Methods and processes for using artificial intelligence to manage and optimize energy consumption across vertical farming and greenhouse hydroponics combined cycle agriculture.

Abstract

A method for using artificial intelligence software to manage and optimize the energy consumption of a vertical farm and greenhouse, comprising a grid of solar panels supplying solar power, solar cells, and sensors throughout the vertical farm, which can heat or cool the greenhouse, with heating and cooling controlled according to plant species and growth stage, and comprising a composite cycle sensor and instrumentation, to which data from both the vertical farm and greenhouse is fed; an outdoor light measuring sensor that measures and estimates the conditions of solar power in the solar grid and transmits the data to the artificial intelligence software; and the artificial intelligence software determines how much light various plants in the hydroponic greenhouse require to maximize the yield from those plants.

Description

Technical Field

[0001] The present invention relates to a method and process for managing and optimizing energy consumption across vertical farming and greenhouse hydroponic composite cycle agriculture using artificial intelligence ("AI").

Background Art

[0002] The description of the background art includes information that may be useful in understanding the present invention. It is not admitted that any of the information provided in this specification is prior art, or is related to the invention described in the claims, or that any of the published documents specifically or implicitly referenced is prior art.

[0003] All published documents identified in this specification are incorporated by reference to the extent that each publication or patent application is as if specifically and individually indicated to be incorporated by reference. If the definition or use of a term in the incorporated reference is not consistent with or is contrary to the definition of that term provided in this specification, then the definition of that term provided in this specification shall apply and the definition of that term in the reference shall not apply. The following description includes information that may be useful in understanding the present invention. It is not admitted that any of the information provided in this specification is prior art, or is related to the invention described in the claims, or that any of the published documents specifically or implicitly referenced is prior art.

[0004] In some embodiments, it is to be understood that the numbers representing properties such as amounts, concentrations of components, reaction conditions, etc., used to describe and claim specific embodiments of the present invention are, in some instances, modified by the term "about".

[0005] Therefore, in some embodiments, the numerical parameters described in the specification and appended claims are approximations that may vary depending on the desired characteristics to be obtained by a particular embodiment.

[0006] In some embodiments, numerical parameters are preferably interpreted in light of the number of significant figures reported and by applying standard rounding methods. Although the numerical ranges and parameters describing a broad range of some embodiments of the present invention are approximations, the numerical values ​​described in the specific examples are reported as accurately as possible for practical purposes.

[0007] The numerical values ​​shown in some embodiments of the present invention may include certain errors that inevitably arise from the standard deviation observed in each test measurement.

[0008] Unless the context indicates otherwise, all ranges described herein should preferably be interpreted as including their endpoints, and non-limiting ranges should preferably be interpreted as including only commercially useful values. Similarly, all listed values ​​should preferably be considered to include intermediate values ​​unless the context indicates otherwise.

[0009] As used in this specification and throughout the attached claims, the meanings of “a,” “an,” and “the” include plural references unless otherwise explicitly stated by the context. Furthermore, as used in this specification, the meaning of “in” includes both “in” and “on” unless otherwise explicitly stated by the context.

[0010] The enumeration of value ranges in this specification is intended solely as a shorthand notation for individually referring to the individual values ​​that fall within those ranges. Unless otherwise indicated herein, each individual value is incorporated herein as if it were individually enumerated. All methods described herein may be performed in any preferred order unless otherwise indicated herein or unless it would be clearly inconsistent with the context. The use of any examples or exemplary language provided herein in relation to a particular embodiment (e.g., "such as") is intended solely to further illustrate the invention and not to limit the scope of the invention as otherwise claimed.

[0011] It is preferable that nothing in this specification be construed as indicating an unclaimed component that is essential to the implementation of the present invention.

[0012] The grouping of alternative components or embodiments of the Invention disclosed herein shall not be construed as limiting. Each member of a group may be claimed by reference individually or in combination with other members of the group or other components found herein. One or more members of a group may be included in or removed from a group for convenience and / or patentability reasons. In the event of any such inclusion or removal, this Specified herein shall be deemed to include the modified group and shall satisfy all descriptions of the Markush group used in the appended claims.

[0013] Energy is the most expensive expense on any farm with vertical lighting. Today, farms use more energy than is necessary to grow crops optimally. In some cases, energy can account for up to 31% of a farm's operating costs.

