# US 11,308,715 B2 · AI-powered autonomous plant-growth optimization system that automatically adjusts input variables to yield desired harvest traits > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,308,715 B2 - Document ID: US11308715B2 - Status: Granted - Kind: B2 - Application number: 17/068,016 - Filed: 2020-10-12 - Published: 2021-01-28 - Granted: 2022-04-19 - Earliest claimed: 2018-06-06 - Assignees: AgEYE Technologies, Inc. - Applicants: AgEYE Technologies, Inc. - Inventors: Nicholas R. Genty; John M. J. Dominic - Web page: https://patentgrep.com/patent/US11308715B2 ## CPC - G06N 3/08: Learning methods - G06N 3/0418: {using chaos or fractal principles} - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G06T 7/0002: {Inspection of images, e.g. flaw detection} - G06T 7/0004: {Industrial image inspection} - G06V 10/82: using neural networks - G06V 20/10: Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59) - G06V 20/188: {Vegetation} - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06N 20/20: Ensemble learning - G06T 2207/10024: Color image - G06T 2207/10048: Infrared image - G06T 2207/30128: Food products - G06T 2207/30188: Vegetation; Agriculture - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G06V 20/194: {using hyperspectral data, i.e. more or other wavelengths than RGB} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## 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. ## Claims ### Claim 1 A method of monitoring plant growth in an indoor farm, the method comprising: receiving, from an image sensor, raw image data that represents a plant being monitored; detecting a growth phase of the plant being monitored based on the received raw image data, wherein the growth phase of the plant being monitored is detected using a neural network that performs computer-based image analysis on the received raw image data; selecting a wavelength of light to apply to the plant being monitored based on the detected growth phase of the plant being monitored, wherein the wavelength of light is selected using an artificial-intelligence model; and applying the selected wavelength of light to the plant being monitored by sending a signal to a light fixture located proximate to the plant being monitored to adjust the output of the light fixture to the selected wavelength. ### Claim 2 (depends on 1) The method of claim 1, wherein the growth phase of the plant being monitored is detected using the neural network by comparing the received raw image data of the plant being monitored against a known growth phase for a plant of the same type as the plant being monitored. ### Claim 3 (depends on 1) The method of claim 1, further comprising detecting plant stress of the plant being monitored based on the detected growth phase. ### Claim 4 (depends on 3) The method of claim 3, wherein a severity of the detected plant stress is determined based on an environmental temperature of the plant being monitored that is received from a temperature sensor located in the indoor farm. ### Claim 5 (depends on 3) The method of claim 3, further comprising notifying a user of the detected plant stress. ### Claim 6 (depends on 1) The method of claim 1, further comprising determining a probability of disease outbreak in the indoor farm, wherein the probability of disease outbreak is determined based on the detected growth phase of the plant being monitored and a humidity in the indoor farm that is received from a humidity sensor, and wherein the probability of disease outbreak in the indoor farm is determined using an artificial-intelligence model. ### Claim 7 (depends on 6) The method of claim 6, wherein the determination of the probability of disease outbreak in the indoor farm is further based on the location in the indoor farm of the plant being monitored. ### Claim 8 (depends on 6) The method of claim 6, wherein the determination of the probability of disease outbreak in the indoor farm is further based on a type of the plant being monitored. ### Claim 9 (depends on 1) The method of claim 1, further comprising detecting a plant pathogen of the plant being monitored based on the received raw image data of the plant being monitored. ### Claim 10 (depends on 1) The method of claim 1, further comprising predicting a harvest date of the plant being monitored based on the detected of the plant being monitored, wherein the predicted harvest date is determined using an artificial-intelligence model. ### Claim 11 (depends on 10) The method of claim 10, further comprising notifying a user of the predicted harvest date. ### Claim 12 (depends on 1) The method of claim 1, further comprising determining whether the plant being monitored is ready for harvest based on the detected growth phase of the plant being monitored, wherein the determination of whether the plant is ready for harvest is made using an artificial-intelligence model. ### Claim 13 (depends on 12) The method of claim 12, further comprising notifying a user when the plant is ready for harvest. ### Claim 14 (depends on 1) The method of claim 1, wherein the wavelength of light is selected to trigger a response in the plant being monitored such that the response results in a harvest trait in the plant being monitored. ### Claim 15 (depends on 14) The method of claim 14, wherein the wavelength of light selected to trigger the response is no light, and wherein the signal sent to the light fixture located proximate to the plant being monitored to apply the selected wavelength of light is a signal that turns off the light fixture.