A Knowledge-Based Expert System for The

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2012 IEEE Student Conference on Research and Development (SCOReD) , December 5-6, 2012, Penang, Malaysia

A Knowledge-Based Expert System for The Smallholder Palm Oil Cultivator in Malaysia Mohd Nor Akmal Khalid, Umi Kalsom Yusof School of Computer Sciences Universiti Sains Malaysia 11800 USM, Penang, Malaysia [email protected], [email protected]

2007 and 2008, palm oil’s production of the smallholder achieved an average of 16 to 18 tons/hectare which is below commercial average yield of about 25 tons/hectare.

Abstract—Palm oil cultivation is one of economic activities that expands Malaysia economy. Statistic shows that approximately 42% of the main contributor of world palm oil resources came from Malaysia. Consequently, the production of palm oil has been emphasized to ensure continuous supply to enhance the Malaysia economy. The use of computer system and Internet had encouraged significant growth of agricultural domain. The current existing knowledge-based system was used in various domains either agricultural or non-agricultural to solve various problems. This paper propose a knowledge-based expert system that focus in helping Rubbers Industry Smallholders Development Authority (RISDA) and Malaysian Palm Oil Association (MPOA) smallholders by providing knowledge and guidance for effective and productive cultivation of palm oil. The system aims to make suitable decision on the suitability of fertilizer, which satisfies the palm oil nutrient needs.

TABLE I D ISTRIBUTION OF WORLD PALM OIL PRODUCTION ADOPTED FROM [1]

Keywords - knowledge-based, string-based retrieval, expert system, smallholder, palm oil

Country

1980

1990

2000

2009

Malaysia Indonesia Nigeria Colombia Cote d’Ivoire Thailand Ecuador Papua New Guinea Others

53.57% 14.37% 9% 1.54% 3.78% 0.27% 0.77% 0.73% 15.97%

56.09% 22.20% 5.34% 2.10% 2.48% 2.13% 1.10% 1.33% 7.23%

49.20% 31.43% 3.37% 2.35% 1.32% 2.32% 0.98% 1.28% 7.75%

38.94% 45.29% 1.93% 1.76% n.a. 2.90% 0.97% 1.04% 7.17%

I. I NTRODUCTION TABLE II D ISTRIBUTION OF PLANTED PALM OIL AREA BY CATEGORY ADOPTED FROM [3]

Statistic shows that Malaysia is the largest palm oil distributors with about 2,978 thousand tons/annum exports in 1984 and increased to 10,733 thousand tons/annum compare to other country of the world. This alone justify that the palm oil export is the most important economic symbol in Malaysia [1]. However, Malaysian’s agricultural production of palm oil has decreased over the recent years while Thailand surpasses about 6.35% of Malaysian’s palm oil production in 2009, as shown in Table I. This causes certain disadvantages to Malaysias current position as a leading palm oil distributor in the world. The palm oil smallholders are important group of an upstream producer of palm oil assets, which play a major effort of expanding the cultivation of the palm oil agricultural domain. Smallholders give a total average production of 668.4 U.S. $/hectare compared to an estate holdings which approximately 238.2 U.S.$/hectare [2]. As shown in Table II, from 3.38 million hectares of palm oil area in Malaysia in year 2000, about 40% of the palm oil area were organized and managed by an independent smallholders. However, the smallholder’s palm oil production capabilities remained the lowest compared to other plantation groups. Between

978-1-4673-5159-1/11/$26.00 ©2012 IEEE

Category

1999

2000

Federal Government/ FELDA* FELCRA RISDA

20.37% 4% 1.25%

17.72% 4% 1.10%

State Scheme

7.11%

7.17%

Organized Smallholders Independent Smallholders Total Smallholders

41.38% 8.65% 50.03%

29.99% 9.50% 39.94%

Limited productivity and degraded performance confronted by the palm oil smallholders are caused by the lack of knowledge and experience for proper costing of fertilizer application and labor. As such, optimizing the application rate of the fertilizer indirectly helps to obtain high yields and profits per unit area of the palm oil plantation. However, these issues are often disregarded [3]. Basically, minor or nonexistent information and experiences on suitable fertilizer application to the palm oil cultivation triggered by the remote

