development of an expert system for pest control in tropical grain stores

Postharvest Biology and Technology, 3 (1993) 335-347 335 © 1993 Elsevier Science Publishers B.V. All rights reserved 0925-5214/93/$06.00

POSTEC 01051

Development of an expert system for pest control in tropical grain stores

T.H. Jones a, J.D. Mumford a, J.A.F. Compton b, G.A. Norton a,. and P.S. Tyler b

a Silwood Centre for Pest Management, Department of Biology, Imperial College at Silwood Park, Ascot, UK

b Grain Technology Department, Natural Resources Institute, CentralAuenue, Chatham Maritime, UK

(Accepted 20 April 1993)

ABSTRACT

The potential of a flexible computer-based system as an aid to grain store managers towards preserving quality has been pointed out previously (Compton et al., 1992). Such an expert system has been developed, with particular emphasis on the logical structure of the decision problem, procedures for data input, and methods whereby the model could be refined in the future. The system is not restricted by store type or storage practice, but emphasizes the options open to the manager. Research in further areas would enable the system to become more comprehensive.

Key words: Expert system; Pest control; Grain storage; Tropical

INTRODUCTION

Large-scale tropical grain stores may include holdings by grain marketing boards, private and para-statal millers, importers and exporters, merchants and national food security reserves. Although the objectives of storage and the final destination of the grain may vary between these different types of stores, one aim which they all have in common is to maintain grain quality during storage at minimal costs.

Pest attack and moisture are among the main causes of losses in stored grains. Pest control in tropical storage poses particular challenges: heat and high humidity provide ideal conditions for pest multiplication. Although techniques for maintaining large stocks of grain in good condition are fairly well-known (the main options

Correspondence to: Dr T.H. Jones, Imperial College at Silwood Park, Ascot, Berkshire SL5 7PY, UK. * Present address: Cooperative Research Centre for Tropical Pest Management, University of Queens-

land, Brisbane, Qld 4072, Australia.

336 T.H. JONES ET AL.

include drying and the use of insecticides and fumigants), difficulties are often encountered in implementing these optimally. Correct and timely decision-making is crucial.

Grain store managers need to make frequent decisions on whether, when and how to treat grain. These are complex decisions which may be influenced by factors such as the planned movement of stocks, availability of transport and deployment of pest control teams, as well as purely technical considerations such as the best choice of pesticide. Decisions may be both strategic (for example, ordering pesticides, planning work programmes) and tactical (deciding on immedi- ate treatment).

Store managers often adopt a fixed treatment regime, thereby avoiding the need to make frequent and often difficult decisions. However, this can mean that some unnecessary and untimely treatments are applied. As a result costs and chemical residues may be increased.

In cases in which store managers do not have particular specialist knowledge, it would be useful for them to have good access to advisers to help them with decision-making. However, in many countries specialists are rare and store managers may work in isolated conditions. Other sources of information are books and manuals. Although these are useful for reference, books cannot give situation- specific advice. Furthermore, some types of information - for example, brands and suppliers of materials - may change frequently and manuals can become outdated.

Expert systems - computer programs which incorporate the knowledge of specialists - have the potential to make this information readily available. In particular, expert systems are useful to decision makers who have access to computers for problems that are narrowly defined but where the human expertise required to determine the best solution to the problem is scarce (Wilkin et al., 1990). The construction of expert systems may also be of considerable value to scientists by providing them with a framework within which to structure the decision process. This can provide a focus for their scientific knowledge. Expert systems have been used in a number of disciplines, including pest management (Jones et al., 1984; Norton, 1987; Edwards-Jones et al., 1989; Adams et al., 1990; Holt et al., 1990; Jones et al., 1990) offering support for practical problem solving, identification of research needs, information provision and processing, and training (Mumford and Norton, 1989). A number of computer systems have been developed specifically for use in grain storage (Perat et al., 1986; Zhang and Otten, 1986; Newton, et al., 1986; Schuller et al., 1986; Norton, 1987; Denne, 1988; Flinn and Hagstrum, 1990; Kawamoto et al., 1990; Wilkin et al., 1990).

