Volume 50 2008 CANADIAN BIOSYSTEMS ENGINEERING 2.9

Precision pesticide deliverybased on aerial spectral imaging

M-A. Michaud1, K.C. Watts1*, D.C. Percival2 and K.I. Wilkie1

1Process Engineering and Applied Science, Dalhousie University, Box 1000, Halifax, Nova Scotia B3J 2X4, Canada; and2Environmental Science, NSAC, Box 550, Truro, Nova Scotia B2N 5E3, Canada. *Email: chris.watts@dal.ca

Michaud, M.-A., Watts, K.C., Percival, D.C. and Wilkie, K.I. 2008.Precision pesticide delivery based on aerial spectral imaging.Canadian Biosystems Engineering/Le génie des biosystèmes auCanada 50: 2.9 - 2.15. Development of a sprayer system to usepreviously obtained aerial scans of Nova Scotian blueberry fieldsrequired the making of a prototype sprayer and then a full modificationof a HARDI sprayer with computer controlled solenoids for eachnozzle. The previously acquired aerial scans were made intoGeographical Information System (GIS) files which delineated theareas of blueberry, weed, and bare ground. These were then correlatedwith the Global Positioning System (GPS) coordinates obtained by areceiver on the tractor. Spraying accuracy was within one meter.Deviation of spray position included GPS error (including the presenceof the tree line), pixel size relative to the presence of the blueberryplants, and spray drift. The direction of travel did not affect theaccuracy. The time for the initiation of spraying (to account for thedelay in time for the spray to travel from the nozzle to the ground) wasfound to be a function of the sprayer system. The cost effectiveness ofthe system is largely a function of the time and personnel to make andprocess the aerial images to generate the GIS files. When this isautomated, the process will be cost effective. Keywords: precisionspraying, GPS, GIS, spectral imaging.

Le développement d’un pulvérisateur agricole partiellementautomatisé en utilisant des images spectrales aériennes débutat avec unprototype puis ensuite avec un système de pulverization HARDIauquel on ajouta des valves solenoidales controllées à partir d’unordinateur. Les images spectrales aériennes obtenus à une dateprécédent furent formattées et processées avec des systèmesd’information géographique (GIS) pour délimiter les mauvaise herbeset les aires dénudées dans les champs de bleuets sauvages de laNouvelle-Ecosse. Les documents informatiques sont ensuite corrélésaux informations d’un récepteur de position mondiale par satellite(GPS) installé sur un tracteur et connecté au programme. Les erreursde pulverization sont limitées à 1 mètre. Les déviations depulvérization sont dues aux erreurs de GPS (incluant la présence desarbres autour des champs), à la résolution des pixels en fonction desplants de bleuets et aux fluctuations éoliennes. La direction dedéplacement du tracteur n’a aucun effet sur les erreurs associées avecle système. Le temps nécessaires pour que le liquide agricole se rendeau sol à partir du moment où le programme initialize la commande estdépendant du système utilisé. Le coût du système dépend du temps etdu personnel requis pour l’acquisition et le traitement des imagesspectrales aériennes. Quand le système sera complètement automatisé,le pulvérisateur agricole sera rentable. Mots clés: agriculture deprécision, pulvérisateur agricole, GPS, GIS, imagerie spectrale.

INTRODUCTION

The implementation of precision agriculture is a growing areaof research and commercialization. Modern technologies suchas high precision Global Positioning System (GPS) receivers,

as well as powerful computers and sensors can enable farmersto keep track of factors such as crop yield, soil nutrient level,and livestock movements and store these factors in databases foranalysis. Furthermore, systems can be devised to correctdeficient factors on-the-go with minimal human input.

