Optimal Utilization of Renewable Energy in Aquaponic Systems

Aquaponic systems require energy in different forms, heat, solar radiation, electricity etc. Typical actuator components of an aquaponic system include pumps, aerators, heaters, coolers, feeders, propagators, lights, etc., which need electrical energy to operate. Hybrid Energy Systems (HES) can help in improving the economic and environmental sustainability of aquaponic systems with respect to energy aspects. Energy management is one of the key issues in operating the HES, which needs to be optimized with respect to the current and future change in generation, demand, and market price, etc. In this paper, a Decision Support System (DSS) for optimal energy management of an aquaponic system that integrates different energy sources and storage mechanisms according to priorities will be presented. The integrated model consists of photovoltaic and solar thermal modules, wind turbine, hydropower, biomass plant, CHP, gas boiler, energy and heat storage systems and access to the power grid and district heating. The results show that the proposed method can significantly increase the utilization of HES and reduce the exchange with the power grid and district heating and consequently reduce running costs.

Effect of stocking density on growth performance of monosex Nile Tilapia (Oreochromis niloticus) in the aquaponic system integrated with lettuce (Lactuca sativa)

Aquaponics is a fish-plant recirculating system where nutrients received from the fish culture are absorbed by the plants for growth.The technology is relatively new for fish culture in Kenya,and the principles and operations remain largely untested for many fish species.This study determined how stocking density affects the growth performance and water quality in a Nile tilapia-lettuce(Lactuca sativa)aquaponics system.The experimental design included five replicates for each of the aquaponic systems stocked at densities of 150,300,and 450 fish/m^(3) for a rearing period of 56 days.Each treatment had a planting density of 16 lettuce/m2.The water quality parameters ranges during the rearing period were 3.83-5.35 mg/L for dissolved oxygen,7.44 to 7.6 for pH,0.014 mg/L to 0.032 mg/L for total ammonium nitrate(TAN),1.11-1.34 mg/L for nitrate,and 0.01-0.08 mg/L for nitrite,and all decreased with increasing stocking density.The final weight of fingerlings was 25.2±4.2 g,32.0±3.8 g and 42.6±3.1 g for 450,300,and 150 fish/m^(3) respectively.Specific growth rate(SGR)was reduced with increasing stocking density whereas food conversion ratio(FCR)increased with stocking density.Aquaponic systems with the lowest stocking densities performed better than 300 and 450 fish/m^(3) respectively.

Intelligent Information Management in Aquaponics to Increase Mutual Benefits

Aquaponics are feedback and two player systems, in which fish and crops mutually benefit from one another and, therefore require close monitoring, management and control. Vast amount of data and information flow from the aquaponics plant itself with its huge amount of smart sensors for water quality, fish and plant growth, system state etc. and from the stakeholder, e.g., farmers, retailers and end consumers. The intelligent management of aquaponics is only possible if this data and information are managed and used in an intelligent way. Therefore, the main focus of this paper is to introduce an intelligent information management (IIM) for aquaponics. It will be shown how the information can be used to create services such as predictive analytics, system optimization and anomaly detection to improve the aquaponics system. The results show that the system enabled full traceability and transparency in the aquaponics processes (customers can follow what is going on at the farm), reduced water and energy use and increased revenue through early fault detection. In this, paper the information management approach will be introduced and the key benefits of the digitized aquaponics system will be given.

Crop diagnostic system:A robust disease detection and management system for leafy green crops grown in an aquaponics facility

