Aquaponics is currently undergoing a transformation into an intensive food production system. The initially applied systems focused on small-scale, fish-centric coupled (CAP, the aquaculture, and the hydroponic units are arranged in a single loop, and the water flows continuously from the fish tanks to the plant unit and back) aquaponics. More recently, the primary area of research interest has shifted toward larger-scale, plant-centric decoupled (aquaculture and hydroponics units are arranged in a multi-loop setup as separate functional units that can be controlled independently) systems, aiming to achieve greater economic benefits and employ more environmentally friendly practices. The objective of this study was to address gaps in the expansion of decoupled larger-scale aquaponics and to provide a comprehensive understanding of the water and nutrient flow in the system. For this purpose, experiments were performed in a greenhouse on CAP and DCAP systems, while this study also included measurements in a pure hydroponic system (HP). This study presents an assessment of the water and nutrient flow in four different crops: basil; cucumber; parsley; and tomato, all co-cultivated with a tilapia aquaculture system. Significant nutrient deficiencies and imbalances were identified in the CAP solution, leading to pronounced impacts on nutrient assimilation, particularly for fruiting vegetables. However, the average nutrient use efficiency (NUE) for nitrogen, phosphorous, potassium, and calcium was found to be 42% higher in the CAP treatment compared to HP and DCAP treatments. The nutrient solution in the DCAP treatment did not exhibit differences in water quality parameters and nutrient efficiency when compared to HP, resulting in similar effects on nutrient assimilation. Nonetheless, it was observed that DCAP plants exhibited superior NUE compared to HP plants.

The circular economy in agriculture aims to reduce waste while also making best use of residues by using economically viable processes and procedures to increase their value. In this study a two-level cascade cultivation system was set up under greenhouse conditions. The research was focused on the identification of crop species as secondary crops and the development/iterative optimization of cultivation practices. For this purpose, different crop-combinations with a primary and different secondary crops were investigated using different system-layouts. Measurements were carried out during two cultivation periods. During the 1st Period a combination of cucumber (Cucumis sativus) as primary crop, with rosemary (Rosmarinus officinalis), basil (Ocimum basilicum), and peppermint (Mentha piperita) as secondary crops, was evaluated. In the 2nd Period the drainage of tomato (Solanum lycopersicum) plants was re-used to irrigate spearmint (Mentha spicata), dill (Anethum graveolens), celery (Apium graveolens) and parsley (Petroselinum crispum) plants. In both periods, different fertigation management strategies based on the drainage solution of the primary crop were employed. The use of the cascade hydroponic system improved both crop water and nutrient use efficiency. Notably, the NO₃ disposal was about 40% less as compared to a monoculture. Average fresh water consumption of secondary crop plants irrigated with diluted drainage solution was reduced by 30% in comparison to plants irrigated with fresh water.

Greenhouse climate control systems are usually based on greenhouse microclimate settings to exert any control. However, to save energy, water and nutrients, additional parameters related to crop performance and physiology will have to be considered. In addition, detecting crop stress before it is clearly visible by naked eye is an advantage that could aid in microclimate control. In this study, a Machine Learning (ML) model which takes into account microclimate and crop physiological data to detect different types of crop stress was developed and tested. For this purpose, a multi-sensor platform was used to record tomato plant physiological characteristics under different fertigation and air temperature conditions. The innovation of the current model lies in the integration of photosynthesis rate (Ps) values estimated by means of remote sensing using a photochemical reflectance index (PRI). Through this process, the time-series Ps data were combined with crop leaf temperature and microclimate data by means of the ML model. Two different algorithms were evaluated: Gradient Boosting (GB) and MultiLayer perceptron (MLP). Two runs with different structures took place for each algorithm. In RUN 1, there were more feature inputs than the outputs to build a model with high predictive accuracy. However, in order to simplify the process and develop a user-friendly approach, a second, different run was carried out. Thus, in RUN 2, the inputs were fewer than the outputs, and that is why the performance of the model in this case was lower than in the case of RUN 1. Particularly, MLP showed 91% and 83% accuracy in the training sample, and 89% and 82% in testing sample, for RUNs 1 and 2, respectively. GB showed 100% accuracy in the training sample for both runs, and 91% and 83% in testing sample in RUN 1 and RUN 2, respectively. To improve the accuracy of RUN 2, a larger database is required. Both models, however, could easily be incorporated into existing greenhouse climate monitoring and control systems, replacing human experience in detecting greenhouse crop stress conditions.

