Water scarcity and inefficient irrigation practices in agriculture have increased the need for smart water management systems capable of making irrigation decisions. In this paper, we propose an IoT embedded smart irrigation system for intelligent water management in agriculture, which includes sensor-based monitoring and optimization of predictive models. Firstly, the agricultural data are analyzed using the Adaptive Fast Desensitized Kalman Filter (AFDKF) to eliminate noise, minimize measurement uncertainty, and improve data consistency. Then, the pre-processed soil and environmental parameters are analyzed using the Kolmogorov-Arnold Recurrent Network (KARN), which is able to analyze the relationships between different agricultural parameters and make predictions regarding the crop needs for irrigation management. Weight parameters of the KARN model are optimized by means of the Arctic Tern Optimizer (ATO) to enhance prediction accuracy and convergence of the model. Moreover, the developed smart irrigation framework involves the use of an ESP32 based sensing unit equipped with sensors measuring soil moisture, temperature, humidity, and water level in real time. Evaluation of the AFDKF-KARN-ATO framework shows that it is able to demonstrate outstanding performance in comparison with existing RFL, ANN, and DNN algorithms with accuracy equal to 99.05%, precision—99.12%, recall—99.17%, specificity—98.4%, and AUC-ROC, 0.93. Besides, the developed smart irrigation framework ensures computation time equal to 97 seconds and throughput equal to 98.6.
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