PII: S0957-4174(96)00049-8 P e r g a m o n Expert Systems With Applications, Vol. 11, No. 3, pp. 343-349, 1996 Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0957-4174/96 $15.00+0.00 PII: S0957-4174(96)00049-8 Using Expert Systems as a Training Tool in the Agriculture Sector in Egypt A . RAFEA Computer Science Department, Institute of Statistical Studies and Research, Cairo University, 5 Tharwat St, Orman, Giza, Egypt K . SHAALAN Computer Science Department, Institute of Statistical Studies and Research, Cairo University, 5 Tharwat St, Orman, Giza, Egypt Abstract--This paper describes the Egyptian experience in using Expert Systems (ES) as a training tool in the agriculture sector. The work described here is part of an ongoing research to study the use orES in human resources development. In particular, we present the use of such a tool as an instructional device f o r increasing the efficiency of extension workers through improving their general decision- making skills in their jobs. To clarify this process, we conducted an experiment and analyzed its results. Copyright © 1996 Elsevier Science Ltd 1. I N T R O D U C T I O N KNOWLEDGE-BASED SYSTEMS (KBS) are computer pro- grams that incorporate heuristic knowledge and emphasize declarative knowledge over procedural prob- lem solving. KBS can be used as a powerful training tool. In general, the goal of training is to produce a motivated user who has the basic skills needed to apply what has been learned and then to continue to learn on the job (Compeau et al., 1995). Two features make KBS an excellent training tool for personnel whose mission is providing advice. The first feature is that KBS incorpo- rate experienced-based knowledge derived from different sources o f a certain domain, e.g. human experts, and is now structured and provided in a very portable and easily accessible medium. For example, by using an expert system for crop management, the crop consultant is forced to go through the entire reasoning process in a systematic manner ensuring consideration o f all factors affecting the decision. Another feature of the KBS is the explanation facility which is inherently an educational tool. Explanation facilities provide for reasoning which is important as a training tool for new personnel, e.g. new extension personnel that are new to certain crop. Literature that sheds light on using KBS in training programs is beginning to emerge. An empirical study o f the use o f ES in U.S.A. business schools and its implications for industry is present ed in (Teer et al., 1994). Several cases and benefits of using KBS as an instructional device i n MBA programs is surveyed in (Dologite, 1991). An interesting study by Mockler (1990) is the use o f KBS for teaching and performing KBS development. This development is to guide both technical and non-technical managers in finding, defining, selecting, evaluating an area, decision or task for potential KBS development. Moreover, an e~pert system can be used to advise managers on selecting employees for training, as in Ntuen et al. (1995), which is time consuming and belongs to a special class o f multiattribute decision making. The study described in this article addresses the use o f ES as a training tool for increasing the efficiency o f extension workers through improving their general decision-making skills in their jobs. The next section briefly presents the needs for ES's technology in the agriculture sector. Then the focus turns to a description o f the agriculture extension service environment. The expert systems involved in this training are then described briefly. It is followed by a description o f the experiment conducted during the training o f extension workers that is the concern o f this study. Next, we discuss the outcome o f applying the experiment and present its results. In a concluding section, we present some final remarks. 2. THE NEEDS F O R EXPERT SYSTEMS T E C H N O L O G Y IN THE A G R I C U L T U R E S E C T O R Agriculture production has evolved into a complex process requiring the accumulation and integration o f knowledge and information from many diverse sources 343 344 A. Rafea and K. Shaalan including marketing, horticulture, insect management, disease management, weed management, accounting and tax laws. Expert systems are tools for agriculture management since they can provide the site-specific integrated and interpreted advice that farmers and consultants need to more efficiently handle management concerns (Rafea, 1995). This section decribes the need for ES as a tool for decision support and as a tool for training. 