- Email: [email protected]
SOYBUG: An expert system for soybean insect pest management
Agricultural Systems 30 (1989) 269-286
SOYBUG: An Expert System for Soybean Insect Pest Management
H o w a r d W. Beck, Pierce Jones & J. W. Jones Computer and Information Sciences,Agricultural EngineeringDepartment, University of Florida, Gainesville,FL 32611, USA (Received4 November 1988; accepted 23 November 1988) A BSTRA CT An expert system has been developed to advise Florida farmers on control of four important insect pests of soybeans: velvetbean caterpillar, stink bug, corn earworm, and soybean looper. SO YBUG integrates a variety of threshold rules based on crop phenology and economics, and gives specific recommendations of pesticides and application rates. A major goal of the SO YBUG project was to develop working knowledge acquisition techniques. The primary technique developed was based on calibration~validation cycles which used large numbers of scenarios to elicit the experts' opinions about very narrow, difficult issues. This technique had the advantage of de-emphasizing intensive personal interviews. The results of these techniques and the details of SO YBUG are presented. Validation tests show that SO YBUG provides better site-specific recommendations than can be obtained through extension bulletins.
INTRODUCTION SOYBUG is an expert system designed to give advice on the control of soybean insect pests in the Florida panhandle. Specifically, four principal pests are involved; velvetbean caterpillar, stink bug, corn earworm, and soybean looper. The expert system gives advice on treating the four insects individually and also for one combination (velvetbean caterpillar plus stink bug). Recommendations are made for seven-day scouting intervals. The operational goal in the development of SOYBUG was to provide better 269 Agricultural Systems 0308-521X/89/$03-50© 1989ElsevierSciencePublishersLtd, England. Printed in Great Britain
270
Howard W. Beck, Pierce Jones, J. W. Jones
recommendations on insect control in soybeans (Johnson & Sprenkel, 1987) than could be obtained through extension bulletins and thereby to show that expert systems can offer a significant improvement over other, more traditional, forms of information delivery. The SOYBUG project was conducted primarily to develop useful knowledge acquisition techniques for an agricultural expert system. The process has resulted in a working expert system which has been validated by comparing expert system recommendations with human expert recommendations and recommendations printed in extension bulletins. The process of developing the expert system consisted of an iterative cycle which made use of scenarios to calibrate and validate the rule set. SOYBUG was developed in about five man-months spread over a period of two years, involved two knowledge engineers, and utilized two expert extension entomologists. Preliminary details of knowledge acquisition techniques were described in an earlier paper (Jones et al., 1986). SOYBUG was built using the I N S I G H T 2 + expert system shell (Level V Research, 1987) running on an IBM-PC compatible microcomputer. This paper presents an overview of the most successful knowledge acquisition techniques developed during the construction of SOYBUG. Details of the SOYBUG expert system are described with an indication of how rules were obtained using these techniques. Finally, the process of validation and results obtained are presented. METHODS
Summary of early efforts The initial attempts at knowledge acquisition consisted mostly of discussion sessions between the experts and a knowledge engineer. These discussions led to a first generation version of SOYBUG in which the engineer translated comments from these discussions/interviews directly into rules. However, the first rule sets did poorly under validation. The process of validation consisted of comparing the expert system's recommendations with those from the human expert for a set of hypothetical scenarios. While the discussions were useful for orientation purposes, it was discovered that the experts behaved differently when confronted with actual situations than when they tried to verbally explain how they arrive at recommendations. As a result the knowledge acquisition approach was altered to concentrate on explicit scenarios (Jones et aL, 1986). Another problem with the early knowledge acquisition techniques was the tendency of the knowledge engineer (and even the experts) to rationalize explanations which imposed a biased opinion on the way experts arrived at
SO YBUG: Soybean insect pest management
271
conclusions (Jones et al., 1986). While these explanations were very logical and well planned, they did not correspond to the way the experts behaved in real situations. For example, everyone on the development team believed that economic considerations played an important role in selecting pest controls. Several plans were proposed about how to handle economic issues, including a model which would determine economic treatment thresholds based on cost of pesticides and improvement in yield from applying pesticides (Jones et al., 1987). It was subsequently discovered that the experts in fact had little confidence in predicting the impact on yield or savings from applying pesticides. In the interest of respecting the expert's opinion, hypothetical economic factors were largely removed from the system. The relatively simple way in which economic factors are considered is described below. Although mathematical models were removed during the early phases of SOYBUG so that a system based entirely on heuristics could be built, this does not mean that models and expert systems should not be coupled. On the contrary, this approach would certainly be valuable (McClendon et al., 1987; Beck & Jones, 1989). The reason this approach is not currently being used in SOYBUG is that it tended to dominate the system to the extent that the experts' knowledge was being ignored. Now that a purely heuristic system has been built, it is clear where modeling can be reintroduced to complement the experts' knowledge. This issue is further discussed below.
Knowledge acquisition procedures Based on experience with developing SOYBUG, most successful knowledge acquisition techniques used in this project were found to correspond to the successful techniques described by people building expert systems in disciplines outside agriculture (Waterman, 1986). However, some effective techniques, not typically discussed, were developed because of organizational constraints in the agricultural extension system. In general, extension specialists cannot spend significant portions of their time working on an expert system project. Furthermore, the experts and engineers are often quite distant geographically and may not be able to meet for intensive interviews on a regular basis. Generally, solutions to these problems involved using telephone conversations in place of direct meetings and presenting scenarios in the form of questionnaires which were mailed and completed by the expert when it was convenient. The general knowledge acquisition procedure can be outlined as follows: Selecting a problem As often stated (e.g. Waterman, 1986), the topic for an expert system should
272
Howard IV. Beck, Pierce Jones, J. W. Jones
be chosen not to be too complex, yet not to be trivial. SOYBUG meets these criteria well since the problem was handled with minimal resources, yet the resulting system could offer significant, non-trivial improvements over existing means of delivering pest control information. A related issue is clearly stating in advance of its development the desired performance level from the expert system. This is why the operational goal, that SOYBUG should perform better than recommendations from extension publications, is important. The operational goal defines a clear stopping point, and must be realistic. Another important criterion in problem selection is the intended target audience. Is the expert system primarily a research project to gain insights into decision making, and/or is it intended as an extension tool for information delivery? SOYBUG was intended to be a research tool for evaluating the potential of expert systems for the delivery of more sophisticated extension information. Initial conversations The process begins with informal, orientation interviews. The purpose of these interviews is to establish a dialogue between the experts and knowledge engineer, and for the engineer to gain an overview of ways to approach the problem. During this phase relevant terminology is given and important issues are identified but not resolved. After this phase the engineer can begin asking informed questions. Also, the engineer should review any available literature on the subject. In SOYBUG, the existing extension publications were studied (Johnson & Sprenkel, 1987). Transcripts Whenever possible, conversations with the expert are tape recorded and transcribed. This includes not just the initial conversations, but discussions about the scenarios. Low-cost microphones which attach to telephone headsets can be used for recording telephone conversations. All the facts and statements which appear in the transcripts should be taken into account by the engineer when constructing rules. Although not all statements by the expert should be taken literally and translated into rules, they at least should be checked for consistency. That is, if the expert makes a statement that conflicts with an existing rule, an explanation for the discrepancy should be pursued. Scenarios Scenarios are the main knowledge acquisition tool. Scenarios focus the expert's attention on particular problems so that the conclusions and the process of arriving at conclusions are easily identified. A scenario is a
SO YBUG: Soybean insect pest management
273
statement of a situation in the problem domain which represents the type of problem the expert system is intended to solve. In SOYBUG, a scenario would be the particular state of insect populations in some soybean field. The scenario should describe the situation as realistically as possible. Ideally, scenarios should come from actual case histories. Often this is not possible, so that hypothetical scenarios must be generated. In SOYBUG, hypothetical scenarios were created by a knowledge engineer. These scenarios were designed to test particular regions of the expert's knowledge. Finally, the process depends on having large numbers of scenarios to adequately cover the problem domain. In SOYBUG, over four hundred scenarios were studied. Two formats for scenarios, the long and the short form, were used. These could be mailed to the expert and the results discussed in interviews. The long form (Fig. 1) is designed to pick up details and subtleties. It is used as a bridge from the initial conversations to focus on particular issues. In the long form situations are presented in detail and the expert makes a recommendation along with an explanation of why the recommendation was made. After the initial recommendation minor variations in the scenario are presented and the expert explains what effect each variation would have on the recommendation. About 15 to 20 long form scenarios with one scenario per page can be processed by an expert in one session. The experts provide many written comments on the long form with sufficient information for the knowledge engineer to create the first version SCENARIO 1--FIELD CONDITIONS Corn Earworm is the only insect present. NEW = 0, SMALL = 2, MEDLRG = 4 (Numbers per row foot). It is early in the season, the soybean canopy is not yet closed, plants are small. Beneficial insect population is well established. Current defoliation is less than 5%. Price of beans is poor. Recommendation (Please explain)? If you did not recommend an insecticide spray ... What would you have recommended if one had been needed? What would you recommend if one had been needed very badly, that is population must be controlled immediately? What if price of beans is average? What if MEDLRG = 2 or 3? What if there were no NEW or SMALL? What if plants larger (Canopy almost closed)? What if plants were smaller (6-8 in)? What if defoliation were much higher (15%)? Other information needed, general comments: Fig. l.
