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Intelligent systems and its application in agriculture
INTELLIGENT SYSTEMS AND ITS APPLICATION IN AGRICUL...
14th World Congress ofIFAC
K-4b-Ol-5
Copyright © 1999 IFAC 14th Triennial World Congress~ Beijing, P.R. China
INTELLIGENT SYSTEMS AND ITS APPLICATION IN AGRICULTURE Xiong Fanlun and Qiao Kezhi
Hefei Institute ofIntelligent Machines, Chinese Academ.y ofSciences P. a.Box 1 J 30, Hefei, .4.nhui 230031 P.R.China flxiong@public. ustc. edu. en T
Abstract: To overcome the complexity and uncertainty of agriculture, intelligent systenls, which utilize some new knowledge representation strategies and integrate with advanced approaches such as case-based reasoning, neural networks, genetic algorithms, and so on, are proposed. Some knowledge representation strategies, which are very suitable for agric·ulture. knowledge:- are also introduced. Many intelligent systems for agriculture \vhich had been developed by our development platforms have been succeeding in adequate fertilization., plant protection~ and cultivation of vvheat, corn, rice, cotton, rape, tea, tobacco, sugur cane, orange, cabbage.) etc in Chinese countryside during the past dozen years. Copyright © 1999IFAC
Keywords: Intelligent systems~ Agriculture
INTRODUCTION
Intelligent infonnation and control techniques have been remarkably progressed during the past dozen years. A great deal of intelligent infoffilatiol1 SystelUS have been developed and applied into many fields. The research of these techniques for agiculture has a tremendous advances as well in recent years, such as for fertilization, irrigation, green house, field managment, etc. Based on the importance of agriculture in china, early in the beginning of 1980's, \ve started and concentrated on developing for agriculture experts systems, and succeeded in applying theJTI to countryside in China (Xiong, 1986) . Xiong et al proposed a series of kno~rledge representation strategies that are suitable for agriculture, and developed a series of tools for building the agriculture intelligent systems based on these kno\~rledge representation approaches and relatively iote 11 igent techniques. And using these tools "\ve have built a lot of experts systems for adequate fertilization, plant protection, and cultivation of wheat, corn, rice, cotton, horticulture, animaJ breeding~ fishery~ and so on
(Xiong, et al~ 1988) . And the integration of intelligent systems with corresponding advanced technologies such as neural networks, genetic aJgorithms~ case-based reasoning) machine learning, data mining, geographical information system are also used in our system. This paper consists of three sections in addition to the introduction. The second section describes some new knowledge representation. The third section describes the system structure and tools. The fourth section introduces application aspects and the prospect.
2
MULrrI-KNOWLEDGE REPRESENTA1"'ION STRATEGIES
Agriculture is a more sophisticated field for the control systems. l'he yields and quality of crops are decided by many factors such as weather, soil, breed~ way of planting, dieases and pests, etc. Agriculture control problems are very complex and there are many types of knowle.dge in agriculture. Intelligent systems integrated with many intelligent techniques are effective approaches for solving these probloms. For example, knowledge of
5597
Copyright 1999 IFAC
ISBN: 0 08 043248 4
14th World Congress of IFAC
INTELLIGENT SYSTEMS AND ITS APPLICATION IN AGRICUL...
fertilization is often decided by the mathematical equations, formulas or the experience of experts; cultivation is represented by descriptive and causal knowledge; diagnose of pests and diseases are represented by uncertain knowledge~ and so on. A series of knowledge representation approaches are proposed by Xiong et ai, \vhich are called as "Rule-skeleton plus Rule-body", Comprehensive kno,"vledge body, Object.. oriented comprihensive knowledge-body, Agent..based multi-knowledge and so on. It is proved in practice that these approaches have been effectively used for intelligent system of agriculture.
2.1 Rule-group Approach When the agriculture experts or other domain experts solve a certain problem, in general, at first he considers \vhich factors are involved in this proble,nl, then the problem is determined (solution or evaluation) by a group of relations (experiences or mathematics formula) among these factors. The determination of each factor separately relies on a group of new factors and the relation of these factors. The rule-group concept is a suitable representation strategy for these klnds of problems. The rule-group kno\vledge representation approach is proposed in a mode of "rule-skeleton plus rule-body". In knowledge base, a rule-group, standing for a group of experience or a kno\vledge unit, is represented through two levels. The first level of rule-group is called as the rule-skeleton that expresses ~~detennined~~ relation betvr'een the premise factors and the conclusion functions. It has the type of multi-premise and multi -conclusion. The second level of rule-group is called as rule... body~ which expresses the knowledge of evaluation or judgment. In the rule-body~ a triple "factor relation value" is used to indicate a description or an assertion. l~he know ledge in the rule-group is organized in hvolevel structure: rule-skeleton and rule-body. In corresponding, inference in rule base is also adopt~d through tv/o-Ievel control. The upper-level reasoning is top-do\vn object-driven inference procedure in ruleskeletons. The lower reasoning is a bottom-up datadriven inference procedure in rule body and is actjvated by upper-level reasoning to determine the value of factors~ BNF description of rule-group knowledge representation of rule-skeleton plus rule-body is as following:
rule-group::=('rule-body) rule-skeleton : /:::; {IF'
premi,~ge factor
set : ~~=(ffictor)
t:
(premise factor
set~>}
calculation factor set : .4:=('faclor» £ (conclusion factor set.» calculation formula : :=(factor)'~' <.algebraic e..tpl-cssion) body rule: :=(IF'(pl-emise sct)'JY1EIV' <'conclusion sot) pl-emise se t : :~(premise) ~ (premise se t)) conclusion set : ~·~(concl[Jsion) ~ (conclusion set)} premise: ;:=(f'actor)('reJRtion symbol) (value) conclu.s~ion : :=:::(factor)~='(value) va.lue .~ :=(numher string)/
2.2 Frame-type Knowledge Representation Many declarative knowledge is existed in agriculture knowledge. And there are some causal relations between this knowledge. The methods of traditional knowJedge representation are difficult to represent this knowledge. We present a frame-type approach, but it is different from traditional frame~ The structure is sbo\\ll as following: FRAME: frame-name STATE: state set while the frame activated CONDITION_FECET: set of active process slots according to state set PROCESS-SL,OT: the advice ofexpen The element of Sl"'ATE-set and PROCESS-SLOT-set can be sub-frame~ so produce a causal nenvork. The frame controls the procedure of the whole system The frame can't compute and is only used to produce the descrjption knowledge, but it may finish the calcuJation by calling rule base. The result of computing and the description results, \vhich the frame reasons:; will be put into conclusion blackboard. We would take some intelligent techniques such as case-based reasoning, neural nen.vorks and so on when we face uncertainty knowledge~ This knowledge is organized in the form of object-body. The frame can call this object-body in the following fonn: RUN CBR name [WITH input parameters TO output parameters] BNF of frame-type knowledge representation is sbo\vn as foUowing: 4
~. :=JFRAME~, fi~ame-name, 'S'TATE', stateset condition-fecet, processing -slot fra.me-name : .'~character~string state~.s~et .' :=state/state) (state-set)
frame
set;>
3
rule-body: : =(calcula tiOll fOZ'Ulllla.>l (ruJebody)}/((hody rule)~ (rule body)}
5598
Copyright 1999 IFAC
ISBN: 0 08 043248 4
INTELLIGENT SYSTEMS AND ITS APPLICATION IN AGRICUL...