Site specific spraying is a form of precision agriculture thatrecognizes that field factors such as nutrient and moisturedeficiencies, as well as weeds and other pest infestations, mayvary significantly within a field and treatment needs to beadjusted accordingly (Bramley and Quabba 2002). OxfordFrozen Foods Limited (OFFL: a wild blueberry processor) andthe Wild Blueberry Producers Association of Nova Scotia haverecognized the need for precision spraying to reduce the amountof herbicide used on wild blueberry fields. Teamed with Dr.David Percival of the Nova Scotia Agricultural College (NSAC)and others under the Atlantic Innovation Fund program, OFFLlaunched an initiative to create a precision spraying systembased on spectral reflectance of the plants found in wildblueberry fields. While the end goal is to have an on-the-gosprayer using spectral or image sensors, a path to this was tocreate a precision sprayer using a pre-processed, GeographicalInformation System (GIS) analyzed map of a field based onaerial spectral imaging.

LITERATURE REVIEW

Thurston et al. (2003) stated that an important part of precisionfarming lies in the content and quality of the GIS. Generally,GIS is used for analysis, simulation, and model buildingpurposes. A number of agricultural equipment manufacturershave been using simple GIS software for the generation of cropyield maps (John Deere Literature 2005). Schou et al. (2001)identified spraying needs for forest agrochemical applicationsand created a GIS program for decision support. Qing andLinvill (2002) have used GIS to simulate regional peanut yields.Neméyi et al. (2003) used GIS as a decision-making andanalysis tool. In these cases, the GIS software is a tool foranalysis and simulation and is not used for on-the-go chemicalapplications.

GIS software has also been used for on-the-go systems,especially in variable rate application research. Larsen et al.(1988) varied the injection sprayer output based on a GIS mapand a differential GPS receiver. The injection of the activeingredient was changed uniformly across the boom, based on theGIS map. Al-Gaadi and Ayers (1999) tested the accuracy andlag time of a variable rate, direct injection sprayer based on aGIS map. It was found that the reaction time was 2.2 seconds,with an application rate error of less than 1%.

LE GÉNIE DES BIOSYSTÈMES AU CANADA MICHAUD et al.2.10

On-the-go sensing is being investigated globally todetermine whether the spectral classification of plants can beused to differentiate between weeds and crop and to detectnutrient deficiencies. The management of nitrogen deficienciesin plants is the most common precision farming activity (Noh etal. 2003). Chen et al. (2003) obtained satisfactory predictionsby using multi-spectral imaging to determine the nitrogencontent of rice crops. Noh et al. (2003) concluded that using amulti-spectral charge-coupled camera (CCD) could providesufficient on-the-go information on nitrogen deficiency in corn.Tumbo et al. (2001) created an on-the-go system for sensingchlorophyll at the low speed of 0.6 km/h. The same system didnot provide useful results at significantly higher speeds.On-the-go systems are being investigated for their potential todetect and correct for other nutritional deficiencies. Adamchucket al. (2002) showed that it is possible to detect the nitrate andpotassium levels of soils using ion-selective electrodes. Adsettet. al. (1999) found that by using ion-selective electrodes, thesoil nitrate level can be detected on-the-go to 95% accuracywith 6-second measurements. Gillis et al. (2001) successfullycreated a system that can detect and spray herbicides on weedsalong highways. This system used vision sensors and boomlessspraying nozzles. The discrimination between weed species canbe achieved using spectral analysis in green, red, andnear-infrared (Brown et al. 1994).

Tian et al. (1999) used real time image processing of spatialand frequency parameters to control solenoid valves on aspraying boom to apply agro-chemicals to a field on-the-go. Thesoftware was developed in C/C++. The system was successfulat a speed of 4.2 km/h.

This paper reports on the development and testing of aprecision sprayer for pesticide delivery based on apre-processed GIS analysis of aerial spectral imaging. Thegeneral objective of the research program was to develop andtest a precision sprayer for the pesticide delivery to wildblueberry field crops based on the pre-processed GIS analysesof aerial spectral imaging field data. The specific objectives ofthe work reported herein were: 1. to develop a system capable of reading GIS maps and

correlating locations on GIS files with current sprayerlocations in order to determine the need for spraying or not,based on the pixel definitions of the GIS maps; and

2. to test and evaluate the program and equipment accuracy.

