Smart sensors can be placed directly in growing environments and utilise the Internet of Things (IoT) network to perform automated data transfer directly to machine learning algorithms.
Controls can be set remotely to manage optimal growing environments while commands are issued automatically through applied machine learning, ensuring that plant health is maximised constantly.
Artificial intelligence can be utilised to logically interpret and analyse data from sensors placed directly in controlled environments. Machine learning platforms can then perform commands to adjust and maintain ideal plant growing conditions.
Sensors can be placed directly in the growing environment to monitor key parameters such as light, temperature, humidity and water availability. Real-time monitoring realises the potential for optimal growing conditions to be constantly maintained, boosting crop growth, health and yield.
Intelligent sensor deployment allows minor changes to be detected as soon as they happen and real-time alerts can then be sent to the farmer. This enables fast action to resolve issues and ultimately safeguard crops and yields.
Growing environments are effortlessly managed through the use of advanced machine learning technology that intuitively controls and commands key factors such as light, temperature, nutrient and water availability.
Sensor values are interpreted by machine learning algorithms that produce real-time alerts sent straight to the producer to signal that sensor readings are outside predefined optimal ranges, enabling simple, effective management and quick action on problems.
Optimising growing environments through real-time monitoring alongside control and command technology enables plant health and growth to be optimised, contributing to increasing knowledge bases and research solutions to real-world problems.
Digitalisation of farm management can ensure that businesses are robust and future-proof, while increasing efficiency and production to achieve increased crop health, growth and yields.