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Most inexpensive portable models usually require that you update the GPS device using a computer.
However, these models usually require extensive computer resources and lengthy computation.
Time-series statistical models usually require the researcher to make assumptions with respect to the distribution of measured responses which may not hold.
However, these models usually require heavily annotated data (such as with ATC codes, protein targets or side effect data)—a complete understanding of the origins and repercussions of synergy has not yet in general been achieved, and thus significant further work is needed, both experimental and in silico.
Those models usually require a large set of input parameters and separate simulations for each pesticide in evaluation.
However, these models usually require a large set of model input parameters for the full descriptions of landscape characteristics, climate conditions, and management practices [8].
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However, building of ANN surrogate models usually requires large training datasets.
The definition of such models usually requires the execution of various experimental tests for the determination of the associated material parameters, constituting a significant cost/effort in terms of characterization.
The relevance of the solution here proposed is threefold: (a) well-known control engineering techniques are applied, (b) modelling and identification of elaborated friction force models usually required for more sophisticated controllers are not needed for the solution here proposed, and (c) the control system stability margin specifications considered are adequate.
In addition, the detailed rules implemented in a micro-simulation model usually require an accurate calibration process, aimed at replicating the actual driving in traffic.
Building a reliable effort estimation model usually requires historical data.
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