application of sensor networks for monitoring of rice...

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Application of Sensor Networks for Monitoring ofRice Plants: A Case Study

Suman Kumar, S. S. Iyengar, Ravi Lochan, Urban Wiggins, Kanwalbir Sekhon,Promita Chakraborty, Raven Dora

Computer ScienceLouisiana State UniversityBaton Rouge, LA, USA

{sumank,iyengar,rlochan,wiggins,kanwalwir,pchakra,rdora}@csc.lsu.edu James H. OardAgronomy Department

Louisiana State UniversityBaton Rouge, LA, [email protected]

I. INTRODUCTION

Sensor network research [1] has enabled large scaleenvironmental monitoring using small sensors with radiolinks. The technological advance in wireless communi-cations and microelectronics has enabled the develop-ment of small, low-cost sensors. Sensor networks aredeveloped to organize and control these sensor nodes,which have sensing, data processing, communication andcontrol capabilities. Information collected from thesesensor nodes is routed to a sink node via diffrent typesof wireless communication approaches. The sink nodecan communicate with remote users through the Internetor satellite network. The research in the sensor networkshas focused primarily on the networks issues such asrouting, data dissemination [2], [3] and aggregation ofco-related data for downstream data delivery [4]. Thecombination of small size, low cost and wireless net-working functionality makes sensor network technologyexceptionally scalable.

Agro technology is changing at a fast rate with theapplication of sensor devices. Diseases encountered incrop fields dramatically affect productivity of a crop.Manual analysis of the causes of diseases in a largecrop field is impossible; hence we need a generic andrapid method for the analysis of the causes and timelyprevention of those diseases. The timely prevention andmonitoring not only enhances the productivity but alsoaids developing new crops varietiesby observing theadaptation of new breed with different climate condi-tions. Clearly, Wireless Sensor Network is a promisingdata mining approach for precision agriculture. Instru-mented with wireless sensors, it will become available tomonitor plants in real time for air temperature, soil water

content, and nutrition stress. The real time informationfrom the fields will provide a solid base for farmers toadjust strategies at any time. As a part of an ongoingresearch related to developing tools and techniques ofconnecting Crossbow motes in the form of a network,we are exploring the coverage of the sensing area forplant monitoring. To address this problem, we havebeen experimented for four months with a localizationmethod of deploying sensor motes in the greenhouseto measure atmospheric parameters such as temperature,barometric pressure, ambient light intensity and humidityto help analyze the effect of these parameters on thegrowth of rice plants. This paper describes the designand implementation of an integrated network that linksseveral communications platforms, wireless technologyand Transfer Control Protocol-Internet Protocol. Themain goal is to develop a system that guarantees a low-cost, high performance and flexible distributed moni-toring system with an increased functionality. The userinteracts with this distributed monitoring system usinga transparent and intuitive graphical user interface madeavailable to remote places.

II. RELATED WORK

As an intial effort [5] showed that modern concepts onmethods and techniques for the management and controlof agricultural systems-such as greenhouse and animallive stocks-claim for the use of computer systems. Themethod basically focussed on control of the environ-mental parameters in an economically way, to producethe best crop or animal living conditions. The specificcombination of diffrent sensor inputs forms an integratedsystem that co-ordinates each action for adequate controlof various production paramters. The current work at

Iowa State University is called the Development ofIn-situ Soil Sensor Network for Temporal and Spatialmonitoring of Agriculture Fields [6]. The eventual goalof this project is to develop a distributed sensor networkto monitor the soil conditions throughout the growingseason, with sensors positioned on a 50 m grid over afield. In the process of their work they are attempting toinclude sensing of soil organic matter, soil nutrients, andother non-agricultural civil and/or military applications.

Another ongoing research [7] at Delft Universityof Technology focussed on wireless sensor networks inpotato field. The main goal of monitoring is to revealwhen the crop is at risk of developing the diseaseand let the farmer treat the field or parts of it withfungicide only when absolutely needed. In [8] a webserver based approach is used where sensor nodes areequipped with a web server to be accessed via theinternet and make use of wireless LAN to provide ahigh speed transmission. The use of a web server helpsto anaylyze distant agricultural fields over long periodsof time whereby the whole dataset is accessible to gen-eral public. The Sandia National Laboratory is alreadyrunning the projects in this area with collaboration of theMexican government [9] . The present work focussedon using sensing technology in hydroponic greenhouses.The Sandia-placed sensors and computer simulationswill tell researchers how to grow crops efficiently. [10] isfocused on the investigation of wireless sensor networksin agricultural applications. With a 2.4 GHz wirelesssensor node, factors such as coverage area and the agri-cultural environmental effects (bare soil, soybean, andcorn fields as their main target) on the radio were studied.Our research differs from the those mentioned abovein that we present a simple design outlining variousconstraints concerning optimal sensor deployment, datacollection and data analysis. Hence, a framework fordesign and implementation of our system have beenpresented with detailed analysis of relevant data.

