Expert systems: Applications to agriculture and farm management

Computers and Electronics in Agriculture, 2 (1988) 173-182 Elsevier Science Publishers B.V., Amsterdam - - Printed in The Netherlands

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Expert Systems: Applications to Agriculture and Farm Management R. DOLUSCHITZ 1and W.E. SCHMISSEUR 2

lInstitut fi~r Landwirtschaftliche Betriebslehre, Universittit Hohenheim, Postfach 70 05 62, 7000 Stuttgart 70 (Federal Republic of Germany) '-'Department of Agricultural and Resource Economics, Oregon State University, Corvallis, OR 97331-3601 (U.S.A.) (Accepted 21 September 1987 )

ABSTRACT Doluschitz, R. and Schmisseur, W.E., 1988. Expert systems: applications to agriculture and farm management. Comput. Electron. Agric., 2: 173-182. Expert systems are just beginning to emerge as a field of research and development within agriculture. This paper provides a brief overview of this widely diverse topic in artificial intelligence, a technology sub-area of computer science. Expert systems are clearly defined and contrasted to conventional computer programs. Generic categories of expert system applications and potential advantages are summarized as well as impediments to their development. State-of4he-art research and development work is summarized, with the focus on agriculture. Furthermore, potential expert system applications in the more specific area of farm management are discussed. Finally a brief outlook is given. The analysis shows that there are numerous potential fields of application in farm management. With respect to the time-consuming and costly development process of expert systems, selection of application fields should be performed carefully and analysis of expected utility should be required.

INTRODUCTION

Interest in expert systems, a recently much-recognized technology sub-area of artificial intelligence, is just beginning to emerge as a field of research and development within agriculture. Motivating this interest are a number of factors, one of which is the development of exciting and promising applications to problems of scientific, technical and commercial interests ( Buchanan, 1986; Waterman, 1986). Another is the wide-spread introduction of specialized development tools which speed expert system construction (Kinnucan, 1985; Richer, 1986). Enthusiasm is also high because some of the existing expert 0168-1699/88/$03.50

© 1988 Elsevier Science Publishers B.V.

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systems have proved to be capable of equalling or surpassing the best performance of rare, scarce and expensive human experts (Hayes-Roth et al., 1983). This position paper was developed because expert systems are a relatively unknown area of study and inquiry for many researchers in agriculture. The first part provides a brief definition and overview of this widely diverse field of computer science. The latter part narrows the focus to agriculture. Within this discipline, state-of-the-art research and development is summarized and potential expert system applications in the more specific field of farm management are advanced. EXPERT SYSTEMS: DEFINITION AND PROPERTIES

"An 'expert system' is an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution. The knowledge necessary to performe at such a level, plus the inference procedure used, can be thought of as a model of the expertise of the best practitioners of the field." This definition is given by Edward A. Feigenbaum, one of the pioneers in expert system development (Feigenbaum, 1981 ). Although there is no general standard for expert systems, most include components as illustrated in Fig. 1: a knowledge base of domain facts and associated heuristics; - an inference procedure or control structure for utilizing the knowledge base; a natural language user interface. The knowledge base component includes both domain facts and heuristics. This component part is usually developed with assistance from at least one human-domain expert. Facts of the domain constitute a body of information -

-

Expert

User

Knowledge acqui(tools) sition facility

Ji

Knowl baseedge

Input/output system Advice~'~ I [ Specific explanati°nsI [ ~;aan%tSdata t

Infsystem ......

I

Fig. 1. Component parts of an expert system computer program (adopted from Feigenbaum and McCorduck, 1983).