Unlike the studies listed above which have been appropriate for use in developed countries, this paper describes the development of an expert system aimed at providing appropriate information and advice to grain store managers in tropical developing countries. The potential for the use of such a system was discussed by Compton et al. (1992). In particular, this paper addresses the problem of choosing the most appropriate strategy for controlling insect infestations in bagged maize in centralized storage sites, reflecting the importance of maize, the prevalence of bag storage in the tropics, and the availability of a pool of specialists with expertise in

EXPERT SYSTEM FOR PEST CONTROL 337

this area. The system does not address the long-term questions of the type of store or general storage practice, but focuses on treatment of grain quality problems. In describing the development of the system, the paper is divided into four sections: the decision problem, the knowledge acquisition procedure, the knowledge base (in the form of facts, rules and hypertext) and discussion on the value of developing the system for understanding the problem and planning future research and management.

THE DECISION PROBLEM

Grain may be stored for a number of reasons, for example to await consumption during the interval between harvests, to await opportunistic sales when market prices are high or for national security. When grain is stored for long periods the need to protect stocks against insect depredations increases and maintaining quality becomes difficult.

Insects may be controlled in grain by a number of physical actions starting before the grain enters the store (Fig. 1). As a routine only dry (less than about 13.5% moisture) grain should enter the store and the store should be kept clean. After grain is already in store, however, practices are less routine. Drying depends on the condition of the grain and the availability of drying equipment. The application of pesticides may be made in response to insect sampling. There are practical limitations on the availability of management expertise which often reduces the flexibility of response and the adoption of optimal strategies.

TYPE OF DECISION SAMPLES OF

CONTROL OPTIONS SELECTION CRITERIA

ROUTINE I [ STORE INSPECTION

STORE HYGIENE SAMPLING INSECTS STANDARD PRACTICE

ACCEPTONLY I SAMPLING MOISTURE DRY GRAIN STANDARD PRACTICE

OPPORTUNISTIC i CLIMATIC CONDITIONS

DRY AFTER - temperature, relative humidity INTAKE GRAIN CONDITION- SAMPLE

EQUIPMENT/COST

I ] THRESHOLD NUMBERS- MARKET TOLERANCE R E S P O N S I V E PESTICIDE SAMPLING INSECTS

EOMIPMENT/COST

Fig. 1. Three types of storage pest control decisions and some example of control options in tropical grain stores. Responses are based on various decision criteria.

338 T.H. J O N E S ET AL.

The expert system described in this paper was designed to answer the following needs: • information on pests, sampling and control methods • problem diagnosis and interpretation of sampling

• pests • moisture • temperature

• advice on appropriate control action.

KNOWLEDGE ACQUISITION

A critical phase in the development of an expert system is the acquisition of relevant knowledge. In this project, this was achieved by consulting thirteen stored grain experts from the Natural Resources Institute, Slough (now at Chatham), and one from Imperial College at Silwood Park, with expertise in inspection procedure, moisture problems, pest biology, fumigation and insecticides. Thus, the knowledge acquired on the storage of grain in the tropics covered a wide range of disciplines. This would not have been possible if only a single expert, or even a smaller number of experts, had been consulted.

Knowledge was obtained by posing a hypothetical case to the experts and noting the process, rules and information they used to solve it. Each expert was asked to consider the following situation: You are visiting a tropical country and are taken to a grain store which contains stacks of bagged maize. Store management is concerned about insect infestation and they ask for your aduice. In particular, they would like you to gitJe them recommendations for pest control

Each expert was then asked to list, in succession, the questions he would ask and the control options he would consider. On the basis of answers received an initial decision tree was formulated. This was then circulated among the experts and from their responses and subsequent interviews it was possible to determine some general procedures involved in giving advice, the logic involved in each of these steps and the decision rules employed to implement these steps. The expert system developer was responsible for translating 'intuitive' knowledge into stan- dardised decision rules by asking relevant questions to stimulate and probe the expert, suggesting possible rationales and hypothesizing concepts and rules for verification.

