ANOVA test for optimization results.

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.

现代可持续农业亟需精准的水资源管理技术，对于土豆这类需要优质灌溉以保障最优生长的作物而言尤为如此。本研究提出一种新颖的混合元启发式框架，将以鸟类觅食行为为原型的生物启发式算法——勺鹬优化算法（Dipper Throated Optimization, DTO）与极地玫瑰搜索算法（Polar Rose Search, PRS）相结合，以优化用于水质预测评估的深度学习模型。所提方法将二值特征选择与元启发式优化整合为统一的优化流程，有效平衡探索与开发能力，可处理复杂高维数据集。我们将该混合策略应用于径向基函数网络（Radial Basis Function Network, RBFN），并通过大量实验验证了其性能提升，包括针对特征选择与优化阶段的方差分析（Analysis of Variance, ANOVA）及威尔科克森检验（Wilcoxon tests）。优化后的模型分类准确率达99.46%，显著优于经典机器学习模型与未优化的深度学习模型。上述结果表明，该框架能够提供准确、可解释且计算高效的预测结果，可为水资源受限农业环境下的智能灌溉决策提供支撑，进而助力可持续作物生产与资源保护。