Soil moisture of maize has an extremely important impact on the growth and development of maize. Failure to accurately estimate soil moisture will lead to severe reductions in maize yields and thus intensify the global food crisis, so it is extremely important to accurately estimate soil moisture of maize. This study proposes a new hybrid machine learning model (SVM-SWOA) that incorporates the Whale Optimization Algorithm (WOA) into sinusoidal chaotic graphs and couples it with a support vector machine (SVM). The model is with both high convergence speed and high accuracy. After using the data from two maize agricultural districts in Iowa, USA for model creation, Taylor plots and significance tests were used to enable the model for identifying input variables. To verify the performance of the model, SVM-SWOA was comprehensively evaluated with both SVM and SVM-WOA models. Results showed that SVM-SWOA was improved 14%, 13%, 41.5%, and 14% over SVM-WOA at 60 cm depth for MAE, RMSE, MAPE, and MBE, respectively, and 20%, 29.5%, 44.5%, and 38% over SVM, respectively. It implies that the SVM-SWOA meta-heuristic algorithm can provide better guidance for smart agriculture and precision irrigation.

玉米的土壤水分对玉米的生长发育有着极其重要的影响。不能准确估算土壤水分将导致玉米产量严重下降，从而加剧全球粮食危机，因此准确估算玉米土壤水分极为重要。本研究提出了一种新的混合机器学习模型 (SVM-SWOA)，它将鲸鱼优化算法 (WOA) 整合到正弦混沌图中，并将其与支持向量机 (SVM) 耦合。该模型收敛速度快，精度高。在使用来自美国爱荷华州两个玉米农业区的数据创建模型后，使用泰勒图和显着性检验使模型能够识别输入变量。为了验证模型的性能，SVM-SWOA 使用 SVM 和 SVM-WOA 模型进行了综合评估。结果表明，在 MAE、RMSE、MAPE 和 MBE 的 60 cm 深度处，SVM-SWOA 比 SVM-WOA 分别提高了 14%、13%、41.5% 和 14%，分别提高了 20%、29.5%、44.5%、和 SVM 分别高出 38%。这意味着SVM-SWOA元启发式算法可以为智慧农业和精准灌溉提供更好的指导。