Long scenario format of the questionnaire sent to entomology experts in the knowledge acquisition process.
newly hatched > new, <½ in > = ½in
0 0 10 0 0
Damage (%)
0 0 0 0 0
NEW
0 0 9 3 0
SMALL
1.25 2 3 6 1-5
MEDLRG
Sizes (No./Row-ft)
late pod senescence late pre harvest late pod
Growth stage
PLEASE WRITE ANY COMMENTS ON THE BACK O F THIS SHEET.
1.25
Sevin (lbs)
CEW Control Recommendations W A I T (days) 3-5 7
plants < 10in > 10in to 10 days to bloom bloom (any flowers present) pin sized beans beans <½full beans >½ full yellowing < 7 5 % of beans are yellow yellowing > 75% yellow beans
Permethyene (lbs) 0-154)-2 0-14)-15
15 March 1987 Growth stages are early prebloom: mid prebloom: late prebloom: new pods: early podfill: late podfill: senescence: harvest:
Value of crop: show recommendation for 2 cases: average ($6"25-$7"00/bu) poor ($4/bu)
CORN EARWORM (CEW) Note: Size classes are NEW: SMALL: MEDLRG:
~2
-,.,.I
8 0 0 10 0
0 0 2 0 0
10 0 0 0 0
0 0 0 0 6
0 0 0 0 pod damage
10 10 10 0 0
0 0 0 10 0
pod damage 0 0 17 18 Fig. 2.
2 0"6 1.5 3 4
1 0.4 0.5 6 1
6 8 6 0-6 4
(~5 2.5 if2 1.5 2
Format of the short scenario questionnaire.
harvest new pod senescence mid pre late pre
late pod new pod early pod mid pre late pod
early pre mid pre late pre new pod harvest
early pod senescence new pod senescence harvest
I,o
276
Howard W. Beck. Pierce Jones, J. W. Jones
of the expert system. At this point, the knowledge engineer can identify the important variables and information which must be obtained from the user. It is also possible to begin developing the set of conclusions (goals) which the expert system can reach based on the set of recommendations given over all the scenarios. Finally, the knowledge engineer can derive rules from the comments and explanations given by the experts. After one or two rounds of long forms, short forms can be used. The purpose of the short form is to process large numbers of scenarios quickly (Fig. 2). About 20 scenarios are placed on a single page with one-line descriptions of each scenario. The expert gives a recommendation in a multiple-choice format by selecting from available recommendations (or specifying other responses on the back of the form). The parameters and set of recommendations used on the short form were obtained from conversations and from analyzing earlier scenarios. The short form is designed by the knowledge engineer to fine-tune the rule set. Borderline cases can be studied by picking scenarios on both sides of an issue. For example, in SOYBUG it was necessary to identify decision points at which the recommendation changed from treat to no treat, or treat with low-dose to treat with high-dose pesticides.
Validation/calibration cycle Each cycle of the knowledge acquisition process which began with informal conversations followed by one or two sets of long form scenarios (about 25) was continued with three or four rounds of short forms. In the SOYBUG project each round of scenarios was treated as a validation/calibration cycle. A new cycle was initiated when a new set of scenarios was generated. The current version of the expert system was then checked in a validation step by testing the rules calibrated in the last cycle with the scenarios for the new cycle. The expert system's recommendations were compared with the experts' recommendations for the new scenarios. After the validation step the set of rules was calibrated so that the expert system agreed with the experts as closely as possible for all the new scenarios. The process continued until there was satisfactory agreement between expert and expert system in the validation step. In SOYBUG, good performance for individual insects was observed after four or five cycles.
RESULTS The rules obtained in SOYBUG can illustrate the knowledge acquisition process. Basically, the rules are analogous to the treatment threshold rules
SO YBUG: Soybean insect pest management
277
which have been used in integrated pest management for many years; namely, if the pest population density is above some threshold, then treatment should be made. However, whereas there is generally only one published rule per pest, the expert has dozens of such rules. The expert divides the season into various phenological stages depending on the crop and pest. Furthermore, the binary decision of 'above' or 'below' threshold is refined into several levels. The decision is also affected by what control methods are available. In contrast to the generalized published control recommendations, the experts tend to give very specific recommendations when confronted with a specific problem. Finally, the expert can take into account multiple pest interactions in making a control recommendation, something which is too complex to express in publications.
Identifying the important phenological stages The first step was to identify the phenological stages within which recommendations would not vary as a function of time. The identification of these stages was made as a result of conversations with the expert, but more importantly on the basis of responses to scenarios. In some cases the expert verbally identified two stages (for example, early pod phase versus middle pod phase), but the recommendations for scenarios from the two stages did not differ. In such cases the two stages were combined into one functionally equivalent stage. The stages identified for the four insect pests illustrate seasonal variation in pest occurrence and response of the soybean crop to damage (Table 1). The boundaries for each stage are defined by rules based on easily observed characteristics. For example, the identification of 'senescence' for velvetbean caterpillar uses RULE $5 shown in Table 1. Note that the definition for each stage depends on the insect, so that 'senescence' for velvetbean caterpillar is not necessarily the same as for stink bug. The user never actually sees these terms, but must answer questions which describe the appearance of the crop. When a recommendation is to be made for multiple pests, the stages identified for individual pests are combined.
