AN ARTIFICIAL INTELLIGENCE METHODOLOGY FOR THE
ADAPTATION OF AGRICULTURAL MODELS
MONU ROHILA, B.E INFORMATION TECHNOLOGY
MANAV RACHNA COLLEGE OF ENGINEERING, FARIDABAD
ABSTRACT
AI techniques can be used in the design of planning and management tools in agriculture for providing the help to the farmers in agricultural processes. The AI application transforms data relating to agricultural factors such as the weather and soil conditions into actionable, understandable information. An AI adaptation methodology designed to assist in transporting agricultural models between regions is presented. Models frequently need adaptation when transported because models developed inone region often do not produce valid results when used in a different region. The methodology prescribesthe linkage of a genetic algorithm to a model. This makes the model more robust because it is able to adaptto the region in which it is being used. This methodology has been implemented within a DSS; andpreliminary testing indicates this methodology has the ability to allow agricultural models developed in onearea to be effectively utilized in other regions.
INTRODUCTION
In the domain of agriculture, the utilization of already developed models in a broad area is often hindered by one or more factors. One frequent factor which impedes transportation is model inaccuracy. For example, when models that perform well in one region, are transported to be used in a different region, they often do not give accurate output (such as, recommendations, results, and/or indicators) in their new environment (i.e., when they are run in a new region). This is one of the major difficulties of model technology transfer. To address this difficulty, an artificial intelligence (AI) methodology is proposed. At the heart of this methodology is a genetic algorithm (GA) (an AI search technique) which is linked to the agricultural model engine (e.g., a risk assessment,
Provisional or crop growth model). The general component created by this combinational methodology will here be called an ’Agricultural Model-GA’ or an AGMOD-GA.
The following sections will describe the overall structure and elements of this methodology, the generic component created by following this methodology, and discuss the application of this methodology.
DESCRIPTION OF THE AI METHODOLOGY FOR ADAPTING AGRICULTURAL MODELS
The theory of this adaptation methodology is that by utilizing historical data from a particular region, a model’s parameter settings can be adapted so that the new parameters allow the model to work well in the particular region. This adaptation is done by trying to match the model parameter settings to the particular region. To find matching model parameter settings, intelligent search are performed which utilizes historical data as part of the objective function.
Overall, by following this methodology, a component will be created which can search for good model parameter settings such that when the given model is applied and run at the location in question, the output values given will be consistent with the historical outcome data; moreover it is hoped that this will also allow the model to be generally used in this region, producing accurate output values on data which it has not seen. The component that performs this search/adaptation can be called an expert system component since it intelligently modifies and adjusts a model to work in a new location in the same way an expert would modify and adjust a model.
Additionally, it should be emphasized that this methodology is particularly appealing because it is not a strictly empirical or analytical, but both. That is, this methodology does not perform a search to fit the historical data from a particular location into an empirical algorithm; rather it performs the search in a larger context, fitting the model parameter settings to a particular location. Therefore, the resulting instantiation of the adapted/localized agricultural model (with the new parameter settings inside) is as good (or as bad) as the original model; consequently, if the model is biologically significant (e.g., if it simulates biological events) then this is not lost by this adaptation methodology since the model is used in the same form (i.e., the structure of the model is left intact), only the model parameter settings are changed.
ELEMENTS OF THIS AI METHODOLOGY
In general, this methodology prescribes the utilization of:
(I) Historical situation data,
(ii) Historical outcome data,
(iii) The agricultural model
(iv) An intelligent search method (in this case, a genetic algorithm, also called a GA, which is an artificial intelligence search technique).
Historical situation data
Historical situation data is the basic data required by the model in question. In the domain of agricultural models, this often includes meteorological data since this is frequently an important input to the model. In this methodology, the more historical situation data that is available, the better. The presence of historical outcome data plays a large part in how accurately a model will be adapted using this methodology. This is due to the basic fact that models accepts situations and computes outcomes.
Historical outcome data
Historical outcome data will be used to fit the model parameter settings to the new region in question. Therefore, when constructing a component using the methodology described here, it must be possible to match model outputs to some combination of outcomes and/or events in the real-world (and there must be one-to-one correspondence). For risk assessment models, historical outcome data regards the occurrence of fungus or pest problems in past years (epidemiological data); or for crop growth models, historical outcome data regards crop yield in past years.
The agricultural model
In this methodology, the agricultural model (i.e., the engine or core of this model) is fundamental because it will be used to obtain evaluations of how well particular model parameter settings work in the given region (i.e., with the given data). In particular, the intelligent search method will repeatedly call upon this model engine as it constructs new model parameter settings that need to have their worth evaluated. This methodology has the capability to address many types of agricultural models: risk assessment models, damage prediction models, crop growth simulation models, etc.
The intelligent search method
The intelligent search method is an important part of this component because, in this particular domain of agricultural models, knowledge of the domain is often hard to codify (i.e., ’rules of thumb’ are vague and difficult to construct), and the selection of an intelligent search method can help to alleviate this difficulty. This is due to the fact that intelligent search methods do not rely on ’rules of thumb’, rather, rules are not required and these methods can actually facilitate the user in identifying ’rules of thumb’.