METHODOLOGY

The development of the precision sprayer system occurred inthree major phases: computer analysis, retrofitting and testingof a smaller garden pull-type sprayer, and finally the retrofittingand testing of a full size 6.1-m boom. The final phase includedthe analysis of the potential chemical cost saving if this systemwas used commercially.

An initial control program was generated for thedevelopment and testing of the individual functions of thecomputer analysis. Another program was created to simulate atractor following a user-selected path across a virtual field. Thispart of the development was crucial in detecting and fixingproblems encountered with the correlation between thecoordinates of the map and the GPS. Also, this phase eliminatedproblems with the verification of the pixel location under theboom for the time period of one second of travel between GPSfixes.

The retrofitting and testing of a smaller prototype wascarried out using a four-nozzle, garden, pull-type sprayer. Thisphase was done to prove the concept of a GIS based precisionsprayer. This proof of concept sprayer was tested on an urbanparking lot where there was sufficient view of the sky to providea GPS fix with sufficient accuracy. The variation in GPScoordinates from one day to another was determined. The testingat this phase was used to demonstrate the problems associatedwith spray delay. Spray delay is defined as the time required forthe water to hit the ground from the time the computer sends asignal to the solenoid valve to open. The signal to the solenoidvalves had to be advanced by the same amount of time.

The last phase in the development of the precision sprayerwas the implementation of the technology into a commercialsystem. The effect of the proximity of the GPS receiver to thetree line was determined. The testing at this final phase did notneed to be extensive since the testing done at theproof-of-concept stage of development had proven thetechnology. However, the chemical saving in four commercialblueberry fields was determined.

Full scale system requirements

The precision spraying system developed for OFFL wasdesigned with the following requirements:

1. It must be able to operate a six-meter boom with 13 nozzles.

2. Each nozzle must be individually operated, initially in anon-or-off mode only, with proportional flow control to beadded at a later time.

3. The system must be easily operated from a laptop computerwhich would also record information necessary to computethe sprayed area of the field and the amount of chemical thathad been sprayed.

4. The system must operate at the standard spraying speed of5.2 km/h.

5. The control program used to operate the nozzles must readan electronic map previously generated from a GIS programand continuously receive information from a Starfire GPSreceiver.

6. The control program must be able to deal with both geodeticand Universal Transverse Mercator (UTM) coordinates toensure versatility when used commercially.

In order for the program to be able to take different GPSinput format, the program must read GPS information from aRS-232 protocol line reading National Marine ElectronicAssociation (NMEA) sentences. The location of the GPSreceiver, along with the speed and direction of travel, determineswhich pixel of the electronic map the control program inspectsto see when spraying was required.

SPRAYING SYSTEM

The project outlined in this paper differs from previous researchby separately controlling each nozzle on the boom, whilemaintaining the identical spraying rate of the active ingredientfrom each operating nozzle. While conceptual testing wascarried out on a retrofitted garden pull type sprayer, the fullsized sprayer used for this research was a HARDI NL (HardiIndustries, Davenport, IA) sprayer with a boom of 6.1 m and atank size of 300 litres. The pump was PTO operated and came

Volume 50 2008 CANADIAN BIOSYSTEMS ENGINEERING 2.11

with its own pressure regulating system. The feed line from thepump was separated into three lines at the distribution valve,each line feeding a section of the boom. The two end sectionsof the boom had four nozzles while the middle section had fivenozzles. A distance of 0.50 m separated each nozzle. Thenozzles were flat fan HARDI nozzles with a spray angle of 110degrees. The recommended boom height for these nozzles wasapproximately 0.5 meter. However, a different boom heightcould be used provided the new height was input to thecomputer program prior to spraying; it must remain unchangedduring spraying.