2.1. The Need for Expert Systems in Decision Support The development o f an agriculture expert system requires the combined efforts o f specialists from many fields of agriculture, and must be developed with the cooperation o f the farmers and extension officers who will use them (Broner et al., 1992). A recent study o f the needs assessment for expert systems in the agriculture sector in Egypt (ESICM, 1994) suggested that the sector needs to use the ES technology to improve the quality o f the products and increase the efficiency of the agri- cultural system. Expert systems are recognized as an appropriate technology because they address the problem o f transfer- ring knowledge and expertise from highly qualified specialists to less knowledgeable personnel. In agri- cuture, this transfer is always taking place from research to extension, from extension to farmers, and even from farmer to farmer. Expert systems present excellent tools for relieving the increasing pressure on the limited expertise available in developing nations. It must be recognized that knowledge, the very foundation o f expertise, is a scarce resource in developing nations. Expert systems can help expand this vital resource by making available, in specific situations, vital knowledge that increases the effectiveness o f less experienced personnel. Expert Systems can be used by decision makers at different levels: operation level and planning level. On the operation level, the extension worker in the village, district, and/or govemorate can use the system to support him in making his decision in giving the appropriate advice to growers. On the planning level, the decision makers can use the expert system for predictions, such as on the needs for water, fertilizers and pesticides for a particular crop in the region given the area cultivated with such a crop. This generated information is very important for different users: the traders, the exporters, the importers o f these materials. Another type o f application is the estimation o f the yield given a simulation model linked with the expert system. The prediction o f yield can serve the decision makers in deciding the amount to be imported in advance, if any, and hence take necessary actions. 2.2. The Need for Expert Systems in Training Although the goal for developing agricultural expert systems in Egypt has to be used as a decision support tool for the extension workers, practical training o f extensionists on the developed ES has revealed that ES can be used for expediting the training o f extension workers. In the near future, it is not expected to install computers in the 4000 villages in Egypt. Installing computers in the 200 district offices, however, is an attainable goal. Therefore, i f these 200 centers could be used to train the extension workers at the village level using the ESs installed at these centers, there will be a tremendous impact. Traditional ways o f training are not sufficient to cope with the fast growing technologies in the different agriculture specialties for the different crops. Using ESs will reduce training time and enhance its quality. 3. AGRICULTURE EXTENSION SERVICES ENVIRONMENT Agriculture development in Egypt depends on the connection between the three sides o f the extension process (CLAES, 1993): (1) research, (2) extension and (3) farmers. The reporting o f problems, and finding solutions to them are the main concern o f the cooperative extension programs. Through the different stages o f technology develop- ment and information transfer to farmers, the extension sector works with the research component to narrow the gap between research results and this application in the field. Extension engineers help in studying the production situation as they can identify farmers' problems through watching the farmers and working with them to diagnose problems and attempt to find the solutions. The Ministry o f Agriculture and Land Reclamation in Egypt is concerned with all activities in agriculture development, and gives special attention to the coopera- tion o f research and extension in order to facilitate continuous training for all people concerned. This is done to spread appropriate agriculture technology all over th e country. 