Identifying levels of infestation Once the phenological stages were identified by the knowledge engineer, rules for the thresholds within each stage were developed. Here the experts became very precise and wanted to determine not just whether the population is above or below threshold, but various levels relative to threshold. For example, levels can range from well below threshold, well below but increasing, near threshold, at threshold, and just above threshold, to well over threshold. These levels were discovered by the experts' shift in
278
Howard IV. Beck, Pierce Jones, J. W. Jones
TABLE 1 Phenological Stages Identified for Treatment Decisions for the Four Insect Pests, and an Example of a Rule Used to Determine the Current Stage Velvetbean caterpillar
Early prebloom Late prebloom Early pods Late pods Senescence Harvest Harvest Harvest
Stink bug
Early prebloom Late prebloom New pods Early pods Late pods Senescence Senescence
Corn earworm
Soybean looper
Early prebloom Mid prebloom Late prebloom New pods Early pods Late pods
Late prebloom Early pods Late pods Senescence
RULE $5 IF Time of season is yellowing leaves but some green leaves, pods or stems THEN Season is senescence
control recommendations. For example, 'Scout again in 7 days' versus 'Scout again in 3 days' is a difference in recommendation caused by a subtle difference in population level. Determining the change points for each level could only be done by using large numbers of scenarios. As an example, the rules for identifying four different levels for stink bug during early podfill are given in Table 2. Another interesting dimension was the division of life stages of each insect. Scouting reports generally used observations such as 'small', 'medium' and 'large' caterpillars. Again, the experts preferred more refinement. In the case of velvetbean caterpillar, five stages were ultimately identified (Table 3). These life stages were taken into account by using weighting factors for calculating population levels. For example: RULE N7 IF Season is early podfill AND SMALL + MED + 2*LRG + VLRG > 4.5 AND % Damage> = 16 THEN VBC near early podfill threshold gives an extra weighting factor (2) to the large (LRG) caterpillars since they consume large amounts of leaf area, and their development stage is such that they will be present in the field for many days. The very large (VLRG) caterpillars have the highest consumption rate of all the stages, but since they will pupate very soon (within 48 h) their importance is diminished. On
SO YBUG: Soybean insect pest management
279
TABLE 2 Rules for Determining Population Levels for Stink Bug During Early Podfill on Poor Priced Beans not Intended for Seed and Showing no Pod Damage RULE Bepod 1 IF Season is early podfill AND Crop value is poor AND NOT Beans for seed AND NOT Pod damage AND 0.02*NEW + 0-02*SMALL + MEDLRG < = 0.3 THEN Stink bug well below threshold RULE Nepod l If Season is early podfill AND Crop value is poor AND NOT Beans for seed AND NOT Pod damage AND 0-02*NEW + 0.02*SMALL + MEDLRG > 0.3 AND 0.02*NEW + 0"02*SMALL + MEDLRG < = 0.4 THEN Stink bug near threshold RULE Nepod 2 If Season is early podfill AND Crop value is poor AND NOT Beans for seed AND NOT Pod damage AND 0.02*NEW + 0~)2*SMALL + MEDLRG > 0-4 THEN Stink bug over threshold RULE Oepod l IF Season is early podfill AND NOT Pod damage AND NOT Beans for seed AND MEDLRG > = 0.3 THEN Stink bug well above threshold
the other hand, while the small (SMALL) and medium (MED) caterpillars are not big consumers, they represent significant future consumption as they develop into larger stages. The weighting factors were determined by watching experts perform calculations on the densities of each size class and on the basis of short form scenarios in which the numbers in each size class were carefully altered. Damage levels Treatment decisions were also affected by the amount of damage already present in terms of per cent defoliation and evidence of pod damage.
280
Howard 14:. Beck, Pierce Jones, J. W. Jones
TABLE 3
Life Stages Identified for Velvetbean Caterpillar Very small Small Medium Large Very large
(VSMALL) (SMALL) (MED) (LRG) (VLRG)
<¼in 1 1. ~-~ln 2 ~ in 31½in > 1½in
Damage was considered at two levels, one at which the damage was insignificant, and one at which the damage was so high that a relatively small pest population could impact yield. The difference is illustrated in two rules for velvetbean caterpillar (Table 4). Control recommendations The actual control recommendation was made once the state of the population was determined. The possible recommendations were identified by recording all of the recommendations revealed in the long form scenarios. These became the hypothetical goals to be tested using the backward chaining inference structure of the INSIGHT2 + expert system shell. The possible recommendations for velvetbean caterpillar are shown in Table 5. It is interesting to note that the recommendations the experts gave in the scenarios tended to differ significantly from recommendations made in conversation. (In casual conversation the expert often talked about control methods which were never actually recommended in the scenarios, these methods were not incorporated into the expert system's knowledge base.) TABLE 4 The Effect of Crop Damage on Identifying Significant Populations of Velvetbean Caterpillar During Early Podfill, % Damage is the Per cent of Defoliation RULE 0 4 IF Season is early podfiil AND MED + LRG + 0.5*VLRG > = 5 THEN VBC well above early podfill threshold RULE 05 IF Season is early podfill AND MED + LRG + 0"5*VLRG > 2 AND % Damage > 17 THEN VBC well above early podfill threshold
SO YBUG: Soybean insect pest management
281
TABLE 5 The Possible Control Recommendations for Veivetbean Caterpillar Appear as Goal Statements in INSIGHT2 + . Also Shown are Two of the Rules which can Conclude these Goals
1. Treat for VBC 1.1. Treat for VBC at full dose dimilin 1.2 Treat for VBC at full dose dimilin or methyl parathion 1.3 Treat for VBC with methyl parathion 1.4 Treat for VBC at low dose dimilin 1.5. Scout again in seven but may treat with dimilin 1.6. Scout again in three but may treat with methyl parathion 1.7. Scout again in three but may treat with dimilin 2. Scout again in three days 3. Scout again in seven days RULE Nl41 I F VBC near early podfill threshold A N D Crop value is A V E R A G E THEN Treat for VBC A N D Treat for VBC at low dose dimilin RULE N142 I F VBC near early podfill threshold A N D Crop value is POOR. THEN Scout again in three days
Table 5 also shows examples of rules which conclude the goals directly based on population level and crop value. Economic considerations In spite of lengthy conversations with the experts about the importance of economics in pest control recommendations, it was determined that the way experts actually used economic information was relatively simple. This is explained largely because the experts do not know how a particular control method will ultimately affect yield. From the expert's point of view, the classic premise that the economic gain in yield should offset the cost of the control was found to be impractical since the precise gains, costs, and increase in profits are unknown. Rather, the experts consider control methods more like insurance; they know that a particular population is potentially damaging and therefore, a treatment should be applied. They are not able to say precisely how the treatment will affect profit (not do they believe such information is likely to come from any source). Economic considerations impact SOYBUG in two ways. First, the actual recommendations are implicitly based on the cost of pesticides. The experts
282
Howard W. Beck, Pierce Jones, J. W. Jones
recommend the lowest cost pesticide which will do the job. Secondly, economics is explicitly treated using an indicator for crop value which can take on the value 'POOR' or ' A V E R A G E ' reflecting the market price of beans. P O O R is about $4.00 per bushel, and A V E R A G E would be roughly $6 to $7 per bushel. At the time the S O Y B U G project began, beans were selling in the A V E R A G E range. The crop value affected the kind of 'insurance' the expert recommended. For example, in Table 5, the same population levels resulted in 'Scout Again' for 'POOR' valued crops, but 'Treat with Low Dose Dimilin' for ' A V E R A G E ' valued crops. In other words, if the crop value was higher, a better quality but more expensive control was recommended.
Multiple-insect interactions Initially, S O Y B U G was built to consider individual pests, that is, assuming only one insect pest was present. One case for multiple insects, velvetbean caterpillar plus stink bug, was also developed. This case was simplified because velvetbean caterpillar is a foliage feeder, whereas stink bug is a pod feeder. Thus, the same population states and threshold levels could be used from the rule sets developed for individual insects, although their validity was tested with additional scenarios. The main difference was in the control recommendation since one pesticide could be capable of controlling both insects (Table 6). Also, the phenological stages for each insect had to be merged.
