Development and implementation of a knowledge acquisition methodology for crop management expert systems

Computers and Electronics in Agriculture, 8 ( 1993 ) 129-146 129 Elsevier Science Publishers B.V., Amsterdam

Development and implementation of a knowledge acquisition methodology for crop management

expert systems

Ahmed Rafea, Ayman E1-Dessouki, Hesham Hassan and Soliman Mohamed Expert System for Improved Crop Management Project (EG Y/88/024), Cairo, Egypt

(Accepted 4 March 1992 )

ABSTRACT

Rafea, A., E1-Dessouki, A., Hassan, H. and Mohamed, S., 1993. Development and implementation of a knowledge acquisition methodology for crop management expert systems. Comput. Electron. Agric., 8: 129-146.

This paper presents methodology developed for knowledge acquisition for crop management expert systems. The proposed methodology is described through an extended waterfall model for knowledge acquisition. The way in which the methodology was implemented is presented, and the experience gained is discussed. Although the methodology has evolved through the development of an expert system for cucumber seedling production, it can be used for other crops. A field prototype of this expert system was implemented and is currently being tested in a real environment.

1. INTRODUCTION

Knowledge acquisition is recognized as the bottleneck in the building of expert systems. Therefore, research efforts have been directed in recent years to developing methods and techniques for this process.

Current approaches to knowledge acquisition are based on a variety of methods and techniques, mostly imported from other disciplines and adapted for use in knowledge acquisition. Psychology has offered entity-attribute grids (Shaw and Gaines, 1987; Boose and Bradshaw, 1987 ), protocol analysis (Er- icsson and Simon, 1980) and some forms of interviewing (Lafrance, 1987 ). Linguistics has offered implicit knowledge structures for text analysis (Wood- ward, 1988), and forms of discourse analysis, ontological and conceptual analysis (Belkin et al., 1986; Hirst and Regoczei, 1989). Epistemology has offered knowledge hierarchies (Hayward et al., 1986 ). The machine-learning

Correspondence to:Ahmed Rafea, Expert System for Improved Crop Management Project (EGY/ 88/024), c/o FAO Rep., P.O. Box 2223, Cairo, Egypt.

0168-1699/93/$06.00 © 1993 Elsevier Science Publishers B.V. All rights reserved.

130 A. RAFEA ET AL.

field has provided induction techniques (Forsythe and Rada, 1980). Artifi- cial intelligence has provided a number of representational and procedural techniques (McDermott, 1988; Chandrasekaran, 1988). System engineering has provided structured procedural methods to conduct the knowledge acquisition process (McGraw and Harbison-Briggs, 1989 ).

I . 1 Objectives

The methodology presented in this paper is actually a result of experience gained in a project for developing expert systems for improved crop management (ESICM). The purposes of this project were: (a) to build a local expert system development group capable of planning, development and packaging of the expert system, and (b) to develop and package two expert systems for enhancing the services of extension workers provided for farmers. One expert system was to assist in crop management of cucumbers grown under pro- tected cultivation; the second expert system to assist in crop management of citrus fruits in open field cultivation.

1.2 Methodology development stages

A systems oriented approach has been used in the proposed methodology as the general framework for the knowledge acquisition process. Develop- ment of the methodology has passed through several stages until it has reached the current status.

These stages can be summarized as follows: - the development of an Expert System for a limited domain, which was the

management for cucumber seedlings production (ESICM, 1990a). - developing a preliminary methodology. - applying this methodology. - revising the methodology continuously until version 1.0 was reached (ES-

ICM, 1990b). These efforts to harness the knowledge acquisition process are presented in

the following sections. A methodology for acquiring knowledge is proposed in Section 2; the way this methodology has been implemented is described in Section 3, followed by concluding remarks in Section 4.