14th World Congress ofIFAC
condition-fecet ~".::=rule/rule, {conditionEecet} 1"'ule"~ "~='IF', condition-set, IT/fEN', active-stateset / 'DEFALfLT') acti ve-state-set condition-set : :~condition-expression/ condi tion-expression, condi tion-se t active-state-set ::~active-statejactjve state~
{active-state-sei} /~lockJ, active-
state-set active-.s·tate ~~ :=state!processil1g-s1ot-llame processing-slot ::~ processin~slotname, proce.~-sing-fece t-se t _processing-fecet-set .- .~= pI-ocessing~fecet / processing-fecet~ ( processing-fcce t-set) processing-slot-name :: ==f.l-ame-name /charac terstring processing-fecet : ~iADVICE"~ processingme thod/condi ti on-set, 'advi ce'; processing~ 0"
metho~
(condition-set,
'ADVrCEJ} processing-
metbo~ } ('-DEFA[ILT ADVICEJj
processing-method
processing-method)
::~character~string
know1 edge -modual-name~·.~ =charBc t er s tr ing condj t ion -modLiBl : : =( CO/y7)J « cOlldi ti on-name.> ( t <' condi lion-name-se t ) f) "(so und-image-se t ) <:cundi tion-J"ump-se t) 'EiVDC01V[)' cOl1dition-namc~' ~·=cha.J-actcr string sound-image-so t: : =emp ty/ ( sound-set)(imageset) condition-name::~charecterstring condition-jump-set::=( conditiDnname>J"~ )(obJect-name-set)/ (condi tionname)(:'(obJect-nAme-set)(conditon-Jump-set) ob..fee t-name-se t: ~.=( ob.lee t-name)/ ( obJ'ec tname»( obJ"ect-name-se t» obJect-name::~chaTacter string processing~modllal~':~'PROCJ ( sound-imagese t)(processing-con ten t ) 'EIVDPROC' processing'~contell t"~ .~=cl1a1~ac ter string sOlJl1d~set;:~(sound-filc-na.mc.>/
string calcu]ation-modual::='COMP'{
image~file~name::~character
2.3 Object l\.1odual
~rule-set)}1EVDCaYP'
In the process of developing intelligent systems, when facing some special problems we found that some knowledge or relations between different factors in agriculture can't be exactly described in the fonns of the frame or the rule-group. So we applied object-oriented techniques to our kno\vledge representation strategies in our systems and present a "object modual"conception. Eveny object modual represents an abstract concept, but hide the construction in the interior of an object modual. An object modual can only be used as a whole body. Every object modual has its private state sets, data and proceduce and can inherit the data of upper levels. It displays an abstract box to the outer, vvithout thinking its interaJ construction. The main body, which calls the object modual in the- outside~ can use and control the object modual and exchange data with another object tllodual by the fixed interface. The objects or procedures in the outside can't interfere with the private state sets and interal structure of an object modual. And so do an object modual interact with another object modual. Every object moduaI consists of condition modual, noncondition modual, processing modual and calculation modual. An object Inodual is a sealed object and interacters ,vith another object modual by MESSAGE. The BNF of object modual is shown as following: knowledge modual: :='BODY~{knowledge-modual name) (('condi tion-modllal) (noncondit ionllJodual) (ca1cula tion-modual,) (processingmodual))'EJ\;VBOD yJ
Ill] e-se t.·
:=(rlll e;>/ (ru] e) (ru] e-se t)
TU] e~~ .~~'IF'<:conditon-set)' THE/'t
(conclusion)'EN.DIF' conditon-set::=(conditon)!(condition) v<'condition-set)/(condition) v( condition) condition: ,,~==(expression)/characterstring conclusion: :~(expressjon)/chal-accte1-string
3
STRUCTURE OF SYSTEM AND TOOLS
Here, the structure of the intelligent systems~ the development tools and kno\vledge acquisition tools will be describled.
3_ I Building ofintelligent Systems Figme 1 shows the whole procedure of building intelligent systems by editing type tools~ Although different systems have different c,haracters in some aspects. The main part of all intelligent system includes main controller, the knowledge base, inference engine, intelligent systems user interface, database etc. The main controller controls different operating tnoduals. When the system start to run, firstly, CBR modual matchs with information provided by user. It would return the conclusion of correspondent history cases if completely matching; expert systems V\'ould begin to reason jf not
5599
Copyright 1999 IFAC
ISBN: 0 08 043248 4
INTELLIGENT SYSTEMS AND ITS APPLICATION IN AGRICUL...