Validation results The most recent round of validation tests is shown in Table 7. For this round scenarios were devised for tough decisions where the expert's recommendations were thought to differ from the published threshold. The results show that this was indeed the case as the published thresholds showed poor
TABLE 6 A Rule which Selects a Control Recommendation for a Multiple Pest Combination (SB = Stink Bug, VBC = Velvetbean Caterpillar) IF Season is early pods AND Stink bug MEDLRG > = 0.25 AND VBC MED + LRG + VLRG > = 2 AND Price of beans is AVERAGE OR % Defoliation > = 17 OR There is pod damage THEN Treat with pydrin
SO YBUG: Soybean insect pest management
283
TABLE 7 Comparison of Experts' Recommendation with SOYBUG and Published Threshold Rules for the Most Recent Round of Scenarios. Numbers under 'SOYBUG' and 'Published Thresholds' are the Number of Times out of 'Number of Scenarios' each agreed with the Expert on a Treat/No Treat Decision. This Test was Designed to Demonstrate Important Differences Between the Expert and the Published Thresholds which have been Captured by SOYBUG Pest
Number of Scenarios
SO YBUG
Published thresholds
Velvetbean caterpillar Corn earworm Stink bug
40 32 22
26 20 10
5 9 9
Total
94
56
23
agreement with the expert recommendations. Specifically, the decision reached from the extension publication agreed with the expert's opinions in only 23 out of 96 scenarios. On the other hand, SOYBUG performed much better than the extension bulletins (agreeing in 56 of the 96 scenarios) since it was able to capture more details of the decision making process. The values in Table 7 are only for the treat versus no treat decision which is all the published thresholds in the extension bulletins provide. The results do not show the advantage that SOYBUG has in giving detailed recommendations on the particular kinds and rates of pesticides. Table 7 shows only the results from the latest round of scenarios. So far, there have been six rounds (about 200 scenarios) for velvetbean caterpillar and four rounds each (about 120 scenarios) for corn earworm and stink bug. Soybean looper has had just one round, and the multiple pest situation for velvetbean caterpillar plus stink bug has had two rounds. Additional work is needed on these latter two modules before they achieve acceptable performance. The validation/calibration cycle is a never ending process and the performance for SOYBUG can be improved even more with additional cycles. Improvement in performance was observed with each round as the set of rules became more finely tuned. From the perspective of the SOYBUG project this demonstrates that the techniques developed for knowledge acquisition can be used to create a working expert system. CONCLUSIONS SOYBUG has demonstrated that functional expert systems for extension information delivery can be built through careful use of knowledge
284
Howard W. Beck, Pierce Jones, J. W. Jones
acquisition techniques and that such expert systems can perform better than recommendations from traditional extension publications. The development process used with SOYBUG also demonstrated that constructing the expert system is an iterative cycle of validation and calibration in which SOYBUG was shown to improve performance with each cycle. In practice, the validation/calibration cycle can continue indefinitely. Once the expert system is put into use it is possible to automatically collect real case histories for scenarios by recording the user's input each time the system is used. These can be kept in a database and studied by the experts and knowledge engineers. Although this technique has not yet been used in SOYBUG, it would be a useful way of maintaining the expert system. One important result from the knowledge acquisition methods was the use of the scenario 'quizzes'. These were completed as time permitted by the experts without requiring the knowledge engineer to be present. This contrasts with other projects which require the expert and knowledge engineer to spend tremendous amounts of time together in intensive interviews. Although the latter may be more desirable for quick turn around, it is seldom practical in the settings where most agricultural expert systems are being built. One problem with the techniques employed is that they are very tedious and demanding for the knowledge engineer. Each of the hundreds of facts observed in the interviews and on the surveys must be evaluated individually and compared with all the rules in the knowledge base. A slight change in one rule could have unknown effects on performance, so testing must be done repeatedly over the entire scenario set whenever changes are made. The process is further complicated by the dynamics of agricultural systems. The introduction of a new pesticide, a change in the market value of the crop, or changes in behavior of the pests would require a modification in the expert system to reflect the new conditions. Thus, maintenance would be required at least annually. However, maintenance is not a simple process because, with each edition, all the facts and rules in the knowledge base must be reevaluated. Often expert systems are built for static situations so that the initial effort expended on knowledge acquisition lasts a long time. In dynamic environments like pest management, other strategies are needed. Asking the expert how decisions would be made under hypothetical, future situations is dangerous because of the observed differences between what experts say and what they actually do. Another approach might be the use of databases for storing generic information for which the particulars might change. Thus, rather than base the rules on specific properties such as trade names of pesticides, an expert system should use generic properties (price, residual
s o YBUG: Soybean insect pest management
285
activity, effectiveness) and a database to map generic information to particular products (Nagarajan et al., 1987). This approach would not work in situations where the generic properties would also change (e.g. a new insect pest appears with radically different behavior). Another useful facility would be an expert system shell which could also maintain a database of scenarios. Many of the tedious aspects of modifying rules result from not having a systematic way of relating all the observations which went into deriving the rule. A database which could store all the scenarios and cross reference scenarios with the rules they derived would greatly speed the process. Furthermore, this system would be coupled with a run-time version of the program so that each time the expert system is used, the user's scenario would be added to the database for further reference by the expert and knowledge engineer. Finally, work is needed to incorporate models. For example, the feeding and development behavior of the insects in SOYBUG are well-known through insect rearing experiments. This is one point where a mathematical model such as SICM (Wilkerson, 1983) could supplement the expert's heuristics. A model could project feeding damage on the basis of the number of insects in each life stage and insect morphological development based on degree days. These values could then be used by the expert system to determine damaging population levels. In interrogating the experts, it was discovered that they were unaware of the precise values for feeding and development rates obtained from experiments, yet they were estimating damage from each life stage based on their general knowledge. Expert systems for the delivery of extension information can be developed. The process is long and tedious and the need for future maintenance must be acknowledged. However, the product can provide more accurate, subtle information on significant issues such as the use of agricultural chemicals than the traditional printed document. From our perspective the SOYBUG project has demonstrated that knowledge acquisition methods can be devised and effectively used to develop agricultural knowledge bases.
ACKNOWLEDGEMENTS Very special thanks to the domain experts Dr Richard Sprenkel and Dr Fred Johnson for answering hundreds of questions. Thanks also to Ramzi Khuri for asking them. Authorized for publication as Paper No. 9461 in the Journal Series of the Florida Agricultural Experiment Station, Gainesville, FL 32611.
286
Howard I4I.Beck, Pierce Jones, J. W. Jones
REFERENCES Beck, H. W. & Jones, J. W. (1989). Simulation and artificial intelligence concepts. Knowledge Engineering in Agriculture. Ed. J. R. Barrett, ASAE Monograph, (In press). Johnson, F. A. & Sprenkel, R. K. (1987). SoybeanInsect Control Guide. Institute of Food and Agricultural Sciences. University of Florida, Gainesville, FL 32611. Jones, J. W., Jones, P. & Everett, P. A. (1987). Combining expert systems and agricultural models: A case study. Trans. of the ASAE., 30(5) 1308-14. Jones, P., Jones, J. W., Everett, P. A. & Beck, H. W. (1986). Knowledge Acquisition: A Case History of an Insect Control Expert System. American Society of Agricultural Engineering Technical Paper No. 86-5041. St. Joseph, MI 96802. Level V Research, Inc. (1987). INSIGHT2 +. 4980 South A-1A, Melbourne Beach, FL 32951. McClendon, R. W., Batchelor, W. D. & Jones, J. W. (1987). Insect Pest Management with an Expert System Coupled Crop Model. American Society of Agricultural Engineering Technical Paper No. 87-4501. St. Joseph, MI 96802. Nagarajan, K., Mishoe, J. W. & Currey, W. L. (1987). Development of an Expert System for Weed Management in Soybean. American Society of Agricultural Engineering Technical Paper No. 87-5024. St. Joseph, MI 96802. Waterman, D. A. (1986). A Guide to Expert Systems. Addison-Wesley Publishing Company. Reading, MA. Wilkerson, G., Mishoe, J., Jones, J., Stimac, J., Boggess, W. & Swaney, D. (1983). SICM: Soybean Integrated Crop Management Model: Model Description and User's Guide, Version 4.2, Agricultural Engineering Department Research Report AGE-1. Agr. Engr. Department, University of Florida, Gainesville, FL 32611.