2. P R O P O S E D M E T H O D O L O G Y

A systems oriented approach was selected for the knowledge acquisition methodology. As knowledge engineers worked with domain experts, they did so in a specified framework of guidelines and procedures that served to provide some degree of standardization over the selection and application of knowledge acquisition techniques. In addition, this methodology included the

CROP MANAGEMENT EXPERT SYSTEMS 1 3 1

use of a variety of templates, or forms, that documented the acquired knowledge. The end result was a knowledge base that could be traced to its original source and well enough documented to enable later maintenance and modification.

2.1 A model for knowledge acquisition

An adaptive Waterfall Model (McGraw and Harbison-Briggs, 1989) has been selected as a general framework for knowledge acquisition activities (see Fig. 1 ). This was adopted because it facilitated rapid prototyping. It is not sequential in nature but more accurately reflects managed iteration and en- hancement. At each stage in the development process, one can stop and evaluate current efforts towards completion. Knowledge acquisition activities, which are shown in the rectangular boxes, are described in the following paragraphs.

Goal analysis. During this activity the knowledge engineer collaborates with the domain expert to set goals for the expert system, given the functional objectives of the system. Next, the knowledge engineer analyzes these goals to determine the required phases and components of the expert system.

Unstructured interview. This is used to familiarize the knowledge engineers with the domain. A group of experts and knowledge engineers meet in sessions for which specific goals are determined to cover all specialities. Each expert briefs the group about his speciality and problems he has observed in the field.

Conceptual analysis. The knowledge engineering group uses the acquired knowledge in the unstructured interview to conceptualize the domain of crop production management.

Domain analysis. Once the domain has been conceptualized into basic blocks, the next step analyzes each block and finds the attributes that describe and characterize this block.

Task analysis. The procedural steps, which the grower uses to produce cucumbers, are analyzed and the problem-solving approach, which the expert appears to be using, identified.

Structured interview. This activity is the mainstay of knowledge acquisition. It is used in all phases of the process to clarify or extend information received via other techniques. The knowledge engineer is to outline specific goals and questions for the knowledge acquisition sessions.

132 A. RAFEA ETAL.

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Prototyping. The research prototype is used as an automated tool for refining the acquired knowledge. This requires each expert and/or group of experts to use the prototype and give their comments.

Evaluation. This activity incorporates simple evaluation procedures, in the sense that domain experts are consulted to evaluate the conclusion of the expert system. Further refinements to the system are based on remarks of the domain expert.

CROP MANAGEMENT EXPERT SYSTEMS 13 3

2.2 Extended model for knowledge acquisition

The Waterfall Model as described in the previous section was found to be insufficient in some parts to cover all aspects of the knowledge acquisition process, and some extensions to the model were necessary. In this section the problems with the initial model will be introduced first and the proposed extensions to solve problems then presented.

2.2.1 Problems with the model The project team encountered several problems with the Waterfall Model

during its implementation throughout the knowledge acquisition phases of the Cucumber Seedlings Expert System. Some extensions to the model were necessary before its adoption in the development phases of the Expert Sys- tem. The problems were:

( 1 ) Failure to reflect the effect of multiple experts on the knowledge acquisition process.

(2) Lack of process tracing and protocol analysis found essential for the project.

(3) The prototyping involved a complete development cycle, which was not indicated by the Waterfall Model.

(4) Record keeping activity was not clearly identified in the model. In the following subsections, extensions to the Waterfall Model to over-

come these drawbacks are presented.

2.2.2 Proposed extensions to the Waterfall Model The following paragraphs describe the extensions that were introduced, to-

gether with the main objectives of each. The extended model is shown in Fig. 2. The new extensions are identified by double lines.

In order to incorporate the effect of multiple experts, an extra activity namely 'Conflict Resolution' was added to the knowledge acquisition activities. During this activity each expert had been individually interviewed, and the knowledge engineers analyzed the results of multiple interviews to iden- tify points of disagreement. Majority consensus rules were applied to reach a conclusion. Opinions of experts were weighted differently, based upon their experience and level of specialization in the area of the conflict.