[0014] While outdoor farming is less expensive than vertical farming, the outdoor spaces used for these farms are typically located far from major metropolitan areas that consume the majority of the produce from those farms. In fact, fruits and vegetables can sometimes be transported thousands of miles, resulting in further pollution and exacerbation of climate change.

[0015] Climate change is also increasing heat all over the globe and reducing the availability of freshwater, both of which are detrimental to farming. [Overview of the Initiative] [Means for solving the problem]

[0016] This invention solves these problems because it produces crops with the lowest energy use using optimal photosynthetic yields both indoors and outdoors.

[0017] No vertical farm exists that uses 100% renewable energy. Nor does a vertical farm that optimally utilizes sunlight. This invention uses 100% renewable energy and optimizes the use of sunlight for solar power.

[0018] Vertical farms in containers can be easily installed in major metropolitan areas. In alternative embodiments, seedbeds are used instead of vertical farms. Hydroponic greenhouses can similarly be installed on rooftops or even in individual residences.

[0019] Both vertical farming and solar power can be costly, therefore, there is a need to optimize the use of energy from solar power and maximize the yield of crops that can be grown.

[0020] This invention represents the most cost-effective use of hydroponics, combining indoor farming with a hydroponic greenhouse that draws power from solar energy. In places like the Middle East and California, where climate change is causing rising heat levels and water scarcity, this invention could potentially utilize large amounts of solar energy to produce crops locally, in contrast to importing crops thousands of miles away.

[0021] Many aspects of this disclosure can be better understood with reference to the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather to emphasize the clear illustration of the principles of the disclosure. Furthermore, similar reference numerals in the drawings point to corresponding parts across multiple figures. [Brief explanation of the drawing]

[0022] [Figure 1] Figure 1 illustrates a method and process for using artificial intelligence to manage and optimize energy consumption across vertical farming and greenhouse hydroponics combined cycle agriculture, according to various embodiments of the present disclosure. [Figure 2] Figure 2 illustrates a method and process for using artificial intelligence to manage and optimize energy consumption across vertical farming and greenhouse hydroponics combined cycle agriculture, according to various embodiments of the present disclosure. [Figure 3] Figure 3 illustrates a method and process for using artificial intelligence to manage and optimize energy consumption across vertical farming and greenhouse hydroponics combined cycle agriculture, according to various embodiments of the present disclosure. [Figure 4] Figure 4 illustrates a method and process for using artificial intelligence to manage and optimize energy consumption across vertical farming and greenhouse hydroponics combined cycle agriculture, according to various embodiments of the present disclosure. [Figure 5] Figure 5 illustrates a method and process for using artificial intelligence to manage and optimize energy consumption across vertical farming and greenhouse hydroponics combined cycle agriculture, according to various embodiments of the present disclosure. [Figure 6]Figure 6 is a diagram of a method and process for managing and optimizing energy consumption across vertical farming and greenhouse hydroponic composite cycle agriculture by artificial intelligence according to various embodiments of the present disclosure. [Figure 7] Figure 7 is a diagram of a method and process for managing and optimizing energy consumption across vertical farming and greenhouse hydroponic composite cycle agriculture by artificial intelligence according to various embodiments of the present disclosure. [Figure 8] Figure 8 is a diagram of a method and process for managing and optimizing energy consumption across vertical farming and greenhouse hydroponic composite cycle agriculture by artificial intelligence according to various embodiments of the present disclosure. [Figure 9] Figure 9 is a diagram of a method and process for managing and optimizing energy consumption across vertical farming and greenhouse hydroponic composite cycle agriculture by artificial intelligence according to various embodiments of the present disclosure. [Figure 10] Figure 10 is a diagram of a method and process for managing and optimizing energy consumption across vertical farming and greenhouse hydroponic composite cycle agriculture by artificial intelligence according to various embodiments of the present disclosure.

Mode for Carrying Out the Invention

[0023] Various embodiments of the present disclosure relate to providing a method and process for managing and optimizing energy consumption across vertical farming and greenhouse hydroponic composite cycle agriculture by artificial intelligence.

[0024] There are vertical farms and hydroponic greenhouses, both of which are equipped with sensors and control devices everywhere and are used in combination. In an alternative embodiment, there is a seedbed instead of a vertical farm. Both the vertical farm and the hydroponic greenhouse transmit data to artificial intelligence energy optimization software.