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widely shared and generally publicly available within the domain. Heuristic knowledge, on the other hand, is mostly privately and individually held. Heuristics include rules-of-thumb, judgements, and sometimes experience-based guesses that typically characterize human expert-level decision making. In order for an expert system to solve a problem, a program must have both kinds of knowledge, facts and heuristics, in its knowledge base. In addition to a knowledge base expert systems include an inference system or procedure also commonly called the inference engine. This system contains the general problem-solving approach. It decides which heuristics are applied to the problem, accesses the appropriate rules in the knowledge base, executes the rule, and determines when an acceptable solution has been found. In effect, the inference system 'runs' an expert system. The remaining component part of the expert system permits bidirectional communication. It is known as the input/output system. Through it, users input knowledge which describes the problem and receive requests for additional information about the problem as well as reasons behind its advice or recommendations. Expert systems, although markedly different, should not be considered as competitors but more as extensions to conventional computer programs. The most basic difference is that expert systems manipulate knowledge while conventional programs manipulate data. T h a t is, conventional programs require users to draw their own conclusions from facts retrieved by the program. In contrast, expert systems consisting of both declarative and procedural knowledge use reasoning to draw conclusions from stored facts. Expert systems also are typically reserved for problems for which algorithmic solutions do not exist. Therefore, heuristic searching is required. This is in sharp contrast to conventional computer programs. However, because of these heuristics, often less than optimum solutions are produced. Also, expert systems typically do not solve sets of equations or perform other extensive mathematical computations which are best handled by conventional algorithmic programs. Instead symbols representing problem concepts can be created and manipulated. This unique feature gives expert systems the ability to take a problem stated in some arbitrary initial form and convert it to a form appropriate for processing by expert rules. This reformulation capability can range from simple to complete reconceptualization of a problem. Another distinguishing feature of expert systems is that the control structure is separate from domain knowledge. This makes it easier to modify, update, and enlarge the expert program. In conventional programs, modifications are generally more difficult because changes in one part of the program must be carefully examined for impacts in other parts of the program. Existing expert system programs range from the very complex to those that are very task-specific and narrowly defined. Extremely complex systems are representative of those being developed by artificial intelligence theorists who

176 are attempting to emulate the thought process of the human brain and those being developed by the broad field of practitioners who are attempting to rival 'world-class' experts in the solution of major practical field problems. No less important, but smaller in scale and scope, are those being developed to analyse narrowly defined but still difficult problems. Application fields are numerous. Expert systems are, for example, used to infere the molecular structure of unknown chemical compounds (DENDRAL; Buchanan and Feigenbaum, 1978), to estimate the likelihood of finding particular types of mineral deposits (PROSPECTOR; Gaschnig, 1981), to diagnose bacterial infections (MYCIN; Shortliffe, 1976) or to design computer systems ( R1, XCON, respectively; McDermott, 1982). Besides interpretation, diagnosis and design, application categories of existing systems also include prediction, planning, monitoring, debugging, repair, instruction and control (Hayes-Roth et al., 1983 ). Major advantages of artificial expertise represented by expert systems over human expertise are numerous. Artificial expertise can easily be preserved and widely distributed. Results are frequently consistent and cost-efficiently produced. Different knowledge sources or even sources from different domains can be fused and literally a new class of 'experts' can be created. On the other hand there are impediments which either make development difficult or impossible. Experts who contribute to the knowledge base of an expert system must be able to articulate their special knowledge, judgement, and experience properly. Acquisition of additional basic domain knowledge is sometimes very complex and time-consuming. Uncertainties in inferences are difficult to consider. More impediments can become relevant in particular fields of application. In spite of these limitations, expert systems seem to be here to stay. Many expert systems in highly different domains are now under development. Expert system researchers believe that there are few constraints on the ultimate use of expert systems (Duda and Shortliffe, 1983; Buchanan, 1985; Gevarter, 1985; Waterman, 1986). TOWARD EXPERT SYSTEM DEVELOPMENTIN AGRICULTURE Connections between expert system development in general and applications to agriculture can be established in several ways. Referring to the general application categories mentioned earlier, agricultural expert systems can be developed, for example, for crop prediction estimates, diagnosing crop and livestock diseases, whole farm planning, and monitoring irrigation and feeding systems. Furthermore, most of the previously mentioned advantages of expert system techniques can be of benefit in agriculture. The preservation of expertise held by extension specialists and experienced farmers and farm managers, or the

