## 3 Methods
### Hybrid Feature Selection  
Feature selection is a process of identifying and preserving the important features from a dataset. It helps in enhancing model efficiency and predictive accuracy [40]. This procedure enhances interpretability, lessens computational complexity, and mitigates overfitting. Models can more successfully generalize to new data by concentrating on important features and removing redundancy, which results in more reliable decision making [41].  
Nature-inspired optimization algorithms may traverse large search spaces while striking a balance between local exploitation and global exploration. They have shown considerable efficacy in solving feature selection problems. To find the best feature subsets, these algorithms use heuristic-based search methods that draw inspiration from biological, physical, or social events. Consider Genetic Algorithms (GAs) [42], which simulate evolutionary selection. Particle Swarm Optimization (PSO) [43] is focused on swarm intelligence and has been widely adopted for feature selection tasks. Gray Wolf Optimization (GWO) [44] replicates the cooperative hunting strategy of wolves and has demonstrated strong performance in refining search efficiency. Artificial Bee Colony (ABC) [45] focuses on the forging patterns of honeybees. The Bat Algorithm (BA) [46] uses the echolocation behavior of bats for adaptive search. These methods are very beneficial for feature selection in cases where data have high dimensionality.  
To improve search effectiveness, we can combine different nature-inspired algorithms to create a hybrid model. These hybrid approaches improve search effectiveness, solution quality, and convergence speed by utilizing the merits of the algorithms used for hybridization [29]. Premature convergence can be avoided and classification performance can be improved by balancing diversification and intensification of the used approaches. The benefits of hybridization have been highlighted in some recent studies conducted in diverse application domains. Consider the example of spam classification. A hybrid bat algorithm with momentum acceleration and Gray Wolf Optimization (GWO) was used to improve spam classification performance [47]. Similarly, a modified bat algorithm was designed to address real-world optimization issues, leading to improved robustness [48]. Another hybrid framework was introduced for telecommunication applications. It introduces the merger of Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) for optimization of resource allocation in cognitive radio networks. The setup ensured superior management of communication resources [49].  
The impact of the hybrids can be observed in other diverse domains like navigation, improving energy efficiency and increasing productivity. The RFO-GWO hybrid was used for path planning in autonomous robots. The sole purpose was to improve navigation efficiency [50]. For high energy efficiency, a GWO-enhanced PSO hybrid was implemented for photovoltaic system optimization [51]. Another GA-GWO hybrid was designed to optimize scheduling in coal mines. The purpose was to increase the operational productivity of coal mines [52]. These accomplishments highlight the use of hybrid approaches in feature selection in cases where dealing with high-dimensional datasets might be difficult.  
These developments motivated us to present a new hybrid approach that combines the Bat Algorithm (BA), Artificial Bee Colony (ABC), and Gray Wolf Optimization (GWO) for feature selection in the smart agricultural application domain. The approach suggested in this work combines the ABC's adaptive foraging mechanism, the BA's dynamic search capability, and the leadership-based exploration and exploitation capabilities of GWO.