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Selection of Lawn and Garden TractorsUsing an Expert SystemB a b ak Neg ah b an , R i ch a rd C . F l u ck , W. Dav i d S h o u p

MEMBERASAE MECHANIZATIONMEMBERASAE

ABSTRACTNO known guidelines have existed for consumerselection of lawn and garden tractors. Certain rulesfor selection were developed from a knowledge extra ctionprocess from experts (top salespersons) for two majorbrands of outdoor products . Their knowledge was usedin constructing expert systems to assist consumerselection. Each brand required its own uniqueprogramming logic in order to accomplish properselection. The accuracy of the expert systems was verifiedby comparing the model output to salesmens' selections.P R OB LEM S TATEM ENT

Selecting the appropriate tractor for one's needs hasserious consequences. A lawn and garden tractor is along-lived and expensive piece of equipment. A tractorthat is too small will require excessive time to operateand have short life and poor reliability, whereas a tractorthat is too large will have needlessly high fixed costs. Inaddition, options are important for they enable certainoperations which are otherwise excluded. Thousands oflawn and garden tractors are purchased annually withlittle knowledge of tractor operation, leading to mistakesin selection.Guidelines did not exist in the lawn and gard en tracto rindustry for assis t ing customers and beginningsalespersons in selecting lawn and garden tractors. Mostoften the consumer must depe nd on the judgm ent of thegarden tractor salesperson to recommend the bestgarden tractor that meets the buyer 's needs.OBJECTIVE

The objective of this work was to develop an expertsystem for lawn and g arde n trac tor selection. This systemcould be used in aiding salesmen and consumers inselect ing appropriate equipment .R EVIEW OF LITER ATUR E

American Society of Agricultural Engineers StandardS323.2 (ASAE, 1986) defines a lawn and garden tractorArticle was submitted for publication in August, 1987; reviewed andapproved for publication by the Power and Machinery Division ofASAE in March, 1988.Approved for publication as Florida Agricultural ExperimentStation Journal Series No. 8326.The use of trade names in this publication does not implyendorsement by the University of Florida of the products named norcriticism of similar ones not named.The authors are: BABAK NEGAHBAN, formerly GraduateResearch Assistant, University of Florida and currently TeachingAssistant, University Center, Dschang, Cameroon; RICHARD C.FLUCK, Professor; and W. DAVID SHOUP, Associate Professor,Agricultural Engineering Dept., University of Florida, Gainesville.

as a self-propelled machine that is designed to supplypower for home lawn, home garden and yard maintenance implements. The tractor should have somemeans to lift implements, and implements should beseparate from the tractor.No literature was found that provided any guidelinesfor lawn an d gard en tra ctor selection. Very little researchhas been published concerning lawn and garden tractorsdespite Outdoor Power Equipment Institute's (1986)forecast of 1,020,000 units of riding lawn and gardentractors to be shipped in the U.S. in 1986, or about twobillion dollars of product. Past shipments indicate thereare a total of several million in use. By comparison, theagricultural tractor market in the U.S. was only 108,795units in 1985, or approximately 4.7 billion dollars ofproduct (Implement & Tractor, 1987).

P R OC EDUR ESIdentifying and Interviewing the ExpertsFlorida regional managers for Ford, John Deere andKubota were identified and contacted. Each regionalmanager was asked to name three of the top salespersonsin the North Central Florida area. The criteria forselecting the salespersons were their records of high salesvolume and long-term sales experience. Top salespersonsfrom Kubota and John Deere organizations were chosento participate in a knowledge extraction processregarding how they selected a tractor to fit a customer'sneeds. Selection rules were derived from the extractionprocess on which to base the expert system.The salespersons identified by the regional managerswere contacted by phone and interview dates wereobtained. The Sondeo approach was used in theinterview process (Hildebrand, 1980). In the Sondeoapproach, interviews are done in a very casual andinformal manner. Very few questions are preplanned.The salespersons were asked to express in their ownwords how they go about recommending a garden tractorto a potential customer.Selecting a Decision Making ToolWeiss and Kulikowski (1984) defined an expert systemas a computer model that can handle real worldproblems requiring an expert 's interpretation. Helping aperson select a proper garden tractor certainly doesrequire assistance from an expert. Expert systems mimicthe deductive reasoning of human experts (Negoita,1985).In order to develop a source of information to aidcustomers, the need for interaction is essential.Knowledge systems or artificial intelligence systems arenecessary for true, logically oriented problem solving andfor two way communication. Expert systems can

