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The analogy seems obvious between diagnosing and solving a medical condition and that of
diagnosing and solving a condition that is constraining or limiting a plant or food crop. This
analogy was first recognized by several plant pathologists and resulted in the development
of a system to detect soybean diseases (Michalski et al., 1981).
This structure was used in the first 'expert systems' developed by the authors. Rules used to
capture the knowledge included, for example:
Rule 1: If the plant observed in the field is
Leucaena leucocephala,
L. and the plant is growing
well then it is very unlikely that soil acidity would limit most food crop yields (80/100).
Rule 2: If the soil of the field is red and in a tropical environment then it is likely that soil
acidity will limit food crop yields (60/100).
Rules 1 and 2 illustrate ways that observational information, i.e. the presence of a particular
plant, can be recorded and can contribute to a conclusion that soil acidity may or may not be
limiting. Rule-based systems were used to develop a wide range of diagnostic systems. In
addition, these two rules illustrate a method not only to capture the logic in the if-then
sequence, but also record some expression of uncertainty in the declaration of the logical
relationship. In advanced rule-based systems combinations or rules with less than 100%
confidence level would be combined to represent that uncertainty in the resulting
conclusion. Some scientists developed methods of checking the consistency of combinations
of various rules, by examining the veracity of the resulting conclusion.
Other methods of knowledge representation have been developed such as frames, semantic
nets, but these are beyond the scope of this chapter. Given the complexity of agricultural
knowledge, improvements in structures supporting knowledge representation continue to
be needed. Specifically challenging are ways to combine qualitative and quantitative
knowledge in ways that conserve both. Unfortunately, many combinations of these types of
knowledge are possible only when the quantitative information is simplified to match the
form of the qualitative and when the qualitative is expressed only in quantitative terms.
5.3 Search strategies
As indicated in Rich (1983) and other references, strategies for efficient search through huge
networks, decision-trees and databases are needed. AI has provided some clear examples of
search strategies such as a) Depth-first, b)Breadth-first, and 3)Best-first (Figure 1). A Depth-
first strategy probes a knowledge-tree or a decision-tree by asking the detailed questions
first in a limb of the tree (top downward) as the first path through the tree. A Breadth-first
strategy, in contrast, searches all nodes at the same depth and then proceeds to the next
lower level of nodes (or questions). The Best-first, however, is a combination of the best
features of the Depth-first and the Breadth-first strategy. The Best features are those in
which a heuristic
2
, or specific knowledge, guides the search to choose at each node either a
Depth-first or a Breadth-first strategy, depending on the knowledge. It's interesting to note
that novices often choose a Depth-first strategy in probing a decision-tree and sometimes
ask far too-detailed questions (deep into the decision-tree) too quickly, resulting in a failed
search. In fact, this occurs so often that when someone exercises a Depth-first search, and it
2
On a personal note, my Brazilian wife has shown me a very practical 'heuristic', she taught me how to
cook rice by using the rule 1) add rice to the pan and 2) add only enough water to cover the rice by the
depth of the distance between the tip of one's index finger and the first joint. Interestingly, some have
speculated that this distance may coincide with the “inch” in English measurements. Less controversial
is that this as an extremely convenient meter stick!
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