Using a series of T-joints, the line connecting thedistribution valve to each section was then connected to eachnozzle to which a solenoid valve was fitted as closely aspossible to each nozzle. The solenoid valves were ASCORedhat Next Generation (Florham Park, NJ) normally closedvalves. These solenoid valves operated on 12-volts andconsumed only 2 watts. Each valve had a maximum operatingdifferential pressure of 1030 kPa at a flow computed using thereported Cv value of the valve. The pressure loss across thesolenoid valve using the reported necessary flow rate across thenozzle at 310 kPa was approximately 4 kPa. Figure 1 shows aschematic of the sprayer after retrofitting. The electrical circuitused for the full sprayer employs solid-state relays obtainedfrom Crydom Co. (San Diego, CA). The controller/dataacquisition unit (DAQ) was the USB-1208FS manufactured byMeasurement Computing (Norton, MA) with 16 digital outputsand 8 single-ended or four differential analog inputs. Additionalsignal ports on the DAQ made it possible for different sensorssuch as a wind vane anemometer or speedometer to be added ata later time if desired.

The control program was written in C usingLabWindows/CVI from National Instruments (Austin, TX) (seehttp://myweb.dal.ca/kcwatts). The factors that were needed toextrapolate the spray width location included the nozzle angle,boom height, speed, and the distance between the GPS receiverand the boom. These were first input by the user prior to

spraying. The program was designed tonot respond until all parameters werekeyed in. The control program wasdesigned to read a file from either oftwo GIS programs; Idrisi Kilimanjarofrom Clark Labs (Worcester, MA) andArcGIS software from ESRI(Redlands, CA). The Idrisi formatstored the map information in a text fileand the pixel values in a byte format, inseparate files. The ESRI exported textfile stored both map information andpixel values in the same file. Bothformat types of GIS maps were readand temporarily stored in arrays priorto spraying. Additionally, to keep theprogram versatile, both UTM andgeodetic coordinates could be usedwhen loading the map. The controlprogram extrapolated the position ofeach nozzle spray width over theone-second interval between each GPS

fix. The program then transformed this position into a pixelnumber matching those of the electronic maps stored in memory.If a pixel was marked for spraying, then the program sent asignal to the DAQ, which in turn activated the correct solenoidvalve.

Additionally an analysis program was designed, also usingthe C programming language, to read and display theinformation such as where the spraying occurred on the field andwhere the tractor traveled after spraying.

TESTING

Prototype system

The purpose of the first set of tests using a smaller pull-typeretrofitted garden sprayer was to determine the ability of thesystem to spray an area fully and accurately. Large pieces oflitmus paper measuring 0.62 by 0.45 meters were fabricated andplaced in chicken wire pouches at a known location of a parkinglot. The parking lot was chosen for its accessibility and itsunobstructed view of the sky, thus ensuring an accurate GPS fix.Prior to testing, the parking lot was mapped so that specificlocations on the parking lot had known coordinates with highaccuracy. An electronic map was constructed using Idrisi GISsoftware with a spray patch of 1.28 by 1.28 m. The corner of thisspray patch corresponded to that of an intersecting parking lotline. The sides of the patches were oriented to the north. Waterand acetic acid were used as the fluid.

For the first two replications, the litmus paper was placed insuch a way that an area of 5 meters square was set out to recordoverspray. Replication #3 had a larger area (6.67 m x 6.67m) torecord more overspray. The test wind condition was reported byEnvironment Canada at Halifax International Airport (21 kmfrom the spraying site) as winds gusting up to 10 km/h comingfrom the north. The area sprayed in this test was wide openminimizing the effect any structures or trees might have had onwind speeds. It was envisioned that an anemometer would beinstalled on the final product. The computer program recorded

Fig. 1. Modified HARDI sprayer.

LE GÉNIE DES BIOSYSTÈMES AU CANADA MICHAUD et al.2.12

when the nozzles were turned on during the test with thecoordinates of the GPS fix. The data were tabulated andgraphically compiled (e. g. Fig. 2) where areas of minimal spraypattern indicate areas of either drift or when part of the spraywas moved by the wind, resulting in an incomplete change ofcolour in the litmus paper.