Acquiring knowledge from a number of expert sources (c.f. use of one expert source as in Denne, 1988; Jones et al., 1990) results in the accumulation of details of numerous approaches (community knowledge as in Schmoldt and Bradshaw, 1989) reflecting a very wide range of experience in many countries. Such an approach (c.f. Delphi technique (Dalkey and Helmer, 1963; Schmoldt and Brad- shaw, 1989)) allows the collection of information from a wide range of sources. It also provides a formal method for experts to communicate together about the complex issues involved in grain storage and allows the opportunity to have several pathways leading to specific advice. It is important to realise that the pathways derived are a compromise not always agreed by every individual expert. The


GENERIC APPROACH (common to all experts)

J PROBLEM PNVSlC~. [ SYMPTOMS

EXAMPLES OF QUESTIONS USED BY INDIVIDUAL EXPERTS

What pests are present? J Is this symptomatic of a more fundamental problem?J How has the infestation adsan? [ What is the extent of the potential loss? J

SOLUTION J J USED IN PAST I

AVAILABLE [

J RECOMMENDED J

What previous treatments have bean used in the past? I What pest control equipment is available? What recommendations have been made e sawhere?

I MANAGEMENT CAPABILITY

[ A D V I C E [ OBJECTIVES I

CONSTRAINTS J

J Why is expert advice being sought? I Is there to be a follow-up? Is advice understood? V/hat is management reaction to advice?

I What are the quality paremetars to be Preserved? I What is the risk ol not treating? What is the cost/benefit of advice?

Is training required? I What are the food/health regulat ons? J Fig. 2. The stages through which experts reach conclusions and advice about stored grain problems. The generic approach is a compromise which may not be specifically tailored to any one particular situation.

A large number of pathways may lead to advice that is more specific.

compromise, or generic, approach is intended to identify the key steps in decision making (Fig. 2).

In some situations there were marked differences in the advice given by individual experts and further questioning was necessary to clarify the issue. There were two main reasons for different advice being given. Firstly, while agreeing on the nature of the problem, some experts prefer different solutions. Secondly, experts, on the basis of their experience, may make different assumptions about a problem. This, again, may result in different solutions being proffered.

The choice of an appropriate contact insecticide was one such problem where a number of solutions were suggested. To clarify the issue, a series of questions was prepared and circulated to eight experts involved with this particular area. Experts were presented with proposals for decision rules and thresholds, based on initial interviews, and asked to mark whether they agreed or disagreed. They were also asked to rank active ingredients for use in a specified situation. Such an approach was found to be particularly useful in that it immediately clarified disagreement and lack of precision in knowledge.

An example of imprecise definition is provided by threshold levels. Many of the interviewed experts, while having well defined "visual thresholds", were unable to


provide quantifiable threshold levels. Various techniques were used to attempt to quantify these "visual thresholds": experts, for example, were confronted with an area of wall and asked to indicate how many insects would be necessary before spraying was recommended on a stack of that size. Secondly, drawings of the surface layer of bag stacks containing various numbers of bags were presented to the specialists, who were asked to mark which bags they would sample. This was then translated into a standard recommended sampling procedure.

Experts validated the system by commenting on both the computer program itself and printouts of the proposed screens of questions and information which would appear on the computer. As well as providing further opportunity to compare the strategies of various specialists this also allowed focussed discussion and ensured verification of an expert's comments to prevent misunderstanding.

T H E K N O W L E D G E B A S E

The knowledge base consists of the rules of the expert system, information for the user and a "user interface" to request inputs and provide information to the user. The knowledge base is based on the knowledge collected from the experts and is the result of the consultative process described above. An example of the paths in the knowledge base is illustrated in Fig. 3.

There are two aspects of the advisory section of the expert system that are of particular significance. Firstly, the system follows the principle of "minimum work" necessary to reach a decision: the expert system program does not require precise knowledge, it presents the user with simple questions and only a few categories of choices. The system is also organised in such a way that the minimum number of questions are asked to differentiate between recommendations. Sec- ondly, even if the user asks for advice on multiple pest problems, the expert system bases its recommendation on the concept of a key pest. The key pest is not necessarily the most damaging pest present; rather, it is defined as that pest which requires the most extensive control measures (Tait, 1987; Compton et al., 1992).

