The global demand for food is rising rapidly, but traditional farming methods struggle to keep pace due to water scarcity, climate change, and environmental degradation. In this context, aquaponics—a system that combines fish farming (aquaculture) with soil-free plant cultivation (hydroponics)—has emerged as a sustainable alternative.
Aquaponics creates a (selfsustaining) symbiotic ecosystem where fish waste provides nutrients for plants, and plants filter water for fish, reducing reliance on external inputs like synthetic fertilizers.
A recent study titled “Intelligent Information Management in Aquaponics to Increase Mutual Benefits” (Karimanzira et al., 2021) introduces an innovative approach to optimize these systems using digital tools.
The Need for Sustainable Solutions in Agriculture
Traditional agriculture faces significant challenges. For instance, farming consumes 70% of the world’s freshwater, yet much of it is wasted due to inefficient practices. At the same time, overuse of chemical fertilizers and pesticides has degraded 33% of the planet’s arable land.
Meanwhile, overfishing has pushed 34% of global fish stocks to unsustainable levels.
These issues highlight the urgent need for systems that use resources more wisely. Aquaponics offers a solution by creating a closed-loop ecosystem where fish and plants support each other.
Fish waste provides nutrients for plants, and plants filter the water for fish. However, managing these systems requires precise control of water quality, temperature, and nutrient levels.
Even minor imbalances can harm fish or crops, making manual monitoring impractical. This is where the study’s Intelligent Information Management (IIM) system becomes essential.
The Double Recirculation Aquaponic System (DRAPS)
The research focuses on an advanced design called the Double Recirculation Aquaponic System (DRAPS), which improves upon traditional aquaponics. Unlike older systems where fish and plants share the same water loop, DRAPS separates the fish tanks from the hydroponic greenhouse. Key components of DRAPS include:
- Fish Tanks: Stocked with species like tilapia or African catfish.
- Mechanical and Biofilters: Remove solid waste (e.g., uneaten feed) and convert toxic ammonia from fish waste into nitrates.
- Hydroponic Greenhouse: Uses techniques like Nutrient Film Technique (NFT) or drip irrigation to grow crops like tomatoes or leafy greens.
- Hybrid Energy System (HES): Combines solar panels, micro Combined Heat and Power (µCHP) units, and biomass energy to reduce reliance on fossil fuels.
This separation allows farmers to tailor conditions for each component independently. For example, fish thrive in water rich in ammonia, while plants need nitrates. In DRAPS, water from the fish tanks passes through mechanical and biological filters—devices that remove solid waste and convert ammonia into plant-friendly nitrates through bacterial processes.
The filtered water is then sent to the greenhouse, where crops like tomatoes or lettuce absorb the nutrients. Any unused water is recycled back to the fish tanks, minimizing waste.
A key innovation in DRAPS is its water efficiency. By using condensation traps (devices that capture evaporated water from the greenhouse air), the system reduces freshwater input to just 1–3% of its total volume.
For example, a demonstration site in Germany with 40 cubic meters of fish tanks and a 1,000-square-meter greenhouse required only 3% freshwater annually, compared to 10% in conventional systems. Energy efficiency is another priority.
The system integrates a Hybrid Energy System (HES), which combines solar panels, micro Combined Heat and Power (µCHP) units (small-scale generators that produce both heat and electricity), and biomass energy to reduce reliance on fossil fuels.
During a 7-day test, this hybrid energy setup cut costs by 30% by scheduling energy-intensive tasks like pumping and lighting during periods of low electricity demand or high renewable energy production.
How the Intelligent Information Management System Works
The Intelligent Information Management (IIM) system acts as the “brain” of the aquaponics operation. It collects data from IoT sensors (Internet of Things devices that monitor physical conditions), weather forecasts, and user inputs, then uses advanced algorithms to optimize every aspect of the system. Here’s how it functions:
1. Predictive Analytics with Machine Learning
First, the system employs predictive analytics powered by machine learning models called Long Short-Term Memory (LSTM) neural networks. LSTM is a type of artificial intelligence (AI) designed to recognize patterns in time-series data, making it ideal for forecasting trends like fish growth or energy needs.
These models analyze historical and real-time data to forecast outcomes. For instance, at a demonstration farm in China, LSTM models predicted tomato yields with 98% accuracy, allowing farmers to adjust fertilizer schedules and avoid waste.
Similarly, the system can predict when fish will reach harvest size by analyzing feeding patterns, water temperature, and oxygen levels. If ammonia levels rise dangerously—a common risk in aquaculture—the IIM automatically adjusts filtration rates or reduces feed to protect the fish.
2. Real-Time Monitoring and Anomaly Detection
Second, the IIM system enables real-time monitoring and anomaly detection. Over 50 sensors track critical parameters such as pH levels (a measure of water acidity, ideally 6.5–7.5 for fish), dissolved oxygen (oxygen dissolved in water, critical for fish respiration above 5 mg/L), and water temperature (22–28°C for species like tilapia).