The domain of crop management has been found to involve procedural as well as declarative knowledge. The activity of Protocol Analysis and Process Tracing was found essential for acquiring this type of knowledge. During this activity, the knowledge engineer performed some form of analysis of the experts' way of thinking and the procedures they followed to solve problems in the domain. This knowledge was acquired by audio casette or as written notes of the expert procedures.

The activity of Prototyping involved a complete software life-cycle. This

134 A. RAFEA ET AL

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Fig. 2. Extended Waterfall Model.

CROP MANAGEMENT EXPERT SYSTEMS 135

included requirement specifications, design, implementation, and verification and validation. Therefore this activity which included several subactivi- ties had to be represented differently to differentiate between it and other simple activities as shown in Fig. 2.

All knowledge acquisition activities required a system of record keeping. A set of forms had to be designed each for a specific activity, and a procedure for record keeping was enforced to ensure accurate and up-to-date documen- tation. These forms were used for documenting: the session itself; results of session and/or its analysis; the technical terms (dictionary); ...etc. Samples of these forms will be presented in Section 3.

3. IMPLEMENTATION AND EXPERIENCE WITH THE METHODOLOGY

In this section, the experience gained in implementing the extended model adopted for knowledge acquisition phases is presented.

3.1 Goal analysis

Initially, a set of Agriculture Engineering requirements was determined in the management domain of cucumber seedlings production, already identified in the project document. These requirements defined a set of functional objectives for the expert system to be developed, namely - Installation and preparation:

• preparing the tunnel infrastructure • preparing media and • sowing seeds.

- Maintenance which includes: • maintaining growth factors in optimal conditions and • protection against insects and diseases.

- Identification of the cause (s) of identified symptoms. - Treatment of identified disorder (s). The following goals were set for the Expert System:

( l ) The Expert System should cover all areas called for by the functional objectives.

(2) The domain should be divided into a set of classes/categories, in such a way as to optimize the relationships within a class and minimize the relationships between classes. Moreover, the attributes of each class should be specified.

(3) The domain description should be properly defined, in the sense that all required relations between attributes of objects should be adequately specified.

(4) The solution had to be complete, in the sense that it provided all pos- sible solutions for the given situation.

136 A. RAFEA ET AL.

(5) The solution suggested by the Expert System should be adequate, in the sense that all questions asked by the system were tested for logical relationships and order.

(6) The knowledge acquired from different domain experts must be con- sistent. In case of disagreement majority consensus rules were applied. These rules had to take into consideration the specialization and seniority of the expert.

(7) The Expert System should give clear responses through a user-friendly interface. The responses should be a result of a well-structured set of knowledge forms.

(8) The results and replies of the Expert System should have a measure of correctness and completeness. The adopted measure was that the system should give correct answers for at least 90% of the inquiries and it should perform better than 80% of the extension service employees.

3.2 Unstructured interview

This was the first knowledge elicitation technique used by the team. The purpose of the interview was to get an overview and the background knowledge about the cucumber seedling production as a whole, and about each function of the expert system to be developed. This technique enabled the knowledge engineering team to become familiar with the domain and aware of all related aspects.

Experts specialized in different areas of cucumber seedling production at- tended the session with two knowledge engineers. The specialities of these experts covered nutrition, protection, irrigation, and production. The sessions were taped on audio-casette. The knowledge engineers did not interfere in the session except for keeping discussion toward the session goals, encour- aging experts to give their views, and/or inquiring about specific items.

3.3 Conceptual analysis

The overall knowledge structure was identified by analysing the outcome of the unstructured interviews, and taking into consideration the system functional objectives. This structure consisted of concepts related to: the physical components of the seedling production system, namely: plastic tunnels, grow- ing media, water, seeds, plant, and climate, ...; growth factors such as temper- ature, relative humidity, light .... ; symptoms, such as yellowish leaves, itu- lated stem, root decay, ...; disorders, which included pests, nutrition deficiency, ...; and agriculture operations, such as media preparation, seed sowing ....


3.4 Domain analysis

The domain analysis included three activities: determining the basic relations between the identified concepts, determining the attributes of these concepts, and identifying relations between concepts and the attributes of other concepts.

