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Ion-specific nutrient management systems would replace conventional electrical conductivity-based management systems for more efficient maintenance of the concentration of each ion in nutrient solutions for closed hydroponic systems. This study reports on the validation testing of a previously developed ion-specific sensing and control system. Specific objectives were to (1) evaluate the ability of the system to maintain the target concentrations of five macronutrients, i.e., NO3−, PO43−, K+, Ca2+, and Mg2+ ions, required for crop growth in closed hydroponic systems based on both real-time measurement of NO3−, K+, and Ca2+ and the use of a concentration ratio method to replenish Mg2+ and PO43−, and (2) investigate the applicability of a cobalt rod-based electrode for hydroponic phosphate sensing. In a lettuce cultivation test conducted with the ebb-and-flow method over 21 days, most of the macronutrient concentrations were successfully maintained at the target levels with RMSE values of 25.2 ± 9.4, 19.1 ± 8.1, and 11.5 ± 6.1 mg∙L−1 for NO3−, K+, and Ca2+, respectively. In the case of Mg2+ and PO43− ions replenished in a proportional ratio to the supply of Ca2+ and NO3− ions, respectively, the Mg2+ concentrations were maintained between 18 and 25 mg∙L−1 at an almost constant level, whereas the PO43− concentrations increased steadily over time with no clear evidence of plant uptake, implying that the use of a concentration ratio method would not be effective in controlling the concentrations of PO43− in hydroponic solutions. As a phosphate sensor, the cobalt electrodes yielded an RMSE result of 10.9 ± 7.1% from a comparison of the electrode method and standard analysis when tested in hydroponic lettuce samples taken once a day during the period of lettuce growing, thereby offering the potential for use in hydroponic phosphate sensing.

Hydroponic solutions used in greenhouses or plant factories are usually evaluated based on their electrical conductivity (EC) and pH. However, EC and pH cannot provide sufficient information about ion imbalances in hydroponic solutions, and this may result in wastage of nutrients or poor yields. This paper reports on the development of an on-site ion monitoring system based on ion-selective electrodes (ISEs) that can automatically calibrate sensors and measure the concentrations of individual ions (NO3−, K+, and Ca2+) in hydroponic solutions. This enables farmers to effectively manage nutrients in reused solutions by rapidly identifying any imbalances that appear in the nutrient ratios. The measurement performance of the developed system was evaluated using hydroponic solutions prepared for growing paprika crops in greenhouses. An application test was conducted to investigate the feasibility of using the developed on-site ion monitoring system for the automated measurement of three macronutrients (NO3−, K+, and Ca2+) in a real greenhouse. The results showed that the developed system was able to measure NO3− concentrations, showing an almost 1:1 relationship with the results of a standard instrument, i.e., ion chromatography (slope of 0.99 and R2 of 0.99). Although the developed system overestimated and underestimated the K+ and Ca2+ concentrations with slopes of 1.17 and 0.75, respectively, the high coefficients of determination of 0.99 and 0.97 made it possible to use calibration factors to compensate for differences in estimation. In fact, relatively low RMSEs of

Greenhouses provide controlled environmental conditions for crop cultivation but require careful management to ensure ideal growing conditions. In this study, we tested three deep-learning-based neural network models (Artificial neural network, ANN; Nonlinear autoregressive exogenous model, NARX; and Recurrent neural networks – Long short-term memory, RNN-LSTM) to determine the best approach to predicting environmental changes in temperature, humidity, and CO2 within a greenhouse to improve management strategies. This study determined the prediction performance for time steps from 5 to 30 min and showed that the accuracy of the time-based algorithm gradually decreased as prediction time increased. The best model for all datasets was RNN-LSTM, even after 30 min, with an R2 of 0.96 for temperature, 0.80 for humidity, and 0.81 for CO2 concentration. The results of this study show that it is possible to apply deep-learning-based prediction models for more precisely managing greenhouse.