Vol. 4 3):September, 1988 1988 American Society of Agricultural Engineers 0883-85 42/88/040 3-02115 02.00 211


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facilitate an analysis of each consumer's unique andspecific needs.Expert systems can show the consumer lines ofreasoning, can show the pool of information being used,can work with nonquantitative data and can work withincomplete information; all conditions normally found inselling. Thus this new tool was tried.Developing the Expert SystemOnce the expert knowledge had been gathered, thenext step was organization of the knowledge into a usableformat. Information that the expert salespersons hadprovided was divided into four different categories. Thefirst category was information critical to the selection ofgarden tractors . The expert system was unable to make arecommendation if this information was not available.Relationships between size of work area, type of activitiesand recom mended garden tractor model were consideredto be essential.The second category was information that did notinfluence the garden tractor selection but did influencethe tone of the recommendation. The customer'sevaluation of his previous garden tractor, if any, was themain component of this category. For example, if thecustomer felt that his previous garden tractor was toosmall and the expert system was recommending an evensmaller garden tractor, the tone of the recommendationwould be more cautious and would ask the customer tolook further into his special conditions. On the otherhand, if both the customer and the expert system agreedon the incapability of the previous garden tractor, thetone of the recommendation would be much stronger.The third category consisted of information that hadno bearing on the recommended garden tractor butnevertheless was appropriate to be included in the expertsystem output, such as information about garden tractormaintenance and safety recommendations.The last category consisted of information obtained inthe interviews with the garden tractor salespersons thatwas irrelevant to the garden tractor selection process.Such information was ignored.The heurist ic information obtained from theinterviews of salespersons of two brands of tractors wassufficiently different so that a single generic expertsystem model could not be constructed to include both.Therefore, separate models were constructed for the twobrands. The first two above categories of informationwere organized into two decision trees for garden tractorselection.Creating the Source Code for the Expert SystemInsight 2 + (Level Five Researc h, 1985) was the ex pertsystem shell selected to incorporate the decision rulesand form the model. Reflex (Borland, 1985) was used toenhance the input and output of Insight 2+ Thisenabled the expert system to be user friendly anddisplay results in a graphical format. These softwareprograms were selected on the basis of availability, costand ease of use. The decision trees for the modelsincluded approximately 4500 steps for the John Deeremodel and 3500 steps for the Kubota model. Thedecision tree for the selection of Kubo ta lawn and gard entractors is shown in Fig. 1. Once the knowledge base hadbeen transfe rred to workab le decision trees, the next step

was to develop the source code for Insight2+ programsthat would duplicate the information in the decisiontrees.Most of the body of programs created with Insight2+is contained in the goals and rules sections. Th e goals forthe garden tractor decision selection system wererelatively simple. The intention was for the system to beable to recommend a garden tractor model for thespecific needs of a particular customer. Therefore, eachgarden tractor model that was covered in the knowledgebase was defined to be a goal in the program. Anexample of one of the possible goals in the program is:1. Appropriate Garden Tractor is KUB OTA G3200

HAVE YOUPREVIOUSLY OWNEDA GARDEN TRACTOR '

WAS ITA KUBOTAGARDEN TRACTOR ?

WHAT MODELWAS THE OLDGARDEN TRACTOR?WHAT WAS THE OLDGARDEN TRACTORENGINE'S HORSEPOWER?

DO YOU FEEL THAT YOUROLD GARDEN TRACTOR WASTOO SMALL, JUST RIGHTOR TOO LARGE ?

