules and collect the values stored in the SD card ofISMSU. The proposed research work is experimented in the study area located in Anna University,

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Chennai, Tamil Nadu, India with latitude 13.0140°N and longitude 80.2360°E. The area has tropicalclimate with relatively high rainfall of about 15-41mm with a peak in summer (March to October).Temperature ranges between 20oC to 21oC in winter and between 38oC to 42oC in summer. The brinjalcrop with drip irrigation is considered for this experiment as shown in Figure 6. The distance betweenadjacent plants is 40cm and the drip irrigation pipe is designed to watering the plants near the rootregion.

5 Results And DiscussionsThis section gives the results and analysis of the proposed system. The performance of the proposedsystem has been evaluated and compared with the existing static depth soil moisture measurementapproach (Prijono & Bana (2015)). The experiments were conducted for 15 days continuously in thedepth of 10cm, 20cm and 30cm, respectively. The Expected Soil Moisture Value (ESM) is a referencevalue that can be con�gured with users choice as it highly depends on the nature of soil, crop type, andcrop period. From the experiments, it is found that the proposed system provides high level of �exibility tothe agriculture scientists to set these values. We can �nd the similar straight line �tting to the data whenthe land depth is 10cm, 20cm, and 30cm. The ESM values and the ASMR calculated by both theproposed and existing static measurement approach proposed by Gutierrz et al. (2014) in different daysare tabulated in the Tables 5, 6 and 7 and comparison graphs are shown in �gures 7, 8 and 9.

Table 5Volumetric Water Content of the soil in 10 cm depth

Day Expected SoilMoisture (ESM) (%)

ASMR calculated byProposed Approach (%)

ASMR calculated by StaticMeasurement Approach (%)

Gutierrz et al. (2014)

03-08-2018

0.06 0.056 0.012

04-08-2018

0.065 0.06 0.019

05-08-2018

0.07 0.065 0.019

06-08-2018

0.075 0.07 0.026

07-08-2018

0.08 0.074 0.029

08-08-2018

0.085 0.081 0.03

From Table 5, it is observed that the proposed model predicts the soil moisture more accurately than thestatic measurement approach at 10cm depth.

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Table 6Volumetric Water Content of the soil in 20 cm depth

Day Expected SoilMoisture

(ESM)

(%)

ASMR calculated byProposed Approach

(%)

ASMR calculated by StaticMeasurement Approach (%)

Gutierrz et al. (2014)

09-08-2018

0.08 0.076 0.04

10-08-2018

0.082 0.077 0.041

11-08-2018

0.084 0.078 0.043

12-08-2018

0.086 0.0799 0.044

13-08-2018

0.088 0.082 0.045

14-08-2018

0.0899 0.084 0.046

From the Table 6, it is observed that on all the six days of observation the proposed approach providesmore accurate results than the static measurement approach.

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Table 7Volumetric Water Content of the soil in 30 cm depth

Day Expected SoilMoisture

(ESM)

(%)

ASMR calculated byProposed Approach

(%)

ASMR calculated by StaticMeasurement Approach (%)

Gutierrz et al. (2014)

15-08-2018

0.09 0.083 0.06

16-08-2018

0.091 0.084 0.061

17-08-2018

0.092 0.085 0.062

18-08-2018

0.093 0.086 0.064

19-08-2018

0.094 0.087 0.066

20-08-2018

0.095 0.088 0.067

From Table 7, it is seen that the proposed approach provides matching results with the estimatedmoisture. This is due to fact that the proposed model uses intelligent rules for making effective decisionon the water usage based on management of soil moisture.

From �gure 7, it is observed that the volumetric content at 10cm depth is measured more accurately bythe proposed model when it is compared with static measurement approach.

From �gure 8, it is observed that the volumetric content at 20cm depth is measured more accurately bythe proposed model when compared with static measurement approach.

From �gure 9, it is observed that the volumetric content at 30 cm depth is predicted more accurately bythe proposed model when it is compared with static measurement approach.

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Table 8Deviation (from ESM) Analysis of Proposed and Static Measurement Approaches

Depth 10 cm Depth 20 cm Depth 30 cm

Deviation

inProposedMethod

(%)

Deviation inStaticMeasurementApproach

(%)

Gutierrz et al.(2014)

DeviationinProposedMethod

(%)

Deviation inStaticMeasurementApproach

(%)

Gutierrz et al.(2014)

DeviationinProposedMethod

(%)

Deviation inStaticMeasurementApproach

(%)

Gutierrz et al.(2014)

0.004 0.048 0.004 0.04 0.007 0.03

0.005 0.046 0.005 0.041 0.007 0.03

0.005 0.051 0.006 0.041 0.007 0.03

0.005 0.049 0.0061 0.042 0.007 0.029

0.006 0.051 0.006 0.043 0.007 0.028

0.004 0.055 0.0059 0.0439 0.007 0.028

From Table 8, it is observed that the proposed multi-depth soil moisture measurement approachoutperforms the existing static measurement approach in terms of accuracy in the calculation of soilmoisture to be maintained in different depth at different periods. This accuracy leads to proper wateringof plants and increases the crop yield. Also, this precise measurement conserves water and electricity byavoiding unnecessary operation of drip irrigation pump.

6 ConclusionThe proposed Intelligent Drip Irrigation System (IDIS) is formed with wireless sensor networks, whereeach ISMSU is a sensor node. The networks of ISMSUs are deployed in the agriculture �eld with a �xedtopology based on the requirements. The data collected by the sensor network using the sensor nodes areprocessed by the ACS. In this proposed model, generally the transmission unit of all ISMSUs are turnedoff for effective power saving. Timers are used in this model to wake up the transmission units of ISMSUwhich are used for data transmission in well planned timing which is set by the ACS. Similarly, eachISMSU measures the soil moisture in de�ned intervals with timers. Here, each ISMSU is equipped withmemory for storing the Soil Moisture Records (SMR). In future, the sensor unit’s depth and number ofrecords to be collected at each session are dynamically con�gurable by the ACS. The ACS consists of alocal coordinator and Region Heads. The raw data obtained from the �eld is sent to pre-processing andalso for Dynamic Terrain management and to the decision manager to provide recommendation to thefarmers.

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DeclarationsCONFLICT OF INTEREST STATEMENTCon�ict of interests - None

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Figures

Figure 1

Architecture of the ISMSU

Figure 2

Architecture of the proposed Data Collection Centre

Page 29/31

Figure 3

Architecture of Agriculture Control Station

Figure 4

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Components of ISMSU

Figure 5

Study area at latitude 13.0140 and longitude 80.2360

Figure 6

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Soil Moisture Content at 10 cm depth

Figure 7

Soil Moisture Content at 20 cm depth

Figure 8

Soil Moisture Content at 30 cm depth