Abstract:
Automation is the effective model to reduce
the human workload and to increase the accuracy of
working process. It is mainly involved by utilizing the
architecture of the Artificial Intelligence (AI). AI is
primarily developed by using the optimization to
reduce the time of the workload. Optimization is the
process of identifying the best solution from the large
combination of solution sets. The best solution is
selected by validating the objective value of the
solution set using the objective function. Without
explicit programming, creating the ability of learning
to the machine is known as machine learning. The
machine learning required to solve the various
problems raises in the power electronics application.
This work mainly involved to perform the pattern
matching process using the CCD sensor. And also
there is need to identify the optimal position of the
CCD sensor in the agriculture region. The knowledge
processing exhibits the higher significance in machine
learning to the pattern matching and optimal
placement. Genetic optimization is the heuristic
approach used in the search process, which executes
the natural selection in the evolutionary process. The
Gravitational Search Algorithm (GSA) is the
optimization model based on the law of gravity and
interaction between the mass. In this paper, unique
solution is designed with the genetic algorithm by
merging with the GSA to identify the optimal placement
position of CCD sensor to identify the Crop disease.
The performance evaluation of GA and proposed GAGSA
is conducted using the Matlab, and the evaluation
exhibits that the proposed solution achieves better
performance in terms of number of iterations and
computational complexity.
