Published Paper Details:

The agricultural sector faces numerous challenges, including optimizing crop yield, managing resources efficiently, and detecting diseases early. Traditional methods often fall short in addressing these complex issues due to their static nature and limited adaptability. Evolutionary algorithms (EAs) offer a promising solution due to their population-based search mechanisms, self-adaptation capabilities, and robustness in handling complex optimization problems. This research focuses on the design and development of a hybrid evolutionary algorithm tailored specifically for agricultural applications. The primary objectives include improving crop yield predictions, enhancing disease detection mechanisms, and optimizing resource management. The study integrates various evolutionary computation techniques, including genetic algorithms, ant colony optimization, and particle swarm optimization, to create a robust hybrid model. The methodology involves generating an initial population of potential solutions, evaluating their fitness, selecting the best individuals, and using crossover and mutation to produce new generations. The developed algorithm is tested using benchmark functions and applied to real-world agricultural problems. The results demonstrate significant improvements in prediction accuracy and resource optimization. This paper also reviews existing literature on the application of EAs in agriculture, discusses the software tools used for algorithm development, and highlights the potential applications and related techniques in the agricultural domain. Future research will focus on refining the algorithm and exploring its applicability in broader agricultural contexts to enhance sustainability and productivity.