SOYBUG: An Expert System for Soybean Insect Pest Management
H o w a r d W. Beck, Pierce Jones & J. W. Jones Computer and Information Sciences,Agricultural EngineeringDepartment, University of Florida, Gainesville,FL 32611, USA (Received4 November 1988; accepted 23 November 1988) A BSTRA CT An expert system has been developed to advise Florida farmers on control of four important insect pests of soybeans: velvetbean caterpillar, stink bug, corn earworm, and soybean looper. SO YBUG integrates a variety of threshold rules based on crop phenology and economics, and gives specific recommendations of pesticides and application rates. A major goal of the SO YBUG project was to develop working knowledge acquisition techniques. The primary technique developed was based on calibration~validation cycles which used large numbers of scenarios to elicit the experts' opinions about very narrow, difficult issues. This technique had the advantage of de-emphasizing intensive personal interviews. The results of these techniques and the details of SO YBUG are presented. Validation tests show that SO YBUG provides better site-specific recommendations than can be obtained through extension bulletins.
INTRODUCTION SOYBUG is an expert system designed to give advice on the control of soybean insect pests in the Florida panhandle. Specifically, four principal pests are involved; velvetbean caterpillar, stink bug, corn earworm, and soybean looper. The expert system gives advice on treating the four insects individually and also for one combination (velvetbean caterpillar plus stink bug). Recommendations are made for seven-day scouting intervals. The operational goal in the development of SOYBUG was to provide better 269 Agricultural Systems 0308-521X/89/$03-50© 1989ElsevierSciencePublishersLtd, England. Printed in Great Britain
270
Howard W. Beck, Pierce Jones, J. W. Jones
recommendations on insect control in soybeans (Johnson & Sprenkel, 1987) than could be obtained through extension bulletins and thereby to show that expert systems can offer a significant improvement over other, more traditional, forms of information delivery. The SOYBUG project was conducted primarily to develop useful knowledge acquisition techniques for an agricultural expert system. The process has resulted in a working expert system which has been validated by comparing expert system recommendations with human expert recommendations and recommendations printed in extension bulletins. The process of developing the expert system consisted of an iterative cycle which made use of scenarios to calibrate and validate the rule set. SOYBUG was developed in about five man-months spread over a period of two years, involved two knowledge engineers, and utilized two expert extension entomologists. Preliminary details of knowledge acquisition techniques were described in an earlier paper (Jones et al., 1986). SOYBUG was built using the I N S I G H T 2 + expert system shell (Level V Research, 1987) running on an IBM-PC compatible microcomputer. This paper presents an overview of the most successful knowledge acquisition techniques developed during the construction of SOYBUG. Details of the SOYBUG expert system are described with an indication of how rules were obtained using these techniques. Finally, the process of validation and results obtained are presented. METHODS
Summary of early efforts The initial attempts at knowledge acquisition consisted mostly of discussion sessions between the experts and a knowledge engineer. These discussions led to a first generation version of SOYBUG in which the engineer translated comments from these discussions/interviews directly into rules. However, the first rule sets did poorly under validation. The process of validation consisted of comparing the expert system's recommendations with those from the human expert for a set of hypothetical scenarios. While the discussions were useful for orientation purposes, it was discovered that the experts behaved differently when confronted with actual situations than when they tried to verbally explain how they arrive at recommendations. As a result the knowledge acquisition approach was altered to concentrate on explicit scenarios (Jones et aL, 1986). Another problem with the early knowledge acquisition techniques was the tendency of the knowledge engineer (and even the experts) to rationalize explanations which imposed a biased opinion on the way experts arrived at
SO YBUG: Soybean insect pest management
271
conclusions (Jones et al., 1986). While these explanations were very logical and well planned, they did not correspond to the way the experts behaved in real situations. For example, everyone on the development team believed that economic considerations played an important role in selecting pest controls. Several plans were proposed about how to handle economic issues, including a model which would determine economic treatment thresholds based on cost of pesticides and improvement in yield from applying pesticides (Jones et al., 1987). It was subsequently discovered that the experts in fact had little confidence in predicting the impact on yield or savings from applying pesticides. In the interest of respecting the expert's opinion, hypothetical economic factors were largely removed from the system. The relatively simple way in which economic factors are considered is described below. Although mathematical models were removed during the early phases of SOYBUG so that a system based entirely on heuristics could be built, this does not mean that models and expert systems should not be coupled. On the contrary, this approach would certainly be valuable (McClendon et al., 1987; Beck & Jones, 1989). The reason this approach is not currently being used in SOYBUG is that it tended to dominate the system to the extent that the experts' knowledge was being ignored. Now that a purely heuristic system has been built, it is clear where modeling can be reintroduced to complement the experts' knowledge. This issue is further discussed below.
Knowledge acquisition procedures Based on experience with developing SOYBUG, most successful knowledge acquisition techniques used in this project were found to correspond to the successful techniques described by people building expert systems in disciplines outside agriculture (Waterman, 1986). However, some effective techniques, not typically discussed, were developed because of organizational constraints in the agricultural extension system. In general, extension specialists cannot spend significant portions of their time working on an expert system project. Furthermore, the experts and engineers are often quite distant geographically and may not be able to meet for intensive interviews on a regular basis. Generally, solutions to these problems involved using telephone conversations in place of direct meetings and presenting scenarios in the form of questionnaires which were mailed and completed by the expert when it was convenient. The general knowledge acquisition procedure can be outlined as follows: Selecting a problem As often stated (e.g. Waterman, 1986), the topic for an expert system should
272
Howard IV. Beck, Pierce Jones, J. W. Jones
be chosen not to be too complex, yet not to be trivial. SOYBUG meets these criteria well since the problem was handled with minimal resources, yet the resulting system could offer significant, non-trivial improvements over existing means of delivering pest control information. A related issue is clearly stating in advance of its development the desired performance level from the expert system. This is why the operational goal, that SOYBUG should perform better than recommendations from extension publications, is important. The operational goal defines a clear stopping point, and must be realistic. Another important criterion in problem selection is the intended target audience. Is the expert system primarily a research project to gain insights into decision making, and/or is it intended as an extension tool for information delivery? SOYBUG was intended to be a research tool for evaluating the potential of expert systems for the delivery of more sophisticated extension information. Initial conversations The process begins with informal, orientation interviews. The purpose of these interviews is to establish a dialogue between the experts and knowledge engineer, and for the engineer to gain an overview of ways to approach the problem. During this phase relevant terminology is given and important issues are identified but not resolved. After this phase the engineer can begin asking informed questions. Also, the engineer should review any available literature on the subject. In SOYBUG, the existing extension publications were studied (Johnson & Sprenkel, 1987). Transcripts Whenever possible, conversations with the expert are tape recorded and transcribed. This includes not just the initial conversations, but discussions about the scenarios. Low-cost microphones which attach to telephone headsets can be used for recording telephone conversations. All the facts and statements which appear in the transcripts should be taken into account by the engineer when constructing rules. Although not all statements by the expert should be taken literally and translated into rules, they at least should be checked for consistency. That is, if the expert makes a statement that conflicts with an existing rule, an explanation for the discrepancy should be pursued. Scenarios Scenarios are the main knowledge acquisition tool. Scenarios focus the expert's attention on particular problems so that the conclusions and the process of arriving at conclusions are easily identified. A scenario is a
SO YBUG: Soybean insect pest management
273
statement of a situation in the problem domain which represents the type of problem the expert system is intended to solve. In SOYBUG, a scenario would be the particular state of insect populations in some soybean field. The scenario should describe the situation as realistically as possible. Ideally, scenarios should come from actual case histories. Often this is not possible, so that hypothetical scenarios must be generated. In SOYBUG, hypothetical scenarios were created by a knowledge engineer. These scenarios were designed to test particular regions of the expert's knowledge. Finally, the process depends on having large numbers of scenarios to adequately cover the problem domain. In SOYBUG, over four hundred scenarios were studied. Two formats for scenarios, the long and the short form, were used. These could be mailed to the expert and the results discussed in interviews. The long form (Fig. 1) is designed to pick up details and subtleties. It is used as a bridge from the initial conversations to focus on particular issues. In the long form situations are presented in detail and the expert makes a recommendation along with an explanation of why the recommendation was made. After the initial recommendation minor variations in the scenario are presented and the expert explains what effect each variation would have on the recommendation. About 15 to 20 long form scenarios with one scenario per page can be processed by an expert in one session. The experts provide many written comments on the long form with sufficient information for the knowledge engineer to create the first version SCENARIO 1--FIELD CONDITIONS Corn Earworm is the only insect present. NEW = 0, SMALL = 2, MEDLRG = 4 (Numbers per row foot). It is early in the season, the soybean canopy is not yet closed, plants are small. Beneficial insect population is well established. Current defoliation is less than 5%. Price of beans is poor. Recommendation (Please explain)? If you did not recommend an insecticide spray ... What would you have recommended if one had been needed? What would you recommend if one had been needed very badly, that is population must be controlled immediately? What if price of beans is average? What if MEDLRG = 2 or 3? What if there were no NEW or SMALL? What if plants larger (Canopy almost closed)? What if plants were smaller (6-8 in)? What if defoliation were much higher (15%)? Other information needed, general comments: Fig. l.