A summarised representation of the knowledge base is shown in Table 1. Each topic contains questions, information, user responses, rules or instructions for screen displays, menus, etc. As can be seen, the knowledge available is in a structured, modular form, thus enabling future developments to be easily incorpo- rated into the system.

Knowledge representation Knowledge and expert opinion have been represented in the expert system in

the form of IF-THEN rules. The user is asked questions about the conditions in the rule. If all the conditions in a particular rule are satisfied, then the conclusion of that rule is met. Since the conclusion of one rule can subsequently become a condition of another, a series of logical sequences can be linked by rules.

Software An expert system shell was used to incorporate the rules into a software

package. This allows the knowledge base developer to concentrate on the logic of


PROBLEM

IINSPECTION I

I S'PUNG I

SEASONAL DATA

HIERARCHY I

I KEY PEST CONCEPT I ~

SOLUTIONS

INFORc~TION OPT ONS

ADVICE

I DECISION RULES

INFORMATION AND RULES ACQUIRED AND ORGANISED FOR INCLUSION IN KNOWLEDGE BASE

~l PROBLEM ] v I DEFINITION

T

DANGER LEVEL?

I

I MOISTURE I

II DANGER LEVEL?

T . . . . . . . t

I DRY l I

I DISPOSE I

PROCESS IN AN EXAMPLE DECISION

Fig. 3. A diagrammatic representation of the process involved in the making of a decision using the grain store expert system. Decision rules are based on the knowledge collected during the consultative

process and categorised as shown on the left hand side of the figure.

the problem rather than the complexities of programming. KnowledgePro (Copyright 1987, Knowledge Garden Inc.) was used and was run on IBM PC compatible computers. For further development it may be desirable to write in a

342 T.H. JONES ET AL

TABLE 1

Summarised knowledge base used in the expert system for pest control in tropical grain stores

Title of expert system [Using the system] [Silwood Centre for Pest Management] [Natural Resources Institute]

User interface (menu)

Stored grain problem Insect problem

What country? [Pest relevance to particular country]

What commodity? [Commodity information]

Seasonal temperatures [Temperature]

Anticipated storage time go to What pest(s)?

Moisture problem What level of moisture?

[Consignment checking] Recommendations

[Moisture information] [Temperature information]

General inspection [General inspection and sampling] * [Inspection for hot-spots] [Inspection for moisture] [Spear samples] [Moisture meter] [Insect inspection]

[Visual inspection] [Hot-spot inspection] [Insect damage]

[Traps] [Sieving and snaking]

What pest(s)? Insect identification

Pest information [Primary pest(s)] [Secondary pest(s)] [Pest groups] [Coleoptera] [Lepidoptera] [Coleoptera species] [Lepidoptera species] [Sitophilus species] [Sitotroga cerealella] [ Pros tephanus t runcatus ] [ Rhyzoper tha domin ica ] [ Tr ibol ium species] [ Trogoderma granar ium ] [Warehouse moths] [Unknown pest(s)]

Identify pest(s) (symptoms)

Cont inued next c o l u m n

cont inued

Check identification? [Explanation of key-pest concept]

Unidentified pest(s) go to Recommendation on key pests

Recommendation on key pests [Infestation level]

Specific recommendations Trogoderma and Pros tephanus Determination of attack levels?

[Pros tephanus - new attack alert] [ Pros tephanus - alert] [ Pros tephanus - control recommendations]

Si tophi lus Determination of attack levels? Rhyzoper tha Determination of attack levels?

[Information of risk of light attack level] Sitophilus and Rhyzoper tha - short-term Si tophi lus and Rhyzoper tha - long-term Si tophi lus - heavy attack [Sitophilus recommendations]

Rhyzoper tha - heavy attack Tribol ium Determination of attack levels? Moth attack?

