Techniques for interaction with, and customization of, production data for farm products

Abstract

The present teachings include systems and methods of providing relatively open access to production data for farm products, e.g., by associating a farm product with a unique identifier, linking production data thereto, and providing access to the data through a user interface. The production data may be accessible by, e.g., scanning a tag on the product. Also or instead, the system may transmit different types of production data, such as videos showing the farm product during one or more stages of production, and / or data related to a characteristic thereof — such as origin, flavor, and so on — or data related to a farm facility and / or production conditions. In aspects, users can provide feedback on the product's characteristics, where the system can adjust production parameters based on received feedback, enhancing the quality and customization of farm products. These techniques can facilitate transparent farming practices, and improve consumer education and satisfaction.

Description

TECHNIQUES FOR INTERACTION WITH, AND CUSTOMIZATION OF, PRODUCTION DATA FOR FARM PRODUCTSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Pat. App. No. 63 / 650,588 filed on May 22, 2024, the entire contents of which is hereby incorporated by reference herein.FIELD

[0002] The present disclosure generally relates to information technology for agriculture, and more specifically to devices, systems, and methods related to providing access, interaction, and / or customization related to production data for farm products.BACKGROUND

[0003] Consumers of farm products often have concerns about how the products are produced. Such concerns may relate to health and safety, environmental impact, animal husbandry, labor practices, efficiency, and more. It may also or instead be desirous to change or otherwise customize one or more characteristics of a farm product, e.g., through selection of certain production parameters used in the production of that farm product. It may also or instead be desirous to change or otherwise customize one or more characteristics of a growing space e.g., a region of a farm, such as through selection of certain farm products and / or production parameters used in the production of such farm products. There thus remains a need for increased access to production data for farm products, and / or for techniques providing interaction and / or customization related to production data for farm products.SUMMARY

[0004] The present teachings include systems and methods of providing relatively open access to production data for a specific farm product. The farm product may be associated with a unique identifier, production data specific to the product may be linked with the identifier, and a user interface may provide access to the data through a data network. The production data may be accessible to the general public by, for example, scanning a tag on the product. Also or instead, a specific farm product may be associated with a user account and accessible via a user interface. The system may transmit different types of production data, such as a video showing the farm product during one or more stages of production, data related to a characteristic of the farm product — such as origin, flavor, and so on — or data related to the farm facility or production conditions. Users can provide feedback on the product’s characteristics, and the system may adjust production parameters based on received feedback, enhancing thequality and customization of farm products. These techniques can facilitate transparent farming practices, and improve consumer education and satisfaction.

[0005] In an example aspect, a computer program product disclosed herein may include computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices, performs the steps of: associating a farm product with a unique identifier, the farm product having one or more characteristics and being produced under one or more production parameters; associating production data specific to the farm product with the unique identifier, the production data including at least information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the farm product; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, feedback associated with the one or more characteristics of the farm product; and adjusting the one or more production parameters responsive to the feedback. Other implementations of this aspect include corresponding computer systems, apparatus, methods, and computer programs recorded on one or more computer storage devices, each configured to perform one or more of the aforementioned steps.

[0006] Implementations of this example aspect, or any other example aspect described in this summary section or otherwise herein, may include one or more of the following features. The production data may include one or more of: a video of production, the one or more production parameters, and a time-series plot. The one or more production parameters may include one or more of: a daily light integral (DLI), a light intensity, a light spectra, a production duration, a feed amount, a feed composition, a water amount, a water savings amount, a temperature, a humidity, a location within a farm, a treatment regimen, an energy usage, and an efficiency. The one or more characteristics of the farm product may include one or more of: a flavor, a texture, a mass, and an appearance. The user interface may be configured to receive the feedback associated with the one or more characteristics of the farm product through one of a slider, a spinner, a point scale, and a text field. The feedback received may be associated with the one or more characteristics of the farm product, and adjusting the one or more production parameters may include an adjustment to one or more of lighting, feed, water, soil, and an environmental condition. The feedback received may be associated with flavor, and adjusting the one or more production parameters may include changing an amount of nitrogen delivered. The computer program product may include code that, when executing on the one or morecomputing devices, performs the step of: automatically adjusting the one or more production parameters responsive to receiving the feedback. The computer program product may include code that, when executing on the one or more computing devices, performs the steps of: storing the feedback associated with the one or more characteristics of the farm product in a training data set; training a model to predict consumer preferences using the training data set; and adjusting the one or more production parameters responsive to a model prediction. The feedback may be associated with one or more of a geographic location, a demographic, a user type, and a use type for the farm product. Implementations of the described techniques may include hardware, a method or process, and / or computer software on a computer-accessible medium.