Long scenario format of the questionnaire sent to entomology experts in the knowledge acquisition process.
newly hatched > new, <½ in > = ½in
0 0 10 0 0
Damage (%)
0 0 0 0 0
NEW
0 0 9 3 0
SMALL
1.25 2 3 6 1-5
MEDLRG
Sizes (No./Row-ft)
late pod senescence late pre harvest late pod
Growth stage
PLEASE WRITE ANY COMMENTS ON THE BACK O F THIS SHEET.
1.25
Sevin (lbs)
CEW Control Recommendations W A I T (days) 3-5 7
plants < 10in > 10in to 10 days to bloom bloom (any flowers present) pin sized beans beans <½full beans >½ full yellowing < 7 5 % of beans are yellow yellowing > 75% yellow beans
Permethyene (lbs) 0-154)-2 0-14)-15
15 March 1987 Growth stages are early prebloom: mid prebloom: late prebloom: new pods: early podfill: late podfill: senescence: harvest:
Value of crop: show recommendation for 2 cases: average ($6"25-$7"00/bu) poor ($4/bu)
CORN EARWORM (CEW) Note: Size classes are NEW: SMALL: MEDLRG:
~2
-,.,.I
8 0 0 10 0
0 0 2 0 0
10 0 0 0 0
0 0 0 0 6
0 0 0 0 pod damage
10 10 10 0 0
0 0 0 10 0
pod damage 0 0 17 18 Fig. 2.
2 0"6 1.5 3 4
1 0.4 0.5 6 1
6 8 6 0-6 4
(~5 2.5 if2 1.5 2
Format of the short scenario questionnaire.
harvest new pod senescence mid pre late pre
late pod new pod early pod mid pre late pod
early pre mid pre late pre new pod harvest
early pod senescence new pod senescence harvest
I,o
276
Howard W. Beck. Pierce Jones, J. W. Jones
of the expert system. At this point, the knowledge engineer can identify the important variables and information which must be obtained from the user. It is also possible to begin developing the set of conclusions (goals) which the expert system can reach based on the set of recommendations given over all the scenarios. Finally, the knowledge engineer can derive rules from the comments and explanations given by the experts. After one or two rounds of long forms, short forms can be used. The purpose of the short form is to process large numbers of scenarios quickly (Fig. 2). About 20 scenarios are placed on a single page with one-line descriptions of each scenario. The expert gives a recommendation in a multiple-choice format by selecting from available recommendations (or specifying other responses on the back of the form). The parameters and set of recommendations used on the short form were obtained from conversations and from analyzing earlier scenarios. The short form is designed by the knowledge engineer to fine-tune the rule set. Borderline cases can be studied by picking scenarios on both sides of an issue. For example, in SOYBUG it was necessary to identify decision points at which the recommendation changed from treat to no treat, or treat with low-dose to treat with high-dose pesticides.
Validation/calibration cycle Each cycle of the knowledge acquisition process which began with informal conversations followed by one or two sets of long form scenarios (about 25) was continued with three or four rounds of short forms. In the SOYBUG project each round of scenarios was treated as a validation/calibration cycle. A new cycle was initiated when a new set of scenarios was generated. The current version of the expert system was then checked in a validation step by testing the rules calibrated in the last cycle with the scenarios for the new cycle. The expert system's recommendations were compared with the experts' recommendations for the new scenarios. After the validation step the set of rules was calibrated so that the expert system agreed with the experts as closely as possible for all the new scenarios. The process continued until there was satisfactory agreement between expert and expert system in the validation step. In SOYBUG, good performance for individual insects was observed after four or five cycles.
RESULTS The rules obtained in SOYBUG can illustrate the knowledge acquisition process. Basically, the rules are analogous to the treatment threshold rules
SO YBUG: Soybean insect pest management
277
which have been used in integrated pest management for many years; namely, if the pest population density is above some threshold, then treatment should be made. However, whereas there is generally only one published rule per pest, the expert has dozens of such rules. The expert divides the season into various phenological stages depending on the crop and pest. Furthermore, the binary decision of 'above' or 'below' threshold is refined into several levels. The decision is also affected by what control methods are available. In contrast to the generalized published control recommendations, the experts tend to give very specific recommendations when confronted with a specific problem. Finally, the expert can take into account multiple pest interactions in making a control recommendation, something which is too complex to express in publications.
Identifying the important phenological stages The first step was to identify the phenological stages within which recommendations would not vary as a function of time. The identification of these stages was made as a result of conversations with the expert, but more importantly on the basis of responses to scenarios. In some cases the expert verbally identified two stages (for example, early pod phase versus middle pod phase), but the recommendations for scenarios from the two stages did not differ. In such cases the two stages were combined into one functionally equivalent stage. The stages identified for the four insect pests illustrate seasonal variation in pest occurrence and response of the soybean crop to damage (Table 1). The boundaries for each stage are defined by rules based on easily observed characteristics. For example, the identification of 'senescence' for velvetbean caterpillar uses RULE $5 shown in Table 1. Note that the definition for each stage depends on the insect, so that 'senescence' for velvetbean caterpillar is not necessarily the same as for stink bug. The user never actually sees these terms, but must answer questions which describe the appearance of the crop. When a recommendation is to be made for multiple pests, the stages identified for individual pests are combined.