Crops grown on aquaponics farms are susceptible to various diseases or biotic stresses during their growth cycle,just like traditional agriculture.The early detection of diseases is crucial to witnessing the efficiency and progress of the aquaponics system.Aquaponics combines recirculating aquaculture and soilless hydroponics methods and promises to ensure food security,reduce water scarcity,and eliminate carbon footprint.For the large-scale imple-mentation of this farming technique,a unified system is needed that can detect crop diseases and support re-searchers and farmers in identifying potential causes and treatments at early stages.This study proposes an automatic crop diagnostic system for detecting biotic stresses and managing diseases in four leafy green crops,lettuce,basil,spinach,and parsley,grown in an aquaponics facility.First,a dataset comprising 2640 images is con-structed.Then,a disease detection system is developed that works in three phases.The first phase is a crop clas-sification system that identifies the type of crop.The second phase is a disease identification system that determines the crop's health status.The final phase is a disease detection system that localizes and detects the diseased and healthy spots in leaves and categorizes the disease.The proposed approach has shown promising results with accuracy in each of the three phases,reaching 95.83%,94.13%,and 82.13%,respectively.The final dis-ease detection system is then integrated with an ontology model through a cloud-based application.This ontol-ogy model contains domain knowledge related to crop pathology,particularly causes and treatments of different diseases of the studied leafy green crops,which can be automatically extracted upon disease detection allowing agricultural practitioners to take precautionary measures.The proposed application finds its significance as a de-cision support system that can automate aquaponics facility health monitoring and assist agricultural practi-tioners in decision-making processes regarding crop and disease management.

Estimation of morphological traits of foliage and effective plant spacing in NFT-based aquaponics system

Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless features.These techniques are also being integrated into modern farming systems,such as aquaponics,to address the challenges hindering its commercialization and large-scale implementation.Aquaponics is a farming technology that combines a recirculating aquaculture system and soilless hydroponics agriculture,that promises to address food security issues.To complement the current research efforts,a methodology is proposed to automatically measure the morphological traits of crops such as width,length and area and estimate the effective plant spacing between grow channels.Plant spacing is one of the key design parameters that are dependent on crop type and its morphological traits and hence needs to be monitored to ensure high crop yield and quality which can be impacted due to foliage occlusion or overlapping as the crop grows.The proposed approach uses Mask-RCNN to estimate the size of the crops and a mathematical model to determine plant spacing for a self-adaptive aquaponics farm.For common little gem romaine lettuce,the growth is estimated within 2 cm of error for both length and width.The final model is deployed on a cloud-based application and integrated with an ontology model containing domain knowledge of the aquaponics system.The relevant knowledge about crop characteristics and optimal plant spacing is extracted from ontology and compared with results obtained from the final model to suggest further actions.The proposed application finds its signifi-cance as a decision support system that can pave the way for intelligent system monitoring and control.

Screen-printed graphite electrode on polyvinyl chloride and parchment strips integrated with genetic programming for in situ nitrate sensing of aquaponic pond water

Nitrate is the primary water-soluble macronutrient essential for plant growth that is converted from excess fish feeds,fish effluents,and degrading biomaterials on the aquaponic pond floor,and when aquacultural malpractices occur,large amounts of it retain in the water system causing increase rate in eutrophication and toxifies fish and aquaculture plants.Recent nitrate sensor prototypes still require performing the additional steps of water sample deionization and dilution and were constructed with expensive materials.In response to the challenge of sensor enhancement and aquaponic water quality monitoring,this study developed sensitive,repeatable,and reproducible screen-printed graphite electrodes on polyvinyl chloride and parchment paper substrates with silver as electrode material and 60:40 graphite powder:nail polish formulated conductive ink for electrical traces,integrated with 9-gene genetic expression model as a function of peak anodic current and electrochemical test time for nitrate concentration prediction that is embedded into low-power Arduino ESP32 for in situ nitrate sensing in aquaponic pond water.Five SPE electrical traces were designed on the two types of substrates.Scanning electron microscopy with energy dispersive X-ray confirmed the electrode surface morphology.Electrochemical cyclic voltammetry using 10 to 100 mg/L KNO3 and water from three-depth regions of the actual pond established the electrochemical test time(10.5 s)and electrode potential(0.135 V)protocol necessary to produce peak current that corresponds to the strength of nitrate ions during redox.The findings from in situ testing revealed that the proposed sensors have strong linear predictions(R2=0.968 MSE=1.659 for nSPEv and R2=0.966 MSE=4.697 for nSPEp)in the range of 10 to 100 mg/L and best detection limit of 3.15μg/L,which are comparable to other sensors of more complex construction.The developed three-electrode electrochemical nitrate sensor confirms that it is reliable for both biosensing in controlled solutions and in situ aquaponic pond water systems.