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distribution of knowledge held by highly qualified researchers, or incorporated in sophisticated simulation and optimization models are desirable. However, since problems in agricultural management routinely become highly complex, the possibility of fusing knowledge from different domains might be the most promising advantage. Before potential application fields for expert systems are discussed in detail, current research is summarized. Agricultural expert systems and current research Since the application of expert systems in agriculture is in a relatively early stage, it is difficult to provide a complete overview of expert system developments and research. Based on limited published information, a listing of existing applications of expert systems to agriculture is presented in Table 1. This listing is by no means exhaustive, but it appears to be very representative of the major types of expert systems work currently underway. PLANT/ds supports the diagnosis of soybean diseases and can be used by growers and county agents alike. It contains diagnostic knowledge represented by decision rules which specify all conditions indicating each disease. Additionally, it uses external data such as weather input, plant growing conditions, and plant symptoms. PLANT/cd predicts corn damage resulting from the black cutworm using a combination of rules and a set of black cutworm simulation models. Required input includes: trap counts, a measure of field weediness, the age spectrum of larvae, soil conditions, and corn variety information. COMAX provides information on integrated crop management in cotton. It is designed for use by farmers, farm managers, and county and soil conservation agents. The system uses a combination of expert-derived rules and results TABLE 1 Existing agricultural expert systems cited in the literature Specification

Field of application

Cited

PLANT/ds

Diagnosis of soybean diseases Prediction of corn damage from the black cutworm Integrated crop management in cotton Pest and orchard management of applies Diagnosis of weeds in turf Determination of grain marketing alternatives

Michalski et al., 1982 Michalski et al., 1983 Boulanger, 1983

PLANT/cd COMAX POMME PLANT/tm GRAIN MARKETING ADVISOR

McKinion and Lemmon, 1985 USDA, 1986 Roach et al., 1985 Fermanian et al., 1985 Uhrig et al., 1985 Uhrig et al., 1986

178 generated by the cotton-crop simulation model named GOSSYM (Baker et al., 1983 ). It requires external information such as weather data, soil physical parameters, soil fertility levels, and certain pest management information. From this input of data, the system produces daily management decision recommendations. POMME provides information about pest and orchard management of apples. This system provides growers with knowledge about fungicides, insecticides, freeze, frost, and drought damage, non-chemical care options as well as information from a disease model. External information such as weather data including forecasts and crop symptoms are utilized by the system to generate management decision recommendations. P L A N T / t m supports turf managers in the diagnosis of weeds. The system contains knowledge rules for the identification of 39 grassy weeds commonly found in turf. Weed characteristics are required input. GRAIN MARKETING ADVISOR is an expert system for determining marketing alternatives and supports grain producers in finding optimal strategies. Individual farm conditions are considered. Information on storage and dryer availability, price trend, price level, basis trend, government program eligibility, and timing is required as input data. Spahr et al. (1985) report a dairy management system which fullfills the formal requirements given by an expert system approach. In addition to the systems which have been cited in agricultural literature, a number of expert system programs are currently under development. A recent Current Research Information System (CRIS) search (May 1986) shows that the main focus of expert system applications research is on crop management areas ( 6 projects ) and plant disease and pest management (5 projects ). These areas are followed by soil erosion prediction and control (3), resource conservation and crop management (3), irrigation management (2), and general farm management and decision making (2). One project each was found in soil management, animal nutrition, and product processing. Many public and private agricultural-related agencies in the North Central Region of the United States have organized a coordinate research, development, and technology transfer effort focusing on conservation production systems. The region-wide project is called EXTRA (EXpert Systems for Technology and Resource conservation in Agriculture). The objective of EXTRA is to develop a package of expert systems to advise farmers how to manage for both soil conservation and profitability. It is anticipated that the final package will contain more than 150 individual expert system programs each designed to provide expert advice in its knowledge domain. Initial efforts are directed to design a standard farm database for the family of proposed expert systems.

