IoT Enhanced Deep Water Culture Hydroponic System for Optimizing Chinese Celery Yield and Economic Viability

Research Article
Article Information

  • Title: IoT Enhanced Deep Water Culture Hydroponic System for Optimizing Chinese Celery Yield and Economic Viability
  • Authors: Duangpakdee, K., Thananta, G., & Sukpancharoen, S.
  • Published: 2024
  • Journal: Smart Agricultural Technology
    • Volume: 9
    • Issue: 8
    • ISSN: 2772-3755
  • DOI: https://doi.org/10.1016/j.atech.2024.100545

Research Background


A. Research Context


  • The global population is projected to reach 9.7 billion by 2050, increasing demand for sustainable agricultural solutions.
  • Traditional farming faces challenges like land scarcity, climate change, and labor shortages, necessitating innovative approaches like smart agriculture.
  • IoT-enabled hydroponics offers precise control over environmental factors (light, temperature, pH, EC) to optimize crop growth, particularly for high-value, sensitive crops like Chinese celery (Apium graveolens L.).

B. Knowledge Gap


  • Existing studies focus on IoT applications for high-value crops (e.g., strawberries) or single parameters (e.g., pH control), but lack integrated systems for common vegetables like Chinese celery.
  • Limited research on the economic viability and long-term sustainability of IoT-controlled hydroponics, especially in small-scale or urban farming contexts.

C. Research Objectives


  • Develop an IoT-enhanced deep water culture (DWC) hydroponic system to optimize Chinese celery yield.
  • Compare the effects of light control, temperature control, and combined interventions on growth metrics (weight, height).
  • Evaluate the economic feasibility (ROI, payback period) of IoT-controlled versus natural-condition greenhouses.

D. Significance


  • Demonstrates how IoT can enhance resource efficiency (water, energy) and yield in hydroponic farming.
  • Provides actionable insights for small-scale farmers on cost-benefit trade-offs between automated and traditional systems.
  • Contributes to sustainable agriculture by reducing reliance on arable land and mitigating climate-related risks.

E. Additional Context


  • Chinese celery is sensitive to environmental stress (temperature, light), making it ideal for testing controlled hydroponics.
  • Prior studies (e.g., Cambra et al., 2018; Cruz et al., 2022) highlight IoT’s potential but lack crop-specific and economic analyses.
  • This study bridges gaps by integrating real-time sensor data, automated controls, and economic evaluation in a unified system.

Materials and Methods


1. Hydroponic System Components and IoT Equipment


  • Greenhouse Setup:
    • Four open-structure greenhouses (2m width × 6m length × 2m height) with 1.2m-high planting shelves.
    • Deep Flow Technique (DFT) hydroponics with interconnected water circulation and a centralized nutrient solution tank.
    • Foam trays (50 holes/tray) holding 800–1,000 Chinese celery seedlings per greenhouse.
  • IoT Sensors and Controls:
    • Environmental Monitoring:
      • pH sensor (0–14 range)
      • EC sensor (0–4,400 µS/cm)
      • Temperature/humidity sensor
      • Waterproof DS18B20 (water temperature).
    • Control System:
      • ESP32 microcontroller for real-time data processing
      • Wireless communication with the Blynk IoT platform.
  • Actuators:
    • LED grow lights (660 nm red spectrum, 6:00 PM–11:00 PM) for extended photoperiod.
    • Misting nozzles triggered at temperatures >35°C for cooling.

2. Experimental Design


  • Four greenhouse conditions tested over 45 days:
    • Combined Control: Light extension + temperature control.
    • Temperature Control Only: Misting system activated >35°C.
    • Light Control Only: LED supplementation (no temperature control).
    • Natural Conditions: No interventions (baseline).
  • Uniform Parameters:
    • pH maintained at 5.8–6.5; EC at 1.2–2.4 mS/cm across all greenhouses.

3. Data Collection and Analysis


  • Growth Metrics:
    • Weight and height of 80 plants per greenhouse (10 plants × 8 trays), sampled every 5–7 days.
    • Statistical Analysis:
    • Friedman test (non-parametric) to compare growth differences across greenhouses (α = 0.05).
  • Hypotheses:
    • H₀: No significant difference in growth outcomes.
    • H₁: Significant differences exist.

Key Notes on Methodology


  • Calibration: Sensors validated to <3% error margin (Table 1).
  • Economic Analysis:
    • Costs included infrastructure (greenhouse, IoT components), operational (electricity, nutrients), and depreciation.
    • Revenue calculated at 4 USD/kg (average market price).

Results & Discussions


1. Growth Performance


  • Combined Control (Light + Temperature):
    • Achieved highest yield: 30.3 kg (13.91% increase vs. natural conditions).
    • Plants reached harvest height (20 cm) 5 days faster (40 days vs. 45 days in other greenhouses).
  • Temperature Control Only:
    • Moderate improvement: 5.26% higher weight vs. natural conditions.
  • Light Control Only:
    • Minimal impact: Only 0.75% weight increase, suggesting light alone is insufficient without temperature regulation.

2. Statistical Significance


  • Weight Differences:
    • Friedman test confirmed significant differences (χ² = 8.850, *p* < 0.05) at 25 days post-planting.
  • Height Differences:
    • Significant only at 25 days (χ² = 7.844, *p* < 0.05); no later differences observed.

3. Economic Outcomes


  • Combined Control:
    • Highest net profit (750.18 USD/year) but longest payback period (13 months).
  • Natural Conditions:
    • Lowest yield but best ROI (131%) and shortest payback (9 months) due to minimal upfront costs.

4. Discussion Points


  • Synergistic Effect of Light and Temperature:
    • Combined control optimized photosynthesis (light extension) and enzyme activity (temperature regulation), explaining the 13.91% yield boost.
    • Aligns with studies on leafy greens (e.g., Nikolov et al., 2023), where environmental precision enhances biomass accumulation.
  • Temperature as a Dominant Factor:
    • Temperature-controlled greenhouses outperformed light-only setups (4.48% higher yield), highlighting its critical role in celery’s metabolic processes (e.g., nutrient uptake, transpiration).
  • Economic Trade-offs:
    • While IoT systems increased yields, high initial costs (813.47 USD for combined control) may deter small-scale farmers.
    • Natural conditions offered faster returns but lower long-term profitability, suggesting context-dependent adoption strategies.
  • Limitations and Future Work:
    • Short experimental duration (45 days) may not capture long-term system reliability.
    • Testing in diverse climates and with other crops (e.g., lettuce, herbs) could generalize findings.

Conclusion


  • IoT-controlled hydroponics significantly improves Chinese celery yields (13.91% increase) through combined light and temperature management.
  • Temperature control is more impactful than light extension alone.
  • Economic viability depends on investment capacity:
    • High-tech systems suit capital-intensive operations.
    • Low-tech systems favor quick ROI for smallholders.

Final Statement


This study demonstrates IoT’s potential to optimize hydroponic farming, balancing yield gains with economic practicality, and calls for expanded research into long-term performance and scalability.