Light attack Attack not important Sitotroga - control recommendation

go to Fumigation - i f advised go to Insecticide spraying - i f advised

Fumigation [Fumigation information] *

[Choosing a fumigant] * [Methyl bromide] [Phosphine]

Fumigation equipment? [Information on equipment] [Application techniques]

Fumigation time? Fumigation history?

[History information] [Residue information]

Trained personnel? Fumigation recommendation

Insecticide spraying [Contact insecticide] [Formulation]

[Emulsifiable concentrates] [Spraying]

[Stacks] [Store walls]

Repeat consultancy go to Stored grain problem

Key: terms in bold represent primary topics; terms in square parentheses [ ] represent hypertext topics; * marks hypertext topics illustrated in Table 2.


TABLE 2

Examples of typical hypertext information provided within the expert system

Topic Information

General Inspection and Sampling

Sub-topic: Moisture

Fumigation

Sub-topic: Definition

Sub-topic: Choice of fumigant

Screen 1

Screen 2

Inspection for moisture: If you have reason to suspect a moisture problem, take spear samples of grain from the section of the stack most likely to be affected and test their moisture content, using a moisture meter. The number of spear samples should be as few as are needed to make a decision. If 3 samples are over the critical moisture level of [13.5%], there is a moisture problem. The maximum number of samples normally needed to be confident that there is NO problem is roughly the square root of the number of bags sampled on each face of the stack. That is, if a stack has a face with 100 bags, a maximum of 10 samples are needed from that face.

Definition of fumigants: Fumigants are materials which give off a poisonous gas. If applied properly, the gas can penetrate the stacks of grain and kill insects deep inside. However, fumigants give no lasting control. Thus, if the grain is to remain in store for some time, fumigants are usually used in conjunction with a contact insecticide which is applied to the outside of the stacks to prevent reinfestation. The two commonly-used fumigants are METHYL BROMIDE (a gas) and PHOSPHINE (sold as a luminium or magnes ium phosphide, a solid). Both are highly poisonous and must be used carefully. More information is available on CHOICE OF FUMI- GANTS, APPLICATION TECHNIQUES and FUM1GA TION EQUIPMENT.

The two commonly-used fumigants are phosphine (sold as aluminium phosphide) and methyl bromide. Both have advantages and disadvantages. Aluminium phosphide (or magnes ium phosphide) is a solid, usually sold as tablets or sachets, which liberates the poisonous gas PHOSPHINE on exposure to moisture in the air. It is easy to handle and needs no special equipment , apart from a fumigation sheet. Workers however, must have special training in safety precautions. Phosphine is about as dense as air and thus moves freely through grain stacks. It leaves a non-toxic residue. Its main disadvantage is the time needed for treatment: the store must be closed for at least 5 days, so it is difficult to use in busy stores. METHYL BROMIDE is a gas under pressure, sold in large cylinders or small canisters. Its main advantage is speed: fumigation can be completed in under 30 hours. However, it needs special application equipment (tubes and pipes), and workers must be highly trained before they can apply it. Methyl bromide is heavier than air, and tends to sink to the bot tom of stacks if carelessly applied. This can result in an uneven distribution of gas, which can lead to two serious problems. First, in areas of low gas concentration, many insects survive and when fumigation is over, these can quickly reinfest the rest of stack. In other areas, overdosing can leave unacceptably high bromide residues, which are harmful to human health, can cause tainting, and may lead to the rejection of the commodity. Even when correctly applied, methyl bromide can give unacceptable residues if used too many times on the same commodity. For this reason, international residue limits have been established for methyl bromide, which must not be exceeded. If the commodity is to remain in store for some time, the strict limits on the number of fumigations with methyl bromide may mean that phosphine must be used for some fumigations.

Terms in bold capitals indicate a link to further hypertext. The context of this hypertext can be seen in Table 1. [ ] indicate a variable value dependent on information supplied by the user.


conventional programming language to reduce space and increase speed. This is much easier after the initial format of the program has been developed in a shell.