14th World Congress of IFAC
-
~
.-.- .-. __ .••. __
--_
,---
---- - -- - -- --_ ••. •. _•.•• __ •.•. --~
--
-- .•..•..••.
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•.••.•.
~
~.~
~
.•••
~
~
•.•.
~._
- ..,
_
·••·••·••·••·•··• ..··_·t
Knowledge Acquisition Units
L:
i ;
Expert knowledge
[Machine learning
User Interface
1Jinetic algorithms
n{lwl~dge Acquisiti
neural networks
n
/Data mining
I
I
nterface
IU seT Database 1
~ I "---M-a-in---~
trr-e-s.-ou-r-ce-s-d-at-a-ba-s""""'le
ControHcr lease-base reasoning
I
t ...., ,.. ,~' . ,.. '_,_,_, , ,_,__,._".,._ __-_..__
neural Fig. 1
T..Q.91;i,.pf.~djr.(!!g.l~yp?_, O.~.Y'?.Ipp!!.(?.!.l.t.-,-_ ,--_.
netv,ork~
etc. for knowledge
acquisition~
Neural
network, genetic algorithms, data mining, etc have been
Procedure of editing type tools developing intelligent systems
introduced above. Machine learning is also very an effective method of knowledge acquisition.
successfully matching. Knowledge base includes rules base, frame base, model base, multi-media base, etc., which can be acquired though knowledge engineers or by knowledge acquisition tools. Database includes user database, resource database, and geographical information system base~ and so on.
3.3 Development To 01 for Intelligent Systen2 According to different users, rn'o kind of too) version have been built and used in our lab: the editing type and the leading type.
3.2 Kna.."vledge Acquisition Tools
The editing type tools, which have 6 versions named as XiongFeng, face mainly to the knowledge engineers.
Generally speaking, general system are often implemented and operated difficultly to many field experts specially. So developing knowledge acquisition system for special field is very meaningful. According to the characters of knowledge in agriculture, we have developed an intelligent development system, \vhich consists of several kinds of knowledge acquisition
The domain experts or technicians \\'ho are familiar \vith computer can also use after training. Under the platfonn of this tool, users can build the knowledge base using discription language of knowledge base, \vhich are introduced in above sections. Then the tool can check up the structure of knowledge base, examines the grammer, inspects the consistency and completeness of kno\vledge. The leading type tools face mainly to agriculture experts \vho directly build their expert system but needn't familiar to our knowledge representation approaches (Lee., et ai, 1998) . Experts sum up their experiences, actual examples, data and knowledge through manmachine interaction. This tool consists of the reference kowledge base, the autonlatic leading ITIodual, the word understanding moduat the Chinese code generator etc. Recently, a tool based on multi-agent mechanism will be completed in our lab. It \-viII be describe,d In the following section,
subsystems. This system integrated some intelligent approaches such as neural nenvork, genetic algorithms, data mining~ and so on. This system, taking the knowledge representation strategies that we presented before and object-oriented & distributed calculation technique, "vas composed of neural networks, genetic algorithnls, nlachine learning, and so 011. Kno\vledge acquisition unit is an integrated and complex system. Some knowledge can be acquised according to domain experts or technicians who familiar ~'ith content needed. User builds the kno\vledge base llsing specific kno~'ledge base description language according to the kno''''ledge sorted out by experts. Fonnat of this specific knowledge base description language includes expression of the explanation information~ function description of the expert system to be built, description of the Jule-gruop. But knowledge of experts is sometimes incOlnplete~ so we had developed some intelligent approaches such as machine learning, data mining,
3.4 A Visualized Tool Based on A1ulti-agent
XiOltgFeng tools have successfully developed dozens of intelligent systenls. But aitning at specific probJ0J11 do[nain~ these tools are not easily grasped by those domain experts who are not acquaint with expert systems
5600
Copyright 1999 IFAC
ISBN: 0 08 043248 4
14th World Congress of IFAC
INTELLIGENT SYSTEMS AND ITS APPLICATION IN AGRICUL...
because the interface of this too1 is not excellent. To overcome the defects eXisting in the old tools; an useroriented visualized tool for AIS have been designed in this Jab (Shoham, ] 993). User !" ...... _.,•• ,•• ,•• ,."., ••,....
_'M'~'M'_'
.. < •• , •• , .. , •• , .. , •• , .. , •• , .. , •• , ..
,~, ,~, ..
.. , .. ' ... M'_,M' •• ,M' •• ,.. ,•• ,.. ,•• ,.. ,.., ... ..,• .,
!Intelligent interaction.Ji:nvironme nt
I
....f-----~---! ~
jlnterface
Lsto rdgc
The visualized tool (figure 2) for agicultllre inte]]jegent systems comprises a series of agents whi ch include control agent, knowledge base management agent, evaluationagent and some problem solving agents ifthe agent infonnation memory unit is not empty) and three units:knov,'ledge acquisition unit problem solving agents generator ,and interface generator etc (Xiong, et aI, 1997). j
1) Control Agent As an indispensable part of the tool~the Control Agent has the responsibilities of integrating and managing each component of the tool.It consists of a controller,an visualized task planner and a pubJic information black board. The controller controls the whole system,accomplishs huamarunachine interaction and infonnation sending/receiving etc. The visualized task planner constructs the diagrams of task analysis and decomposition through interac.tion.For task planner,the complexity of the given problem determines the number of hierarchies of task decomposition.The public blackboard plays the role of an information pivot because it retains the present states)data and the control infonnation of the whole system. 2)CKB Management Agent The visualized knowledge (including text,video,sound,graph and data etc.) represented by icons win be checked in this agent if they have not semantic or gralnmaticaI error in order to be filled into the cOluprehensive know [edge base.The eKB management agent also provides KB and DB management functions such as browsing,modification,dcletion and insertion etc. 3)CQmprehensive Kuo\'r'ledge Base (CKB)
This comprehensive knowledge base stores the hierarchical know [edge made up of icons and the corresponding content according to their attribute types such as text, video,sound,graph or data etc. 4)Problem Solving Agent Generator In multi-agent system,tbe intelligent entity--agent has some favorable characteristics such as:autonomy,coording and communication etc.In this tool,an generator is used to construct different kinds of problem solving agents according to task planning diagram and the available knowledge through humancomputer interaction.Tne generated agents are taskoriented and can be of different size. 5)Eva]uatjQo Agent Since there still may be some kno\vledge redundancy, c.onflicts or small errOf5 exist although eKB Management Agent has checked the input knowledge.So,after different PSAs are generated ,they are evaluated and checked by the Coordination Agent in order to find (if there are some) latent errors in knowledge/data and conflicts in results caused by knowledge inconsistency.