DO YOU PLAN TO DOGARGEtt&iG WfXH YOURGARDEN TRACTOR

WHAT IS YOURESTIMATED MOWINGAREA (ACRES) ?

SELECTG 5 2 0 0

10 TO 15 1SELECTB 7200

20 OR MOREGARDEN TRACTORNOT APPROPRIATE

| 5 TO IP' SELECTB 8 2 0 0SELECTG 6 2 0 0

0 TO 1.5 | [ 1.5 TO 2. 5~ |

SELECTG 3 2 0 0 SELECTG 4 2 0 0

WHAT t$ YGU&ESTIMATED MQW-IN6AREA NACRES* 1

(Fig. 1 continues)212 APPL IE D E NGINE E RING in AGRICUL T URE


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2 OR LESS 1 | 2 TO 6 "| | 6 TO I 2 ~ | | 12 TO 2o" "| 1 20 OR MORE

SELECTB 5 2 0 0 SELECT8 6 2 0 0 SELECTB 7 2 0 0 SELECTB 8 2 0 0 GARDENTRACTORNOTAPPROPRIATE]

3 5 TO 10

WHAT IS YOURESTIMATED GARDENINGAREA (ACRES)?

: oSELECTB 6 2 0 0 SELECTB 7 2 0 0 SELECTB 8 2 0 0 GARDENTRACTORNOTAPPROPRIATE

H 'O TO 15

WHAT IS YOURESTIMATED GARDENINGAREA (ACRES)?

SELECTB 7 2 0 0 SELECTB 8 2 0 0 GARDENTRACTORNOTAPPROPRIATE

0WHAT IS YOURESTIMATED GARDENINGAREA (ACRES)?

SELECTB 82

GARDENTRACTORNOTAPPROPRIATE^

Fig. 1Decision tree for the selection of Kubota lawn and garden tractors.

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Insight 2+ would ask the user the relevant q uestionsand try to verify this goal. If wha t the user entered wouldnot verify this goal, then In sigh t 2 + would try to verifyanother goal. Through the process of elimination,Insight 2 + would eventually find a goal tha t it couldverify and recommend a specific garden tractor model.To take care of situations where no garden tractor isrecommended one final goal was added to the list. Thisgoal stated that a garden tractor is not recommended forthe kind of work the customer wants to do. An exampleof such a situation was that the customer wanted to dogardening on more than 20 acres.The next step in the program was to define the rulesneeded to achieve the stated goals. It must be understoodthat there must be a rule defined for every possiblepathway in the decision tree that has been created. If apossible pathway in the decision tree is left out of theprogram , Insight 2+ wil l fail to make a recomm endationwhen the customer's situation is the same as the missingpathway. In the decision tree for the garden tractorselection there are from 16 to 48 different situations inwhich any single garden tractor model is recommended.Therefore, for any given tractor model there are from 16to 48 different rules that can achieve the goal ofrecomm ending that model . For example, rule num ber 10for recommending the Kubota B-7200 tractor is asfollows:

RULE For Kubota Option 10If You have previously owned a garden tractorAND It was a KUBOTA garden tractorAND Your previous garden tractor model IS toolargeAND You will use this tractor for gardeningAND Estimated mowing area in acres < 10AND Estimated gardening area in acres > 6AND Estimated g ardening area in acres < = 12THEN Appropr ia te Garden Tractor i s KUBOTAB7200AND Selection := B7200AND Mowing := 15AND Gardening := 12AND DISPLAY Recommendation 8

