A Survey on AI-Driven Bio-Inspired Algorithms in Agriculture

Abstract

Bio-inspired algorithms are now considered to be highly effective computational methods for resolving difficult agricultural optimization issues. Inspired by natural processes like evolution, swarm intelligence, and neural systems, these algorithms have been widely used in agriculture. This work presents the comprehensive analysis of bio-inspired algorithms, such as, Ant Colony Optimization (ACO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Flower Pollination algorithm (FPA) focusing on their uses and performance in agricultural problem solving. To increasing the yields, the precision of Bio inspired algorithms (BIAs) reduced the possibility of failures in the application of fertilizer, pesticides, irrigation and crop monitoring. This study presents review of different Bio-Inspired Algorithms employed in agriculture and also compares various Bio-Inspired algorithms to make it more computationally useful for farming in the future.