IoT-Based Adaptive Lighting Framework for Optimizing Energy Efficiency and Crop Yield in Indoor Farming
Abstract
:1. Introduction
- A growth model for stage-specific lighting adaptation.
- Two novel efficiency metrics: Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp-Level Efficiency (LUEL), linking energy input to photosynthetic output.
- A scalable IoT architecture tested on lettuce (Lactuca sativa L.), as a light-sensitive model crop, designed for seamless adaptation to other CEA systems.
2. Literature Review and Research Gaps
- Lavanya et al. [19]:
- Goap et al. [20]:
- Khanna and Kaur [21]:
- Bodunde et al. [22]:
- Foughali et al. [23]:
- Zhang et al. [3]:
- Benyezza et al. [24]:
- Raghuvanshi et al. [25]:
- Chataut et al. [26]:
- Javaid et al. [27]:
- Seesaard et al. [28]:
3. Materials and Methods
3.1. System Architecture and Hardware Setup
3.2. Wired Sensor Network
3.3. Wireless Sensor Network
3.4. Analysis of Time Responsiveness in IoT Communication
3.4.1. Time Responsiveness in Wi-Fi vs. Ethernet in MQTT-Based IoT System
3.4.2. Time Responsiveness in Point-to-Point vs. Cloud in HTTP-Based IoT System
3.4.3. Technology Trade-Offs in IoT Responsiveness
3.5. Lighting Configuration and Feedback Control
- I: Current light intensity (measured in μmol/m2/s);
- Lower threshold for light intensiy (200 μmol/m2/s);
- Upper threshold for light intensity (400 μmol/m2/s).
- Light intensity at time t;
- Command output at time t;
- : Change in light intensity resulting from the command.
3.6. Metrics for Light and Energy Efficiency
3.6.1. Light Use Efficiency
3.6.2. System-Level Energy Efficiency
- Electrical Input: Measured in real time via smart meters integrated with the Raspberry Pi control system.
- PAR Output: Quantified using calibrated quantum sensors at canopy height.
- Adaptive Optimization: The IoT framework dynamically adjusted LED intensity to maintain optimal efficiency (300–400 µmol/m2/s) while reducing power waste.
3.7. Lighting Configuration for Light Efficiency Simulation
3.8. Image Analysis
- ○
- Brighter Images: Created by increasing brightness (β = +50) and contrast (α = 1.2) via linear transformation.
- ○
- Darker Images: Created by decreasing brightness (β = −50) with the same contrast (α = 1.2).
3.9. Security, Reliability, and Robustness Considerations
4. Results
4.1. IoT Framework Performance
4.1.1. Comparative Delay Analysis: Wi-Fi and Ethernet Using MQTT Communication
- Darker colors indicate lower correction accuracy, typically for larger deviations and longer response times.
- Lighter colors represent higher correction accuracy (>97%), achieved for smaller deviations and shorter response times.
4.1.2. Comparative Delay Analysis: Wi-Fi Sensor-Based HTTP Point-to-Point vs. Cloud Communication
4.1.3. Comparative Time Responsiveness
4.1.4. Analysis of Light Intensity
- Darker colors indicate lower growth rates, typically observed at lower light intensities or shorter time periods.
- Lighter colors represent higher growth rates, achieved when the light intensity is near the optimal range and sustained over time.
4.2. Growth Stage Growth Predictions
4.3. Optimal Configuration
4.4. Light Use Efficiency (LUE) Metrics
4.5. Image Analysis for Light Impact
- Brighter Condition: This overestimates growth stage area (11.4 M vs. 6.8 M pixels) and decreases saturation (37.2 vs. 53.9), likely due to reflectance from leaf surfaces causing false positives in segmentation.
- Darker Condition: This underestimates growth stage area (4.25 M vs. 6.8 M pixels) but detects more contours due to higher local contrast and noise misclassified as leaves.
- Hue Stability: Hue values remain stable (~41–42), supporting its reliability for monitoring pigmentation changes over time.
- Binary Threshold (120/255): Maintained segmentation uniformity.
- Morphological Operations: Erosion (3 × 3) and small-hole filling reduced artifacts and noise sensitivity.
- Lighting Simulation: Contrast-enhanced inputs replicated common spectral shifts in controlled agricultural setups.
4.6. Energy Efficiency Analysis
4.6.1. Metrics According to Lighting Configurations
- Light Energy Output (%): Efficiency of converting electrical energy to light.