4
APPLICATIONS AND PROSPECTS
4. 1 Application Surnrnary
In our .lab, the fertilization expert system for sandly black soil, which is first agriculture expert system in China, is built in Oct., 1985. It was applied in more than 10 counties in north~Hua plane. We have developed dozens of agriculture experts systems by our development tools under support of National Science Tec,hno]ogy Committee~ Advanced Technology Developing Plan, Chinese National Science Foundation, etc. They were listed as national key spreading project in science and teclmoJogy fields fram 1991 to 2000~ In many provinces such as Anhui) Yunnan~ Henan etc the province, the leading committees for spreading our systems have been set up and are directly managed by government of provinces. These agriculture intelligent systems have functions in the follo\ving: Fertilization intelJigent systems can estimate the level of in accordance with physical & chemistry parameters of soil and geographical map of soil fertility. These systems recommend the quantity, time and the ways of applying fertilizer after reasoning and analyzing the type of soil, the breed of plant, time of so\ving, planting density, irrigation, the relation benveen the fertilizers and the yield, and so on, and teach how to apply fertilizer so that raising the yield and efficiency of fertilizers. fertility
The plant protection intelligent systems can forecast the occurrence of pests and diseases, diagnose possible diseases and pests according to the conditions of
5601
Copyright 1999 IFAC
ISBN: 0 08 043248 4
INTELLIGENT SYSTEMS AND ITS APPLICATION IN AGRICUL...
14th World Congress of IFAC
environment and the sytnptonl of plants in different tinles and introduce the effective ways of preventing diseases and pests. The cultivation management intelligent systems include cultivation, fertilization, management of water resources, plant protection etc. The animal raising inteHigent system teaches user how to select the breed of animal, how to make up forage, ho\v to scientificly raise and how to prevent from diseases,
etc. Some sample areas are arranged in different districts of China for how' to apply our agriculture intelligent systems. For example, 14 counties in Liaoning province have applied our expert system for fertilization of rice from 1989. 34 counties ofYunnan province, v/here many minorities live, are arranged as extension areas of agriculture intelligent system for corn, rice, apple, wheat, tobacco. Fertilization intelligent system for cabbage has been applied to a high school as "little star.. fire~' scientific activity in Tianjin City. 20 counties in Anhui province have used the cultivation management intelligent system for rice, cotton, \vheat, rape, etc. Our systems have been effectively used in five miJlion hectares of more than 200 countries of 20 provinces in China. The yield of grains increases 2.3 million kilogram~ cotton increases 350 thousand piculs; the chemical fertilizer was saved nearly 485 thousand tons. Recent years it is speeding the extension in \vhole country.
4.2 Prospects
Our achievements about R&D in agriculture intelligent systems have been got the support from National Scientific and Technology COlllillittee, Chinese National Science Foundation, agriculture ministry and many relative sectors, and are welcomed by grass-roots technicians and fanners in villages. Agriculture intelligent systems developed by us satisfy particular purposes with agriculture development in China. 1) T'he land in countryside of China is very distributive. The farming custom of fanners is different each other. Agriculture depends on cUrnate, breed, soil, etc and is very regional. Agriculture intelligent systems are a good tool, which can give exact and different advice, anaJyzing and inferring according to different situations. It can be sajd it is promising farming of China. 2) E·ducation level offanners in China is very low, and fanners are not good at farming with scientific methods. Under the intelligence and multi-media, agriculture intelligent system can give exact and audio-visual advice about the fertilization, the prevention and control of plant diseases and pests. management of fields, etc. This is an excellent tool for the science propagation. 3) Amount of agricu lture experts in China is very
scarely. They can't usually go into the midst of the common fanners. Agriculture intelligent system can substitute for agriculture experts to go down to the grassroots villages and guide fanners in any needed time. 4) The capability of grass-roots teclmicians is not high~ They need to renew the knowledge and technology. AgricuJture intelligent system integrates with knowledge, experiences, and model from experts of masters hip and is very suitable for training grass-roots technicians in
anytime. 5) In China~ collection and acculnulation of data, building of simulation model etc.have many problems, but the agriculture intelligent system is using the experience, knowledge and models. In addition, modification of knowledge base is very convenient in use of the 100]s. Ifs djfferent from the electronic books~ 6) Agriculture intelligent systems can effectively integrate ,",'ith database, geographical information system, multi-media, Internet, dec·ision support system etc and coordinate with product management, market economy, macro-decision, etc so those agriculture infonnation techniques can be implemented. 1
It is very meaningful that information techniques are applied to agriculture domain. China is a large agriculture countr~y. In the critical periods of converting from traditional agriculture to mode·m agriculture at present the in fOffil atics , automation and intelligence are the main objects we should major in. So we have developed the intelligent infonnation systems for gratifying these needs since 19805. These achievements had been a\\rarded by govenunent,. Academia Sinica and some departments of nation. With the development of netvvork of and INTERNET technology, in our future work, Vole shall develop the net\Vork information developing system based on IN"TERNET & WWW.
