Abstract In irrigation systems, salinity is a critical problem as it has undesirable impacts on crop health, agricultural throughput and farming management. Considering these, it is imperative to regularly monitor and develop measures to predict salinity of the soil to negate the salinization effects on agriculture. This paper constructs and evaluates the performance of the hybrid machine learning model of multilayer perceptron (MLP)-Grey Wolf Optimizer (MLP-GWO) for electrical conductivity (EC). MLP-GWO model is trained with soil sample data (i.e., parameters for organic matter, OM and soil constituents Ca + 2, Mg + 2, K+, Na+, Cl-, SO4-2, HCO3-) from Khuzestan province in Iran. Seven modelling scenarios representing different combinations of salinity parameters are investigated to establish hybrid MLP-GWO model that aims to reduce the error rate of the resulting forecasts of EC. To ascertain conclusive results, the MLP-GWO model is cross-validated with its classical counterpart without the add-in (i.e., GWO) optimizer, and the model error metrics are evaluated by coefficients of determination (R2), root mean squared error (RMSE) and relative root mean square error (RRMSE) in independent test data. For all tested predictive models, the performance of MLP-GWO hybrid model is superior to a classical model, evidenced by larger R2 (~0.552–0.711 relative to ~0.430–0.711) and a lower RMSE and RRSE (~1.293–3.537 vs. 1.616–4.421 and ~3.736–9.899 vs. 4.613–12.133). The proposed GWO as an optimizer leads to a plausible improvement in an MLP model due to the most optimal weights attained in the neuronal layer that facilitates a robust feature extraction process to predict EC. The relatively low RMSE and RRSE and the high R2 attained in the testing phase shows the effectiveness of the hybrid model for different soil properties, which has potential implications in precision agriculture where salinity needs to be modelled for crop management practices.

中文翻译：

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使用与灰狼优化器集成的多层感知器预测土壤电导率

摘要 在灌溉系统中，盐度是一个关键问题，因为它对作物健康、农业产量和农业管理产生不利影响。考虑到这些，必须定期监测和制定措施来预测土壤盐度，以消除盐渍化对农业的影响。本文构建并评估了多层感知器 (MLP)-灰狼优化器 (MLP-GWO) 的混合机器学习模型对电导率 (EC) 的性能。MLP-GWO 模型使用来自伊朗胡齐斯坦省的土壤样本数据（即有机质、有机质和土壤成分 Ca + 2、Mg + 2、K+、Na+、Cl-、SO4-2、HCO3- 的参数）进行训练。研究了代表不同盐度参数组合的七个建模场景，以建立混合 MLP-GWO 模型，旨在降低 EC 预测结果的错误率。为了确定结论性结果，MLP-GWO 模型与其经典对应模型进行了交叉验证，没有加载项（即 GWO）优化器，并且模型误差指标通过确定系数 (R2)、均方根误差 ( RMSE) 和独立测试数据中的相对均方根误差 (RRMSE)。对于所有经过测试的预测模型，MLP-GWO 混合模型的性能优于经典模型，这可以通过更大的 R2（~0.552-0.711 相对于~0.430-0.711）和更低的 RMSE 和 RRSE（~1.293-3.537 与1.616–4.421 和 ~3.736–9.899 与 4.613–12.133）。由于在神经元层中获得了最优化的权重，因此建议的 GWO 作为优化器导致了 MLP 模型的合理改进，这有助于稳健的特征提取过程来预测 EC。在测试阶段获得的相对较低的 RMSE 和 RRSE 以及较高的 R2 显示了混合模型对不同土壤特性的有效性，这对精准农业具有潜在影响，其中需要为作物管理实践对盐度进行建模。