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Potential fields of development in farm management Expert systems appear to be a next step in the progression of computersupported information and decision systems first initiated by agriculturalists in the early fifties ( for a summary see Eisgruber, 1973 ). This optimism is based on several reasons. One is that expert systems are able to not only indicate management problem areas and suggest the dimensions of the problems, but also point to prescriptive actions. This critical ability, according to Connor and Vincent (1970), enables the realization of the full potential of computers in applied farm management. Another reason for optimism is that expert systems as surrogate human consultants resolve analytical shortcomings of the end user. This issue, according to Harsh et al. (1986), is a major limitation of existing computerized systems. A third reason is that expert systems are particularly adept at transforming raw data through analysis and 'expert' interpretation into information that is problem context-specific and therefore, when combined with adequate heuristics, directly usable for decision making. For these and other reasons there is renewed interest within the agricultural research community about development of extended management information and decision support systems, including expert systems. In determining potential fields of computer applications for supporting applied farm management relevant to a large number of farms, Connor and Vincent (1970) suggested that the primary object of study is not the computer, but rather the manager and the information he needs in performing his functions. The suggested approach also seems to be a first step in evaluating where to begin expert system development in farm management, even though the object of interest is knowledge and not only information anymore. Knowledge to perform farm management tasks can basically be divided into two major categories: The first includes several kinds of special knowledge which usually is demanded by farmers and farm managers from non-domain specialists. Characteristics of problems which ask for this kind of more general applicable expertise can be: less frequent occurrence, e.g. long-range farm planning, investment and tax planning, loan organisation, and real-estate decisions; - unexpected occurrence, e.g. livestock and plant diseases; - occurrence under fairly different conditions, e.g. feed optimization, breeding planning, marketing decisions. -

In general, costs of acquisition of sophisticated knowledge in these fields for farmers or farm managers are much greater than payoffs. Therefore, presently this expertise is frequently provided by consultants and both public and private specialists servicing farmers and ranchers. These and similar types of services are candidates for expert system developments. Much of this domain knowl-

180 edge, although directly related to agriculture, is non-agricultural domain knowledge. Thus these systems should be developed with support of corresponding domain specialists. They should produce extremely high payoffs because of their general applicability and their anticipated wide-spread use throughout all of agriculture. Furthermore, not only should they prove useful in actual decision making, but also they should facilitate the ongoing business planning efforts of many farm and ranch managers. The other kind of knowledge is related to actual farming operations including single and multiple-enterprise operations, and special tasks within these enterprises. Problems in this field are often more specific and less obvious. An expert system approach in this case has to be able to identify problems or problem areas on particular farms and ranches and to recommend feasible solutions. Expertise for the solution of these kind of problems could include both expert knowledge of the most successful farmers and results from suitable simulation and optimization models. Ex post-type analysis might use domain knowledge obtained from expert farmers, recorded in databases and prepared in relevant form. An expert system in this situation has to combine functions like interpretation, diagnosis and to some extent design and planning. Depending on the problem and application area it also has to fuse expert knowledge from more than one domain within agricultural research. Problems which can be covered are, for example, crop management problems, dairy and cattle management problems, swine production and marketing problems, and general financial and farm management problems. Compared to the programs listed in Table 1 these kinds of systems are more complex. Admittedly, prioritizing expert system development in such a broad and diverse field as farm management is a very difficult task. A natural progression, already occurring within agriculture, would result in the independent development, over a number of years, of rather isolated and narrowly domain-defined systems. Following these developments, broader but still rather narrowly domain-defined expert systems would eventually emerge which will fuse knowledge domains of related expert-domain programs. Finally, sufficient systems will be available to create the expert decision and information system for overall farm management. Researchers' learning curves and interests, funding constraints, and different regional interests give credence to this development scenario. CONCLUSIONS

Expert system development potential exists in agriculture and farm management. These systems could help to make non-domain knowledge more frequently available to farmers and farm managers. Existing management information systems will become logically extended in their ability to support decision-making. Finally, agricultural database systems become more useful

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for farmers. In general the knowledge transfer in agriculture could be improved and the extension-service work performed more efficiently. Most importantly, an enormous amount of research information could be translated to levels of applicability to end users. A look at the artificial intelligence product market (programming languages, expert system development shells, special hardware) indicates, on the other hand, fast technological progression in the field and increasing efforts in marketing (Davis, 1986; Kinnucan, 1985). Furthermore, the cost of computer hardware is decreasing and its performance is improving rapidly. Finally, improvements in knowledge acquisition and representation techniques are other driving forces to expert system development. Currently one of the major impediments in developing expert systems is the great complexity and time-consuming nature of the process. Therefore, selection of foremost application fields must be performed carefully and analysis of expected utility should be required. The special situation in agriculture additionally requires a fusion of different knowledge sources (human experts, databases, research models ). Problems of this nature are widely unsolved at the moment. Finally, expert systems in farm management have to deal with a wide diversity of enterprises which makes it difficult to develop support systems with a broad relevance. Problems as mentioned above have not been unknown in the past; however, before expert system development can be successful, solutions have to be found.

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