The basic relations were identified. The first relation was the is-part-of relation which joins the cultivation physical objects to the tunnel environment. The second relation was the is-done-on relation which joins the agriculture operation to the physical objects. Figure 3 demonstrates these two relations.

The attributes and the meta-attributes of each of the identified concepts were determined. Figure 4 shows an example of the media concept. The meta- attributes, sometimes called facets, describe the attributes of a concept. Fig- ure 5 gives another example of a concept - the media preparation operation. Agriculture operations are identified as one type of the expert system outputs. It is the responsibility of the inference engine to determine an operation to be done. Some of the agriculture operation attributes had been provided by the domain expert (static), some by the user (dynamic), and some had to be inferred from the system.

The third activity in domain analysis was identifying relations between concepts and other concept attributes. The relation between the disorders and their symptoms is an example of this activity. The symptom appearance order relation was also found to be very important. A third relation needed was that between a disorder and other disorders sharing the most important symptoms. The relation between the symptom and its cause (disorder) was found

Tunnel environment

Plastic M e d i a W a t e r Seeds Plant Climate Tunnel

Operations : Operations : Operations : Operations : Operations : Operations : - T r a y d i s in fec t ion - M e d i a P ~ p a r a t i n n - R e d u c i n g Sa l in i ty

T u n n e l d i s in fec t ion - M e d i a fe r t i l i za t ion - A d d i n g pest icides - S o a k i n g - Sp ray ing - T e m p e r a t u r e Cont ro l - Reduc ing m e d i a for t r e a t m e n t or - H u m i d i t y Cont ro l

S a l i n i t y pro tec t ion

- R e d u c i n g m e d i a P.H - S c h e d u l i n g &

- M e d i a d i s in fec t ion r e~cheduhmg I r r iga t ion

Fig. 3. Overall domaine structure.

138 A. RAFEA ET AL

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to have a degree of certainty. It was also found that the relation between the disorder and the appearance of symptoms had some degree of certainty. The accumulation of more than one symptom increases the degree of certainty when diagnosing disorders. As a result of this analysis a form was designed to acquire the identified relations as shown in Fig. 6. The column marked S/D indicates the degree of certainty with which the disorder might be diagnosed if the corresponding symptom existed. The column marked D/S indicates the degree of certainty with which the symptom might have appeared if the disease existed. The column marked ACC indicates the degree of certainty with which the disorder might be diagnosed if symptoms from the first up to the corresponding symptom existed. The second part provides knowledge of ob- servations about the order of symptoms. The third part provides knowledge of disorders that share the most important symptoms with the current disorder.

3.5 Protocol analysis

Analyzing the outcomes of the unstructured interview did not provide knowledge on how domain experts reached conclusions. Therefore, special sessions were dedicated to getting this knowledge by field visits in which the expert was requested to explain his behavior in a typical situation relating to the expert system functional objectives already determined as described in Section 1.1. This self-explanation was recorded for further analysis.

1 4 0 A. RAFEA ET AL.

NAME OF THE DISORDER : White Ro

SYMPTOMS S/D D/S ACC.

1 Stem Decay 70 100

2 Small Water Spots on the Stem 50 100 100 ( Turned to Brown )

3 Dead Seedlings 30 70 100

4 Leaf Wilt 30 50 100

5 Yellowish Leaf 10 40 100

6

7

8

SYMPTOMS THAT APPEAR 1N THIS DISEASE ONLY, ORDERED ACCORDING TO OBSERVATION

SI Abnormal Leaf $5

$2 Decay Stem $6

$3 Small water spots on the stem $7

$4 $8

DISEASES THAT SHARE THE MOST IMPORTANT SYMPTOMS

D1 Gummy stem Blight D3

172 I)4

NOTES:

Fig. 6. Diagnos i s form.

3.6 Task analysis

The outcome of the protocol analysis elicitation technique was used for deciding the procedural steps followed by the expert to reach conclusions within each expert system function.

