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.

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

“…This work demonstrated how ANFIS and genetic algorithms may be used to improve hydrological forecasting prediction accuracy, which aligns to optimize PET predictions via hybridization. Maraveas et al ( 2022 ) have emphasized using bio-inspired algorithms, including particle swarm optimization (PSO), to estimate plant evapotranspiration rates. When considering the optimization component of PET forecasting, this reference’s insights on applying optimization algorithms in agricultural engineering may be helpful.…”

Emerging Perspectives on Diverse Nature-Oriented Sustainability Strategies

“…Specific nature-inspired computing algorithms include the following: genetic algorithms, swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithms, firefly algorithms, cuckoo searching, virus colony searching, etc. [26,34,35]. Advanced optimization algorithms are discipline-focused, to solve various complex engineering design or problems of software testing.…”

Combination of a Nondestructive Testing Method with Artificial Neural Network for Determining Thickness of Aluminum Sheets Regardless of Alloy’s Type

“…Researchers in a wide variety of disciplines, including electronics [22], oil and petrochemicals [23,24], agricultural engineering [25,26], etc., have been interested in neural networks due to their potential as useful tools. In the mentioned research, different neural networks, different feature extraction methods, and different machine learning algorithms have been used in different fields, which can inspire further research.…”