• Tasmanian Government


    Expert Systems used to disseminate complex information in agriculture and horticulture

    The Department of Primary Industry and Fisheries of Tasmania has used Expert Systems technology to assist the delivery of information to farmers. Farming in the developed world has become technologically advanced, and, as a result, the decisions that farmers face are more complex. Printed notes rapidly become out of date and for these reasons we looked to a technology solution.

    An early application was the development of “Pasturepak” an expert system designed to enable counter staff at seed retailers deliver the Department’s expert advice on pasture seed mixtures and appropriate cultivars (varieties). The staff get questions from farmers who wish to sow pastures for different purposes, climate and soil types. Pasturepak was developed using a DOS based Expert System shell called Crystal. An advantage was that the Department did not have to rely on the farmers owning computers

    We searched for a product that would suit our need. We selected XpertRule® because it was a true Expert System, rather than a front end for a data base. It also had connectivity with MS Office programs, plus ODBC, DDE and OLE connectivity. The software looked truly professional with the ability to include Winhelp, and the appearance of the runtime products on screen was better than others that we considered. Although XpertRule was not the cheapest Expert System shell on the market, our experience with Crystal had taught us that a good shell saves in development time and would be the cheapest option in the long run

    It was not possible to read the rules from Crystal into XpertRule. This did not deter us because the real work in developing an Expert System is the time spent in developing rules and the models contained within an Expert System. It took us a short while to learn XpertRule, because it is a new technology with a different structure. In XpertRule the logic is broken down into “Tasks” and these may be forward chained or backward chained, concepts familiar from previous development with Crystal. We found the graphic representation in XpertRule brilliant, and the ability to include code has not been lost. In XpertRule the code is bundled into “Procedures” and it is easy to keep track of where these are in the program. Building dialog boxes is quick, and there is a facility to duplicate and edit for even more efficient development.

    The “Apple Thinning Program”

    Apple orchards are important in primary production in Tasmania, and there has been a long history in the process of apple thinning. Apples are naturally biennial bearing,. Trees flower heavily one year producing a large crop of small fruit (called the “On” year) followed by light flowering the next year with a small crop of large poor quality fruit.

    Thinning is most economically done by applying sprays of chemicals that act similarly to plant hormones and cause the abortion of flowers and fruitlets at an early stage of development. Early thinning favours the development of the desirable high density of cells in the fruit.

    Orchardists have to make the difficult decision about the concentration of the thinning agent at blossom time. If the concentration is too low, then thinning will not be effective and the cost of hand thinning is prohibitive,. And if the concentration is too high, then there is the risk of loosing all of the fruit. The decision is made more difficult because of the large number of variables that need to be taken into account.

    The Department decided to use Expert System technology to encode rules about how to make the decision on apple thinning. The research has been published in scientific journals, and this, in conjunction with other related research in Europe and the USA, was our basis upon which to build an expert system. But, as so often happens, the published data was insufficient to make specific recommendations. The researchers, who were also giving advice to individuals and groups of growers, were classic examples of experts who were good at articulating their expert knowledge, but less clear at explaining how they arrived at the advice. (cf XpertRule“Structured Decision Tasks Methodology”).

    In the knowledge acquisition sessions we developed models from tables on the white board. These are not the same as the “Truth Tables” in XpertRule, but numerical tables that we use to capture the range of response that the experts expect from one or other of the variables. In developing these tables I encouraged the experts to look for patterns. In this way we assigned numerical values for the variables important in determining the concentration of the thinning agent. These variables are:

    • About the trees themselves – cultivar, rootstock and age.
    • About the physiology of the trees – previous crop, vigour, number of blossom buds.
    • About pruning – severity of detailed pruning, limb thinning, and penetration of light into the canopy.
    • About the market – size of fruit required for the market.
    • About spraying – type of spray machinery and volume of water to be used in the machinery.

    The Apple Thinning Program gives advice on five cultivars with options that encompass four chemical thinners. The application in XpertRule has 60 tasks, (some with 50 decision tree leafs (i.e. rule paths), plus 30 other variables and 40 procedures supported by a customized help file of 5,000 words. The program has made a significant contribution to the way apple growers who have acquired it can access advice for their spray thinning. The knowledge engineering of the problem itself has changed the way in which those involved in spray thinning research think about new experiments for other cultivars and possibly new spray thinners.

    In its first year there were some 20 Tasmanian apple growers who have acquired the Apple Thinning Program, but next year this will increase as the benefits become more widely known. The Department has the view that its Expert Systems are “living software”. This means that there is regular revision of the knowledge. In the case of the Apple Thinning Program, knowledge of thinning emerging cultivars is added. Because of this the license period for the program is limited to one or two years.

    XpertRule has proved to be a very good platform for delivering this advice. We have found that, in addition to the obvious advantages of having a Windows environment, our program is far more robust than it was in the DOS environment with the Crystal development tool. The visual way in which the knowledge is laid out in XpertRule is excellent. In our opinion XpertRule lives up to its claim of being a tool that is able to maintain knowledge as well as deliver it.

    By Peter Gillard, Department of Primary Industry and Fisheries of Tasmania.

    This story was also published in PC AI magazine.

     

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