4. A BRIEF DESCRIPTION OF THE EXPERT SYSTEMS USED IN TRAINING The expert systems being used are mainly for crop management which are developed by the Central Labo- ratory for Agriculture Expert System (CLAES) at the Agriculture Research Center o f Ministry o f Agriculture and Land Reclamation in Egypt. They are the Cucumber Expert System (CUPTEX) and the Citrus Expert System (CITEX). CUPTEX (E1-Dessouki et al., 1993; Rafea et al., 1995) is an expert system for cucumber production management in a plastic tunnel. CITEX (Salah et al., 1993) is an expert system for citrus production in open Expert Systems as a Training Tool in Agriculture 345 fields. Both the two expert systems were modeled using the KADS methodology (Schreiber et al., 1993; Wielinga et al., 1991). A laboratory prototype was implemented using the NEXPERT Object shell (Neuron Data Inc., 1991). Currently, they have been transferred to a knowledge representation language based on object- oriented and logic programming paradigms (ESICM, 1992). These expert systems are intended to be used by the agricultural extension service within the Egyptian Ministry o f Agriculture and by the private sector. The following are components or subsystems of the two expert systems: irrigation, fertilization, verification and treatment. The main objective of the irrigation and fertilization subsystems is to generate schedules, that include the water quantity, irrigation interval, nutrient quantity and application interval. These outputs are based on quantita- tive reasoning rather than heuristic reasoning. The objective o f the verification subsystem is to confirm the user suggestion o f particular disorder according to the symptoms provided by the user. The objective o f the treatment subsystem is to recommend treatment opera- tion according to a case description. 5. EXPERIMENT DESCRIPTION This section describes the experiment conducted during the training of extension workers at the project premises for CUPTEX and CITEX at CLAES. CLAES provided an excellent research site for this study. To date, 749 man/days training were realized. This experience pro- vides a useful vehicle for evaluating t h e effect of using ES as a training tool to increase the decision-making skills o f extension workers. Concerning this study, 11 extension workers who specialized in protected cultiva- tion were involved in the evaluation using CUPTEX and 8 extension workers who specialized in horticulture were involved in the evaluation using CITEX. The objective o f conducting this experiment was two-fold: first, to measure the effect o f using expert systems on the performance o f the extension workers and second to assess the decision-making skills o f the extension workers compared with decisions generated by the expert systems. The methodology followed to achieve this objective is presented in the first subsection, whereas, its application is given in the second subsec- tion. 5.1. The Methodology The proposed methodology is based on tests conducted during one training cycle of competent extension work- ers to measure the effect o f using expert systems on their performance, and to assess their decision-making skills compared to decisions generated by the expert systems. It can be summarized in the following steps: (1) Design forms to document the cases and their results. (2) Prepare cases which are described b y a set o f input data. (3) Distribute these cases to the trainees to give their decisions before using the expert system. (4) Train the extension workers on the expert system. (5) Distribute again the same cases without their previous decisions and ask them to give their decisions again. (6) Evaluate the cases before and after training TABLE 1 Irrigation Results Average score Average score Percentage of before (%) after (%) Enhancement enhancement Water qty 38.1 73.6 35.5 93.18 Interval 41.9 71.2 29.3 69.93 Average 40.0 72.4 32.4 81.00 TABLE 2 Fertilization Subsystem Results Average score Average score Percentage of before (%) after (%) Enhancement enhancement Nitrogen 44.8 64.4 19.6 43,75 Phosphorus 36.3 50.9 14.6 40,22 Potassium 40.1 60.6 20.5 51.12 Magnesium 7.0 62.3 55.3 790 Manure 0.0 91.8 91.8 infinity Average 25.64 66.0 40.36 157.41 346 A. Rafea and K. Shaalan TABLE 3 Verification Subsystem Results Average score Average score Percentage of before (%) after (%) E n h a n c e m e n t enhancement Symptoms on leaves 16.5 Symptoms on stem 28.8 Symptoms on root 40.3 Symptoms on fruits 34.0 Average 29.9 38.9 22.4 135.76 46.5 17.7 61.46 87.5 47.2 117.12 36.0 2.0 5.88 52.23 22.33 80.06 taking the expert system results as a reference (the result o f the expert system for these cases had been verified with domain experts before conducting the experiment). The following formula is used to compute the percentage (%) o f the enhancements: Enhancement Average Score before using the ES × 100 where the E n h a n c e m e n t is the difference between the average score before and after using the ES. 5.2. The Application of the Methodology The methodology was applied as follows: (1) Forms were designed for the different sub- systems o f C U P T E X and CITEX, namely, irrigation, fertilization, verification and treat- ment. In effect, the irrigation and fertilization were grouped in one f o r m whereas the ver- ification and treatment were grouped in another form. (2) Sets o f cases covering the different aspects