The AI Gronomy controller was developed with the professional and amateur in mind to fully be able to monitor, control, and adapt to any size grow environment using a radio mesh network. The controller called “Atlas” uses multiple embedded and remote sensors to gather environmental data, make decisions on that data maintaining optimum growing conditions. Along with the sensors there is an onboard camera that visually monitors your plants and can identify nutrient, mold and insect problems at first indication to enable early intervention. Image classification is at the heart of visual monitoring and can guide the grower to the type of problem that exists in the environment or on the
plant surface.
AI Gronomy uses multiple neural network architectures to learn your style of growing, the conditions you as a grower prefer, providing a system and related method to optimize plant growth in a controlled environment while minimizing the need for direct intensive labor to do so. The Atlas melds data processing with machine vision image classification via the deep convolutional neural networks. Data processing of image information with other sensed information to generate automated tasking and event planning based on a learned behavior from human input. The programmed processor is trained to remedy issues and maintain optimal control of the environment associated with a plant’s growth activity. The user accesses and controls the data through a dashboard based cloud server, enabling remote or offsite monitoring of grow conditions. This type of remote monitoring frees the user of direct intervention and allows for reduction in labor and the risk of grow room contamination