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In modern agriculture, effective water management is a major concern, particularly in the areas where unpredictable climates and water scarcity. Conventional irrigation methods often depend on manual observation, which can result in excessive or insufficient irrigation, which has a detrimental effect on crop productivity, soil health, and resource utilization. Real-time, accurate, and scalable methods for tracking Soil Hydration (SH) levels in a variety of agricultural fields are lacking. This paper introduces an IoT-based Soil Hydration Estimation Model (ISHEM) for smart agriculture, utilizing Multiple Linear Regression (MLR) to estimate soil hydration (SH) content in agricultural fields. The performance of proposed model is compared with commonly used Machine Learning (ML) algorithms such as Adaptive Boost (AdaBoost), Random Forest (RF), Extreme Gradient Boost (XGBoost) and Support Vector Machine (SVM) using various performance measure metrics such as the Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), Ratio between Prediction to Deviation (RPD) and Ratio of Performance to the Inter-Quartile distance (RPIQ). The ISHEM model demonstrate superior performance over existing ML algorithms by achieving lower values in key error metrics (MAE, MSE, and RMSE), as well as higher values in RPD and RPIQ. This developed model enables accurate estimation of Soil Hydration (SH) content, which can play a crucial role in enhancing the efficiency and effectiveness of smart agriculture practices.
Maity et al. (Wed,) studied this question.