Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Abstract The article reviewed the four major Bioinspired intelligent algorithms for agricultural applications, namely ecological, swarm-intelligence-based, ecology-based, and multi-objective algorithms. The key emphasis was placed on the variants of the swarm intelligence algorithms, namely the artificial bee colony (ABC), genetic algorithm, flower pollination algorithm (FPA), particle swarm, the ant colony, firefly algorithm, artificial fish swarm, and Krill herd algorithm because they had been widely employed in the agricultural sector. There was a broad consensus among scholars that certain BIAs' variants were more effective than others. For example, the Ant Colony Optimization Algorithm and genetic algorithm were best suited for farm machinery path optimization and pest detection, among other applications. On the contrary, the particle swarm algorithm was useful in determining the plant evapotranspiration rates, which predicted the water requirements and optimization of the irrigation process. Despite the promising applications, the adoption of hyper-heuristic algorithms in agriculture remained low. No universal algorithm could perform multiple functions in farms; different algorithms were designed to perform specific functions. Secondary concerns relate to data integrity and cyber security, considering the history of cyber-attacks on smart farms. Despite the concerns, the benefits associated with the BIAs outweighed the risks. On average, farmers can save 647–1866 L on fuel which is equivalent to US$734-851, with the use of GPS-guided systems. The accuracy of the BIAs mitigated the risk of errors in applying pesticides, fertilizers, irrigation, and crop monitoring for better yields.