The conclusion of the first set of tests was that an advancedtime of activation of the solenoids needed to be incorporatedinto the control program to account for the delay from the timeof the electrical signal to the solenoid to when the spray reachedthe ground. To determine the spray delay, a simple moisturesensor was made using two wires twisted together with papertissue between. These sensors were placed on the ground andthe time from the electrical signal to the solenoid to the timethere was a resistance change between the two twisted wires wasdefined as the delay time. An average delay of 0.57 seconds forthe prototype system was obtained. The wind speed was nottaken into account when determining spray delay. Sincespraying and wind speed varies slightly within the guidelines setout by the sponsoring company, it was assumed that one valuefor spray delay for each type of hardware would be sufficient tocover all spraying conditions.

The purpose of the second set of tests using the prototypewas to verify that the timing advance indeed corrected for thediscrepancies encountered in the first tests. Six cardboardsquares, each representing four 0.50 m pixels, were placed onthe same parking lot in two rows with the center of each pixelat a previously surveyed location. The prototype was thenpulled across the two rows and the spray deposition wasobserved. Since the day was chosen with low relative humidity,the over-spray caused the parking lot to darken. The center ofthe spray deposition was measured from the center of thecardboard squares at each area and a digital picture was taken.Different angles of approach and different speeds were alsotested.

Full scale sprayer

Testing of the system using the full-size Hardi sprayer wasconducted on one of the Wild Blueberry Research Centre fields

in Debert, Nova Scotia, using a GIS map with simulatedspraying locations. The sprayer was driven over the spray areasand a wooden stake was dropped by a researcher walking behindthe sprayer, when he observed the center nozzle to turn on oroff. The location for the wooden stake was then surveyed andrecorded. Another test performed at the Debert location was tophysically mark the intended spray area using a rope and tomeasure the distance between the rope and the location wherethe water from the center nozzle was observed to hit the ground.The variation of the GPS position was also noted as the sprayerapproached the tree line surrounding the field.

Simulated test for chemical cost saving

The final test was a simulated spray test using GIS maps createdfrom actual aerial spectral data. The purpose of this test was tocreate an output file which could be analyzed for the costsavings had the precision sprayer been used with the path acrossthe field generated by a GPS simulator. The aerial spectral datafiles were generated using a CASI (Compact AirborneSpectrographic Imager). The sensor output was analyzed by alocal company to generate binary maps to be used for the test.The results were analyzed for the cost saving of sprayingherbicides and fungicides. Four fields were analyzed; Scots field(near Mt. Thom), Kempton, Londonderry, and Mt. Thom, all inNova Scotia. Each field was analyzed twice, once for thedelivery of a herbicide commonly used on blueberry fields, andthe other for a fungicide. Two GIS files were needed per field,one representing the weed population and the other theblueberry plant population. One example is shown in Fig. 3(blueberries) and one in Fig. 4 (weeds). Bare spots appear inboth figures as white. The information on pesticide costs camefrom data sheets from the manufacturing company. The sprayingrate was established after consultation with the sponsoringcompany, OFFL. It was assumed that, during blanket spraying,no effort is made to adjust the spraying parameters thus coveringthe entire field.

RESULTS

Prototype testing

There were differences among the spray patterns of the threepasses in the first test sequences using the prototype sprayer.These are readily explained by the amount of drift created by thegusting wind, the GPS error, and the spray delay error. Thelatter was not accounted for at the time of the test. (Figure 2shows only one of the passes.) Also the velocity and path of thesprayer were not consistent during the test since the vehicle wasmanually controlled and so it was very difficult to have the towvehicle trace exactly along the same parking lot line every timeand at the same desired speed. The operating speed was setbelow 10 km/h, remaining near 5.2 km/h which was the requiredmaximum speed set by the sponsoring company.