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geographical location and insignificant technological infrastructure. As such, palm oil smallholders disconnected from knowledgeable people regarding the fertilizer and exposure to better crop management efficiency and agronomic practices were trivial [4]. Additionally, support from government agencies, such as RISDA and MPOA, have faced with difficulties in reaching palm oil smallholders in a whole country continuously and simultaneously [5]. Inspiration of undertaking the research task arose from the will to assist RISDA and MPOA, reaching the smallholder palm oil’s cultivator of any levels of experiences in cultivating palm oil, through practiced and effectively-proven way, by designing a knowledge-based expert system, named as Smart Agro System (SAS). It will draws recommendation of fertilizer based on the inputs given by the users in a webbased application. This system can systematically educate the smallholders on relevant and suitable information regarding the fertilizer of palm oil plantation with no delay via computer technology. Indirectly, the system provides medium for RISDA and MPOA, to channel their efforts by taking responsibility in managing and maintaining the system in dispersing up-to-date information and minimize the need of face-to-face meeting with smallholders. The remainder of this paper is organized as follows: Section II discusses about background study. Section III emphasizes on the proposed solution. Section IV elaborates on the results. Finally, Section V concludes the paper.

Any changes to the system required a significant effort and cost. As a part of artificial intelligence technology, KBS solves problems in a certain problem domain by mimicking human expert reasoning skills and knowledge acquired from a human expert of a particular domain. The main components of knowledge-based system consist of knowledge base and knowledge acquisition mechanism. The knowledge-base stores the domain knowledge of a particular domain problem where the knowledge acquisition mechanism helps to expand knowledge-base in order to adapt in a changing environment. When compared to ES, Web-based KBS has the capabilities of portability and adaptability which often preferable, where the engineered knowledge of the knowledge-base component is structured and manageable. Subsection III-A will further explain the detail of KBS framework. A web-based ES and knowledge-based application, beginning to capture commercial and agricultural implementation as its proven to be reliable and effective. Some researcher reported the usage of web-based ES and KBS in business strategy development and interviews [7], legal area and its evaluation [8], decision making [9], education [10], and others. The methods used are mostly the integration of KBS with the help of unique, automated and semi-automated, knowledge acquisition techniques. The efforts for further implementation are also reported in varying areas of agriculture such as fish farming [11], weeding control and detection [12], plant tissue culture [13], disease identification [6], fruit grading and classification [14], agricultural decision support [15], and agricultural yield prediction [16]. This paper proposes a knowledge-based expert system called SAS as a solution for fertilizer application problem faced by palm oil smallholder. Normally, rule-based, casebased, or condition-based system is sufficient. However, palm oil fertilizer properties are uncertain and varied that based on external and internal conditions of the palm tree environment. Rule-based, case-based, or condition-based alone cannot implicitly justify the suitability of the fertilizer recommended entirely. However, SAS supports any inference process with a suitable justification obtained from a human expert. As such, SAS that uses combination of knowledge-based and expert system approaches through a web-based application, is the best solution to solve the problem. This opportunity is seen as a chance to implement SAS in agriculture domain, specifically on the suitability recommendation of fertilizer, which in turn, helps RISDA and MPOA in disseminating knowledge on fertilizer.

II. BACKGROUND S TUDY The present of the network access encouraged the emergence of World Wide Web (WWW) resulted in a Webbased application that can be easily accessed anytime and everywhere in the world. However, the real-world society is already dense and overloaded with information. As such, knowledge sharing becomes more important compared to information sharing. To solve a complex problem, Web-based expert system (ES) and knowledge-based system (KBS) capabilities are much more appealing when compared to the conventional web-based application. Since the creation of the Internet, it has been seen as an opportunity to integrate the WWW technology with the ES and KBS throughout the network [6]. As reported by Duan et al. [7], an early application of ES is adopted on a standalone, mainframe-based of an artificial intelligence workstation or personal computer platforms. An implementation of ES in a web-based application is widely recognized but faced with the knowledge bottleneck. Most popular methodologies of ES are rule-based, casebased, condition-based, model-based, or their combinations. Although these methodologies are widely used in different domain problems, potential setback occurs when the size of the knowledge is substantial. A web-based ES application can be easily outdated when a narrow domain was concentrated.