[0025] Artificial intelligence is the intelligence exhibited by machines, particularly computers. It includes software that enables machines to perceive their environment and uses learning and intelligence to maximize the likelihood of achieving defined goals. One form of AI is machine learning, which involves statistical algorithms that can learn from data and generalize to unseen data, thereby enabling them to perform tasks without explicit instructions. Training data is provided from time to time to ensure that AI software based on machine learning learns the correct lessons. Another form of AI is a neural network, which is a model inspired by the structure of the brain. AI based on neural networks includes nodes called artificial neurons, which are modeled after neurons in the brain. These are connected by edges that model synapses in the brain. Each artificial neuron receives signals from connected neurons, processes those signals, and transmits them to other connected neurons. These "signals" are real numbers, and the output of each neuron is calculated by some nonlinear function of the sum of the inputs to that neuron, which is called the activation function. The strength of the signal at each connection is determined by weights, which are adjusted during the learning process. Typically, neurons are aggregated into layers. Different layers can perform different transformations on the inputs they receive. Signals travel from the first layer (input layer) to the last layer (output layer), sometimes passing through multiple hidden layers. When a network has at least two hidden layers, it is typically called a deep neural network. Machine learning that uses deep neural networks is called deep learning.

[0026] Solar power is absorbed through the solar grid of solar panels. The solar grid measures solar power generation data. There are solar cells that store some of the energy absorbed by the solar grid.

[0027] The plants begin as seedlings and plugs, in which case, plugs in horticulture are small seedlings grown in seed trays filled with potting soil. In one embodiment of the present invention, the seedlings and plugs begin in larger containers similar to or identical to standard shipping containers. In another embodiment of the present invention, the vertical farm can be incorporated into a greenhouse without containers. They are placed close together and spaced apart. Once the plants have grown beyond the seedling stage, they are moved into the greenhouse and placed in a planting medium.

[0028] The planting medium may be deep hydroponics or nutrient film technique ("NFT"), or ebb & flow, or rockwool slab, or Dutch bucket. Plants are transferred from indoor vertical hydroponics for germination to seedlings and planted in a greenhouse hydroponic system. In one embodiment of the present invention, plants are spaced more sparsely than in containers to allow time for the plants to grow to full size. In another embodiment of the present invention, the vertical farm can be maintained without transplanting.

[0029] Solar energy is used to power the containers and greenhouses. Power usage includes heating, cooling, and pumping. Light is provided through sunlight within the greenhouse. If sunlight is insufficient, for example during a storm or in the middle of winter far from the equator, artificial light may be used to supplement the lighting. The solar grid charges the solar cells. The batteries last longest when maintained at 60-80% charge. Power goes directly from the solar grid to the heating, cooling, and pumps, balancing the load with the solar cell charge, while artificial intelligence energy optimization software balances the load. The solar cells transmit battery percentage data to the artificial intelligence energy optimization software.

[0030] The optimal range for extending battery life is between 20% and 60% charge.

[0031] Fertilizer is added as needed, both at the seedling stage and later. Either chemical or organic fertilizers can be used, depending on what is best for each type of plant.

[0032] Heating and cooling are controlled according to the type of plant and its stage of growth. Some plant seedlings require a specific temperature, while other stages of growth require different temperatures; different plants require different temperatures at each stage. Typically, plants can exhibit greater heat tolerance as they grow towards maturity.

[0033] There are combined cycle sensors, instrumentation, and control systems. There are also outdoor light measurement sensors and instrumentation. Data from both the vertical farm and the hydroponic greenhouse are fed to the combined cycle sensors and instrumentation. Data from the outdoor light measurement sensors and instrumentation are fed to artificial intelligence energy optimization software. The outdoor light measurement sensors and instrumentation measure the estimated photosynthetic yield and solar power generation. The outdoor light measurement sensors and instrumentation also measure the amount of photosynthetically active radiation ("PAR") and the total amount of radiation flow. The data from the outdoor light measurement sensors and instrumentation allows the artificial intelligence energy optimization software to determine how much light is needed for the various plants in the hydroponic greenhouse. The outdoor light measurement sensors and instrumentation also measure and estimate the solar power conditions within the solar grid.

