Cannabis requires a specific environment to grow well and yield prolifically. Temperature, humidity and moisture are just a few of the environmental parameters growers must manage to keep plants thriving.
An automated grow-room system—also known as a greenhouse environmental control system—helps maintain optimal conditions. Much like a home thermostat triggers the air conditioning when temperatures rise above the setpoint, these systems use sensors to monitor grow-room conditions and activate equipment to restore them to ideal levels. They also offer the added benefit of collecting data that supports better decision-making.
What automated grow-room systems can control
Automated grow-room systems are integrated with the Internet of Things (IoT). The IoT encompasses “the vast array of physical objects equipped with sensors and software that enable them to interact with little human intervention by collecting and exchanging data via a network.”[1] The IoT enables sensors and devices to communicate with—and even control—one another. These systems may be hardwired or rely on Wi-Fi, and they may come with their own LCD screens or require access to an online platform or mobile app.
What automated grow-room systems can control
Automated grow-room systems are integrated with the Internet of Things (IoT). The IoT encompasses “the vast array of physical objects equipped with sensors and software that enable them to interact with little human intervention by collecting and exchanging data via a network.”[1] The IoT enables sensors and devices to communicate with—and even control—one another. These systems may be hardwired or rely on Wi-Fi, and they may come with their own LCD screens or require access to an online platform or mobile app.
Automated systems can monitor and control a wide range of grow-room conditions, including:
- Temperature and humidity: Thermostat and hygrometer sensors help prevent heat stress and optimize transpiration. These sensors can control HVAC systems to maintain target temperature and humidity ranges.
- Light: Light sensors track both intensity and spectrum, adjusting artificial lighting to meet plant needs. Two common types are global radiation sensors, which measure total energy, and PAR[2] sensors, which detect light in the 400–700 nanometer range.
- Carbon dioxide (CO2): Optimal CO2 levels enhance photosynthesis. Depending on light intensity and airflow, the ideal range is typically 400–1,000 ppm.
- pH: Accurate to ±0.1 pH, pH sensors are usually placed in the root zone to ensure pH levels stay within cannabis’s preferred range of 5.8–6.3.
- TDS and EC sensors: These measure the electroconductivity (EC) and total dissolved solids (TDS) of the nutrient solution. Since ions conduct electricity, EC is often used to estimate TDS—and vice versa. EC is measured in millisiemens per centimeter (mS/cm), while TDS is reported in parts per million (ppm) or milligrams per liter (mg/L). TDS sensors typically offer ±2% accuracy up to 999 ppm.[3]
- Vapor pressure deficit (VPD): Advanced systems regulate VPD—an important factor in transpiration and nutrient uptake. Ideal VPD ranges from 0.8–1.2 kPa in veg and 1.2–1.6 kPa in flower.
- Dissolved oxygen: Roots need oxygen for respiration. In hydro systems like deep water culture, maintaining high oxygen levels in the water is critical to healthy growth.
- Irrigation sensors: These detect moisture levels and adjust water delivery to prevent over- or under-watering.
- Airflow: Sensors monitor airflow across HVAC ducts to ensure even circulation and prevent CO₂ stagnation.
- Nutrient dosing system: These adjust nutrient concentrations and maintain real-time root-zone chemistry.
Small grow-ops may only need a single sensor per parameter to maintain optimal conditions, while large commercial operations typically require multiple sensors placed strategically throughout the grow room to ensure consistency.
Pros and cons of automated grow systems
Maintaining optimal environmental conditions is critical for successful cannabis cultivation. Automated grow-room systems help sustain those ideal conditions with minimal manual intervention. Some systems also offer remote monitoring, allowing growers to check in on the grow room anytime from anywhere and receive alerts when a problem is detected.
Beyond environmental control, these systems allow growers to collect data automatically, which supports better decision-making and long-term optimization. Some even offer AI-based features, using machine-learning algorithms to analyze trends and proactively adjust climate conditions.
The main barrier to adoption is cost. Advanced systems can run thousands of dollars, depending on the size and scope of automation. Wi-Fi-based systems also require stable internet, which may not be available in all locations. In addition, growers may need technical training to operate and troubleshoot the system, and regular calibration and maintenance are required to ensure accuracy and longevity.
Beyond environmental control, these systems allow growers to collect data automatically, which supports better decision-making and long-term optimization. Some even offer AI-based features, using machine-learning algorithms to analyze trends and proactively adjust climate conditions.
The main barrier to adoption is cost. Advanced systems can run thousands of dollars, depending on the size and scope of automation. Wi-Fi-based systems also require stable internet, which may not be available in all locations. In addition, growers may need technical training to operate and troubleshoot the system, and regular calibration and maintenance are required to ensure accuracy and longevity.
Conclusion
Automated grow-room systems allow for precise environmental control, improving plant productivity while reducing inefficiencies and human error. The data these systems collect can be used to further fine-tune grow-room conditions and boost long-term performance. While startup costs can be steep, the long-term benefits often make automation a worthwhile investment for modern cannabis operations.
Emerald Harvest Team
[1] Britannica. 2025. “Internet of Things.” Last updated June 7. https://www.britannica.com/science/Internet-of-Things.
[2] Photosynthetically active radiation.
[3] Abu Sneineh, Anees, and Arafat A. A. Shabaneh. 2023. “Design of a Smart Hydroponics Monitoring System Using an ESP32 Microcontroller and the Internet of Things.” MethodsX 11: 102301. https://doi.org/10.1016/j.mex.2023.102401.