Identifying levels of infestation Once the phenological stages were identified by the knowledge engineer, rules for the thresholds within each stage were developed. Here the experts became very precise and wanted to determine not just whether the population is above or below threshold, but various levels relative to threshold. For example, levels can range from well below threshold, well below but increasing, near threshold, at threshold, and just above threshold, to well over threshold. These levels were discovered by the experts' shift in
278
Howard IV. Beck, Pierce Jones, J. W. Jones
TABLE 1 Phenological Stages Identified for Treatment Decisions for the Four Insect Pests, and an Example of a Rule Used to Determine the Current Stage Velvetbean caterpillar
Early prebloom Late prebloom Early pods Late pods Senescence Harvest Harvest Harvest
Stink bug
Early prebloom Late prebloom New pods Early pods Late pods Senescence Senescence
Corn earworm
Soybean looper
Early prebloom Mid prebloom Late prebloom New pods Early pods Late pods
Late prebloom Early pods Late pods Senescence
RULE $5 IF Time of season is yellowing leaves but some green leaves, pods or stems THEN Season is senescence
control recommendations. For example, 'Scout again in 7 days' versus 'Scout again in 3 days' is a difference in recommendation caused by a subtle difference in population level. Determining the change points for each level could only be done by using large numbers of scenarios. As an example, the rules for identifying four different levels for stink bug during early podfill are given in Table 2. Another interesting dimension was the division of life stages of each insect. Scouting reports generally used observations such as 'small', 'medium' and 'large' caterpillars. Again, the experts preferred more refinement. In the case of velvetbean caterpillar, five stages were ultimately identified (Table 3). These life stages were taken into account by using weighting factors for calculating population levels. For example: RULE N7 IF Season is early podfill AND SMALL + MED + 2*LRG + VLRG > 4.5 AND % Damage> = 16 THEN VBC near early podfill threshold gives an extra weighting factor (2) to the large (LRG) caterpillars since they consume large amounts of leaf area, and their development stage is such that they will be present in the field for many days. The very large (VLRG) caterpillars have the highest consumption rate of all the stages, but since they will pupate very soon (within 48 h) their importance is diminished. On
SO YBUG: Soybean insect pest management
279
TABLE 2 Rules for Determining Population Levels for Stink Bug During Early Podfill on Poor Priced Beans not Intended for Seed and Showing no Pod Damage RULE Bepod 1 IF Season is early podfill AND Crop value is poor AND NOT Beans for seed AND NOT Pod damage AND 0.02*NEW + 0-02*SMALL + MEDLRG < = 0.3 THEN Stink bug well below threshold RULE Nepod l If Season is early podfill AND Crop value is poor AND NOT Beans for seed AND NOT Pod damage AND 0-02*NEW + 0.02*SMALL + MEDLRG > 0.3 AND 0.02*NEW + 0"02*SMALL + MEDLRG < = 0.4 THEN Stink bug near threshold RULE Nepod 2 If Season is early podfill AND Crop value is poor AND NOT Beans for seed AND NOT Pod damage AND 0.02*NEW + 0~)2*SMALL + MEDLRG > 0-4 THEN Stink bug over threshold RULE Oepod l IF Season is early podfill AND NOT Pod damage AND NOT Beans for seed AND MEDLRG > = 0.3 THEN Stink bug well above threshold
the other hand, while the small (SMALL) and medium (MED) caterpillars are not big consumers, they represent significant future consumption as they develop into larger stages. The weighting factors were determined by watching experts perform calculations on the densities of each size class and on the basis of short form scenarios in which the numbers in each size class were carefully altered. Damage levels Treatment decisions were also affected by the amount of damage already present in terms of per cent defoliation and evidence of pod damage.
280
Howard 14:. Beck, Pierce Jones, J. W. Jones
TABLE 3
Life Stages Identified for Velvetbean Caterpillar Very small Small Medium Large Very large
(VSMALL) (SMALL) (MED) (LRG) (VLRG)
<¼in 1 1. ~-~ln 2 ~ in 31½in > 1½in
Damage was considered at two levels, one at which the damage was insignificant, and one at which the damage was so high that a relatively small pest population could impact yield. The difference is illustrated in two rules for velvetbean caterpillar (Table 4). Control recommendations The actual control recommendation was made once the state of the population was determined. The possible recommendations were identified by recording all of the recommendations revealed in the long form scenarios. These became the hypothetical goals to be tested using the backward chaining inference structure of the INSIGHT2 + expert system shell. The possible recommendations for velvetbean caterpillar are shown in Table 5. It is interesting to note that the recommendations the experts gave in the scenarios tended to differ significantly from recommendations made in conversation. (In casual conversation the expert often talked about control methods which were never actually recommended in the scenarios, these methods were not incorporated into the expert system's knowledge base.) TABLE 4 The Effect of Crop Damage on Identifying Significant Populations of Velvetbean Caterpillar During Early Podfill, % Damage is the Per cent of Defoliation RULE 0 4 IF Season is early podfiil AND MED + LRG + 0.5*VLRG > = 5 THEN VBC well above early podfill threshold RULE 05 IF Season is early podfill AND MED + LRG + 0"5*VLRG > 2 AND % Damage > 17 THEN VBC well above early podfill threshold
SO YBUG: Soybean insect pest management
281
TABLE 5 The Possible Control Recommendations for Veivetbean Caterpillar Appear as Goal Statements in INSIGHT2 + . Also Shown are Two of the Rules which can Conclude these Goals
1. Treat for VBC 1.1. Treat for VBC at full dose dimilin 1.2 Treat for VBC at full dose dimilin or methyl parathion 1.3 Treat for VBC with methyl parathion 1.4 Treat for VBC at low dose dimilin 1.5. Scout again in seven but may treat with dimilin 1.6. Scout again in three but may treat with methyl parathion 1.7. Scout again in three but may treat with dimilin 2. Scout again in three days 3. Scout again in seven days RULE Nl41 I F VBC near early podfill threshold A N D Crop value is A V E R A G E THEN Treat for VBC A N D Treat for VBC at low dose dimilin RULE N142 I F VBC near early podfill threshold A N D Crop value is POOR. THEN Scout again in three days
Table 5 also shows examples of rules which conclude the goals directly based on population level and crop value. Economic considerations In spite of lengthy conversations with the experts about the importance of economics in pest control recommendations, it was determined that the way experts actually used economic information was relatively simple. This is explained largely because the experts do not know how a particular control method will ultimately affect yield. From the expert's point of view, the classic premise that the economic gain in yield should offset the cost of the control was found to be impractical since the precise gains, costs, and increase in profits are unknown. Rather, the experts consider control methods more like insurance; they know that a particular population is potentially damaging and therefore, a treatment should be applied. They are not able to say precisely how the treatment will affect profit (not do they believe such information is likely to come from any source). Economic considerations impact SOYBUG in two ways. First, the actual recommendations are implicitly based on the cost of pesticides. The experts
282
Howard W. Beck, Pierce Jones, J. W. Jones
recommend the lowest cost pesticide which will do the job. Secondly, economics is explicitly treated using an indicator for crop value which can take on the value 'POOR' or ' A V E R A G E ' reflecting the market price of beans. P O O R is about $4.00 per bushel, and A V E R A G E would be roughly $6 to $7 per bushel. At the time the S O Y B U G project began, beans were selling in the A V E R A G E range. The crop value affected the kind of 'insurance' the expert recommended. For example, in Table 5, the same population levels resulted in 'Scout Again' for 'POOR' valued crops, but 'Treat with Low Dose Dimilin' for ' A V E R A G E ' valued crops. In other words, if the crop value was higher, a better quality but more expensive control was recommended.
Multiple-insect interactions Initially, S O Y B U G was built to consider individual pests, that is, assuming only one insect pest was present. One case for multiple insects, velvetbean caterpillar plus stink bug, was also developed. This case was simplified because velvetbean caterpillar is a foliage feeder, whereas stink bug is a pod feeder. Thus, the same population states and threshold levels could be used from the rule sets developed for individual insects, although their validity was tested with additional scenarios. The main difference was in the control recommendation since one pesticide could be capable of controlling both insects (Table 6). Also, the phenological stages for each insect had to be merged.