[0034] Photosynthetically active radiation tells the artificial intelligence energy optimization software how much light from the sun is available for the plants to grow. Then, if more lighting needs to be turned on to maximize the yield of a particular crop, the AI ​​energy optimization software will turn on more lights until the amount of light reaching that crop results in the maximum yield for that crop. Alternatively, if more lighting needs to be turned off to maximize the yield of a particular crop, the AI ​​energy optimization software will turn off more lights until the amount of light reaching that crop results in the maximum yield for that crop.

[0035] The combined cycle sensor, instrumentation, and control system measures ambient temperature, water temperature, lighting on or off time, flow rate, carbon dioxide levels, humidity, nutrient solution levels, electrical conductivity, and pH.

[0036] The combined cycle sensor, instrumentation, and control system transmit data to artificial intelligence energy optimization software.

[0037] The artificial intelligence energy optimization software transmits data to the combined cycle sensor, instrumentation, and control system, informing them of what to do.

[0038] There is a non-networked artificial intelligence architecture that integrates solar grid data with real-time data, solar cells with real-time data, and outdoor light measurement sensors and instrumentation with real-time data.

[0039] Artificial intelligence energy optimization software analyzes inputs from vertical farms, hydroponic greenhouses, solar grids, and solar panels to optimize conditions in vertical farms and hydroponic greenhouses to maximize crop yields. The software performs this optimization by modifying heating, cooling, flow rates, lighting, carbon dioxide levels, humidity, nutrient solutions, electrical conductivity, and pH. It makes these changes based on what is optimal for the yield of each plant at each stage of its life cycle. The software attempts to minimize overall energy consumption per crop.

[0040] The vertical farm and hydroponic greenhouses operate year-round using artificial intelligence energy optimization software. This software constantly attempts to maximize crop yields within the vertical farm.

[0041] If the sensor detects that there is too much carbon dioxide for a particular plant, the artificial intelligence energy optimization software will open vents to reduce the carbon dioxide until the sensor signals that the optimal amount of carbon dioxide for that plant has been achieved.

[0042] If the sensor detects that the humidity is too high for a particular plant, the artificial intelligence energy optimization software will either turn on a fan near the plant, turn on a dehumidifier near the plant, or place a cooling pad near the plant to reduce the humidity until the sensor signals that the optimal humidity level for that plant has been achieved. Alternatively, if the sensor detects that the humidity is too high for a particular plant and that the outdoor environment is not humid, the artificial intelligence energy optimization software will open vents to facilitate humidity reduction until the sensor signals that the optimal humidity level for that plant has been achieved.

[0043] If the sensor detects that the air is too hot for a particular plant, the artificial intelligence energy optimization software will either turn on the air conditioning near that plant, or if the sensor detects that the outdoor air is not hotter than the indoor air, the artificial intelligence energy optimization software will open vents to facilitate a temperature reduction, and will continue this until the sensor signals that the optimal temperature for that particular plant has been achieved.

[0044] If the sensor detects that the air is too cold for a particular plant, the artificial intelligence energy optimization software will turn on a water heater near that plant, or if the sensor detects that the outdoor air is not colder than the indoor air, the artificial intelligence energy optimization software will open vents to promote a temperature increase, and will continue this until the sensor signals that the optimal temperature for that particular plant has been achieved.

[0045] If the sensor detects that the water is too hot for a particular plant, the artificial intelligence energy optimization software will turn on the water chiller supplying the plant until the sensor signals that the optimal temperature for the water going to that plant has been achieved.

[0046] If the sensor detects that the water is too cold for a particular plant, the artificial intelligence energy optimization software will turn on the water heater supplying that plant until the sensor signals that the water has reached the optimal temperature.

[0047] If the sensor detects that a certain component of the nutrient solution is deficient for a particular plant, the artificial intelligence energy optimization software will instruct the machine to add those components to the nutrient solution going to that plant until the sensor signals that the optimal level of those components has been achieved.

[0048] If the sensor detects that the amount of a certain component in the nutrient solution is too high for a particular plant, the artificial intelligence energy optimization software will instruct the machine to remove those components from the nutrient solution going to the plant until the sensor signals that the optimal level of those components has been achieved for the nutrient solution going to that plant.

[0049] If the sensor detects that the electrical conductivity in Siemens per meter is too low for a particular plant, the artificial intelligence energy optimization software will instruct the machine to increase the Siemens per meter of electrical conductivity going to that plant until the sensor signals that the Siemens per meter of electrical conductivity going to that plant has reached an optimal level.