Water and nitrogen deficit stress are some of the most important growth limiting factors in crop production. Several methods have been used to quantify the impact of water and nitrogen deficit stress on plant physiology. However, by performing machine learning with hyperspectral sensor data, crop physiology management systems are integrated into real artificial intelligence systems, providing richer recommendations and insights into implementing appropriate irrigation and environment control management strategies. In this study, the Classification Tree model was used to group complex hyperspectral datasets in order to provide remote visual results about plant water and nitrogen deficit stress. Soilless tomato crops are grown under varying water and nitrogen regimes. The model that we developed was trained using 75% of the total sample dataset, while the rest (25%) of the data were used to validate the model. The results showed that the combination of MSAVI, mrNDVI, and PRI had the potential to determine water and nitrogen deficit stress with 89.6% and 91.4% classification accuracy values for the training and testing samples, respectively. The results of the current study are promising for developing control strategies for sustainable greenhouse production.

Minimum environmental impact and improved resource efficiency is attainable for soilless cascade systems where the nutrient solution drained from a primary (donor) crop is reused to fertigate a secondary (receiver) crop. However, it is not clear whether the nutrient solution drained from the primary crop can completely satisfy the needs of a secondary crop and if the productivity of the secondary crop is compromised. To test this hypothesis, a prototype soilless cascade system was developed and evaluated. To assess the performance of the system in terms of yield, water and nutrient productivity, a tomato crop was used as the primary crop, while lettuce, spinach and parsley were tested as secondary crops under different drainage management strategies. Measurements of plant growth, crop fresh and dry matter production, leaf chlorophyll and nutrient content, and photosynthesis rate were performed in the secondary crops. In addition, the water productivity and nutrient use efficiency for the fertigation of the primary and secondary crops were recorded. The results showed that the yield of the cascade spinach crop increased by up to 14% compared to the control treatment (monoculture of secondary crop fertigated by standard nutrient solution). The yield of the lettuce and parsley crop was not affected by the reuse of the tomato crop drainage solution. The water productivities of the lettuce, spinach and parsley plants fertigated with pure drainage solution were 50%, 30% and 14% higher than in the control treatment, respectively. The nitrogen and phosphorus use efficiency was improved by more than 50% compared to the control treatments.

Cascade hydroponics, that is, the application of the circular economy concept in greenhouse hydroponic crops, may be considered as an alternative means to increase water and nutrient use efficiency in greenhouses. In such systems, the drained nutrient solution from a crop may be used as input in a second crop. However, the second (secondary) crop in the loop must be a crop that is less sensitive to salinity than the first (primary) crop. In the present study, the salinity tolerance of basil plants grown in rockwool and nutrient film technique (NFT) systems was investigated in order to study the potential of using a basil crop as a secondary crop in a cascade hydroponic system. In total, 4 electrical conductivity (EC) levels of the irrigation nutrient solution were tested (2, 4, 6, and 8 dS m⁻¹), and salinity was imposed by NaCl or by macronutrients. Plant growth varied across the different substrates, with those grown in the NFT system being less affected as opposed to the rockwool-grown basil plants, which showed a significant growth decrease with EC values higher than 4 dS m⁻¹. This relatively low growth pattern was associated with a decrease in water use efficiency (WUE) in the rockwool system. On the contrary, in the NFT system, the continuous flow of the nutrient solution in the root zone of the plants contributed to the alleviation of negative salinity effects, yielding up to 30 kg FM m⁻² WUE even for the plants irrigated with the highest salinity treatment (8 dS m⁻¹). The majority of macro- and micronutrients in the leaf tissue of basil were positively affected by the higher levels of conductivity in the nutrient solution. Therefore, basil cultivation could be efficiently incorporated as a secondary crop in a cascade NFT cropping system. This would contribute to drainage management in hydroponics, as the crop could be irrigated through the moderately saline drainage from a primary crop due to either NaCl or high nutrient accumulation in the leachates.