Validation results The most recent round of validation tests is shown in Table 7. For this round scenarios were devised for tough decisions where the expert's recommendations were thought to differ from the published threshold. The results show that this was indeed the case as the published thresholds showed poor
TABLE 6 A Rule which Selects a Control Recommendation for a Multiple Pest Combination (SB = Stink Bug, VBC = Velvetbean Caterpillar) IF Season is early pods AND Stink bug MEDLRG > = 0.25 AND VBC MED + LRG + VLRG > = 2 AND Price of beans is AVERAGE OR % Defoliation > = 17 OR There is pod damage THEN Treat with pydrin
SO YBUG: Soybean insect pest management
283
TABLE 7 Comparison of Experts' Recommendation with SOYBUG and Published Threshold Rules for the Most Recent Round of Scenarios. Numbers under 'SOYBUG' and 'Published Thresholds' are the Number of Times out of 'Number of Scenarios' each agreed with the Expert on a Treat/No Treat Decision. This Test was Designed to Demonstrate Important Differences Between the Expert and the Published Thresholds which have been Captured by SOYBUG Pest
Number of Scenarios
SO YBUG
Published thresholds
Velvetbean caterpillar Corn earworm Stink bug
40 32 22
26 20 10
5 9 9
Total
94
56
23
agreement with the expert recommendations. Specifically, the decision reached from the extension publication agreed with the expert's opinions in only 23 out of 96 scenarios. On the other hand, SOYBUG performed much better than the extension bulletins (agreeing in 56 of the 96 scenarios) since it was able to capture more details of the decision making process. The values in Table 7 are only for the treat versus no treat decision which is all the published thresholds in the extension bulletins provide. The results do not show the advantage that SOYBUG has in giving detailed recommendations on the particular kinds and rates of pesticides. Table 7 shows only the results from the latest round of scenarios. So far, there have been six rounds (about 200 scenarios) for velvetbean caterpillar and four rounds each (about 120 scenarios) for corn earworm and stink bug. Soybean looper has had just one round, and the multiple pest situation for velvetbean caterpillar plus stink bug has had two rounds. Additional work is needed on these latter two modules before they achieve acceptable performance. The validation/calibration cycle is a never ending process and the performance for SOYBUG can be improved even more with additional cycles. Improvement in performance was observed with each round as the set of rules became more finely tuned. From the perspective of the SOYBUG project this demonstrates that the techniques developed for knowledge acquisition can be used to create a working expert system. CONCLUSIONS SOYBUG has demonstrated that functional expert systems for extension information delivery can be built through careful use of knowledge
284
Howard W. Beck, Pierce Jones, J. W. Jones
acquisition techniques and that such expert systems can perform better than recommendations from traditional extension publications. The development process used with SOYBUG also demonstrated that constructing the expert system is an iterative cycle of validation and calibration in which SOYBUG was shown to improve performance with each cycle. In practice, the validation/calibration cycle can continue indefinitely. Once the expert system is put into use it is possible to automatically collect real case histories for scenarios by recording the user's input each time the system is used. These can be kept in a database and studied by the experts and knowledge engineers. Although this technique has not yet been used in SOYBUG, it would be a useful way of maintaining the expert system. One important result from the knowledge acquisition methods was the use of the scenario 'quizzes'. These were completed as time permitted by the experts without requiring the knowledge engineer to be present. This contrasts with other projects which require the expert and knowledge engineer to spend tremendous amounts of time together in intensive interviews. Although the latter may be more desirable for quick turn around, it is seldom practical in the settings where most agricultural expert systems are being built. One problem with the techniques employed is that they are very tedious and demanding for the knowledge engineer. Each of the hundreds of facts observed in the interviews and on the surveys must be evaluated individually and compared with all the rules in the knowledge base. A slight change in one rule could have unknown effects on performance, so testing must be done repeatedly over the entire scenario set whenever changes are made. The process is further complicated by the dynamics of agricultural systems. The introduction of a new pesticide, a change in the market value of the crop, or changes in behavior of the pests would require a modification in the expert system to reflect the new conditions. Thus, maintenance would be required at least annually. However, maintenance is not a simple process because, with each edition, all the facts and rules in the knowledge base must be reevaluated. Often expert systems are built for static situations so that the initial effort expended on knowledge acquisition lasts a long time. In dynamic environments like pest management, other strategies are needed. Asking the expert how decisions would be made under hypothetical, future situations is dangerous because of the observed differences between what experts say and what they actually do. Another approach might be the use of databases for storing generic information for which the particulars might change. Thus, rather than base the rules on specific properties such as trade names of pesticides, an expert system should use generic properties (price, residual
s o YBUG: Soybean insect pest management
285
activity, effectiveness) and a database to map generic information to particular products (Nagarajan et al., 1987). This approach would not work in situations where the generic properties would also change (e.g. a new insect pest appears with radically different behavior). Another useful facility would be an expert system shell which could also maintain a database of scenarios. Many of the tedious aspects of modifying rules result from not having a systematic way of relating all the observations which went into deriving the rule. A database which could store all the scenarios and cross reference scenarios with the rules they derived would greatly speed the process. Furthermore, this system would be coupled with a run-time version of the program so that each time the expert system is used, the user's scenario would be added to the database for further reference by the expert and knowledge engineer. Finally, work is needed to incorporate models. For example, the feeding and development behavior of the insects in SOYBUG are well-known through insect rearing experiments. This is one point where a mathematical model such as SICM (Wilkerson, 1983) could supplement the expert's heuristics. A model could project feeding damage on the basis of the number of insects in each life stage and insect morphological development based on degree days. These values could then be used by the expert system to determine damaging population levels. In interrogating the experts, it was discovered that they were unaware of the precise values for feeding and development rates obtained from experiments, yet they were estimating damage from each life stage based on their general knowledge. Expert systems for the delivery of extension information can be developed. The process is long and tedious and the need for future maintenance must be acknowledged. However, the product can provide more accurate, subtle information on significant issues such as the use of agricultural chemicals than the traditional printed document. From our perspective the SOYBUG project has demonstrated that knowledge acquisition methods can be devised and effectively used to develop agricultural knowledge bases.
ACKNOWLEDGEMENTS Very special thanks to the domain experts Dr Richard Sprenkel and Dr Fred Johnson for answering hundreds of questions. Thanks also to Ramzi Khuri for asking them. Authorized for publication as Paper No. 9461 in the Journal Series of the Florida Agricultural Experiment Station, Gainesville, FL 32611.
286
Howard I4I.Beck, Pierce Jones, J. W. Jones
REFERENCES Beck, H. W. & Jones, J. W. (1989). Simulation and artificial intelligence concepts. Knowledge Engineering in Agriculture. Ed. J. R. Barrett, ASAE Monograph, (In press). Johnson, F. A. & Sprenkel, R. K. (1987). SoybeanInsect Control Guide. Institute of Food and Agricultural Sciences. University of Florida, Gainesville, FL 32611. Jones, J. W., Jones, P. & Everett, P. A. (1987). Combining expert systems and agricultural models: A case study. Trans. of the ASAE., 30(5) 1308-14. Jones, P., Jones, J. W., Everett, P. A. & Beck, H. W. (1986). Knowledge Acquisition: A Case History of an Insect Control Expert System. American Society of Agricultural Engineering Technical Paper No. 86-5041. St. Joseph, MI 96802. Level V Research, Inc. (1987). INSIGHT2 +. 4980 South A-1A, Melbourne Beach, FL 32951. McClendon, R. W., Batchelor, W. D. & Jones, J. W. (1987). Insect Pest Management with an Expert System Coupled Crop Model. American Society of Agricultural Engineering Technical Paper No. 87-4501. St. Joseph, MI 96802. Nagarajan, K., Mishoe, J. W. & Currey, W. L. (1987). Development of an Expert System for Weed Management in Soybean. American Society of Agricultural Engineering Technical Paper No. 87-5024. St. Joseph, MI 96802. Waterman, D. A. (1986). A Guide to Expert Systems. Addison-Wesley Publishing Company. Reading, MA. Wilkerson, G., Mishoe, J., Jones, J., Stimac, J., Boggess, W. & Swaney, D. (1983). SICM: Soybean Integrated Crop Management Model: Model Description and User's Guide, Version 4.2, Agricultural Engineering Department Research Report AGE-1. Agr. Engr. Department, University of Florida, Gainesville, FL 32611.