HydroFormer: Deep Learning-Based IoT Control and Prediction in Smart Hydroponic Systems

Hydroponic cultivation demands intelligent and adaptive regulation of nutrient chemistry and environmental parameters; however, existing IoT-based monitoring frameworks often struggle to accurately interpret irregular, noisy, and highly interdependent sensor streams, limiting autonomous decision-making and stable crop development. To address these challenges, this work proposes HydroFormer, a unified deep learning-driven hydroponic intelligence framework that integrates continuous-time dynamic modeling, graph-aware feature learning, transformer-based predictive analytics, and cooperative optimization for autonomous hydroponic management. The proposed framework introduces a Graph Neural Ordinary Differential Equation architecture capable of modeling nonlinear temporal interactions among pH, TDS, temperature, humidity, and water-level variables while preserving continuous system dynamics and physicochemical dependencies. To enhance representation quality, Echidna Optimization selectively refines latent features by eliminating redundant and weak system descriptors, thereby improving predictive robustness and computational efficiency. Furthermore, HydroFormer employs a cross-patch attention transformer mechanism to simultaneously perform hydroponic state classification, multivariate growth prediction, anomaly recognition, and intelligent actuator inference through joint short-term and long-range dependency learning. In addition, Mandarin Duck Cooperative Optimization adaptively tunes the network and training hyperparameters to achieve globally optimized learning behavior under complex IoT sensing conditions. Experimental analysis demonstrates that the proposed framework achieves superior hydroponic intelligence performance with 99.27% accuracy, 99.41% precision, 99.18% recall, and 99.29% F1-score, alongside highly reliable continuous predictions with a mean absolute error of 0.021, root mean square error of 0.042, and R2 value of 0.996.