The first line in this rule is the rule title and adeclaration that what comes after is a rule. The secondthrough fourth lines determine the tone of therecommendation (this issue has been previouslyexplained). The next four lines determine whether thegoal (line 9) can be verified. The last four lines are usedto make Insight2+ more user friendly. The entire exp ertsystem for garden tractor selection was a collection ofrules similar to the one presented above. Insight 2+sorts through these rules until it can carry out one to thevery end and verify its stated goal.One final consideration was to develop a program thatwould be easy to use, even for a person with no c omp uterknowledge. This required that there be adequateinstructions on the use of the computer. A number ofscreens were created at the beginning of the programthat presented the user with detailed information aboutthe computer keyboard and the keys that he would needto use (i.e. number keys, curser keys and function keys).In addition, every time the user was prompted with a

question he was given instructions on how to enter hisanswer.In order to make quest ions understandable, detai ledexplanations were presented to the user each time he wasprompted with a question. In some cases the user had theoption of going to a second screen of information formore detailed explanations.Finally, the user received a full screen of informationthat discusses the recommended garden tractor (theDISPLAY command line). This screen also includedopinions about the customer's previous graden tractor, ifany. If differences of opinion existed between thecustomer and the expert system, this was also includedon this screen. Essentially, an attempt was made tocreate an expert system that could make effectiverecommendations about garden tractors and also befriendly enough to be easily used by the customer.The expert system developed provides each dealershipwith capability to accumulate customer data. Thecustomer accesses the expert system via an IBMcompatible microcomputer which can be located at thedealership or which can be a portable unit provided by asalesperson. The accumulated data could be used toimprove or update the expert system. Reflex stores eachresponse from each cu stomer for any given time frame ina data base. Reflex also contains a report writer programwhich organizes the data into tables or graphs.Field Testing the Expert SystemOnce the two models were created, each was taken forevaluation to eight expert salespersons for that brand inthe region. Salespersons were given the expert system inorder to test hypothetical consumer situations. Thesalespersons then evaluated the expert system bycomparing their conclusions with those of the expertsystem.A questionnaire was used as a tool for evaluating thesalesmen's agreement that the expert system properlyreflected their decision process. The questionnaireconsisted of 14 statements; in addition to approximationof reality, it also included statements relating to themodel's ease of use and value to the salesperson.Salespersons were asked to give each statement a scorebetween 1 and 5; 1 being excellent, 2 good, 3 average, 4fair and 5 poor. Salespersons were also given theopportuntiy to write comments on the second page of theevaluation sheet.

RESULTS AND DISCUSSIONAll of the evaluators stated verbally that they felt theexpert system properly reflected the decision process formaking recommendations. An analysis of the question

naires is provided in Table 1. The mean scores for all ofthe statements were between 1.250 and 1.875.The rangewas from 1 to 5. The s tand ard deviations of the scoresranged between 0.447 to 0.957. The 95% confidenceintervals for the mean scores ranged from a minimumlow of 0.880 to a maximum high of 2.334.Amount of instructions, ease of use after workshopand usefulness of saving customer information receivedthe lowest (best) mean scores (1.250) in the evaluation.The first two factors emphasized the user friendliness ofthe expert system.214 APPLIED ENGINEERING in AGRICULTURE


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TABLE1 STATISTICAL ANALYSIS OF MODEL EVALUATION SCORESSta tement

Reasonable amountofins t ruc t ionsLanguageis unders tandableProgram isconcise,notwordyScreen legibilityCouldbeused after a shortworkshopEasyto useApproxim ates rea l i tyProgram proceedsin alogicalmanner to solve problemMakesareasonablerecommendat ionPotent ia l asasalespersontra ining toolPotent ia l asa sales toolSaving custom er inform ationis usefulInformation summary and graphscreated from retainedinformation are usefulR e c om m e nd theuseofthisprogram

Mean score

1.2501.3131.6251.875

1.2501.3121.625

1.375

1.313

1.4381.500

1.250

1.375

1.500

Std. dev.

0.4470.4790.7190.957

0.4470.6020.619

0.619

0.479

0.5120.730

0.500

0.619

0.632

95% C . I .

1.03-1.471.09-1.551.27-1.981.41-2.34

1.03-1.471.07-1.611.32-1.93

1.07-1.68

1.08-1.55

1.18-1.691.14-1.86

0.88-1.37

1.07-1.68

1.19-1.81

The rangeofscores was from 1(best) to 5 (worst) .