[0050] If the sensor detects that the electrical conductivity is too high in Siemens per meter for a particular plant, the artificial intelligence energy optimization software will instruct the machine to reduce the Siemens per meter of electrical conductivity going to that plant until the sensor signals that the Siemens per meter of electrical conductivity going to that plant has reached an optimal level.

[0051] If the sensor detects that the pH of a particular plant is too low, the artificial intelligence energy optimization software will instruct the machine to add a basic liquid to that plant to increase its pH until the sensor signals that the plant's pH has reached the optimal level.

[0052] If the sensor detects that the pH is too high in a particular plant, the artificial intelligence energy optimization software will instruct the machine to add an acidic liquid to that plant to lower its pH until the sensor signals that the plant's pH has reached an optimal level.

[0053] Artificial intelligence energy optimization software utilizes different types of artificial intelligence depending on the situation. It can employ machine learning, deep learning, neural networks, or any other useful neural network architecture.

[0054] Solar panels may never reach 100% charge. If a solar panel reaches 1%, the artificial intelligence energy optimization software may stop directing energy from the solar grid to the vertical farm and hydroponic greenhouse and instead focus on charging the solar panel. Alternatively, if a solar panel is above 80%, the artificial intelligence energy optimization software may direct all energy from the solar grid to the vertical farm and hydroponic greenhouse and not charge the solar panel until it drops to 60%. If a solar panel is charged beyond 100%, it can cause battery damage, battery degradation, and potentially fire. Therefore, if a solar panel has already reached 99% charge, energy from the solar grid will stop charging the solar panel and instead focus on powering the vertical farm and hydroponic greenhouse.

[0055] Outdoor light measurement sensors and instrumentation also measure all the energy available for solar conversion. The outdoor light measurement sensors transmit this data to artificial intelligence energy optimization software, which estimates the power generation of the solar grid. This data is available in real time. One unit for measuring energy from the solar grid is the kilowatt-hour.

[0056] Different crops that can be grown using this invention include lettuce, tomatoes, leafy vegetables, and climbing crops. Many other fruits, vegetables, lentils, and other plants can also be grown using this invention.

[0057] In one embodiment of the present invention, artificial intelligence energy optimization software balances energy generation from the solar grid, energy storage in solar cells, and energy consumption in the vertical farm and hydroponic greenhouse to maximize the yield of crops grown in the vertical farm and hydroponic greenhouse.

[0058] In one embodiment of the present invention, the solar cells have a backup energy source. In environments with less sunlight, increasing the solar grid can compensate for the reduced amount of sunlight, still providing enough energy to power vertical farms, hydroponic greenhouses, and artificial intelligence energy optimization software. In an alternative embodiment, a nursery is used instead of a vertical farm.

[0059] Figure 1 is a flowchart of one embodiment of the present invention, showing how the vertical farm and hydroponic greenhouse interact with the AI ​​software. Figure 2 is the same flowchart as Figure 1, except that the operations described in Figure 1 are shown numerically. There are vertical farm inputs 201 for seeds, fertilizer, heating, cooling, pumps, and lighting. There are hydroponic greenhouse inputs 202 for seedlings, fertilizer, heating, cooling, and pumps. There are composite cycle sensors / instruments and control devices 203 for air temperature (heating or cooling or ventilation), water temperature (on and off of water heater or chiller), lighting on and off, flow sensors, carbon dioxide on or vent opening, humidity (fan and pad cooling or dehumidifier or greenhouse window opening), nutrient solution electrical conductivity, and pH. There are outdoor light measurement sensors and instruments 204 that give estimated photosynthetic yield and photovoltaic power generation. There is AI energy optimization 205 that includes balancing energy generation, energy storage, and energy consumption. There is a solar grid 206 for solar panels that give photovoltaic power generation data. There is a solar cell 207 that stores energy from the solar panel and provides battery percentage data.

[0060] Figure 3 shows sensors located throughout the greenhouse. These include a humidity sensor 301, a soil pH sensor 302, a temperature sensor 303, and a flow sensor 304. These sensors can be swapped and moved to different locations throughout the greenhouse and vertical farm.