REFERENCES F.L Xiong. (1986) Expert System for Decision-making in Complex Environment_ Proc. 25th IEEE Conference on Decision and Control~ pp1374-1379 F.L Xiong, J.M Zhou et al.(1988) Expert Systems for Fertilization and Their Building Tool. Proc. 1988 IEEE International Conference on Systems, Man, and Cybernetics, pp802-805 F.L Xiong, Y Zheng et al (1998). A Visualized Tool for Developing AIS. 3rd IFAC/CIGR Workshop on Artificial Intelligence in Agriculture, Japan April pp147-151 H.Y Lee, Y.F Xu et al. (1998)An Intelligent Framework forAgri culture System Based on the 1-1 euristic I.. .eading Mechanisln. 3rd IFAC/CIGR Workshop on Artificial Intelligence in Agriculture, Japan April pp36-40 Shoham, Y. (1 993)Agent-oriented Programlning. Artificial Intelligence, pp:27-32
5602
Copyright 1999 IFAC
ISBN: 0 08 043248 4
14th World Congress ofIFAC
K-4b-Ol-5
Copyright © 1999 IFAC 14th Triennial World Congress~ Beijing, P.R. China
INTELLIGENT SYSTEMS AND ITS APPLICATION IN AGRICULTURE Xiong Fanlun and Qiao Kezhi
Hefei Institute ofIntelligent Machines, Chinese Academ.y ofSciences P. a.Box 1 J 30, Hefei, .4.nhui 230031 P.R.China flxiong@public. ustc. edu. en T
Abstract: To overcome the complexity and uncertainty of agriculture, intelligent systenls, which utilize some new knowledge representation strategies and integrate with advanced approaches such as case-based reasoning, neural networks, genetic algorithms, and so on, are proposed. Some knowledge representation strategies, which are very suitable for agric·ulture. knowledge:- are also introduced. Many intelligent systems for agriculture \vhich had been developed by our development platforms have been succeeding in adequate fertilization., plant protection~ and cultivation of vvheat, corn, rice, cotton, rape, tea, tobacco, sugur cane, orange, cabbage.) etc in Chinese countryside during the past dozen years. Copyright © 1999IFAC
Keywords: Intelligent systems~ Agriculture
INTRODUCTION
Intelligent infonnation and control techniques have been remarkably progressed during the past dozen years. A great deal of intelligent infoffilatiol1 SystelUS have been developed and applied into many fields. The research of these techniques for agiculture has a tremendous advances as well in recent years, such as for fertilization, irrigation, green house, field managment, etc. Based on the importance of agriculture in china, early in the beginning of 1980's, \ve started and concentrated on developing for agriculture experts systems, and succeeded in applying theJTI to countryside in China (Xiong, 1986) . Xiong et al proposed a series of kno~rledge representation strategies that are suitable for agriculture, and developed a series of tools for building the agriculture intelligent systems based on these kno\~rledge representation approaches and relatively iote 11 igent techniques. And using these tools "\ve have built a lot of experts systems for adequate fertilization, plant protection, and cultivation of wheat, corn, rice, cotton, horticulture, animaJ breeding~ fishery~ and so on
(Xiong, et al~ 1988) . And the integration of intelligent systems with corresponding advanced technologies such as neural networks, genetic aJgorithms~ case-based reasoning) machine learning, data mining, geographical information system are also used in our system. This paper consists of three sections in addition to the introduction. The second section describes some new knowledge representation. The third section describes the system structure and tools. The fourth section introduces application aspects and the prospect.
2
MULrrI-KNOWLEDGE REPRESENTA1"'ION STRATEGIES
Agriculture is a more sophisticated field for the control systems. l'he yields and quality of crops are decided by many factors such as weather, soil, breed~ way of planting, dieases and pests, etc. Agriculture control problems are very complex and there are many types of knowle.dge in agriculture. Intelligent systems integrated with many intelligent techniques are effective approaches for solving these probloms. For example, knowledge of
5597
Copyright 1999 IFAC
ISBN: 0 08 043248 4
14th World Congress of IFAC
INTELLIGENT SYSTEMS AND ITS APPLICATION IN AGRICUL...
fertilization is often decided by the mathematical equations, formulas or the experience of experts; cultivation is represented by descriptive and causal knowledge; diagnose of pests and diseases are represented by uncertain knowledge~ and so on. A series of knowledge representation approaches are proposed by Xiong et ai, \vhich are called as "Rule-skeleton plus Rule-body", Comprehensive kno,"vledge body, Object.. oriented comprihensive knowledge-body, Agent..based multi-knowledge and so on. It is proved in practice that these approaches have been effectively used for intelligent system of agriculture.
2.1 Rule-group Approach When the agriculture experts or other domain experts solve a certain problem, in general, at first he considers \vhich factors are involved in this proble,nl, then the problem is determined (solution or evaluation) by a group of relations (experiences or mathematics formula) among these factors. The determination of each factor separately relies on a group of new factors and the relation of these factors. The rule-group concept is a suitable representation strategy for these klnds of problems. The rule-group kno\vledge representation approach is proposed in a mode of "rule-skeleton plus rule-body". In knowledge base, a rule-group, standing for a group of experience or a kno\vledge unit, is represented through two levels. The first level of rule-group is called as the rule-skeleton that expresses ~~detennined~~ relation betvr'een the premise factors and the conclusion functions. It has the type of multi-premise and multi -conclusion. The second level of rule-group is called as rule... body~ which expresses the knowledge of evaluation or judgment. In the rule-body~ a triple "factor relation value" is used to indicate a description or an assertion. l~he know ledge in the rule-group is organized in hvolevel structure: rule-skeleton and rule-body. In corresponding, inference in rule base is also adopt~d through tv/o-Ievel control. The upper-level reasoning is top-do\vn object-driven inference procedure in ruleskeletons. The lower reasoning is a bottom-up datadriven inference procedure in rule body and is actjvated by upper-level reasoning to determine the value of factors~ BNF description of rule-group knowledge representation of rule-skeleton plus rule-body is as following:
rule-group::=
premi,~ge factor
set : ~~=(ffictor)
t:
(premise factor
set~>}
calculation factor set : .4:=('faclor» £ (conclusion factor set.» calculation formula : :=(factor)'~' <.algebraic e..tpl-cssion) body rule: :=(IF'(pl-emise sct)'JY1EIV' <'conclusion sot) pl-emise se t : :~(premise) ~ (premise se t)) conclusion set : ~·~(concl[Jsion) ~ (conclusion set)} premise: ;:=(f'actor)('reJRtion symbol) (value) conclu.s~ion : :=:::(factor)~='(value) va.lue .~ :=(numher string)/
2.2 Frame-type Knowledge Representation Many declarative knowledge is existed in agriculture knowledge. And there are some causal relations between this knowledge. The methods of traditional knowJedge representation are difficult to represent this knowledge. We present a frame-type approach, but it is different from traditional frame~ The structure is sbo\\ll as following: FRAME: frame-name STATE: state set while the frame activated CONDITION_FECET: set of active process slots according to state set PROCESS-SL,OT: the advice ofexpen The element of Sl"'ATE-set and PROCESS-SLOT-set can be sub-frame~ so produce a causal nenvork. The frame controls the procedure of the whole system The frame can't compute and is only used to produce the descrjption knowledge, but it may finish the calcuJation by calling rule base. The result of computing and the description results, \vhich the frame reasons:; will be put into conclusion blackboard. We would take some intelligent techniques such as case-based reasoning, neural nen.vorks and so on when we face uncertainty knowledge~ This knowledge is organized in the form of object-body. The frame can call this object-body in the following fonn: RUN CBR name [WITH input parameters TO output parameters] BNF of frame-type knowledge representation is sbo\vn as foUowing: 4
~. :=JFRAME~, fi~ame-name, 'S'TATE', stateset condition-fecet, processing -slot fra.me-name : .'~character~string state~.s~et .' :=state/state) (state-set)
frame
set;>
3
rule-body: : =(calcula tiOll fOZ'Ulllla.>l (ruJebody)}/((hody rule)~ (rule body)}
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condition-fecet ~".::=rule/rule, {conditionEecet} 1"'ule"~ "~='IF', condition-set, IT/fEN', active-stateset / 'DEFALfLT') acti ve-state-set condition-set : :~condition-expression/ condi tion-expression, condi tion-se t active-state-set ::~active-statejactjve state~
{active-state-sei} /~lockJ, active-
state-set active-.s·tate ~~ :=state!processil1g-s1ot-llame processing-slot ::~ processin~slotname, proce.~-sing-fece t-se t _processing-fecet-set .- .~= pI-ocessing~fecet / processing-fecet~ ( processing-fcce t-set) processing-slot-name :: ==f.l-ame-name /charac terstring processing-fecet : ~iADVICE"~ processingme thod/condi ti on-set, 'advi ce'; processing~ 0"
metho~
(condition-set,
'ADVrCEJ} processing-
metbo~ } ('-DEFA[ILT ADVICEJj
processing-method
processing-method)
::~character~string
know1 edge -modual-name~·.~ =charBc t er s tr ing condj t ion -modLiBl : : =( CO/y7)J « cOlldi ti on-name.> ( t <' condi lion-name-se t ) f) "(so und-image-se t ) <:cundi tion-J"ump-se t) 'EiVDC01V[)' cOl1dition-namc~' ~·=cha.J-actcr string sound-image-so t: : =emp ty/ ( sound-set)(imageset) condition-name::~charecterstring condition-jump-set::=( conditiDnname>J"~ )(obJect-name-set)/ (condi tionname)(:'(obJect-nAme-set)(conditon-Jump-set) ob..fee t-name-se t: ~.=( ob.lee t-name)/ ( obJ'ec tname»( obJ"ect-name-se t» obJect-name::~chaTacter string processing~modllal~':~'PROCJ ( sound-imagese t)(processing-con ten t ) 'EIVDPROC' processing'~contell t"~ .~=cl1a1~ac ter string sOlJl1d~set;:~(sound-filc-na.mc.>/
string calcu]ation-modual::='COMP'{
image~file~name::~character
2.3 Object l\.1odual
~rule-set)}1EVDCaYP'
In the process of developing intelligent systems, when facing some special problems we found that some knowledge or relations between different factors in agriculture can't be exactly described in the fonns of the frame or the rule-group. So we applied object-oriented techniques to our kno\vledge representation strategies in our systems and present a "object modual"conception. Eveny object modual represents an abstract concept, but hide the construction in the interior of an object modual. An object modual can only be used as a whole body. Every object modual has its private state sets, data and proceduce and can inherit the data of upper levels. It displays an abstract box to the outer, vvithout thinking its interaJ construction. The main body, which calls the object modual in the- outside~ can use and control the object modual and exchange data with another object tllodual by the fixed interface. The objects or procedures in the outside can't interfere with the private state sets and interal structure of an object modual. And so do an object modual interact with another object modual. Every object moduaI consists of condition modual, noncondition modual, processing modual and calculation modual. An object Inodual is a sealed object and interacters ,vith another object modual by MESSAGE. The BNF of object modual is shown as following: knowledge modual: :='BODY~{knowledge-modual name) (('condi tion-modllal) (noncondit ionllJodual) (ca1cula tion-modual,) (processingmodual))'EJ\;VBOD yJ
Ill] e-se t.·
:=(rlll e;>/ (ru] e) (ru] e-se t)
TU] e~~ .~~'IF'<:conditon-set)' THE/'t
(conclusion)'EN.DIF' conditon-set::=(conditon)!(condition) v<'condition-set)/(condition) v( condition) condition: ,,~==(expression)/characterstring conclusion: :~(expressjon)/chal-accte1-string
3
STRUCTURE OF SYSTEM AND TOOLS
Here, the structure of the intelligent systems~ the development tools and kno\vledge acquisition tools will be describled.