The area of the pixel sprayed during the first test was alsorecorded (Table 1). The control program was designed to turnon all nozzles over an area needing to be sprayed, regardless ofwhether the spray of the nozzle extended beyond the designatedspray area. The analysis and control programs used the pixel asthe unit area of measurement. In this test, as is evident in Fig. 2,the direction of travel is oblique to the pixel. Thus as the sprayerpassed diagonally across a corner of an adjacent pixel, theappropriate nozzle was turned on for a brief moment. Therefore,

Fig. 2. One replication of the prototype sprayer.

Volume 50 2008 CANADIAN BIOSYSTEMS ENGINEERING 2.13

when this second nozzle was turned on, the pixel adjacent to thetarget spray pixel was partially sprayed but recorded as beingfully sprayed by the programs.

In this test, the pixel size was approximately the spray widthof one nozzle. It is evident that when the required spray areawas considerably greater than one pixel, the amount ofoverspray would be a much smaller percentage than it was inthe first round of tests. Similarly, the underspray or the area ofthe spray pattern not sprayed, would also be a smallerpercentage.

It also became evident that, using the SF2 protocol, the GPSposition varied slightly from one day to another and themagnitude of this needed to be documented as a potential sourceof error. These GPS position results as well as for the second setof tests can be found in Table 2. The distance between the centerof the spraying patch and that of the designated spraying areawas measured and compared to that of the estimated distancebased on the computer analysis of the test. The positionalprecision of the centers of the six pixels was measured andtabulated in Table 2. It was found that the positional variabilityof the GPS receiver ranged between 0.1 to 0.89 m for any given

day. Those errors were not large enough toprevent the designated spray area beingfully sprayed during the test regardless ofdirection of approach. The results using theprototype gave a good overview of what toexpect in the final design testing. Althoughboth sprayers were different, the parametersused in the prototype were the same as thoseused in the final design. Some notableexceptions are the values representing boomheight, nozzle spray angle, and the distanceof the GPS from the boom.

Fig. 3. GIS map of Kempton field showing blueberriespotentially requiring a fungicide application.

Fig. 4. GIS map of Kempton field showing weedpopulation requiring herbicide application.

Table 1. Areas sprayed during the first prototype test.

AreasReplication 1

(m2)Replication 2

(m2)Replication 3

(m2)

Total recorded area sprayedArea of pixelArea of pixel sprayedArea under-sprayedArea over-sprayedArea analysis program record as sprayed

3.171.641.420.221.76

4.91

3.201.641.260.381.94

3.28

3.771.640.740.893.03

4.91

LE GÉNIE DES BIOSYSTÈMES AU CANADA MICHAUD et al.2.14

It was concluded that the program achieved its goal ofspraying a pixel accurately at speeds of less than 10 km/h.Also, it was found that the area of overspray for a smallpatch (~ one pixel) was quite large. The direction of travelhad an affect on the shape of the area of spray deposition butthe pixel was fully sprayed in all tests.

Final design field testing

The first test results using the full scale system at Debertshowed a different spray delay time factor from that of theproof-of-concept sprayer. A new value was needed in thecontrol program of the HARDI sprayer based on when the spraywas observed to hit the ground. The results in Fig. 5a showsignificant under-spray as the result of an incorrect time factoradjusting for the spray delay. It was found that the advance timefactor needed for the HARDI sprayer was 0.8 seconds ratherthan the 0.57 seconds used for the proof-of-concept sprayer.Even with the spray delay corrected, a small amount ofover-spray and under-spray resulted. This discrepancy may bedue to human error in placing the wooden stake or positionalvariability of the GPS receiver. The accuracy of sprayplacement with the correct delay (the distance between theboundary of the spray area and the surveyed location of thewooden stake) can be seen in Fig. 5b. The pixel resolution forthis test was 0.5 m. The points on Fig. 5 represent the recordedGPS positions, whereas, the lines between those points show theapproximate path and direction of travel. The speed of thesprayer was approximately 5.2 km/h.