[0007] In an example aspect, a method of receiving feedback related to production data for a farm product disclosed herein may include: associating a farm product with a unique identifier, the farm product having one or more characteristics and being produced under one or more production parameters; associating production data specific to the farm product with the unique identifier, the production data including at least information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the farm product; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, feedback associated with the one or more characteristics of the farm product; and adjusting at least one of the one or more production parameters responsive to the feedback. Other implementations of this aspect include corresponding computer systems, apparatus, methods, and computer programs recorded on one or more computer storage devices, each configured to perform one or more of the aforementioned steps.

[0008] In an example aspect, a system disclosed herein for receiving feedback related to production data for a farm product may include a data network, a database configured to store production data for a plurality of farm products, and a remote computing resource coupled to the data network and the database, the remote computing resource including a processor and a memory, the memory storing code executable by the processor to perform the steps of: associating a farm product with a unique identifier, the farm product having one or more characteristics and being produced under one or more production parameters; associating production data specific to the farm product with the unique identifier, the production data including at least information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computingresource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the farm product; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, feedback associated with the one or more characteristics of the farm product; and adjusting at least one of the one or more production parameters responsive to the feedback. Other implementations of this aspect include corresponding computer systems, apparatus, methods, and computer programs recorded on one or more computer storage devices, each configured to perform one or more of the aforementioned functions.

[0009] In an example aspect, a method of adjusting production parameters for a region of a farm disclosed herein may include: associating a region of a farm with a unique identifier, the region having one or more production parameters; associating production data specific to the region with the unique identifier, the production data including at least a plant type and information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the region; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, a change request associated with one or more of the plant type and the production parameters; and adjusting one or more of the plant type and the production parameters responsive to the change request. Other implementations of this aspect include corresponding computer systems, apparatus, methods, and computer programs recorded on one or more computer storage devices, each configured to perform one or more of the aforementioned steps.

[0010] Implementations of this example aspect, or any other example aspect described in this summary section or otherwise herein, may include one or more of the following features. The one or more production parameters may include one or more of: a plant spacing, a crop yield, a number of seeds per unit area, a daily light integral (DLI), a light intensity, a light spectra, a production duration, a feed amount, a feed composition, a water amount, a water savings amount, a temperature, a humidity, a location within the farm, a treatment regimen, an energy usage, and an efficiency. The method may include automatically adjusting one or more of the production parameters responsive to receiving the change request. The region of the farm may include one or more plant support apparatus. The region of the farm may include a portion of a plant support apparatus. The production data may include one or more of: a video of production, the one or more production parameters, and a time-series plot. The user interfacemay be configured to receive the change request associated with one or more of the plant type and the production parameters through one of a slider, a spinner, a point scale, a menu, and a text field. The change request received may be associated with one or more characteristics of a farm product, and adjusting one or more of the plant type and the production parameters may include an adjustment to one or more of lighting, feed, water, soil, and an environmental condition. Implementations of the described techniques may include hardware, a method or process, and / or computer software on a computer-accessible medium.

[0011] In an example aspect, a computer program product disclosed herein may include computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices, performs the steps of associating a region of a farm with a unique identifier, the region having one or more production parameters; associating production data specific to the region with the unique identifier, the production data including at least a plant type and information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the region; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, a change request associated with one or more of the plant type and the production parameters; and adjusting one or more of the plant type and the production parameters responsive to the change request. Other implementations of this aspect include corresponding computer systems, apparatus, methods, and computer programs recorded on one or more computer storage devices, each configured to perform one or more of the aforementioned steps.

[0012] In an example aspect, a system for adjusting production parameters for a region of a farm disclosed herein may include a data network, a database configured to store production data, and a remote computing resource coupled to the data network and the database, the remote computing resource including a processor and a memory, the memory storing code executable by the processor to perform the steps of associating a region of a farm with a unique identifier, the region having one or more production parameters; associating production data specific to the region with the unique identifier, the production data including at least a plant type and information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the region; providing, responsive to receiving the data related to theunique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, a change request associated with one or more of the plant type and the production parameters; and adjusting one or more of the plant type and the production parameters responsive to the change request. Other implementations of this aspect include corresponding computer systems, apparatus, methods, and computer programs recorded on one or more computer storage devices, each configured to perform one or more of the aforementioned functions.

