Abstract Soil moisture is a key parameter for the modeling practices in agriculture, hydrology, and climate, but at the same time, it has to bear the consequences of the drawbacks in conventional methods regarding real‐time accuracy and agility. To make the scenario more friendly, we introduce SoilNet‐TF, an innovative deep‐learning (DL) ecosystem composed of tabular neural network (TabNet) and DenseNet‐121 working together through the attention fusion mechanism that gives the possibility of forecasting and monitoring soil moisture with very high precision and reliability. The dynamic attention of TabNet is applied to the most important soil properties along with sparse attention, whereas DenseNet‐121 allows the reuse of deep features, which leads to the uncovering of the nonobvious interactions among soil parameters. Self‐adaptive pufferfish optimization adjusts feature selection for adaptive real‐time optimization. The model, besides including a bidirectional long short‐term memory for the learning of sequential patterns, also consists of a variational autoencoder for anomaly detection, which aids in accurate soil moisture prediction and provides early warning systems for smart irrigation. The experimental results show a mean absolute error of 0.0129, mean squared error of 0.0013, and R 2 of 0.9728, which means that prediction accuracy has been significantly improved compared to traditional models. The combination of optimization methods and DL in SoilNet‐TF is a very accurate, scalable solution for sustainable agriculture and, subsequently, for water resources management.
R et al. (Wed,) studied this question.