Soil moisture simulation of rice using optimized Support Vector Machine for sustainable agricultural applications

The growth and development of rice crops primarily depend on appropriate soil water balance for which soil moisture is the key determinant. Soil moisture is a crucial parameter in the hydrological cycle, which has a vital role in optimal water management for sustainable agricultural growth as it has a significant impact on hydrological, ecological, and climatic processes. Thus, accurate estimation of soil moisture is important otherwise it will drastically reduce crop yields, intensifying the global food crisis. A novel soil moisture prediction model (SVM-COLGWO) that incorporates the Grey Wolf Optimizer (GWO) into Chebyshev chaotic maps and opposition-based learning to optimize the Support Vector Machine (SVM) model is proposed. The suggested model simultaneously increases the simulated model's accuracy while speeding up global convergence. To evaluate the proposed model, the prediction performance is compared with other hybrid and standalone models where the feasibility of the proposed model is validated through superior simulation results (MAE = 0.167, MSE = 0.179, RMSE = 0.423, MAPE = 0.162, and R2 = 0.949) including Shannon's Entropy. Thus, based on accurate soil moisture simulation through the proposed model, irrigation can be effectively scheduled for sustainable rice growth.