Controlling and regulation of integrated aquaponic production systems – An approach for a management execution system (MES)

Manufacturing execution systems(MESs)play a significant role in the manufacturing paradigm.MES is there to link between the Enterprise Resource Planning(ERP)systems and the plant equipment control or Supervisory Control and Data Acquisition(SCADA)applications.In this paper the MES of the INAPRO aquaponics system which was developed to support and advise the aquaponics managers in operating the complex aquaponic farms,will be presented.One important feature of the INAPRO aquaponics system is to minimize fresh water<3%,energy and nutrient supplies.This can only be achieved by appropriate design of the fish and crop mixture,considering the fish to crop ratio,when to sow the crops etc.and to monitor the system to see whether it performing as designed or not.Therefore,the MES has a view to show the designed system with all the material flow(water,energy and nutrients)balances and also how the system will be performing for a given predictive horizon.Knowing the future developments of the system,the operator can taking corrective measures to make sure that the system is behaving as required.An example of water balance of a system with 40 m3 fish tanks coupled with a hydroponic NFT system with 1,000 m2 which can produce five tons of Tilapia and 75 tons of tomato yearly is given.

An ontology model to represent aquaponics 4.0 system’s knowledge

Aquaponics,one of the vertical farming methods,is a combination of aquaculture and hydroponics.To enhance the production capabilities of the aquaponics system and maxi-mize crop yield on a commercial level,integration of Industry 4.0 technologies is needed.Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics,internet of things,robotics,cloud computing,and artificial intelligence.The realization of aquaponics 4.0,however,requires an efficient flow and inte-gration of data due to the presence of complex biological processes.A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources.An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing,extracting,and sharing the domains’knowledge.In the field of agriculture,several ontologies are developed for the soil-based farming methods,but so far,no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model.Therefore,this study proposes a unified ontology model,AquaONT,to rep-resent and store the essential knowledge of an aquaponics 4.0 system.This ontology pro-vides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem.AquaONT is built from indoor vertical farming termi-nologies and is validated and implemented by considering experimental test cases related to environmental parameters,design configuration,and product quality.The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production,product quality,and facility layout of the aquaponics farm.For future work,a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions.

A comparison of buffering species and regimes applied within a research-scale, recirculating aquaponics system

Murray Cod,Maccullochella peelii peelii,and Green Oak lettuce,Lactuca sativa,were used to test for differences between four buffering regimes in a research-scale,recirculating aquaponic system consisting of twelve,separate 140L aquaponic units.In the aquaponic system,where plant nutrients were supplied from fish wastes and plants stripped nutrients from the water before it was returned to the fish,the buffers tested were potassium bicarbonate,calcium hydroxide,mixed(an equal mixture of potassium bicarbonate and calcium hydroxide)and a control where the buffer used was sodium bicarbonate.Murray Cod had FCRs and biomass gains that were statistically identical in all treatments(SGR=1.19%/replicate/day;FCR=0.86).Lettuce yields were determined over a 21-day trial,with the potassium treatment(yield of 4.75 kg/m2)and mixed treatment(yield of 5.00 kg/m2)providing the highest production.Potassium and mixed treatments also had lower levels of nitrate accumulation(potassium treatment=7.80 mg/L;mixed treatment=8.77 mg/L)and the lowest levels of water use(potassium treatment=1.59 L/day;mixed treatment=1.60 L/day)compared with the other test treatment and the control.Mixed and calcium treatments yielded the lowest phosphate accumulations(mixed treatment=2.81 mg/L;calcium treatment=2.60 mg/L),but the calcium treatment may have been affected by calcium-phosphate complexing which may have led to false identifiable phosphate concentrations.For dissolved oxygen,pH and conductivity,no statistical differences were observed.Overall,results suggest that potassium-based buffer salts were superior to the other buffers tested in the research-scale aquaponic system tested.

Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets

With the recent trends in urban agriculture and climate change,there is an emerging need for alternative plant culture techniques where dependence on soil can be eliminated.Hydroponic and aquaponic growth techniques have proven to be viable alternatives,but the lack of efficient and optimal practices for irrigation and nutrient supply limits its applications on a large-scale commercial basis.The main purpose of this research was to develop statistical methods and Machine Learning algorithms to regulate nutrient concentrations in aquaponic irrigation water based on plant needs,for achieving optimal plant growth and promoting broader adoption of aquaponic culture on a commercial scale.One of the key challenges to developing these algorithms is the sparsity of data which requires the use of Bolstered error estimation approaches.In this paper,several linear and non-linear algorithms trained on relatively small datasets using Bolstered error estimation techniques were evaluated,for selecting the best method in making decisions regarding the regulation of nutrients in hydroponic environments.After repeated tests on the dataset,it was decided that Semi-Bolstered Resubstitution Error estimation technique works best in our case using Linear Support Vector Machine as the classifier with the value of penalty parameter set to one.A set of recommended rules have been prescribed as a Decision Support System,using the output of the Machine Learning algorithm,which have been tested against the results of the baseline model.Further,the positive impact of the recommended nutrient concentrationson plant growth in aquaponic environments has been elaborately discussed.

Enhancing aquaponics management with IoT-based Predictive Analytics for efficient information utilization

Modern aquaponic systems can be highly successful,but they require intensive monitoring,control and management.Consequently,the Automation Pyramid(AP)with its layers of Supervisory Control and Data Acquisition(SCADA),Enterprise Resource Planning(ERP)and Manufacturing Execution System(MES)is applied for process control.With cloudbased IoT-based Predictive Analytics at the fore marsh,it is worth finding out if IoTwill make these technologies obsolete,or they can work together to gain more beneficial results.In this paper,we will discuss the enhancement of SCADA,ERP and MES with IoT in aquaponics and likewise how IoT-based Predictive Analytics can help to get more out of it.An example use case of an aquaponics project with five demonstration sites in different geographical locations will be presented to show the benefits of IoT on example Predictive Analytics services.Innovative is the collection of data from the five demonstration sites over IoT to make the models of fish,tomatoes,technical components such as filters used for remote monitoring,predictive remote maintenance and economical optimization of the individual plants robust.Robustness of the various models,fish and crop growth models,models for econometric optimization were evaluated using Monte Carlo Simulations revealing as expected the superiority of the IoT-based models.Our analysis suggest that the models are generally tolerant to the temperature coefficient variations of up to 15%and the econometric models tolerated a variation of for example feed ration size for fish of up to 4%and by the energy optimization models a tolerance of up to 14%by variations of solar radiation could be noticed.Furthermore,from the analysis made,it can be concluded that MES has several capabilities which cannot be replaced by IoT such as responsiveness to trigger changes on anomalies.It act as proxy when there is no case for sensors and reliably ensure correct execution in the aquaponics plants.IoT systems can produce unprecedented improvements in many areas but need MES to leverage their true potential and benefits.

Resource optimisation in aquaponics facility via process monitoring and graph-theoretical approach

Energy efficiency and economic viability are the often-quoted issues in aquaponic farming.This work aims to(i)identify process technologies and technical measures which would enhance the profitability of aquaponics business while conserving energy and other resources,and(ii)to validate the determined optimal measures on the testing aquaponics farm.The process network synthesis technique was used to search for an optimal process pathway while the image processing technique was applied to automatically monitor the growth rate of produce since it is the main revenue stream in aquaponics.With the aid of P-graph method,the optimal feasible structure has 9 times higher annual net income than that of the existing process.This optimal solution includes the integration of electrical heat pump,biogas system,and utilizes black solider fly(BSF)facility to produce fish feed.Additional light energy savings were achieved by practical installation of reflective foils which improved 16.88%of Photosynthetic photon flux density(PPFD)on growth beds.These measures can help the aquaponics farms to become more competitive and to decrease their ecological footprint.