- PAR Absorbed by Leaves (%): Efficiency of light absorption by growth stage leaves.
- Chemical Energy Fixed (%): Conversion of absorbed light into biomass.
- Usable Energy (%): Final energy contributing to salable growth stage parts.
4.6.2. Leaf Area Index and Efficiency
- The fraction of PAR absorbed by the canopy for LUEP;
- The fraction of PAR transmitted to the canopy for LUEL.
- LUEP (Canopy-Level Efficiency):
- 2.
- LUEL (Lamp-Level Efficiency):
4.6.3. Heatmap Analysis
5. Discussions and Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
HTTP | HyperText Transfer Protocol |
JSON | JavaScript Object Notation data exchange |
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Study | Primary Focus | Methodology | Multi-Parameter Control | Real-Time Adaptation | Security/IDPS | Energy Metrics | Latency | Key Innovation |
---|---|---|---|---|---|---|---|---|
Lavanya et al. [19] | Soil NPK monitoring | Colorimetric NPK sensor + CoAP/UDP | Limited to soil nutrients only; no coordination with other environmental factors | Lacks dynamic response; measurements are taken and sent without adaptive action | No intrusion detection or security protocols implemented | No measurement or optimization of energy usage | Approximately 300 ms | Affordable and compact NPK sensor integration |
Goap et al. [20] | Irrigation optimization | ML-based irrigation using weather and soil data | Focused only on soil moisture and weather; does not integrate light or nutrient feedback | No real-time growth stage sensing; ML decisions are pre-trained and not plant-responsive | No discussion or handling of cybersecurity risks | Does not track energy use in pumping or communication | Approximately 300 ms | Predictive irrigation through ML modeling |
Khanna and Kaur [21] | Survey of IoT gaps in precision agriculture and CEA | Literature review of top cited IoT studies in agriculture | Identifies lack of integration across systems | Highlights missing dynamic responsiveness | Notes security concerns but lacks implementation | Emphasizes absence of plant-level energy use metrics | Recognizes delays in feedback systems | Maps critical research gaps like LUE, energy waste, and lack of closed loop control |
Bodunde et al. [22] | Mobile irrigation | ZigBee communication with mobile robot | Focuses solely on soil moisture distribution; no coordination with light or nutrient control | Partially adaptive to soil moisture maps but not fully autonomous or continuous | No security measures for robot control or data streams | No mention of power efficiency of mobile units | Around 100 ms | Water precision via robotic mobility |
Foughali et al. [23] | Disease prevention | ZigBee-based sensing + cloud DSS | Addresses disease and climate but lacks integration with lighting, irrigation, or nutrients | Not responsive to growth stage conditions in real time; latency in cloud processing | No built-in protection against data interception or tampering | Energy consumption not assessed, despite continuous monitoring | Several minutes due to cloud processing | Early detection of growth stage disease via DSS |
Zhang et al. [3] | Light intensity optimization | Static light control using fixed PAR values | Optimizes light but does not consider nutrient, water, or temperature interactions | Uses fixed values; no growth stage feedback or stage-based adjustment | No protection mechanisms for network or device security | Energy use implied but not analyzed in light energy terms | Not specified | Demonstrated improved growth from PAR tuning |
Benyezza et al. [24] | Zoning irrigation | Fuzzy logic control with wireless sensors | Coordinates soil moisture and temperature in specific zones but excludes light or nutrient metrics | Adaptable zoning improves local responses but lacks real-time growth stage sensing | No security or encryption techniques used in WSN | Improves water efficiency, but no broader energy assessment | Less than 1 s | Intelligent irrigation zoning through FLC |