To clarify the outcome of the task analysis phase, a simple example is given concerning media preparation. In this it was found the expert followed certain steps in reaching a final decision: ( 1 ) Determine the raw material components ratio. (2) Specify additions to the raw material according to its components. ( 3 ) Determine the time needed to leave the mixture before sowing. Acquiring this type of procedural knowledge was important in ensuring the logical sequence for requesting data when the system runs. For each step, the


Rule Co.tenl Form

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Associated certainty : 100% I.:sedBy: Prepmli0~andlnstalla~i0nK,B.

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'omnkaDl • (T~s p~l/l ~ 1o ~ fl~Cd by lhg dolilalll cXpell)

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rules which the expert used were documented in a rule content form. An example of this form is shown in Fig. 7, which documents one of the rules used to specify additions to the raw material.

3.7 Conflict resolution

Conflict of domain experts was expected from the very beginning and for this reasons an agriculture domain coordinator had been suggested for the development team. However, experience showed that this position was very difficult for any of the agriculture experts because of potential bias. Where there was disagreement between two experts in the same speciality, but different from his, the agriculture domain coordinator could not manage the situation. Therefore, the idea of an agriculture coordinator was abandoned and this responsibility given to the knowledge engineer.

Conflict of domain experts involved: - conflict of experts in different specialities - conflict of experts in the same field.

Resolving conflict of experts in different specialities was easier than resolving the conflict where the same speciality was involved. In the case of different specialities, the points of conflict were determined. The experts them-

142 A. RAFEA ET AL.

selves recognized the expert whose decision must be followed. In the case of the same speciality, the conflict was resolved by consensus. The knowledge engineer and the experts held sessions and went through parts of conflict, case by case. Each expert explained his view and how he reached his decision. At the end, they agreed on one decision.

An example of one of the problems encountered was disagreement over the order of symptoms relating to plant disorders (see Fig. 6 ).

3.8 Structured interview

This elicitation technique followed all knowledge analysis phases. It was used to complete the conceptual structure of the domain, to complete the attributes of the concepts, to acquire the attribute values and facets, to fill in designed forms, to enquire about any missing information, and to resolve conflicts if needed.

In the structured interview session, a major problem facing knowledge engineers was the deviation of domain experts from the session goals already decided. It took a great effort on the part of the knowledge engineers to get domain experts to focus on the session goals. This deviation was found to be due to two main causes. Firstly, the experts were trying to refer to other crops which were not within the scope of the investigation. Second, they did not give concrete answers to the questions posed. To overcome this problem the knowledge engineers had to control the session in a firm, but polite, way and use the designed forms to get the domain experts focusing on the required knowledge.

3.9 Prototyping

This activity included: requirement specifications, design, implementation, and verification and validation. The use requirements were analyzed and a requirement specification document produced (ESICM, 1990c). A preliminary design was conducted (ESICM, 1990d ), and the implementation of a laboratory prototype performed, using EXSYS professional shell (EXSYS, 1988 ). The laboratory prototype was verified and validated (ESICM, 1990e ). The evaluation of the verification and validation results was used to develop a field prototype (ESICM, 1990f).

In effect, the prototype itself was used as a tool for knowledge elicitation through dedicated sessions in which the knowledge engineer used a data pro- jection system to review the acquired knowledge with the domain experts.

Remarks of the domain experts during this session were documented and the prototype was updated. This process was repeated for each function of the expert system. In some situations gaps in the knowledge were discovered. Ac-


cordingly, the appropriate elicitation technique was conducted to fill these gaps.

3.10 Evaluation

The evaluation activity included measures for each of the knowledge acquisition phases described above. These measures were determined in the goal analysis phases as ment ioned in section III- 1. There was a 1-1 correspondence between the goals determined in that section and the knowledge acquisition phases. The evaluation procedure corresponding to each phase was conducted to assure the completeness of this phase. This can be seen in Fig. 2, which depicts the extended model.

