A multi-objective algorithm for crop pattern optimization in agriculture.

The crop growth and crop yield in agriculture depend upon many factors such as weather conditions, soil type, and application of fertilizers. The crop net return can be increased by deciding suitable crops for a particular land depending upon its weather conditions. The application of an appropriate amount of fertilizers also promotes crop growth and yield. On the other hand, the use of fertilizers needs to be minimized to reduce the capital cost as well as to prevent its harmful effect on soil and the environment. This paper models the problem of increasing net crop benefit and reducing the application of fertilizers as multi-objective optimization functions. A hybrid CSA-PSO optimization algorithm is proposed for solving multi-objective problems by combining crow search algorithm (CSA) and particle swarm optimization (PSO). The performance of the proposed algorithm is evaluated against CEC 2009 benchmark functions. This algorithm is implemented for crop pattern optimization in India's Telangana state, which depicts the proposed algorithm's feasibility and effectiveness. • A multi-objective crop model is designed to maximize crop net return. • A new hybrid multi-objective optimization algorithm CSA-PSO is introduced. • The flight length is defined randomly to increase the exploration of the CSA-PSO. • The CSA-PSO is implemented for crop planning in Telangana, India. [ABSTRACT FROM AUTHOR]