3_ I Building ofintelligent Systems Figme 1 shows the whole procedure of building intelligent systems by editing type tools~ Although different systems have different c,haracters in some aspects. The main part of all intelligent system includes main controller, the knowledge base, inference engine, intelligent systems user interface, database etc. The main controller controls different operating tnoduals. When the system start to run, firstly, CBR modual matchs with information provided by user. It would return the conclusion of correspondent history cases if completely matching; expert systems V\'ould begin to reason jf not
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-
~
.-.- .-. __ .••. __
--_
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~
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~
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~
~
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·••·••·••·••·•··• ..··_·t
Knowledge Acquisition Units
L:
i ;
Expert knowledge
[Machine learning
User Interface
1Jinetic algorithms
n{lwl~dge Acquisiti
neural networks
n
/Data mining
I
I
nterface
IU seT Database 1
~ I "---M-a-in---~
trr-e-s.-ou-r-ce-s-d-at-a-ba-s""""'le
ControHcr lease-base reasoning
I
t ...., ,.. ,~' . ,.. '_,_,_, , ,_,__,._".,._ __-_..__
neural Fig. 1
T..Q.91;i,.pf.~djr.(!!g.l~yp?_, O.~.Y'?.Ipp!!.(?.!.l.t.-,-_ ,--_.
netv,ork~
etc. for knowledge
acquisition~
Neural
network, genetic algorithms, data mining, etc have been
Procedure of editing type tools developing intelligent systems
introduced above. Machine learning is also very an effective method of knowledge acquisition.
successfully matching. Knowledge base includes rules base, frame base, model base, multi-media base, etc., which can be acquired though knowledge engineers or by knowledge acquisition tools. Database includes user database, resource database, and geographical information system base~ and so on.
3.3 Development To 01 for Intelligent Systen2 According to different users, rn'o kind of too) version have been built and used in our lab: the editing type and the leading type.
3.2 Kna.."vledge Acquisition Tools
The editing type tools, which have 6 versions named as XiongFeng, face mainly to the knowledge engineers.
Generally speaking, general system are often implemented and operated difficultly to many field experts specially. So developing knowledge acquisition system for special field is very meaningful. According to the characters of knowledge in agriculture, we have developed an intelligent development system, \vhich consists of several kinds of knowledge acquisition
The domain experts or technicians \\'ho are familiar \vith computer can also use after training. Under the platfonn of this tool, users can build the knowledge base using discription language of knowledge base, \vhich are introduced in above sections. Then the tool can check up the structure of knowledge base, examines the grammer, inspects the consistency and completeness of kno\vledge. The leading type tools face mainly to agriculture experts \vho directly build their expert system but needn't familiar to our knowledge representation approaches (Lee., et ai, 1998) . Experts sum up their experiences, actual examples, data and knowledge through manmachine interaction. This tool consists of the reference kowledge base, the autonlatic leading ITIodual, the word understanding moduat the Chinese code generator etc. Recently, a tool based on multi-agent mechanism will be completed in our lab. It \-viII be describe,d In the following section,
subsystems. This system integrated some intelligent approaches such as neural nenvork, genetic algorithms, data mining~ and so on. This system, taking the knowledge representation strategies that we presented before and object-oriented & distributed calculation technique, "vas composed of neural networks, genetic algorithnls, nlachine learning, and so 011. Kno\vledge acquisition unit is an integrated and complex system. Some knowledge can be acquised according to domain experts or technicians who familiar ~'ith content needed. User builds the kno\vledge base llsing specific kno~'ledge base description language according to the kno''''ledge sorted out by experts. Fonnat of this specific knowledge base description language includes expression of the explanation information~ function description of the expert system to be built, description of the Jule-gruop. But knowledge of experts is sometimes incOlnplete~ so we had developed some intelligent approaches such as machine learning, data mining,
3.4 A Visualized Tool Based on A1ulti-agent
XiOltgFeng tools have successfully developed dozens of intelligent systenls. But aitning at specific probJ0J11 do[nain~ these tools are not easily grasped by those domain experts who are not acquaint with expert systems
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INTELLIGENT SYSTEMS AND ITS APPLICATION IN AGRICUL...
because the interface of this too1 is not excellent. To overcome the defects eXisting in the old tools; an useroriented visualized tool for AIS have been designed in this Jab (Shoham, ] 993). User !" ...... _.,•• ,•• ,•• ,."., ••,....
_'M'~'M'_'
.. < •• , •• , .. , •• , .. , •• , .. , •• , .. , •• , ..
,~, ,~, ..
.. , .. ' ... M'_,M' •• ,M' •• ,.. ,•• ,.. ,•• ,.. ,.., ... ..,• .,
!Intelligent interaction.Ji:nvironme nt
I
....f-----~---! ~
jlnterface
Lsto rdgc
The visualized tool (figure 2) for agicultllre inte]]jegent systems comprises a series of agents whi ch include control agent, knowledge base management agent, evaluationagent and some problem solving agents ifthe agent infonnation memory unit is not empty) and three units:knov,'ledge acquisition unit problem solving agents generator ,and interface generator etc (Xiong, et aI, 1997). j
1) Control Agent As an indispensable part of the tool~the Control Agent has the responsibilities of integrating and managing each component of the tool.It consists of a controller,an visualized task planner and a pubJic information black board. The controller controls the whole system,accomplishs huamarunachine interaction and infonnation sending/receiving etc. The visualized task planner constructs the diagrams of task analysis and decomposition through interac.tion.For task planner,the complexity of the given problem determines the number of hierarchies of task decomposition.The public blackboard plays the role of an information pivot because it retains the present states)data and the control infonnation of the whole system. 2)CKB Management Agent The visualized knowledge (including text,video,sound,graph and data etc.) represented by icons win be checked in this agent if they have not semantic or gralnmaticaI error in order to be filled into the cOluprehensive know [edge base.The eKB management agent also provides KB and DB management functions such as browsing,modification,dcletion and insertion etc. 3)CQmprehensive Kuo\'r'ledge Base (CKB)
This comprehensive knowledge base stores the hierarchical know [edge made up of icons and the corresponding content according to their attribute types such as text, video,sound,graph or data etc. 4)Problem Solving Agent Generator In multi-agent system,tbe intelligent entity--agent has some favorable characteristics such as:autonomy,coording and communication etc.In this tool,an generator is used to construct different kinds of problem solving agents according to task planning diagram and the available knowledge through humancomputer interaction.Tne generated agents are taskoriented and can be of different size. 5)Eva]uatjQo Agent Since there still may be some kno\vledge redundancy, c.onflicts or small errOf5 exist although eKB Management Agent has checked the input knowledge.So,after different PSAs are generated ,they are evaluated and checked by the Coordination Agent in order to find (if there are some) latent errors in knowledge/data and conflicts in results caused by knowledge inconsistency.