[0061] Figure 4 shows the outdoor light measurement sensors of the PAR. Sensor 401 absorbs UV light across the entire color spectrum and analyzes it. Sensor 402 also absorbs UV light and analyzes it. Display 403 shows the analysis of the PAR based on sensors 401 and 402.

[0062] Figure 5 shows multiple solar cells and solar panels arranged in a grid configuration.

[0063] Figure 6 shows a sensor for measuring soil pH in either a greenhouse or a vertical farm.

[0064] Figures 7 through 10 are different images of vertical farms. These are different arrangements of vertical farms, and different sensors can be placed in various different locations within these vertical farms, depending on where those sensors will obtain accurate measurement results.

[0065] Each of the following further embodiments can be combined with any other in any combination. Furthermore, in each of the following embodiments, the vertical farm can be replaced with a seedling bed. Moreover, all of the embodiments described can be combined with one or more other embodiments in any combination.

[0066] In a further embodiment, there is a method for having artificial intelligence software manage and optimize energy consumption across a vertical farm and greenhouse, comprising: a grid of solar panels providing solar power; solar cells capable of storing solar power from the grid of solar panels; sensors throughout the vertical farm; the ability to heat or cool the vertical farm and greenhouse; heating and cooling controlled according to plant species and growth stage; a combined cycle sensor and instrumentation; data from both the vertical farm and greenhouse being fed to the combined cycle sensor and instrumentation; an outdoor light measuring sensor; the outdoor light measuring sensor measuring photosynthetically active radiation ("PAR"); the outdoor light measuring sensor also measuring and estimating the conditions of solar power within the solar grid; data from the outdoor light measuring sensor being transmitted to artificial intelligence software; and the artificial intelligence software determining how much light various plants in a hydroponic greenhouse need to maximize their yield.

[0067] In a further embodiment, there is a method for having artificial intelligence software manage and optimize energy consumption across vertical farming and greenhouses, comprising: a sensor that measures carbon dioxide; the sensor that transmits data to the artificial intelligence software; and the artificial intelligence software that uses this data to make adjustments to the conditions within the vertical farm and greenhouse to maximize plant yields.

[0068] In a further embodiment, there is a method for having artificial intelligence software manage and optimize energy consumption across vertical farming and greenhouses, comprising: a sensor that measures the pH of the soil in which the plants are located; the sensor transmitting data to the artificial intelligence software; and the artificial intelligence software using this data to make adjustments to the conditions within the vertical farm and greenhouse to maximize plant yields.

[0069] In a further embodiment, there is a method for having artificial intelligence software manage and optimize energy consumption across vertical farming and greenhouses, comprising: a sensor that measures greenhouse humidity; the sensor transmitting data to artificial intelligence software; and the artificial intelligence software using this data to adjust conditions within the greenhouse to maximize plant yields.

[0070] In a further embodiment, there is a method for using artificial intelligence software to manage and optimize energy consumption across vertical farming and greenhouses, comprising: a sensor that measures nutrient solutions in plants within the greenhouse; the sensor transmitting data to the artificial intelligence software; and the artificial intelligence software using this data to adjust the conditions within the greenhouse to maximize plant yields.

[0071] In a further embodiment, there is a method for having artificial intelligence software manage and optimize energy consumption across vertical farming and greenhouses, wherein the type of artificial intelligence is machine learning.

[0072] In a further embodiment, there is a method for having artificial intelligence software manage and optimize energy consumption across vertical farming and greenhouses, wherein the type of artificial intelligence is deep learning.

[0073] In a further embodiment, there is a method for having artificial intelligence software manage and optimize energy consumption across vertical farming and greenhouses, wherein the type of artificial intelligence is a neural network.

[0074] In a further embodiment, there is a method for having artificial intelligence software manage and optimize energy consumption across vertical farming and greenhouses, wherein some of the changes that the artificial intelligence software can make to both the vertical farm and greenhouse include: changing the temperature, changing the flow rate of water or other liquids, changing the lighting, changing the carbon dioxide levels, changing the humidity, changing the nutrient solution for each plant, changing the electrical conductivity, and changing the pH of either the soil or water for the plants.

[0075] In a further embodiment, there is a method for using artificial intelligence software to manage and optimize energy consumption across vertical farming and greenhouses, wherein the greenhouse can contain hydroponically grown plants.