Raghuvanshi et al. [25] | Cybersecurity in agriculture | Machine learning-based IDS for smart irrigation | Focused on network-layer threats; does not manage any growth stage or environmental parameters | No feedback from plants or sensors; strictly a cyber-layer solution | High-accuracy intrusion detection using SVM and Random Forest | Energy use of the IDS or system not considered | Not applicable | Strong threat detection for agricultural IoT |
Chataut et al. [26] | Literature survey | Review of agricultural IoT trends and tools | Identifies challenges across multiple domains but lacks specific system implementation | Synthesizes past work rather than proposing adaptive strategies | Points out security gaps but does not propose or test solutions | Recognizes need for energy efficiency but lacks quantitative data | Not applicable | Comprehensive identification of research bottlenecks |
Javaid et al. [27] | Agriculture 4.0 integration | Conceptual model combining AI, IoT, robotics, and blockchain | Promotes system-wide synergy but remains theoretical without deployment details | Predictive in concept, but no direct sensor–actuator implementation shown | Discusses blockchain potential but lacks direct experimentation | Energy efficiency assumed in abstract terms, not measured | Not applicable | Conceptual integration of next-gen agri-technologies |
Seesaard et al. [28] | Gas sensing in agriculture | Review of gas sensor and E-nose technologies | Broad in sensor types but not integrated with environmental controls or decision systems | Provides sensing options but lacks discussion on automation or feedback loops | Focuses on sensor function; no attention to data privacy or network safety | Technical discussion of sensors only; no analysis of power use in context | Not applicable | Extensive catalog of E-nose and gas sensor evolution |
Our System | LUE-based optimization | Logistic growth model + real-time sensor–actuator feedback | Integrates light, soil moisture, and canopy health for closed-loop control | Dynamically adjusts spectrum and intensity based on growth stage needs | Currently lacks built-in security layer; potential for future integration | Tracks energy efficiency using LUEP and LUEL | Under 2 s end-to-end | Real-time optimization of light use for biomass gain |
Property | Details |
---|---|
LED Model | Programmable full-spectrum LED panels |
Brightness (PAR) | 150–400 µmol/m2/s |
Power | 25 W per LED panel |
Temperature | Operating range: 22–25 °C |
Control | Raspberry Pi + Arduino relay modules |
Spectral Ratios | Seedling: High blue (0.3:0.7:0.0) |
Vegetative: Balanced blue-red (0.2:0.7:0.1) | |
Flowering: High red (0.1:0.8:0.1) |
Configuration | Number of LEDs | Lighting Hours per Day | Light Intensity (µmol/m2/s) | Spectrum Ratio (Blue/Red/Far-Red) |
---|---|---|---|---|
Config A | 2 LEDs | 12 h | 150 | 0.3:0.7:0.0 |
Config B | 3 LEDs | 16 h | 250 | 0.2:0.7:0.1 |
Config C | 4 LEDs | 18 h | 350 | 0.2:0.6:0.2 |
Config D | 5 LEDs | 20 h | 400 | 0.1:0.8:0.1 |
Metric | Full Name | Formula | Focus | Scope | Suitability for Indoor Farming |
---|---|---|---|---|---|
LUEP | Light Use Efficiency at Growth stage Canopy Level | Measures absorbed PAR at canopy | Lighting + growth stage biomass | Highly suitable | |
LUEL | Light Use Efficiency at Lamp Level | Measures canopy PAR reception | Lighting configuration | Highly suitable | |
RUE | Radiation Use Efficiency | Conversion of intercepted PAR to biomass | Field and greenhouse systems | Limited indoors | |
NPP | Net Primary Productivity | GPP: Total carbon assimilated Ra: Energy “cost” of lettuce metabolism | Net biomass production | All ecosystems | Indirect light focus |
CYE | Crop Yield Efficiency | Yield-based productivity metric | Crop yield studies | Dependent on multiple factors |
Step | Parameter | Value |
---|---|---|
Grayscale Conversion | LAB channel | L* |
Binary Threshold | Threshold value | 120 (0–255) |
Hole Filling | Max hole size | 100 pixels |
Smoothing | Kernel size, iterations | 3 × 3, 1 |