o f the developed expert systems were prepared in forms. Each set consisted o f approximately 20 cases. (3) Each trainee was given around 10 cases before conducting the training and was asked to give his decisions for these cases. The decision is either irrigation schedule, fertilization sched- ule, s y m p t o m s to be observed i f a disorder is suspected, or a treatment schedule. It should be noted that some o f the trainees have the same cases while some others m a y have different cases. (4) The training was conducted by letting the trainees run the expert system, providing the inputs in the cases and observing the outputs o f the expert system. During training, each trainee was given all the cases, and other cases he/she created were also run on the system. (5) The same cases (cases before training) were given to each trainee after conducting the Iraining. H e / s h e was not told that they were the same cases nor had he/she access to the forms completed before training. (6) The cases were evaluated in the following way: • The irrigation subsystem was evaluated taking into account the water quantity and irrigation interval. • The fertilization subsystem was evaluated taking into account the quantifies o f nitro- gen, phosphorus, potassium, magnesium and manure. • The verification subsystem was evaluated taking into account symptoms on root, stem, leaves and fruits. • The treatment subsystem was evaluated taking into account the treatment materials and their corresponding doses. In all cases the forms before and after training were analyzed taking results produced b y the expert system as a reference. I f the decision given by the trainee matches the expert system result, the trainee is given full marks. I f the decision given by the trainee mismatches the TABLE 4 Treatment Subsystem Result Average score Average score Percentage of before (%) after (%) Enhancement enhancement Material (1) 41.8 72.4 30.6 73.21 Dose (1) 25.8 64.8 39.0 151.16 Material (2) 24.7 36.5 11.8 47.77 Dose (2) 10.5 20.0 9.5 90.48 Average 25.7 48.43 22.73 90.66 Expert Systems as a Training Tool in Agriculture 347 TABLE 5 Irrigation Results Average score Average score Percentage of before (%) after (%) Enhancement enhancement Water qty 31.3 67.5 36.2 115.65 Interval 39.7 60.6 20.9 52.64 Average 35.5 64.05 28.55 84.15 expert system result, the trainee is given zero. In cases where the decision is a quantity or a dose, the trainee is given a score relative to the quantity or dose produced b y the expert system. In other cases, a score was estimated b y a domain expert who was responsible for this evaluation. 6. R E S U L T S O F A P P L Y I N G O U R T R A I N I N G M E T H O D O L O G Y T O T H E A G R I C U L T U R E S E C T O R I N E G Y P T Evaluators have used all the components o f the above- mentioned methodology in their effort to measure the effect o f using expert systems on extension personnel performance. The outcome o f applying all components o f the methodology to C U P T E X and C I T E X is discussed below. The verification results are summarized in Table 3. As can be seen, the average enhancement o f 80.06% was noticed after using the expert system for one-day training. The most remarkable enhancement was related to the symptoms on leaves, whereas the s y m p t o m s on fruits has slightly increased. The treatment subsystem results are summarized in Table 4. As can b e seen f r o m the table, an average enhancement of 90.66% was noticed after using the expert system. A remarkable enhancement was noticed in determining the dose for material (1). It is also worth noting that the trainees were not aware o f the second material application nor its dose (the lowest score before and after the training). 6.2. CITEX Results and Discussion 6.1. CUPTEX Results and Discussion The irrigation results are summarized in Table 1. As can be seen, an average increase o f 81% has been achieved in the decision taken b y the trainees after using the expert system. The enhancement in deciding the water quality is m u c h better than the enhancement in deciding the irrigation interval. The fertilization results are summarized in Table 2. It is worth noting that manure was not included b y any o f the trainees although it is well known that manure should be added before cultivation. They might have assumed that this is a well-known fact. So, they did not put it. The second important remark is that they did not know much about magnesium; the percentage o f enhancement regarding m a g n e s i u m was 790%. Eliminating these two odd cases, an average enhancement o f 45.12% can b e observed in three fertilizers, namely, nitrogen, phospho- rus and potassium. The irrigation results are summarized in Table 5. As can be