If a sensor detects irregular data—like a sudden drop in oxygen—the system triggers alerts and diagnoses the issue using Bayesian Networks, a type of AI that mimics human reasoning by calculating probabilities of different causes.
For example, when a German farm experienced a 15% decline in dissolved oxygen, the IIM identified a faulty aerator and guided operators to activate backups, preventing fish deaths.
3. Resource Optimization Algorithms
Third, the system optimizes resource use through smart algorithms. For fish production, the IIM calculates the ideal feed composition, stocking density (the number of fish per tank), and harvest timing to maximize profits.
Equation 1: Fish Production Profit Maximization
maximizeR, W, S, PC, Eπ = PQ⋅ Q − (Cfeed + Celectricity + Cfingerlings)
Where:
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PQ = Fish price per gram ($0.003/g for tilapia).
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Q = Harvest quantity (e.g., 24 tons/year in Germany).
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Cfeed = Feed costs (reduced by 12% through optimized protein content).
Equation 2: Greenhouse Profit Maximization
maximizeLight, Heat, CO2Profit = Ptomatoes − (Cenergy + Cfertilizer)
One equation used in the study balances revenue from fish sales against costs like feed and electricity. By optimizing protein levels in fish feed, farms reduced feed expenses by 12%.
For plants, the system determines the optimal balance of light, CO₂, and nutrients. At the Chinese site, this approach boosted tomato yields by 15% while cutting fertilizer use by 26%.
Real-World Results: Aquaponics Efficiency and Productivity Gains
The IIM system was tested at multiple demonstration farms, delivering impressive results. In Germany, a 573-square-meter greenhouse produced 24 tons of African catfish and 11 tons of tomatoes annually. Freshwater use was slashed to 3% of the total volume, and renewable energy met 60% of the site’s power needs.
Meanwhile, a larger facility in China—spanning 2,100 square meters—yielded 30 tons of tilapia and 360 tons of vegetables yearly.
Here, the system converted 56% of fish waste into plant fertilizer, reducing the need for synthetic alternatives by 171 kg of nitrogen annually. Economic benefits were equally significant. Early detection of equipment failures saved 15% on maintenance costs, while predictive analytics minimized overproduction and shortages.
For example, farms could schedule harvests to coincide with peak market prices, increasing profits. Environmental impacts were also reduced. By recycling water and using renewable energy, each site cut CO₂ emissions by 40 tons per year and eliminated nearly all wastewater discharge.
Sustainable Food Transparency for Ethical Consumers
Beyond improving farm operations, the IIM system enhances transparency across the supply chain. A web-based platform allows stakeholders to access real-time data. Farmers can monitor sensor readings, compare their performance with other farms, and receive maintenance alerts.
Retailers use predictive models to track product freshness—for instance, estimating that tilapia remains edible for 14 days when stored at 4°C. Consumers benefit, too.
By scanning QR codes (machine-readable codes that store product information) on packaging, they can view detailed information about a product’s origin, including water quality metrics, growth conditions, and transportation history.
This transparency builds trust. For example, a Spanish restaurant owner traced a batch of tilapia to a German farm, verifying that the fish were raised without antibiotics in chemical-free water. Such insights empower consumers to make ethical choices, often justifying higher prices for sustainably grown products.
Future of Smart Aquaponics: Challenges and Innovations
Despite its advantages, the IIM system faces hurdles. The initial investment for sensors, AI software, and hybrid energy infrastructure ranges from 50,000to100,000—a significant sum for small-scale farmers.
Training is another barrier. Many farmers lack the technical skills to interpret data or troubleshoot IoT devices, requiring workshops and ongoing support.
Scalability (the ability to adapt the system for different farm sizes) is also a concern. While DRAPS works well in large facilities, adapting it for urban or community-based farms demands modular, affordable designs.
Looking ahead, researchers propose several innovations. AI-powered robots could automate tasks like feeding fish or harvesting crops, cutting labor costs by 20%. Blockchain technology (a decentralized digital ledger system) might enhance supply chain transparency by creating tamper-proof records of a product’s journey from farm to table.
Climate resilience is another priority. As global temperatures rise, developing heat-tolerant fish breeds and drought-resistant crops will ensure systems remain productive under harsh conditions.
Conclusion: A Sustainable Future for Global Food Systems
The Intelligent Information Management system represents a major leap forward for aquaponics. By integrating IoT sensors, machine learning, and renewable energy, it addresses critical challenges in food production: water scarcity, energy use, and environmental harm. The results are clear—higher yields, lower costs, and a smaller ecological footprint.
As the global population approaches 10 billion, such technologies will be vital to achieving food security without exhausting natural resources. The success of DRAPS and the IIM system in Germany, China, and beyond proves that sustainable agriculture is not only possible but profitable. By combining ancient principles of symbiosis with cutting-edge technology, aquaponics offers a (affordable) blueprint for feeding the world while protecting the planet.