III. EXPERIMENTAL SET-UP

The green house is a controlled environmental systemfor research in the area of food production. LouisianaState University Agcenter has a green house equippedwith fans artificial lamps for light intensity control andis isolated from outside environmental conditions. Thissection discusses the design part of the experiment.The goal of the project is to create a wireless sensornetwork that will provide 24 hour/ 7 days a weekmonitoring of environmental greenhouse conditions oflsu greenhouse and its effect on growth of rice plants.Five distinct varieties of rice plants each having fourreplicas were placed in the LSU greenhouse and studied

for four months. The plants are placed in a random blockexperiment design throughout.

A. System Design

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Fig. 1. (a).Greenhouse floor plan and Randomized block diagram,(b). LSU Agcenter greenhouse design of distributed sensor networkfor studying the rice plants and (c). Placement of sensors accordingto the sensing regions and optimal coverage of all the rice plants

In figure 1(b) the LSU agcenter design of sensornetwork is presented. Data is gathered from sensor nodesby hop-by-hop communication in the database serverrunning on the PC that acts as a sink node with thehelp of MTS420 sensor board1. The webserver interactswith database server running on the sink, with thehelp of ADSL link to enable the remote access of theinteresting data. Furthermore, data are analysed remotelyby researchers. Data are collected by using the sensorboard and stored in a postgres sql data base. Further,analysis is run on the data using Matlab version 7 2 andStatistics tool. Further analysed data are plotted for clearunderstanding using graphics tools like microsoft excel.

B. Sensor NodesSensor nodes are setup in a wireless network to

collect various environmental readings. These nodes area common mix between the ability to sense real timedata with the ability to instantaneously communicatethe data findings. The sensors are usually set up in an

1All the sensor boards are from crossbow, www.xbow.com2www.mathworks.com/products/matlab/

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ad hoc environment, and the battery operated sensorsare referred to as motes. There are three very essentialcomponents of the sensor networks namely sensing,communications, and computation to have a successfuldeployment. These mentioned factors are the equiva-lent of obtaining the correct and necessary hardware,software, and algorithms. A wireless sensor mote isprogrammed to monitor various conditions. The possibleconditions that can be monitored are light, humidity,temperature, as well as GPS readings. These readingsare routed to the system and the data is converted forhuman interpretation.

The Cross bow Mica2 wireless sensor network deviceswere utilized with the environmental board 420i. TheMica2 nodes have the following characteristics:

• Program Flash Memory: 128k bytes• Battery: 2x AA batteries• User Interface: 3 LEDs• Size(in): 2.25 x 1.25 x 0.25• Weight(oz): 0.7• Multi-Channel Tranceiver: 315, 433, or 868/916

MHz

The Mica2 sensors were outfitted with the MTS420sensor board. The sensor nodes fascilitates measuring ofBarometric Pressure, Ambient Light, Relative Humidity,Temperature. The GPS hardware has been removed asits not needed here. It also has EEPROM which couldbe programmed as per user requirement. Each sensor hasto be programmed with a unique ID to uniquely identifyit in the network.

C. Rice BackgroundThe LSU Agcenter and the United States Depart-

ment of Agriculture(USDA) researchers has spent severaldecades in the understanding and cultivation of rice tomaximize production and yield quantity under variedenvironmental conditions. The growth structure of ricecan be classified into three(3) basic phases: vegetative,reproductive, and ripening, respectively. Traditionally,rice being a tropical plant has a reproductive phase of25 days anad the ripening phase is approximately 30days. A very interesting property that can be inferredin the rice plants is that the differences in the growthduration [11] are categorized by changes in the lengthof the vegetative phase. The following rice varieties areused in the current experiment:

• Variety 1 = Cypress, LA variety first released in1992 known for its high grain milling quality.