[0076] In a further embodiment, there is a method for using artificial intelligence software to manage and optimize energy consumption across vertical farming and greenhouses, wherein the greenhouse can include aquatic plants.

[0077] In a further embodiment, a method for using artificial intelligence software to manage and optimize energy consumption across vertical farming and greenhouses: the method comprises a sensor that measures carbon dioxide; the sensor transmits data to artificial intelligence software; the artificial intelligence software uses this data to adjust conditions in the vertical farm and greenhouse to maximize plant yield; the method comprises a sensor that measures the pH of the soil in which the plants are located; the sensor transmits data to artificial intelligence software; the artificial intelligence software uses this data to adjust conditions in the vertical farm and greenhouse to maximize plant yield; the method comprises a sensor that measures humidity in a greenhouse; the sensor transmits data to artificial intelligence software; the artificial intelligence software uses this data The method involves adjusting greenhouse conditions to maximize plant yield; the method includes sensors that measure nutrient solutions in plants in the vertical farm and greenhouse; the sensors transmit data to artificial intelligence software; the artificial intelligence software uses this data to adjust greenhouse conditions to maximize plant yield; the type of artificial intelligence is machine learning; the type of artificial intelligence is deep learning; the type of artificial intelligence is a neural network; and some of the changes the artificial intelligence software can make in both the vertical farm and greenhouse include: changing the temperature, changing the flow rate of water or other liquids, changing the lighting, changing the carbon dioxide level, changing the humidity, changing the nutrient solution for each plant, changing the electrical conductivity, and changing the pH of either the soil or water for the plants.

[0078] From the above, it will be understood that specific embodiments of the present invention have been described herein for illustrative purposes, but that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, the present invention is not limited to the appended claims.

Claims

1. A method for using artificial intelligence software to manage and optimize the energy consumption of seedbeds and the entire greenhouse: A grid of solar panels that provides solar power; A solar cell capable of storing solar power from the grid of the aforementioned solar panels; It includes, Sensors are placed throughout the seedling beds and greenhouses; The seedbed and greenhouse can be heated or cooled; Heating and cooling are controlled according to the type of plant and stage of growth; It has a combined cycle sensor and instrumentation; Data from both the vertical farm and the greenhouse is fed to the combined cycle sensor and instrumentation; There is an outdoor light measurement sensor; The aforementioned outdoor light measuring sensor measures photosynthetically active radiation ("PAR"); The outdoor light measurement sensor also measures and estimates the conditions of solar power within the solar grid; The data from the outdoor light measuring sensor is transmitted to the artificial intelligence software; The artificial intelligence software determines how much light each plant in the hydroponic greenhouse needs to maximize its yield. method.

2. moreover, A sensor that measures carbon dioxide; It includes, The sensor transmits data to the artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions in the vertical farm and greenhouse in order to maximize the yield of the plants. The method according to claim 1.

3. moreover, A sensor for measuring the pH of the soil containing the aforementioned plants; It includes, The sensor transmits data to the artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions in the vertical farm and greenhouse in order to maximize plant yields. The method according to claim 1.

4. moreover, A sensor for measuring humidity in the seedling bed and greenhouse; It includes, The sensor transmits data to the artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions inside the greenhouse in order to maximize the yield of the plants. The method according to claim 1.

5. moreover, A sensor for measuring the nutrient solution of the plants in the seedbed and greenhouse; It includes, The sensor transmits data to the artificial intelligence software; The aforementioned artificial intelligence software uses this data to adjust the conditions inside the greenhouse in order to maximize plant yield. The method according to claim 1.

6. moreover, The aforementioned type of artificial intelligence is machine learning. The method according to claim 1.

7. moreover, The aforementioned type of artificial intelligence is deep learning. The method according to claim 1.

8. moreover, The aforementioned type of artificial intelligence is a neural network. The method according to claim 1.

9. moreover, Some of the changes that the artificial intelligence software can make to both the vertical farm and the greenhouse are: Temperature change, Changing the flow rate of water or other liquids, Lighting changes, Changes in carbon dioxide levels, Humidity changes, Modification of nutrient solutions for a certain group of plants, Changes in electrical conductivity, and Changing the pH of either the soil or water for plants including, The method according to claim 1.

10. moreover, The greenhouse can include hydroponic plants. The method according to claim 1.

11. moreover, If a greenhouse can contain aquatic plants, The method according to claim 1.