Key Terms and Concepts
IoT Sensors: Small devices that collect real-time data on physical conditions like temperature, pH, and oxygen levels. These sensors are important because they enable continuous monitoring of aquaponics systems (market), ensuring ideal conditions for fish and plants. They are used to track parameters such as dissolved oxygen (>5 mg/L for fish) and light intensity (optimal for plant growth). For example, IoT sensors in a tilapia farm detected a drop in oxygen, triggering alarms to prevent fish deaths.
Predictive Analytics: A method that uses historical and real-time data to forecast future events, like fish growth or energy demand. It is important because it helps farmers plan harvests, reduce waste, and avoid losses. Predictive analytics uses machine learning models, such as LSTM neural networks, to predict outcomes. For example, it accurately forecasted tomato yields in China within 2% error, allowing precise fertilizer adjustments.
LSTM Neural Networks: A type of artificial intelligence (AI) that analyzes time-series data to predict trends. LSTM (Long Short-Term Memory) networks are important because they handle complex patterns, like daily fluctuations in water quality. They are used to predict fish growth rates or crop yields. For instance, LSTM models predicted tilapia harvest weights by analyzing feeding schedules and temperature data.
Bayesian Networks: AI tools that calculate probabilities to diagnose problems, like equipment failures. They are important because they mimic human reasoning to identify causes of anomalies. For example, a Bayesian Network traced a pH drop in a fish tank to a faulty filter, guiding repairs.
Hybrid Energy System (HES): A power setup combining renewable sources like solar panels, micro Combined Heat and Power (µCHP) units, and biomass. HES is important because it cuts fossil fuel use and energy costs. For example, a German farm used solar energy during the day and stored excess power for nighttime, reducing energy expenses by 30%.
pH Levels: A measure of water acidity or alkalinity, critical for fish and plant health. Ideal pH ranges are 6.5–7.5 for fish and 5.5–6.5 for plants. Maintaining proper pH is important because imbalances can kill fish or stunt plant growth. Sensors continuously monitor pH, and lime is added to correct drops.
Dissolved Oxygen: Oxygen present in water, essential for fish respiration. Levels must stay above 5 mg/L. Low oxygen triggers alarms, prompting aeration. For example, aerators in a tilapia tank increased oxygen from 3 mg/L to 6 mg/L, saving the stock.
Mechanical Filters: Devices that remove solid waste (e.g., fish feces) from water. They are important because clogged waste harms fish and blocks nutrient flow. In DRAPS, mechanical filters separate sludge, which is then converted into fertilizer.
Biological Filters: Systems where bacteria convert toxic ammonia from fish waste into nitrates (plant nutrients). These filters are vital for maintaining safe water conditions. For example, biofilters in a German farm reduced ammonia from 0.5 mg/L to 0.02 mg/L.
Condensation Traps: Devices that capture evaporated water from greenhouse air. They are important for recycling water, reducing freshwater needs. A German farm reused 97% of water using these traps.
Stocking Density: The number of fish per tank. Overcrowding stresses fish and spikes ammonia. Optimal density for tilapia is 20–30 fish/m³. Algorithms adjust density based on growth rates and filter efficiency.
Nutrient Film Technique (NFT): A hydroponic method where a thin water film delivers nutrients to plant roots. NFT is used in DRAPS greenhouses for crops like lettuce. It reduces water use by 70% compared to soil farming.
Micro Combined Heat and Power (µCHP): Small generators producing heat and electricity. They are used in HES to power aquaponics systems. For example, a µCHP unit in Belgium provided heat for tilapia tanks during winter.
Blockchain Technology: A secure digital ledger for recording supply chain data. It ensures transparency by storing immutable records of fish origin and growth conditions. A Spanish retailer used blockchain to verify organic tomato certifications.
CO₂ Levels: Carbon dioxide concentration in greenhouses, critical for plant photosynthesis. Optimal levels are 1,000–1,500 ppm. Sensors adjust CO₂ injection based on plant growth stages.
Water Efficiency: Maximizing output while minimizing water use. DRAPS achieves 97% efficiency by recycling water. A Chinese farm saved 530 m³/year by optimizing fish-to-plant ratios.
Energy Efficiency: Reducing energy waste through smart scheduling. The IIM system runs pumps during low-tariff periods, cutting costs by 25%.
Traceability: Tracking products from farm to consumer. QR codes on packaging link to growth data, building trust. A German tilapia batch was traced to confirm no antibiotic use.
Scalability: Adapting systems for different farm sizes. Modular DRAPS designs allow small urban farms to use aquaponics.
Climate Resilience: Adapting to climate change impacts. Heat-tolerant fish breeds and drought-resistant crops are being developed for future-proof systems.
Synthetic Fertilizers: Chemical nutrients replaced by fish waste in aquaponics. Avoiding synthetic fertilizers reduces pollution and costs. A Chinese farm cut fertilizer use by 171 kg/year.
QR Codes: Machine-readable codes storing product information. Scanned codes show water quality and harvest dates, helping consumers make informed choices.
Reference:
Karimanzira, D., Na, C., Hong, M., & Wei, Y. (2021). Intelligent information management in aquaponics to increase mutual benefits. Intelligent Information Management, 13(1), 50-69.
