Hybrid deep learning optimization for smart agriculture: Dipper throated optimization and polar rose search applied to water quality prediction

Abstract

Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. The optimized model achieved a classification accuracy of 99.46%, significantly outperforming classical machine learning and unoptimized deep learning models. These results demonstrate the framework’s capability to provide accurate, interpretable, and computationally efficient predictions, which can support smart irrigation decision-making in water-limited agricultural environments, thereby contributing to sustainable crop production and resource conservation.