• Variety 2 = Rosemont, TX long-grain variety, verysusceptible to fungus

• Variety 3 = Pecos, TX variety, tolerant to fungus• Variety 4 = Cocodrie, LA variety, known for high

yield, but susceptible to fungus• Variety 5 = IR64, popular variety grown in Asia,

susceptible to fungus

There are four replicas of each variety of rice plants stud-ied for the effect of spatial distribution of environmentalconditions on plant growth.

IV. IMPLEMENTATION

Implementation issues of the current work is discussedin this section.

A. Greenhouse Randomized block designFloor plan of green house is shown in Figure 1(a).

The LSU Agcenter greenhouse is 26’ long and 24’wide with two 24” fan. Plant varieties are labeled withthe corresponding variety number and placed on fourequidistant benches. Location of heater, fan and pumphave been shown. Clearly, floor plan and randomizedblock design of the experiment affect the growth ratesof plants. We further comment on this in section V andVI.

B. Wireless sensor network designA group of sensors (in our case five sensors are

distributed in field and one outside) are distributed insideand outside of the greenhouse to form a sensor network.The sensor nodes are placed in the greenhouse in such away that different variety of plants come in the sensingrange of every node.

The placement of the sensors is based on the followingconstraints:

• Each rice plant is in the coverage region of at leastone sensor

• The overlap between the sensing regions has to beminimized (thus minimizing the number of sensors)

• Multiple communication paths exist between thesensors and the base station (laptop). This providesreliability in case of temporary failures of the sen-sors.

In the current scenario, there are five sensors sensingand forwarding the data to the base station. Each sensorcoverage region is labeled as shown in Figure 1(c). Thuseach rice plant falls into one of the regions I(coveringplant variety 5,4 and 1), II(covering plant varieties 5, 3, 1and 2), III (4 and 3), IV(1, 2, 4 and 5), and VII(Coveringplant varieties 1,2,3 and 4. Sensors IV and VI are forforwarding purposes.

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Fig. 2. (a). Sensor placement in a coke bottle (b). Routing sensoroutside the green house (c)Plant varieties in the clay pot(germinationphase) and (d,e). Reproductive and Ripening phase respectively ofrice plants and placement of sensors

The sensor motes implemented in the greenhouseforms a self-organizing robust mesh network, with IEEE802.11 as shown in figure 1(b). The architecture isdivided into three main layers. Layer 1 is the mote layeror sensor mesh network layer. This layer is programmedwith TinyOS firmware as platform. The interface, calledMOTE-VIEW interface, is application oriented and pro-grammed to do specific tasks like weather monitoring,target detection and tracking. Layer 2 is the Server layerwhich is responsible for data collection and logging andother database related services. The sensor readings arestored on a server when they arrive at the base stationand the server layer takes care of all procedures involved.Layer 3 is the client layer which provides software forusers for visualization, data monitoring and data analysistools to display, calibrate and interpret sensor data.

C. OS/Software ApplicationMOTE-VIEW [12] interface in our sensors supports

the MICA2 platform, based on TinyOS3 framework. Inaddition to this, it supports certain other features likesecurity/intrusion detection system which is based onMSP. Further, we have used the XMesh Applicationavailable in MOTE-VIEW to program our MTS420 sen-sors. XMesh is Crossbow.s multi-hop mesh networkingprotocol and it has features like low-power listening,time synchronization, scheduling sleep modes, any-to-base and base-to-any routing. Thus, the XMesh applica-tion has helped our sensor motes to form a mesh within

3TinyOS: A Component-Based OS for the Networked SensorRegime, http://webs.cs.berkeley.edu/tos/

the greenhouse, by forwarding data to the base stationand finally to the server for storage. Our MOTE-VIEWinterface runs on Windows XP Professional platformwith remote viewing facility.

D. Sensor PlacementDuring the germination phase, a sensor has been

placed in the coke bottle as in figure 2(a) to measurediffrent environmental factors such as humidity, light,temperature etc. Special care has been taken to placethe sensor near the top of the plants. Further, the sensorhas been moved to the top of the plant to measure theenvironmental parameters such as temperature, humidity,pressure and light as suggested by agriculture researchersas in Figure 2(d,e). In the same figure( 2(c)) plant varitieswere placed in a small pots. Additional sensors for thehop to hop communication have been placed outsideof the greenhouse. Outside sensor has been placed ina small container with a hole in a base to save it fromrain and other environmental disturbances that may affectintegrity of sensor network operation.