Most evaluators felt that accumulating customer datawas very important. However, evaluatorsatsomeofthesmaller dealers felt that this feature would not be ofmuchuse tothem . They felt that theyhadsucha smallnumber of customers that they could mentally keep trackof their customer base. Nevertheless, they gave highscoresto thestateme nt conce rning this feature becau sethey understood how such an option couldbeuseful inother situations. The larger dealerships, on the otherhan d, liked this feature very much. One dealer felt tha t ifhe had had suchacapability d uring the previous year,hewouldnothave been stuck with someofthe mo dels th athe hadwhen interviewed.A number of differences exist between the way theKubota and John Deere salespersons perceive theabilities of each of their garden tractor m odels . As aresult, there are a n u m b er of differences between theexpert systems developed for each company's productline, because the ex p er t s do not always agree.Therefore the two expert systems did not alwaysrecommend similar powered modelsin agiven situation.John Deere salespersons recommended their lawnandgarden tractorsfor use onareasup to 5acres.Onareaslarger than 5 acres they recommended a larger lineoftheir tractors termed utility tractors.Incontrast , K ubotasalespersons recommended their lawn and gardentractorsforuse on area saslarge as 20 acres. Asa result,the expert system for Kubota lawn andgarden tractorscan recommend lawn andgarden modelsfor a broaderrange of customer situations.One of the major differences b etween the expertsystems for the twocompanies is the criteria used for

recommending larger modelsineach compan y's produ ctline. Kubota salespersons hadcriteria for their full lineof lawn andgard en trac tor mo dels. Consequently, theexpert system for Ku bota ga rden trac tor selectioncanmake sol id recommendations on the use of the largermodels in appropriate s i tuat ions. On the other hand,John Deere salespersons did not have criteria forselecting their larger garden tractor models. Rather, theyfelt th at m any of their larger m odels were selected onthebasisofluxury features r ather th an ca pacity. Asa result,the expert system for John Dee re garden tracto r selectiondoesnot make recommendationsfor thelarger models.John Deere salespersons could not provid e sufficientinformationfor alogic tree to suppo rt the e xpert system'smaking decisionsfor their larger models.Limitationsofthe ModelsThe models have limitations. The mo st obviouslimitations aretha t they cover only garde n tra ctorsandonly thoseof theJohn Deere andKubota b rands .Theexpert system technique could be expan ded into othe ragricultural machinery and manufacturers .Another likely limitation is the mod els' geo graphiclimitations. Since the expertise of the salespersonsinterviewed is only in North and Central Florida, themodelscanonlybecertaintohandle s i tuat ions thatarecommon to that area. Toexpand themodel into othe rgeographic areas would require interviewing gardentractor experts in each homogeneous geographic zoneand incorporating their knowledge intothemodel.

CONCLUSIONSAn evaluation of the information obtained in thisstudyhasresultedin thefollowing conc lusions:1. While manufacturers oflawn and ga rden tractorsprovide limited guidelines for theselection oflawnandgarden tractors ,nodecision criteriaareprovided exceptfor salespersons' guidance.2. Themost imp orta nt factors involving lawnandgarden tracto r selection are size of work area and type ofactivity.3 . Differences between brands in the obtainedinformation and tractor m odels required th at twodistinct expert systems rather than a generic expertsystembeconstructed for thetwo lin es.4. The minimum threshold land area for whichmachines arerecommended is larger with the Kubotamodel than with the John Deere model . Furthe r, them ax i m u m l an d a rea for which ma chines arerecommended islarger with the Kub ota m odel than withthe John Deere model.5. It is possible to verify an expert system by field

study.6. Expe rt systems can be developedfornon-technicalareas suchas marketing. While expert systems couldbeused in market ing agricul tural equipme nt , theseapplications would require entirely different logic trees.References

1. ASAE. 1986. ASAE Standards 1986. ASAE, St. Joseph, MI49085.

2. Borland. 1985. Reflex-The Analyst. Borland/Analytica Inc.,4585 Scotts Valley Drive, Scotts Valley, CA.

3 . Hildebrand, P. E. 1980. Combining disciplines in rapid

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