4
APPLICATIONS AND PROSPECTS
4. 1 Application Surnrnary
In our .lab, the fertilization expert system for sandly black soil, which is first agriculture expert system in China, is built in Oct., 1985. It was applied in more than 10 counties in north~Hua plane. We have developed dozens of agriculture experts systems by our development tools under support of National Science Tec,hno]ogy Committee~ Advanced Technology Developing Plan, Chinese National Science Foundation, etc. They were listed as national key spreading project in science and teclmoJogy fields fram 1991 to 2000~ In many provinces such as Anhui) Yunnan~ Henan etc the province, the leading committees for spreading our systems have been set up and are directly managed by government of provinces. These agriculture intelligent systems have functions in the follo\ving: Fertilization intelJigent systems can estimate the level of in accordance with physical & chemistry parameters of soil and geographical map of soil fertility. These systems recommend the quantity, time and the ways of applying fertilizer after reasoning and analyzing the type of soil, the breed of plant, time of so\ving, planting density, irrigation, the relation benveen the fertilizers and the yield, and so on, and teach how to apply fertilizer so that raising the yield and efficiency of fertilizers. fertility
The plant protection intelligent systems can forecast the occurrence of pests and diseases, diagnose possible diseases and pests according to the conditions of
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environment and the sytnptonl of plants in different tinles and introduce the effective ways of preventing diseases and pests. The cultivation management intelligent systems include cultivation, fertilization, management of water resources, plant protection etc. The animal raising inteHigent system teaches user how to select the breed of animal, how to make up forage, ho\v to scientificly raise and how to prevent from diseases,
etc. Some sample areas are arranged in different districts of China for how' to apply our agriculture intelligent systems. For example, 14 counties in Liaoning province have applied our expert system for fertilization of rice from 1989. 34 counties ofYunnan province, v/here many minorities live, are arranged as extension areas of agriculture intelligent system for corn, rice, apple, wheat, tobacco. Fertilization intelligent system for cabbage has been applied to a high school as "little star.. fire~' scientific activity in Tianjin City. 20 counties in Anhui province have used the cultivation management intelligent system for rice, cotton, \vheat, rape, etc. Our systems have been effectively used in five miJlion hectares of more than 200 countries of 20 provinces in China. The yield of grains increases 2.3 million kilogram~ cotton increases 350 thousand piculs; the chemical fertilizer was saved nearly 485 thousand tons. Recent years it is speeding the extension in \vhole country.
4.2 Prospects
Our achievements about R&D in agriculture intelligent systems have been got the support from National Scientific and Technology COlllillittee, Chinese National Science Foundation, agriculture ministry and many relative sectors, and are welcomed by grass-roots technicians and fanners in villages. Agriculture intelligent systems developed by us satisfy particular purposes with agriculture development in China. 1) T'he land in countryside of China is very distributive. The farming custom of fanners is different each other. Agriculture depends on cUrnate, breed, soil, etc and is very regional. Agriculture intelligent systems are a good tool, which can give exact and different advice, anaJyzing and inferring according to different situations. It can be sajd it is promising farming of China. 2) E·ducation level offanners in China is very low, and fanners are not good at farming with scientific methods. Under the intelligence and multi-media, agriculture intelligent system can give exact and audio-visual advice about the fertilization, the prevention and control of plant diseases and pests. management of fields, etc. This is an excellent tool for the science propagation. 3) Amount of agricu lture experts in China is very
scarely. They can't usually go into the midst of the common fanners. Agriculture intelligent system can substitute for agriculture experts to go down to the grassroots villages and guide fanners in any needed time. 4) The capability of grass-roots teclmicians is not high~ They need to renew the knowledge and technology. AgricuJture intelligent system integrates with knowledge, experiences, and model from experts of masters hip and is very suitable for training grass-roots technicians in
anytime. 5) In China~ collection and acculnulation of data, building of simulation model etc.have many problems, but the agriculture intelligent system is using the experience, knowledge and models. In addition, modification of knowledge base is very convenient in use of the 100]s. Ifs djfferent from the electronic books~ 6) Agriculture intelligent systems can effectively integrate ,",'ith database, geographical information system, multi-media, Internet, dec·ision support system etc and coordinate with product management, market economy, macro-decision, etc so those agriculture infonnation techniques can be implemented. 1
It is very meaningful that information techniques are applied to agriculture domain. China is a large agriculture countr~y. In the critical periods of converting from traditional agriculture to mode·m agriculture at present the in fOffil atics , automation and intelligence are the main objects we should major in. So we have developed the intelligent infonnation systems for gratifying these needs since 19805. These achievements had been a\\rarded by govenunent,. Academia Sinica and some departments of nation. With the development of netvvork of and INTERNET technology, in our future work, Vole shall develop the net\Vork information developing system based on IN"TERNET & WWW.
REFERENCES F.L Xiong. (1986) Expert System for Decision-making in Complex Environment_ Proc. 25th IEEE Conference on Decision and Control~ pp1374-1379 F.L Xiong, J.M Zhou et al.(1988) Expert Systems for Fertilization and Their Building Tool. Proc. 1988 IEEE International Conference on Systems, Man, and Cybernetics, pp802-805 F.L Xiong, Y Zheng et al (1998). A Visualized Tool for Developing AIS. 3rd IFAC/CIGR Workshop on Artificial Intelligence in Agriculture, Japan April pp147-151 H.Y Lee, Y.F Xu et al. (1998)An Intelligent Framework forAgri culture System Based on the 1-1 euristic I.. .eading Mechanisln. 3rd IFAC/CIGR Workshop on Artificial Intelligence in Agriculture, Japan April pp36-40 Shoham, Y. (1 993)Agent-oriented Programlning. Artificial Intelligence, pp:27-32
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