12. moreover: A sensor that measures carbon dioxide; A method according to claim 1, comprising: The sensor transmits data to artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions in the vertical farm and greenhouse in order to maximize the yield of the plants; The method is a sensor for measuring the pH of the plant's nutrient solution; It includes, The sensor transmits data to the artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions in the vertical farm and greenhouse in order to maximize the yield of the plants; The method involves a sensor that measures the humidity in the seedbed and greenhouse; It includes, The sensor transmits data to the artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions inside the greenhouse in order to maximize the yield of the plants; The method includes a sensor for measuring the nutrient solution of the plants in the seedbed and greenhouse; It includes, The sensor transmits data to the artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions inside the greenhouse in order to maximize the yield of the plants; The type of artificial intelligence is either machine learning, deep learning, or a neural network; Some of the changes that the artificial intelligence software can make to both the vertical farm and the greenhouse are: Changing the flow rate of water or other liquids, Lighting changes, Changes in carbon dioxide levels, Humidity changes, Changes to nutrient solutions for each plant, Changes in electrical conductivity, and Changing the pH of either the soil or water for the aforementioned plants, including, method.

13. A method for using artificial intelligence software to manage and optimize energy consumption across vertical farming and greenhouses: Sensors are placed throughout the seedling bed and greenhouse. The seedbed and greenhouse can be heated or cooled. Heating and cooling are controlled according to the plant type and growth stage. It has a combined cycle sensor and instrumentation; Data from both the vertical farm and the greenhouse is supplied to the combined cycle sensor and instrumentation. There is an outdoor light measurement sensor. The aforementioned outdoor light measuring sensor measures photosynthetically active radiation ("PAR"); Other sensors measure and estimate the conditions of solar power within the solar grid; Data from the outdoor light measuring sensor is transmitted to the artificial intelligence software; The artificial intelligence software determines how much light each plant in the hydroponic greenhouse needs to maximize its yield. method.

14. moreover, A grid of solar panels that supplies solar power; A solar cell capable of storing solar power from the grid of the aforementioned solar panels, The method according to claim 13, comprising:

15. moreover, Multiple sensors for measuring carbon dioxide in both the seedbed and the greenhouse; It includes, The sensor transmits data to the artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions in the vertical farm and greenhouse in order to maximize the yield of the plants. The method according to claim 13.

16. moreover, A sensor for measuring the pH of the nutrient solution containing the aforementioned plant; It includes, The sensor transmits data to the artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions in the vertical farm and greenhouse in order to maximize the yield of the plants. The method according to claim 13.

17. moreover A sensor for measuring humidity in the seedling bed and greenhouse; It includes, The sensor transmits data to the artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions inside the greenhouse in order to maximize the yield of the plants; The method according to claim 13.

18. moreover, A sensor for measuring the nutrient solution of the plants in the vertical farm and greenhouse; It includes, The sensor transmits data to artificial intelligence software; The artificial intelligence software uses this data to adjust the conditions inside the greenhouse in order to maximize the yield of the plants. The method according to claim 13.

19. moreover, The type of artificial intelligence used by the aforementioned artificial intelligence software is either machine learning, deep learning, or a neural network. The method according to claim 13.

20. A method for using artificial intelligence software to manage and optimize energy consumption across vertical farming and greenhouses: Sensors are placed throughout the aforementioned vertical farm and greenhouse; The aforementioned vertical farm and greenhouse can be heated or cooled; Heating and cooling are controlled according to the type of plant and its growth stage; It has a combined cycle sensor and instrumentation; Data from both the vertical farm and the greenhouse is fed to the combined cycle sensor and instrumentation; There is an outdoor light measurement sensor; An outdoor light measurement sensor measures photosynthetically active radiation ("PAR"); Other sensors measure and estimate the conditions of solar power within the solar grid; Data from the outdoor light measuring sensor is transmitted to the artificial intelligence software; The artificial intelligence software determines how much light each plant in the hydroponic greenhouse needs to maximize its yield; Some of the changes that the artificial intelligence software can make to both the vertical farm and the greenhouse are: Temperature change, Changing the flow rate of water or other liquids, Lighting changes, Changes in carbon dioxide levels, Humidity changes, Changes in electrical conductivity, and Changing the pH of either the soil or water for the aforementioned plants, including, method.

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