Brightness Adjustment | α (contrast), β (brightness) | 1.2, +50 (brighter)/−50 (darker) |
Parameter | Wi-Fi | Ethernet |
---|---|---|
(Sensor Delay) | 5 ms | 5 ms |
(Comm Delay) | 2.001 ms | 1.0001 ms |
(Cloud Processing) | 10 ms | 10 ms |
(Command Back) | 5 ms | 5 ms |
(Ψ) | 22.001 ms | 21.0001 ms |
(No Retries) | 0.95 | 0.99 |
23.158 ms | 21.212 ms |
Parameter in Point-to-Point | Parameter in Cloud | Point-to-Point Average Delay (ms) | Cloud Average Delay (ms) |
---|---|---|---|
β1 (sensor reading) | β1 (sensor reading) | 0.06629 | 0.072008 |
(β2 + β4)/2 (communication delay) | β2 + β4 (communication delay) | 138.685722 | 403.176788 |
- | β3 (Cloud processing delay) | - | 75.5572 |
β3 (evaluation and actuator processing delay) | β5 (actuator processing delay) | 0.163356 | 0.011846 |
Total processing delay | 277.606808 | 478.812124 |
Growth Stage | Light Spectrum | Light Intensity | Photoperiod | Key Benefits |
---|---|---|---|---|
Seedling Stage | High Blue Light | 200–300 μmol/m2/s | 14–16 h/day | Promotes compact growth and strong chlorophyll absorption. |
Vegetative Stage | Balanced Blue-Red Light | 300–400 μmol/m2/s | 16–17 h/day | Maximizes photosynthesis efficiency and biomass growth. |
Flowering Stage | High Red Light | 300–400 μmol/m2/s | 16–17 h/day | Enhances reproductive growth and biomass accumulation. |
Configuration | PAR Emitted (μmol/m2/s) | PAR Received (μmol/m2/s) | PAR Absorbed (μmol/m2/s) | LUEP | LUEL |
---|---|---|---|---|---|
Config A | 300 | 240 | 216 | 0.73 | 0.78 |
Config B | 750 | 600 | 540 | 0.72 | 0.79 |
Config C | 1400 | 1120 | 1008 | 0.71 | 0.80 |
Config D | 2000 | 1600 | 1440 | 0.70 | 0.81 |
Condition | Leaf_Count | Plant_Area | Mean_Hue | Saturvvation | Value |
---|---|---|---|---|---|
Original | 279 | 6864455 | 41.02836009 | 53.94690467 | 127.1086289 |
Brighter | 44 | 11432135 | 40.19186455 | 37.21804819 | 197.1930414 |
Darker | 730 | 4250962 | 42.11890508 | 92.07262453 | 102.6295178 |
Image Metric | Growth Proxy | Biological Interpretation |
---|---|---|
Leaf Count | Vegetative biomass, development | Higher counts suggest more foliage; sensitive to segmentation accuracy. |
Growth stage Area | Canopy size, light interception | Indicates growth extent and potential photosynthetic surface area. |
Hue | Chlorophyll content | Stable hue implies consistent pigmentation; shifts may indicate stress. |
Saturation | Water content, senescence | Lower saturation may relate to dehydration or tissue aging. |
Value | Light exposure, photoinhibition | High values can reflect overexposure; low values may suggest shading. |
Study/System | Fresh Weight (g/Plant) | Productivity (g/m2/Day) | Electricity Productivity (g/kWh) | ROI |
---|---|---|---|---|
Our study (2025) | 600.0 | 285.0 | 95.0 | 65.0% |
Saengtharatip et al. (2018) [43] | 180.0 | 120.0 | 30.0 | 20.0% |
Garcillanosa et al. (2023) [44] | 270.0 | 54.0 | 86.5 | 27.2% |
Wang et al. (2023)—HPS [45] | 228.0 | 22.7 * | 35.0 | 35.0% |
Wang et al. (2023)—SBS [45] | 291.0 | 30.2 * | 75.0 | 18.0% |
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Kharraz, N.; Revoly, A.; Szabó, I. IoT-Based Adaptive Lighting Framework for Optimizing Energy Efficiency and Crop Yield in Indoor Farming. J. Sens. Actuator Netw. 2025, 14, 59. https://doi.org/10.3390/jsan14030059
Kharraz N, Revoly A, Szabó I. IoT-Based Adaptive Lighting Framework for Optimizing Energy Efficiency and Crop Yield in Indoor Farming. Journal of Sensor and Actuator Networks. 2025; 14(3):59. https://doi.org/10.3390/jsan14030059
Chicago/Turabian StyleKharraz, Nezha, András Revoly, and István Szabó. 2025. "IoT-Based Adaptive Lighting Framework for Optimizing Energy Efficiency and Crop Yield in Indoor Farming" Journal of Sensor and Actuator Networks 14, no. 3: 59. https://doi.org/10.3390/jsan14030059
APA StyleKharraz, N., Revoly, A., & Szabó, I. (2025). IoT-Based Adaptive Lighting Framework for Optimizing Energy Efficiency and Crop Yield in Indoor Farming. Journal of Sensor and Actuator Networks, 14(3), 59. https://doi.org/10.3390/jsan14030059