III. S MART AGRO S YSTEM (SAS) A. System Development And Framework SAS was implemented using HTML (Hypertext Markup Language) and CSS (Cascading Style Sheet) for the user interface. Dreamweaver CS4 was used as the integrated

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development environment. A multimedia user interface was used in the system. The system used multimedia aids, such as images, to help the user to understand and aware of the correct decision or preferred selection. PHP (Hypertext Preprocessing) and AJAX (Asynchronous JavaScript and XML) were used for inference engine mechanism and database accessing. JQuery was used as the event handler and client-side data processing of the web application. The output of SAS was displayed by categorizing it based on the specific category to cater smallholder understanding and comprehension of the conveyed information. There are three major components of SAS: knowledgebase, acquisition mechanism, and inference engine. The knowledge-base and inference engine are the main viable component for both KBS and ES, where the knowledge base stores domain knowledge of a particular domain problem. The acquisition mechanism acquires knowledge continuously, enabling SAS to evolve with time. However, the inference engine manipulates the stored knowledge to produce results which remain as the fixed component of SAS. The SAS reasoning process consist of two types: forward chaining and backward chaining. Forward chaining is a data-driven mechanism which makes use of facts and attempts to draw conclusion from it, where backward chaining is the opposite. The SAS also has the ability to learn new rules through inductive or deductive learning. Inductive learning creates new rules, that are not derivable from existing rules about the domain where deductive learning is the opposite. SAS framework consists of user interface, knowledge acquisition interface, explanation subsystem, inference engine, and databases. The user interface displays the system output and act as an input medium for the smallholder’s request in the Web-based application. The knowledge acquisition interface is used for acquiring knowledge from the palm oil expert and translates it into a knowledge base schema. The explanation subsystem is essential for the knowledge to be available for the user interface in order for the palm oil smallholder to be able to understand the result displayed in simple and understandable manner. SAS implements forward chaining inferencing mechanism because it makes use of available facts to draw appropriate recommendation. The inference mechanism will be explained further in subsection III-C. The SAS database consists of two parts, internal knowledge base component of SAS and external fertilizer database. This separation is essential to reduce the needs of making changes to the knowledge base when huge amount of data entry performed [7]. The knowledge base structure consists of knowledge representation and rules structure for the inference engine, while the fertilizer database stores all the facts of the fertilizer. The level of the knowledge is extended only for the physical properties of the palm oil environment, such as the palm oil tree conditions and the soil properties of the planted palm oil trees. As such, in this study,

SAS concerns only for the recommendation module where the knowledge acquisition interface was excluded from the this study. The overall proposed framework for SAS can be referred to Fig. 1.

Smart Agro System

Inference Engine

Knowledge Acquisition Interface

Human Expert

User Interface

User

Explanation Subsystem

Knowledge Base (Internal)

Fertilizer Database (External)

Fig. 1.

Framework for SAS

B. Knowledge Representation The popular methods of knowledge representation used in knowledge-based systems mostly are attribute-value pair, object-attribute-value triplets, semantic networks, frames and logic. The knowledge of the fertilizer is collectives and declaratives, but may vary from one to another. It also describes the specification of the fertilizer but different context is embedded in a single knowledge which indirectly complicates the knowledge representation. To generalize the knowledge representation, combination of rule-based and semantic network are used. The semantic network is used as a mapping structure for the available rules of the rule-based structure. The semantic network acts as a ‘bridge’ between the fact that triggered by the rule(s) and related knowledge. The combination of the semantic and rule-based structure involves multiple levels of matching processes. Both complement each other where the rule-based structure supports the semantic network as the triggering mechanism for firing rule. The binary-string keys, which the details are explained in the next subsection, used as the facts matcher of the rule-based mechanism. As such, the antecedent value of a plausible rule structure (IF antecedent THEN consequence) will be matched against the binary-string key, conforming to the structure of the semantic network. Then, the suitable result will be

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retrieved from the fertilizer database accordingly. As an example, IF (has property(BSK , some parameter)) THEN do something. This structure are cumulated in a cumulative environment to ease of knowledge retrieval process. The rulebased semantic network structure in a cumulative environment is shown in Fig. 2.