The maximum error due to the proximity of the tree line wasfound to be 1 m.

Simulated spray test

The final testing, measuring the estimated chemical cost savingwhile using the precision sprayer, was performed using asimulated path through the field with a GPS simulator. Theaerial spectral images were taken one or more years before theywere used to generate the GIS maps and therefore the sprayercould not be used on the actual field since the weed andblueberry plant distribution in the fields had changed. Asimulated spraying test alone was used. Tables 3 and 4 show thechemical cost saving and the cost of creating the precisionsprayer. The cost of the GIS map creation for this project wasconsiderably higher than the figure noted in Table 4 since theprocess for developing the GIS maps was being researched asthe maps were being created. If the process could be automated,the cost for the four fields would significantly decrease toapproximately $880, the cost of the flights. This would make theprecision sprayer an economically viable technology. The onlyrecurring cost would be the creation of the GIS map every year.

Table 3. Chemical cost ($) saving using the precision sprayer.

Scots field(15 ha)

Kempton(8 ha)

Londonderry(24 ha)

Mt. Thom(9 ha)

HerbicideFungicide

2932264

1171161

3291549

1133190

Table 2. GPS positional variability (m) for locations used in the second prototype test.

Point 1 Point 2 Point 3 Point 4 Point 5 Point 6

Maximum positional variability on survey day 1 (70 points)Maximum positional variability on survey day 2 (70 points)Maximum positional variability on survey day 3 (70 points)Maximum position difference from average position of all 3 daysPositional variability survey on day of testing

Recorded difference between center of target spray pixel to actual spray patternEstimated difference between center of target spray pixel to actual spray patternArea of spray patch displayed by analysis program (m2)

0.110.080.891.381.1@270E

1.25@145E

0.25@90E8.25

2.560.040.121.260.5@315E

1.16@180E

0.35@210E10.5

0.120.040.130.930.4@225E

0

0.55@180E9.75

0.060.040.010.70

*

1.25@80E

0.5@90E7.75

0.070.040.060.63

0.56@180E0.9@135E

0.35@135E9.25

0.060.040.040.42

0.67@210E

1.00@90E

0.50@90E7.25

*Measurement taken for this point was done immediately after the GPS power had been cut off.

Fig 5. Effect of the time delay factor on the applicationerror for the full size sprayer.

Table 4. Equipment and GIS map creation costs of theprecision sprayer.

Item Cost ($)

GPS receiverLaptop computerUSB-1208FS controller13 solenoid valvesHydraulic and electrical componentsGIS map creation

73102644269

2830560

880 (aerial time only)

Total 14,493

Volume 50 2008 CANADIAN BIOSYSTEMS ENGINEERING 2.15

In the eventuality of an economically viable technology, thelaptop computer could be replaced by a computer using solidstate memory only, to reduce the likelihood of damage to thehard drive from vibrations of the tractor in the rough fields.

The errors associated with the cost saving may be quite highdue to the uncertainty of the time and costs associated with theGIS map creation. There also was some uncertainty as to theresolution and accuracy of the GIS data. The time needed toprocess the map may allow weed distributions to change,introducing errors in actual plant composition. The errorassociated with slight overspray or underspray of a particulararea may be increased if the field is spotty instead of having afew large areas needing spray, thus directly affecting costsavings.

CONCLUSIONS

The developed precision sprayer delivered spray to the targetareas successfully at the designated spraying speed of 5.2 km/h.The system could handle relatively high positional inaccuraciessince the system always chose to spray even if only a smallpatch needed spraying. The maximum positional inaccuracydepended on the size of the pixel and the spray width of anozzle. Tree lines and hilly terrain degraded the GPS signal, buttypical blueberry-field topography and tree lines did not preventthe system from spraying the field within the GPS and GISnominal accuracy. A further correction to the GPS accuracycould be provided by real time kinematics (RTK). Based on thisstudy, the chemical cost of using the precision sprayer is notenough to offset the cost of the GIS map creation. The chemicalcost savings increase as the field area is increased but so wouldthe cost of the GIS map creation.