Hyper tex t

An important feature of the system, and a characteristic of the expert system shell used - KnowledgePro - is the use of hypertext. Certain key words or phrases can be marked in the program so that when they appear on screen they are highlighted. Information screens on particular topics can be called by pressing pre-defined keys. Hypertext has three clear advantages: information is not forced on the user but is available when needed, the system is so structured that the same information is available both from the information bank and from the advice section and finally, it is easy to add or modify hypertext at any time (Warwick et al., 1992). Some examples of the various ways in which hypertext has been employed in this system are listed in Table 2.

D I S C U S S I O N

The development of the expert system for pest control in tropical grain stores described in this paper demanded a disciplined approach to the design of specific control recommendations across a range of situations, decision points and levels of information availability. It makes specialised advice readily available to the non- specialist user. Like other knowledge-based systems in pest management, it offers support for practical problem solving, information processing and training (Mum- ford and Norton, 1989; Compton et al., 1992).

Wilkin et al. (1990) commented on the problems encountered in the development of a storage expert system. Access to experts and the quality of available data were considered major limiting factors. The development of the expert system described above identifies other difficulties. In particular, although access to expertise was relatively easy, acquiring knowledge from a number of expert sources results in the accumulation of details of numerous approaches to each individual situation. Thus the expert system developer must determine both the common and individual routes (Fig. 2) to the various forms of advice given by the range of experts. A number of serious drawbacks to multi-expert knowledge acquisition have been suggested (Shields et al., 1987). For example, it may be difficult to achieve a satisfactory consensus or compromise within the group. In addition, in the course of collecting and exchanging information with the experts, they often modify their rules in response to learning the views of their colleagues. However, the process contributes greatly to the overall understanding of how advice is given by experts and how these experts believe advice should be presented to decision makers under a wide range of circumstances.

In developing an expert system such as the one described here, another problem frequently encountered is that objectives for store management are not always clearly defined. Store managers may have objectives ranging from avoidance of complaints to maintaining profits. Furthermore, the ranking of objectives by expert advisers and store managers may not coincide, for example, the relative impor-


tance of quality and safety. Economic circumstances may also differ in individual cases. Store managers often have to make decisions under unique local circumstances, for example, where exchange rates are distorted a store manager might prefer an expensive chemical, which is artificially cheap on the local market, while an adviser might think in terms of its foreign currency costs and recommend a cheaper product. In theory, these problems could be addressed by the expert system, simply by asking questions about the objectives of the user, for example: 'At what grade do you want to sell the maize?', 'Are you prepared to lose one grade?'

The systematic process of knowledge acquisition involved in developing this expert system has revealed information gaps that clearly specify the need for future research. There are three areas, in particular, that deserve mention:

1. The need for more standardisation of inspection and sampling procedures. Most experts have highly individual ways of conducting inspections. Such idiosyn- cratic rules do not lend themselves well to training managers and maintaining good management practices in large storage organisations.

2. Differing objectives for grain storage make it difficult to clarify what levels of infestation are acceptable in certain circumstances. For example, the threshold level for fumigation of Sitophilus, Rhyzopertha and Tribolium was defined as anything higher than a "light infestation". No agreement was reached on actual numbers. While there will be no universal answer to this threshold problem a computerised expert system provides a way of testing the sensitivity of decisions with a range of thresholds. As the decision rules involve the acquisition of information for each of the parameters individually it is relatively easy to make adjustments to complex rules. As a result the expert system provides an opportunity for experts to test and develop rules for applicability for a wide range of circumstances.

3. There is a need for an economic framework (Mumford and Norton, 1984; 1990) that will take into account such factors as distorted exchange rates, grain security and subsidies when making decisions. In cases where costs, etc. are not well known then the incorporation of a more probabilistic approach would also be desirable. However, it is difficult for managers to calculate numerous probabilities and to visualise the consequences of them. An expert system could both provide a check-list of information to be used in decision making and could easily run a number of calculations to show how different probability distributions would affect the advice that is given.

ACKNOWLEDGEMENTS

This work was carried out through an extra-mural contract from the Natural Resources Institute. We thank the numerous colleagues at Silwood Centre for Pest Management and the Natural Resources Institute who collaborated with us in the development of the prototype system, and Conrad Warwick (Silwood Park) for discussion.


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development of an expert system for pest control in tropical grain stores

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