of 3 charac ers b nary-s r ng key (110 011 010 ) based on he arrangemen of he proper y cho ces TART

G U

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00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0T 00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T n T

Cumulative Environment

B

S

B C

T

K

R

B K V

END

M

B K

P

V

F P C

Q

R

R

R F

T F

K w

R

S

Decembe 5-6 2012 Penang Ma ays a

B

H H H

A

A

E

H

T p

F

H

F

mD R H

D

H

P

Fg 3

P

F owcha o SAS n e ence mechan sm

R

S ub

Qu

The key s a so used o re r eve any re evan nforma on regard ng se ec ed user fac s SAS conforms o s r c and gu ded fac s wh ch presen ed n he form of he combo boxes n he user n erface As such any s ng e key or comb na on of keys w be used as a bas s for re r eva of any re a ed and re evan nforma on o he proper y se ec ed wh ch n urn he ps o us fy he fer zer resu recommended In order o d fferen a e he keyword a dash (-) opera or s used be ween he keys of he fu b nary-s r ng key The nd v dua key w be merged o a or he ru e-based s ruc ure for he cond on ma ch ng and re r eva purposes of any re a ed know edge The use of h s me hod neg ec s any dup ca on occurrence as he fer zer sugges ed may vary based on user needs and goa Thus mu p e resu s w be sugges ed for any par a y ma ched b nary-s r ng key va ue

D

Q D

A

n F m

Fg 2

Ru e based seman c ne wo k s uc u e n cumu a ve env onmen

C B nary-S r ng Key Re r eva (BSK) The BSK s a ru e-based nference mechan sm adop ed w h n he know edge-based s ruc ure a ongs de he seman c ne work Referr ng o F g 3 user npu se ec on s cap ured and processed n o a s r ng da a ype named as b nary-s r ng keys Every key cons s s of a number of charac ers wh ch quan y s based on he se ec on cho ces ‘1’ w nd ca es se ec ed proper y wh e ‘0’ o herw se Comb na on of severa se ec ons w resu n fu b nary-s r ng key wh ch requ red o forma y s ruc ures he know edge ha or g na es n a var e y of forms (s a emen proof symbo s e c ) The arrangemen of he proper y va ue w de erm ne he correc va ue of he key The key can have more han one ‘1’ n wh ch nd ca es an add ona proper y ha va d for ha se ec on cho ce As an examp e f fer zer A have wo d fferen proper es ou of 3 so he key w be any form

IV R ESULTS AND D SCUSS ON To us ra e how SAS works da a regard ng fer zer and s proper es co ec ed and s ored n he da abase manua y A samp e of 29 fer zers s s ored n he SAS know edge base The user npu se ec on cho ces are presen ed as a phabe c code as shown n Tab e III No e ha h s a phabe ca represen a on used on y for us ra on of he SAS work ng mechan sm For examp e fer zer ype has hree cho ces hus poss b e cho ce s e her A B or C These parame ers are he componen s needed o us fy he recommended fer zer as he bes and appropr a e ou pu (s) for he pa m o cu va on

305

S5-2


TABLE III U SER INPUT CHOICES Selection Parameters

Type

Price

Quality Fertilizer

Element

Straight Mixture Compound Complex Expensive Affordable Cheap Best Average Worst Nitrogen Phosphorus Potassium Magnesium Calcium Boron Copper Sulphur Zinc

Alphabetic Code A B C D A B C A B C A B C D E F G H I

Seedling Growing Matured

Alphabetic Code A B C

Flat Rolling Gentle Rolling Undulating Steep

A B C D E

Terric Troposaprist Typic Sulfaquept Typic Hapludox Xanthic Kandiudox Typic Paleudult Typic Hapludult Typic Kandiudult Typic Melanudand

A B C D E F G H

Selection Parameters

Palm Age

Terrain Type

Soil Type

TABLE IV S AMPLE DATA OF INPUT CHOICES AND ITS RESPECTIVE OUTPUT ( S ) No. 1 2 3 4 5 6 7 8 9 10

Type C A A A D C B C D C

Fertilizer Pricing Quality A B C C A A B B C A A A A A B C B B A A

Element B C A E C G H D B F

Palm Age B C C C A C B C B B

Terrain Type A B A C E B E D C B

Soil Type A G B D G G E B H G

Output (Fertilizer) Single Super Phosphate, Triple Super Phosphate, Monoammonium Phosphate Bunch Ash, Empty Fruit Bunch Ammonium Nitrate Rock Phosphate Finely Powdered Nitrophoska Foliar 25:10:17+Te Copper Chelate n.a. Dolomite N.P.K Mix Borax