RECOMMENDATIONS

An automated process to generate the GIS files from the aerialsurvey would greatly reduce the cost of the precision sprayingsystem allowing the saving in chemical costs to offset the othercapital and operating costs of the GIS map creation andoperation of the precision sprayer.

ACKNOWLEDGEMENTS

The authors acknowledge the Natural Science and EngineeringResearch Council and Oxford Frozen Foods Limited for theirsupport throughout the development of this system.

REFERENCES

Adamchuck, V., A. Dobermann, M.T. Morgan and S.M.Brouder. 2002. Feasibility of on-the-go mapping of soilnitrate and potassium using ion-selective electrodes. ASAEPaper 02-1183. St. Joseph, MI: ASABE.

Adsett, J.F., J.A.Thottan and K.J. Sibley. 1999. Development ofan automated on-the-go soil nitrate monitoring system.Applied Engineering in Agriculture 15(4):351-356.

Al-Gaadi, K.A and P.D. Ayers. 1999. Integrating GIS and GPSinto a spatially variable rate herbicide application system.Applied Engineering in Agriculture 15(4): 255-262.

Bramley, R.G.V. and R.P. Quabba. 2002. Opportunities forimproving management of sugarcane production through theadoption of precision agriculture: An Australian perspective.International Sugar Journal 104(1240):152 -161.

Brown, R.B., J.-P.G.A. Steckler and G.W. Anderson. 1994.Remote sensing for identification of weeds in no-till corn.Transactions of the ASAE 37 (1): 297-302.

Chen, S., C.-W. Huang, C.-C. Huang, C.-K. Yang, T.-H. Wu,Y.-Z. Tsai and P.-L. Miao. 2003. Determination of nitrogencontent in rice crop using multi-spectral imaging. ASAEPaper 03-1132. St. Joseph, MI: ASABE.

Gillis K.P., D.K. Giles, D.C. Slaughter and D. Downey. 2001.Injection and fluid handling system for machine-visioncontrolled spraying. ASAE Paper 01-1114. St. Joseph, MI:ASABE.

John Deere Literature. 2005. Ag management solutionsGreenStar common components. http://www.deere.com(2005/06/01).

Larsen, W.E., D.A. Tyler and G.A. Neilson. 1988. Fieldnavigation using global positioning system (GPS). ASAEPaper 88-1604. St. Joseph, MI: ASABE.

Neméyi, M., P.Á. Mestweházi, Zs. Pecze and Zs. Stéphán. 2003.The role of GIS and GPS in precision agriculture.Computers and Electronics in Agriculture 40:45-55.

Noh, H.K., Q. Zang, B.S. Shin and S. Han. 2003. Multispectralimage sensor for detection of nitrogen deficiency in corn byusing an empirical line method. ASAE Paper 03-1135. St.Joseph, MI: ASABE.

Qing, Z. and D.E. Linvill. 2002. Integrating crop models intoGIS to predict regional crop yield. ASAE Paper 02-9916. St.Joseph, MI: ASABE.

Schou, W.C., B. Richardson, M.E. Teske and H.W. Thistle.2001. SpraySafe Manager 2 integration of GIS with an aerialherbicide application decision support system. ASAE Paper011-050. St. Joseph, MI: ASABE.

Thurston T., T.K. Poiker and J.P. Moore. 2003. Integrated

Geospatial Technologies: A Guide to GPS, GIS and Data

Logging. New Jersey, NY: John Wiley & Sons, Inc..

Tian, L., J.F. Reid and J.W. Hummel. 1999. Development of aprecision sprayer for site-specific weed management.Transactions of the ASAE 42(4):893-900.

Tumbo S.D., D.G. Wagner and P.H. Heinemann. 2001.On-the-go sensing of chlorophyll status in corn. ASAE Paper01-1175. St. Joseph, MI: ASABE.