The most important evaluation procedure was the analysis of the validation results. The validation was conducted using a set of typical cases for each expert system function, generated from the acquired knowledge. Each case had more than one conclusion. The problem was how to measure the performance of the prototype relative to the domain experts, taking into consideration that the domain experts themselves may give different results.

Therefore, a formula for computing the performance factor was developed to measure the degree of agreement between the system and the experts, and between the experts themselves (ESICM, 1990e).

The performance factor of expert (i) in function (j), denoted by (PVij), has been defined as follows:

PFij = E PFijl X 1 0 0 ( 1 ) /=1

where nj is the number of cases in function (j), and PFijt is the performance factor of expert (i) in function (j) for case (l) which is given by this formula:

m--I

PFijl= ~, NCijkt*k/m (2) k=0

NCim is the number of conclusions in which the expert (i) in function (j), agreed with (k) experts in case (I), and m is the total number of experts including the expert system.

Applying this formula on the validation cases, the results in Table 1 were obtained. This table showed the following features: - Domain experts disagreed in several cases. - The system performance varied from function to function, which reflects

the maturity of the acquired knowledge in a certain function. Conse- quently, conflicts between experts were resolved using the suggested conflict resolution method and the ensuing results so obtained are shown in

144 A. RAFEA ET AL.

TABLE 1

Performance factors of the system and the five domain experts

Function name System Expert Expert E x p e r t Expert Expert 1 2 3 4 5

Agricultural practices 67% 65% 67% 68% 68% 68% Protection 44% 66% 58% 57% 66% 67% Disorders diagnosis 63% 50% 55% 55% 62% 57% Disorder treatments 62% 66% 50% 52% 63% 69%

Average 59% 61.75% 57.5% 58% 64.75% 65.25%

TABLE 2

System performance relative to domain experts

Function name System Performance

Agricultural practices 78% Protection 40% Disorders diagnosis 72% Disorder treatments 67%

Average 64.25%

Table 2 which reflects the performance of the system relative to the domain experts.

- It is worth noting that the performance of the system had increased in three out of the four expert system functions, indicating that some of the resolved cases were in favor of the system.

- The system performance had decreased in only one function, indicating that some of the resolved cases were against the system. Results obtained from these experiments were used to develop the field

prototype.

3.11 Knowledge record-keeping

The procedure for knowledge record-keeping has been found to be very use- ful. Frequently the knowledge engineers referred to the knowledge notebook during the preparation of the design document, coding the knowledge, and/ or verifying the prototype. The building of the dictionary did not go well because the knowledge engineers concentrated more on domain and task anal- yses. However, the importance of the dictionary appeared when an explanation facility had to be integrated with the expert system.


4. CONCLUSION

The knowledge engineering team has succeeded in harnessing the knowledge acquisition phase for developing an expert system for cucumber seedling production management. The proposed methodology is suitable for building other expert systems for managing the production of any other crop.

The multiple expert approach was necessary in the domain of crop production management for two main reasons, namely: the domain is by its nature a multi-disciplinary one, and it was found that a single opinion in any of these specializations was not sufficient to reach a proper and concrete conclusion. The team believes that this is the case in almost all real life domains of expert system development.

Enhancing the methodology is a continuous activity of the project. Record- keeping procedure needs to be automated as it was found tedious to refer to papers, however well organized, to find the knowledge elicited on a specific point.

The performance evaluation of the laboratory prototype identified gaps in the knowledge base. These gaps were filled and the laboratory prototype updated. This mature laboratory prototype (field prototype) will be tested in the field. Testing is currently being performed in the real environment.

This methodology is currently being used in project activities on the development of two other expert systems: one for the cucumber production management under plastic tunnels, and the second for citrus production management in open field agriculture.

ACKNOWLEDGEMENTS

The project described in this paper was funded by the Egyptian Ministry of Agriculture and Reclamation (MOALR) and the United Nations Develop- ment Programme (UNDP) , and was executed by the Food and Agriculture Organization (FAO).

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146 A. RAFEA ET AL.

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Documents

Development and implementation of a knowledge acquisition methodology for crop management expert systems