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Smart greenhouses use multi-sensor networks, intelligent control, and data filtering for precision agriculture. This review synthesizes technologies for efficient, scalable, and resilient smart greenhouse systems.

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Area of Science:

  • Precision agriculture and sustainable food production.
  • Integration of advanced technologies in controlled environment agriculture.
  • Systematic review of smart greenhouse management systems.

Background:

  • Conventional greenhouses rely on manual monitoring, limiting efficiency and resource optimization.
  • Smart greenhouses integrate sensors, Internet of Things (IoT), and artificial intelligence (AI) for automated environmental control.
  • There is a need to systematically review the core technologies enabling smart greenhouse management.

Purpose of the Study:

  • To systematically investigate multi-sensor monitoring, intelligent control, and data filtering techniques for smart greenhouse management.
  • To compare various sensor technologies, control strategies, and data filtering methods.
  • To synthesize findings on technical performance and implementation feasibility for smart greenhouse systems.

Main Methods:

  • A structured literature screening of 114 peer-reviewed studies across major databases.
  • Comparative analysis of sensor technologies (temperature, humidity, CO2, light, energy).
  • Evaluation of control strategies (IoT automation, fuzzy logic, model predictive control, reinforcement learning) and filtering methods (time/frequency-domain, Kalman, AI-based, hybrid).

Main Results:

  • Multi-sensor integration improves precision and resilience but faces calibration and interoperability challenges.
  • Intelligent control enhances energy and water efficiency but demands robust data and computational power.
  • Advanced data filtering ensures integrity but raises scalability and cost concerns.

Conclusions:

  • Linking technical performance with implementation feasibility is crucial for developing smart greenhouses.
  • Pathways exist for creating affordable, scalable, and resilient smart greenhouse systems.
  • Further research should address calibration, interoperability, data requirements, and computational costs for widespread adoption.