SAS was tested with 10 datasets as shown in Table IV. For every datasets, different parameters of the user input selection choices are considered. As an example, in No.1, which fertilizer type, preferred pricing, quality, and element composition, palm age, terrain type and soil type are C, A, B, B, B, A, and A respectively, output the results of three fertilizers (Single Super Phosphate, Triple Super Phosphate, and Monoammonium Phosphate). All three fertilizers share the same properties which are compound, expensive, average, phosphorus, growing, rolling, and terric troposaprist, which respective to the selection parameters.

trogen or phosphorus. The differences are only visible in the knowledge base and its validation can be seen if the parameters of the fertilizer element content changes from phosphorus to either nitrogen or calcium. Another example of user input choices of A, C, C, C, C, B, and G for No.2, which results only return two fertilizers output (Bunch Ash and Empty Fruit Bunch). Compared to No.1, the two fertilizer results are completely similar in their individual properties. Any changes to the selection parameter for No.2 will result in completely different fertilizer output. On the other hand, for No.7, user input choices of B, A, A, H, B, E, and E do not return any fertilizer result because the requested property choice has no matched rule and none of the available fertilizer have any properties selected. The other sample input selection choices only result in a single fertilizer result because the parameter selected exactly matched to their

The fertilizer result for No.1, ‘Single Super Phosphate’ and ‘Triple Super Phosphate’, shared a common property of fertilizer element content, which can be either phosphorus or calcium. Both fertilizers are different compared to ‘Monoammonium Phosphate’ which contains either ni-

306

S5-2


respective fertilizer property, following the knowledge base schema.

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V. C ONCLUSION SAS was successfully implemented, but there always some room for future improvement. Some benefits SAS introduced are the knowledge inferring mechanism that enabled multiple rules fired simultaneously, creating a ‘knowledge push’ to the smallholders. Improvement can be enhanced in the binarystring key automation retrieval as it only bounded to a fixed single or multiple rules. A non-generalized binary-string key merging convention was used and the rule structure is not dynamically allocated suitable binary-string key value. Additionally, any performance issue was not considered in the implementation of SAS. As such, in a large database structure, SAS may be affected due to performance downgrade. The knowledge represented by the BSK value was limited to a certain number but support large quantities of knowledge when the duplication issue was nonexistent. SAS is a fundamental system in achieving a better palm oil yield production for the smallholder community. In the future, the proposed system has the potential of expanding further by not limiting to the fertilizer aspect of the palm oil cultivation. Hypothetically, it also will increase the efficiency of the overall smallholder palm oil productivity in multiple aspects. The system also hopes to aid and assist RISDA and MPOA, in helping palm oil smallholders in disseminating knowledge regarding the fertilizer and its practices. The system was tested based on expert knowledge that was obtained mostly from open literatures. As such, to prove SAS effectiveness, efforts such as extensive knowledge acquisition technique should be employed. A pilot run should be applied in a long-term preplanned period, considering subjective feedbacks from actual farmers or cultivators regarding the system, and a more comprehensive data testing should be performed. However, SAS output correctness is highly subjective where limited storage of expert knowledge obtained from literatures and direct interview does not emphasize the total view of the palm oil agricultural domain. The future works may be extended to a much more complex, long-term, and broad real-world problems of agricultural or non-agricultural domain. ACKNOWLEDGMENT The main author wishes to thank Universiti Sains Malaysia for the support it has extended in the completion of the present research through APEX University Grant (1002/PKOMP/ARSP13100). R EFERENCES [1] T. Cheng Hai, “Key sustainability issues in the palm oil sector,” Discussion Draft. [2] Y. Basiron, Baileys Industrial Oil and Fat Products, Sixth Edition, F. Shahidi, Ed. John Wiley and Sons Incorporation, 2005.

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