seen, an average increase o f 84.15% has been achieved in the decision taken b y the trainees after using the expert system. The enhancement in deciding the water quantity is much better than the enhancement in deciding the irrigation interval. The fertilization results are summarized in Table 6. An average enhancement o f 35.89% can be observed after using the expert system. It is worth noting that the least enhancement was in adding manure, only 11.65%. This m a y b e due to the fact that manure application is done once a year during preparation. The verification results are summarized in Table 7. As can be seen, the average enhancement o f 1464.08% was noticed after using the expert system for one-day training. However, the scores o f zeros, before using the expert system, in recognizing the symptoms are very odd. But as can be seen, this recognition reached 90% in the case o f symptoms on roots after using the expert TABLE 6 Fertilization Subsystem Results Average score Average score Percentage of before (%) after (%) Enhancement enhancement Nitrogen 43.1 63.6 20.5 47.56 Phosphorus 46.4 66.9 20.5 44.18 Potassium 38.1 53.4 15.3 40.16 Manure 78.1 87.2 9.1 11.65 Average 51.43 67.78 16.35 35.89 348 A. Rafea and K. Shaalan TABLE 7 Verification Subsystem Results Average score Average score Percentage of before (%) after (%) Enhancement enhancement Symptoms on leaves 14.2 Symptoms on stem 0.0 Symptoms on root 0.0 Symptoms on fruits 0.0 Average 3.55 65.5 51.3 361.27 33.3 33.3 infinity 90.0 90.0 infinity 33.3 33.3 infinity 55.53 51.96 1464.08 system. So, even i f the first row o f Table 7 which relates to symptoms on leaves is considered, the enhancement is 361.27% which is still very high. The treatment subsystem results are summarized in Table 8. As can be seen f r o m the table, an average enhancement o f 732.61% was noticed after using the expert system. A remarkable enhancement was noticed in determining the dose. The improvement in determining the material is also very high. 7. C O N C L U S I O N S The results o f this study suggest that the expert system can be an effective training tool in agriculture extension programs. The experiment showed that there is enhance- ment in the performance o f the extension workers after the usage o f the expert systems which developed after a very short time. Although the expert systems developed are thoroughly verified with domain experts, there is always r o o m for further improvement. Regardless o f the quantitative measures which m a y be arguable regarding how these values are calculated, the trend o f the trainees was to accept the advice given b y the system, which is why their measured performances were increasingly taking the decision o f the expert system as a reference. Table 9 presents a summary o f the results for both CUPTEX and CITEX. As can be seen from Table 9 , the average score percentages for trainees on the four subsystems o f C U P T E X and C I T E X were approximately in the same range (30.31 and 27.13). But i f we look at each individual subsystem, we can find a tangible difference between the verification and treatment sub- systems in the favor o f the C U P T E X trainees, while there is a tangible difference in the fertilization subsystem in the favor o f C I T E X trainees. This remark is related to the percentage o f enhancement, we can see that the best enhancement for CUPTEX was in the fertilization subsystem, whereas the best enhancements for C I T E X were in the verification and treatment subsystems. The average enhancement for C I T E X was approximately six times the average enhancement for CUPTEX. This is because the performance o f the C I T E X trainees in the TABLE 8 Treatment Subsystem Result Average score Average score Percentage of before (%) after (%) E n h a n c e m e n t enhancement Material 11.0 63.9 52.9 480.91 Dose 5.1 55.3 50.2 984.31 Average 8.05 59.6 51.55 732.61 TABLE 9 Results Summary CUPTEX CITEX CUPTEX CITEX (average (average (% of (% of score %) score %) enhancement) enhancement) Before After B e f o r e After Irrigation 40.0 72.4 35.5 64.05 81.0 84.15 Fertilization 25.64 66.0 5 1 . 4 3 67.78 157.41 35.89 Verifiction 29.9 52.23 3.55 5 5 . 5 3 80.06 1464.08 Treatment 25.7 48.43 8.05 59.60 90.66 734.61 Average 30.31 59.77 27.13 61.74 102.28 579.18 Expert Systems as a Training Tool in Agriculture 349 v e r i f i c a t i o n a n d t r e a t m e n t s u b s y s t e m s w a s v e r y l o w (3.55 a n d 8.05), a n d c o n s e q u e n t l y t h e i r p e r f o r m a n c e h a s i n c r e a s e d d r a m a t i c a l l y a f t e r u s i n g t h e s y s t e m ( p e r c e n t - a g e s o f e n h a n c e m e n t a r e 1 4 6 4 . 0 8 a n d 7 3 2 . 6 1 ) . 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