V. EXPERIMENTAL-RESULT

The placement of sensors accounts for the differentgrowth variation for different plant varieties and replicasas spacing and fixing the sensors are important as itsensed the regions around it. We assumed that sensorshave circular sensing region and likewise we recordedthe reading for different plants. Sensors collectivelytransfer the data to forwarding sensors which finallyreach to the sink. Temperature, humidity, light intensityare considered important factors for the growth of theplant. This section contains data collection and analysispart. Because of space limitation, all the results can notbe presented here. Our main focus of this section is toexplain the usefulness of the data of interest, which areuseful for agriculture researchers.

A. Weekly StatisticsThe current project is real time monitoring of the

growth of the plants with different environmental param-eters. The weekly statistics for temperature and humidityvariation is shown in Table ?? for the period of 10thMarch to 16th March, 2006. As we can see from thetable, standard deviation and mean variation of theparameter for interest has been calculated and also giventhe variation. As we can see in the green house, thevariation is not the same for all the nodes and it solelydepends on the design of the greenhouse like heaterplacement, fan plancement etc. and that variation will

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TABLE IWEEKLY STATISTICS FOR ENVIRONMENTAL PARAMETER

VARIATION SENSED SENSOR NODE REGION 1 (A) AND NODEREGION 2 (B)IN THE GREEN HOUSE

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account for different growth rate for different plants. Asit is already mentioned that we covered all the regionsof interest using the sensor node,s hence for each sensornodes we have different variation as mentioned earlier.

B. Node-Region wise Growth Variation

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Fig. 3. (a). Growth variation of rice plants of different varietiesin node region 1, (b). Growth variation of rice plants of differentvarieties in node region 2, (c). Variation of rice variety 1 as observedin different sensing regions and (d). Variation of rice variety 2 asobserved in different sensing regions.

As shown in the Figure 3(a) and Figure 3(b), thegrowth variation of different plant varieties have beenshown in different node regions. As we can see differentvarieties has different growth rates as expected. Forexample the 3rd replica of variety 4 grew longer ascompared to other plant varieties which accounts forthe environmental parameters that are more favorablefor this variety and among other varieties, this givesgood performance. As we can see in the Figure 3(c)

and Figure 3(d), one particular plant varieties givesdifferent growth rate in different regions which is due tothe fact that environmental paramters are not same foreach region. Hence, It gives valuable information aboutthe placement of the plants in the greenhouse.

C. ANOVA AnalysisANOVA4. test has been run on multiple variables to

see the effect of diffrent variables on other variables,hence it provides an important statistics result. Hence,Table II shows the effect of temperature and humidityeffect on variety 1 plants. Since, diffrent varieties havediffrent growth patterns, ANOVA analysis has been per-formed and shown in the Table III. In the table, HRep1,HRep2, HRep3, and HRe4 correspond to replicas 1, 2, 3,and 4 of varity 1. The favorable factor for the growth ofvariety 1 and replica 1 governs good growth environmenthaving a little higher temperature and lower humidity.Growth differences between different varieties helps uschoosing the variety of the plant corresponding to fa-vorable environmental conditions and depending on theenvironmental factor we should choose plant variety formaximum growth. The data helps us in different domainsfirst choosing the variety and favorable conditions forthat variety as we have differences between growth ofdifferent replicas.

TABLE IIANOVA ANALYSIS SHOWING THE EFFECT OF HUMIDITY AND

TEMPERATURE ON PLANT VARIETY 1

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TABLE IIIANOVA ANALYSIS SHOWING PERFORMANCE RELATION AMONG

DIFFRENT REPLICAS OF PLANT VARIETY 1

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VI. CONCLUSION AND DISCUSSION

The present paper outlines the method through whichthe measurement data are available from the greenhouseto analyse and predict the various environmental factorsaffecting the plants in various manners. In the agricul-tural research adaptance of engineering system to thecondition of severe environment, open system, and long-term operation makes it easy to adapt and control theagricultural field. Furthermore, it will help in a lab-basedmeasurement in the actual field. However, the followingdesign constraint must be considered while designing arobust sensor network:

