IoT sensors can be placed directly into controlled environments to intelligently monitor growing conditions and send data remotely in real-time for up to date, accurate analysis.
Artificial intelligence can be utilised to automatically interpret sensor data to offer value-added insights into plant health and growth, while providing effective management tools to ensure maximum yields.
Applied sensor analytics combined with deep learning technology can intuitively interpret big data to produce intelligent, useful information for comprehensive management decisions.
Machine learning platforms are adaptable to endless applications and integrate easily into any modern systems, enabling effortless data transfer and interpretation.
Sensors analyse environmental conditions in real-time with instantaneous responses from machine learning platforms that enable effective control of growing environments to ensure optimal conditions are maintained at all times.
Applied machine learning technology is highly capable of implementing automatic command actions in direct response to sensor values. This allows the remote control of switches, fans, humidifiers, water and nutrient solutions.
Plant health can be affected by a wide range of factors including temperature, light, water and nutrient availability. Controlled environments enable these factors to be optimised, with real-time changes made instantaneously so that plant health is never compromised.
Sensors are capable of monitoring continuously 24 hours a day, giving significant advantage over manual measurement methods. This enables minor changes to be detected and alerts sent in real-time to solve issues immediately.
Sensor deployment has infinite applications for controlled environments and opportunities are continually expanding to offer further intelligent solutions to optimise management and production.