Forecasting Nutrient Concentration Dynamics in Hydroponic Lettuce Cultivation Using a Hybrid Fuzzy Time Series and Long Short-Term Memory Approach for Internet of Things–Based Systems - Agus, Muh.; Akhsa, Alvian Tri Putra Darti; Marka M, Ilham Ali; Hasyim, Muhammad Fadel (Jurnal Teknik Informatika (Jutif), 2026)
Proper nutrient management is crucial for the optimal growth and yield of hydroponically cultivated lettuce. This study proposes a hybrid time-series forecasting model that integrates Fuzzy Time Series (FTS) and Long Short-Term Memory (LSTM) networks to predict nutrient concentration dynamics in hydroponic lettuce cultivation within an Internet of Things–based environment. Experimental data from four lettuce plant samples with different nutrient treatments (control, 400 PPM, 600 PPM, and 1000 PPM) were analyzed for 26 days, with the prediction extended to 40 days, representing the complete growth cycle using a TDS Sensor as a PPM value reader and a Solenoid Valve to accurately control the PPM value via ESP32 with Internet of Things (IoT) communication. This hybrid model incorporates growth-stage awareness through an adaptive weighting mechanism, resulting in a superior forecasting accuracy. The results showed that the ensemble approach achieved a Mean Absolute Percentage Error (MAPE) of 2.43% for the control, 3.12% for the 400 PPM, 3.45% for the 600 PPM, and 3.78% for the 1000 PPM sample. The 600 PPM treatment showed optimal development with 82% compliance with the recommended PPM range (560-840 ppm). The proposed model provides actionable insights for precision nutrient management, potentially reducing fertilizer use by 23-35% while maintaining crop quality. This study contributes to hybrid intelligent systems and time-series forecasting by demonstrating an effective integration of rule-based fuzzy modeling and deep recurrent neural networks in Internet of Things–driven environments for hydroponic systems, supporting efficient resource utilization and increased crop productivity.