Robustness: The overall robustness of a sensor net-work depends on many factors: the ability of networkprotocols to recover from errors, the quality of radiotransmissions, the quality of the gateway link, how wellthe nodes withstand harsh outdoor conditions and mis-adventure from wildlife or humans, the accuracy of thesensors and software used for measurment of humidity,temperature, light intensity and lifetime of battery powersources. Hence, if the network does not perform reliablyin any one of these areas, then it may fail to deliver datafrom the field. In the present case we did not observe anydata loss and the sensor-network deployed in the green-house has been monitored through the remote connectiononce a week. Since the greenhouse environemnt is acontrolled environment, no-loss case may seem obviousbut in the case of real time monitoring of crop fieldloss of data communication is expected due to outside

interference in the communication.Accuracy: We want to know how feed grows from

changes in temperature and location and time of water-ing. That will help us modify the design and operationof the greenhouses. In the present work, sensor nodesare located at diffrent location and the placement oftemperature and light controlling devices will give adiffrent reading at diffrent location. Hence, the plantgrowth expected to be diffrent in diffrent geographicallocations inside the greenhouse. The accuracy of datacorresponds to such diffrences.

Lifetime of the sensor network: For the long timemeasurement battery life should be sufficient so that wehave lowest number of battery replacements. We ran theexperiment for four months and recorded data for every4 hours as suggested by agriculture researcher. Due tolow frequency of data collection, we only had to replacebattery once during the whole duration of experiment.To employ the optimization of battery power, the nodeshave been programmed to go to sleep mode when notsensing.

It is difficult to perform a fine judgment by automaticsensor, and there is understandable anxiety about sensorerror because of the possible consequences to livingthings. In our proposed system, the sensor-nodes areconstructed, not with the artificial intelligence to judgefor themselves, but managed by manual operating usingthe field users judgment. This system can also constructa man-machine system in which the field user himselfcan check and operate the peripheral equipment with asense of security in the case of an important subject. Withregard to future work, sensor network with embeddedintelligent should be explored for the automatic controland adaptability to the various environmental conditionswithout manual interference of field users. Intelligentsensors surely revolutionise the environmental research.The proposed design and implementation described inthis paper is effective and can be a guideline for furtherresearch in this area.

REFERENCES

[1] S. Sitharama Iyenger and Richard R. Brooks, Distributed SensorNetworks, Chapman and Hall/CRC publishing house, 2005.

[2] SD. Servetto and G. Barrenechea, “Constrained random walkson random graphs: Routing algorithms for large scale wirelesssensor networks,” in WSNA, Sept. 2002.

[3] F. Ye, H. Luo, J. Cheng, S. Lu, and L. Zhang, “A two-tier data dissemination model for large-scale wireless sensornetworks,” in Proceedings of the international conference onMobile Computing and Networking (ACM MOBICOM), Sept.2002.

[4] Seung-Jong Park, Ramanuja Vedantham, Raghupathy Sivaku-mar, and Ian F. Akyildiz, “A scalable approach for reliabledownstream data delivery in wireless sensor networks,” in

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Proceedings of ACM International Symposium on Mobile Adhoc Networking and Computing (MOBIHOC), Japan, May2004.

[5] C.M.J Alves-Serodio, J. L. Monteiro, and C.A.C. Couto, “Anintegrated network for agricultural management applications,”in IEEE International Symposium on Industrial Electronics,Pretoria, South Africa, 1998, pp. 679–683.

[6] ICUBE, “http://www.ece.iastate.edu/research/researchcenters/icube/projects/birrell/,” .

[7] Aline Baggio, “Wireless sensor networks in precision agricul-ture,” in Workshop on Real-World Wireless Sensor Networks,Stockholm, Sweden, 2005.

[8] Tokihiro FUKATSU and Masayuki HIRAFUJI, “Field monitor-ing using sensor-nodes with a web server,” Journal of Roboticsand Mechatronics, vol. 17, no. 2, pp. 164–172, 2005.

[9] Sandia experiments may reduce possibility of future waterwars and create additional solar power sources[Online],“http://www.sandia.gov/news/resources/releases/2004/gen-science/greenhouse.html,” .

[10] Zhuohui Zhang, “Investigation of wireless sensor networks forprecision agriculture,” American Society of Agricultural andBiological Engineers, 2004.

[11] Rice Growth Stages, “http://www.knowledgebank.irri.org/rp/growthstages/growthstages.htm,” .

[12] M Turon, “Mote-view: A sensor network monitoring andmanagement tool,” in The Second IEEE Workshop on EmbeddedNetworked Sensors, May 2005, pp. 11–18.

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application of sensor networks for monitoring of rice...

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