Abstract: Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light.

Abstract: Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light.

Abstract: Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light.

Abstract: Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light.

Abstract: Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light.

Abstract: Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light.

Abstract: Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light.