कृषि विकास में एक्सपर्ट सिस्टम
An expert system is a software application that attempts to reproduce the performance of one or more human experts. Expert systems are mostly based on a specific problem domain, and are a traditional application of artificial intelligence. The expert system is used to behave like a human expert to solve the problem with the help of pre-set conditions in the software application.
A wide variety of methods can be used to simulate the performance of the expert, which are 1) the creation of "knowledge base" which uses some knowledge representation formalism to capture the Subject Matter Experts (SME) knowledge, and 2) a process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering.
Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or a supplement to some information system.
Importance of Expert Systems
The complexity of problems faced by farmers are yield loses, soil erosion, selection of crop, increasing chemical pesticides cost, pest resistance, diminishing market prices from international competition, and economic barriers hindering adoption of farming strategies. The farmer may not become expert manager of all these aspects of farming operations. On the other hand, agricultural Researchers need to address problems of farm management and discover new management strategies to promote farm success. Numerical methods have failed to provide better solution because understanding about crop systems is qualitative based on experience and cannot be mathematically represented.
Expert system are computer programs that are different from conventional computer programs as they solve problems by mimicking human reasoning processes, relying on logic, belief, rules of thumb opinion and experience. The experience and knowledge of scientists will be used to develop expert system on various issues of agriculture, which in turn will provide advisory support to the farmers.
In agriculture, expert systems are capable of integrating the perspectives of individual disciplines such as plant pathology, entomology, horticulture and agricultural meteorology into a framework that best addresses the type of ad hoc decision-making required of modern farmers. Expert systems can be one of the most useful tools for accomplishing the task of providing growers with the day-today integrated decision support needed to grow their crops.
Components of Expert Systems
Expert systems are composed of several basic components such as a user interface, a database, a knowledge base, and an inference mechanism. Moreover, expert system development usually proceeds through several phases including problem selection, knowledge acquisition, knowledge representation, programming, testing and evaluation.
The function of the user interface, is to present questions and information to the user and supply the user's responses to the inference engine. The questions are mostly in the form visuals that are developed as images, animation clips, and video clips. Any values entered by the user must be received and interpreted by the user interface. Some responses are restricted to a set of possible legal answers, others are not. The user interface checks all responses to ensure that they are of the correct data type. Whenever the user enters an illegal response, the user interface informs the user that his input was invalid and prompts him to correct it.
The knowledge, the experts uses to solve a problem must be represented in a fashion that can be used to code into the computer and then be available for decision making by the expert system. There are various formal methods for representing knowledge and usually the characteristics of a particular problem will determine the appropriate representation technique employed.
The knowledge base is a collection of rules or other information structures derived from the human expert. Knowledge bases can be represented by production rules. These rules consist of a condition or premise followed by an action or conclusion (IF condition...THEN action). Production rules permit the relationships that makeup the knowledge base to be broken down into manageable units. Having a knowledge base that consists of hundreds or thousands of rules can cause a problem with management and organization of the rules. Organizing rules and visualizing their interconnectedness can be accomplished through dependency networks. The knowledgebase can be used to good relational database management systems (DBMS) like Oracle, SQL Serer, MySQL, Access databases to develop the rule base and the query system can be used to retrieve the knowledge from DBMS systems.
The inference mechanism will be integrated as a software program (inference engine) that the part of the program containing reasoning capability. It interacts with a knowledge base (IF...THEN...ELSE statements), which contains information about how to solve problems within the problem domain. This is the global memory where the knowledge based system records information relating to a specific problem that it is trying to solve. Much of this information comes from the user but the memory is also used by the inference engine to record its own conclusions and to remember its chain of reasoning. By comparing what it knows about the problem domain in general with what it knows about the specific problem, the inference engine tries to proceed logically towards a better solution.
An understanding of the "inference rule" concept is important to understand expert systems. An inference rule is a statement that has two parts, an if-clause and a then-clause. This rule is what gives expert systems the ability to find solutions to diagnostic and prescriptive problems. An example of an inference rule is: If the symptom of crop is X, Then the nutrition deficiency is Y.
An expert system's rule base is made up of many such inference rules. They are entered as separate rules and it is the inference engine that uses them together to draw conclusions. Because each rule is a unit, rules may be deleted or added without affecting other rules. One advantage of inference rules over traditional programming is that inference rules use reasoning, which more closely resembles human reasoning. Thus, when a conclusion is drawn, it is possible to understand how this conclusion was reached. Furthermore, because the expert system uses knowledge in a form similar to the human expert, it may be easier to retrieve this information from the expert.
The knowledge that is represented in the system appears in the rulebase. In the rulebase described in the cross-referenced applications, there are basically four different types of objects, with associated information present.
Classes - these are questions asked to the user
Parameter - a parameter is a placeholder for a character string, which may be a variable that can be inserted into a class question at the point in the question where the parameter is positioned.
Procedures - these are definitions of calls to external procedures
Rule Nodes - The inferencing in the system is done by a tree structure, which indicates the rules or logic that mimics human reasoning. The nodes of these trees are called rule nodes. There are several different types of rule nodes.
The rule base comprises a forest of many trees. The top node of the tree is called the goal node, in that it contains the conclusion. Each tree in the forest has a different goal node. The leaves of the tree are also referred to as rule nodes, or one of the types of rule nodes. A leaf may be an evidence node, an external node, or a reference node. An evidence node functions to obtain information from the operator by asking a specific question. In responding to a question presented by an evidence node, the operator is generally instructed to answer "Yes" or "No".
Advantages and disadvantages of Expert Systems
- Expert Systems are useful in many aspects and ready to use by end user as advisory system.
- Provides consistent answers for repetitive decisions, processes and tasks.
- Holds and maintains significant levels of information.
- Encourages human expert to clarify and finalise the logic of their decision-making.
- Never "forgets" to ask a question, as a human might.
- Lacks common sense needed in some decision making.
- Cannot make creative responses as human expert would in unusual circumstances.
- Domain experts not always able to explain their logic and reasoning.
- Cannot adopt to changing environments, unless knowledge base is changed
www.manage.gov.in (National Institute of Agricultural Extension Management)
Sumit Rajendra Salunkhe1 and Surendra Kumar Rai2
Research fellows, Department of Extension Education, NMCA, Navsari Agricultural University