[0013] In an example aspect, a method of providing access to production data for a farm product disclosed herein may include: associating the farm product with a unique identifier; associating production data specific to the farm product with the unique identifier, the production data including at least a video of production specific to the farm product; tagging the farm product with a tag having the unique identifier; receiving, over a data network from a user device, data related to the unique identifier associated with the tag; and transmitting, responsive to receiving the data related to the unique identifier, the production data associated with the unique identifier, from a remote computing resource over the data network. Other implementations of this aspect include corresponding computer systems, apparatus, methods, and computer programs recorded on one or more computer storage devices, each configured to perform one or more of the aforementioned steps.

[0014] Implementations of this example aspect, or any other example aspect described in this summary section or otherwise herein, may include one or more of the following features. The video may include a time-lapse of a least a portion of the production of the farm product. The farm product may be a plant and the video may include a time-lapse showing the plant over at least a portion of a growing period. The farm product may be an unmanufactured animal product and the video may include a time-lapse of a harvest of the unmanufactured animal product. The tag includes one or more of a quick response (QR) code, a barcode, an alphanumeric identifier, a microchip, a radio frequency identification tag, and an integrated circuit. The production data may include an attribute of one or more production parameters. The production data may include a time-series plot of one or more production parameters. The farm product may be one of an edible leafy green plant, a flowering plant, a root vegetable, a tuber plant, a vine, a hemp plant, a tobacco plant, a cactus, and a mushroom. The farm product may be one of lettuce, spinach, beets, cabbage, quinoa, endive, carrots, arugula, fennel, and watercress. Implementations of the described techniques may include hardware, a method or process, and / or computer software on a computer-accessible medium.

[0015] In an example aspect, a computer program product disclosed herein may include computer executable code embodied in a non-transitory computer readable medium that,when executing on one or more computing devices, performs the steps of: associating a farm product with a unique identifier; associating production data specific to the farm product with the unique identifier, the production data including at least a video of production specific to the farm product; tagging the farm product with a tag having the unique identifier; receiving, over a data network from a user device, data related to the unique identifier associated with the tag; and transmitting, responsive to receiving the data related to the unique identifier, the production data associated with the unique identifier, from a remote computing resource over the data network. Other implementations of this aspect include corresponding computer systems, apparatus, methods, and computer programs recorded on one or more computer storage devices, each configured to perform one or more of the aforementioned steps.

[0016] In an example aspect, a system for providing access to production data for a farm product disclosed herein may include a data network, a database configured to store production data for a plurality of farm products, and a remote computing resource coupled to the data network and the database, the remote computing resource including a processor and a memory, the memory storing code executable by the processor to perform the steps of associating a farm product with a unique identifier; associating the production data specific to the farm product with the unique identifier, the production data including at least a video of production specific to the farm product; tagging the farm product with a tag having the unique identifier; receiving, over a data network from a user device, data related to the unique identifier associated with the tag; and transmitting, responsive to receiving the data related to the unique identifier, the production data associated with the unique identifier, from a remote computing resource over the data network. Other implementations of this aspect include corresponding computer systems, apparatus, methods, and computer programs recorded on one or more computer storage devices, each configured to perform one or more of the aforementioned functions.

[0017] These and other features, aspects, and advantages of the present teachings will become better understood with reference to the following description, examples, and appended claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The foregoing and other objects, features, and advantages of the devices, systems, and methods described herein will be apparent from the following description of particular embodiments thereof, as illustrated in the accompanying drawings. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of thedevices, systems, and methods described herein. In the drawings, like reference numerals generally identify corresponding elements.

[0019] Fig. 1 shows a system related to providing access to production data for a farm product, in accordance with a representative example.

[0020] Fig. 2 is a flow chart of a method for providing access to production data for a farm product, in accordance with a representative example.

[0021] Fig. 3 is a flow chart of a method for receiving feedback related to production data for a farm product, in accordance with a representative example.

[0022] Fig. 4 is flow chart of a method for adjusting production parameters for a region of a farm, in accordance with a representative example.DETAILED DESCRIPTION

[0023] The embodiments will now be described more fully hereinafter with reference to the accompanying figures, in which preferred embodiments are shown. The foregoing may, however, be embodied in many different forms and should not be construed as limited to the illustrated embodiments set forth herein. Rather, these illustrated embodiments are provided so that this disclosure will convey the scope to those skilled in the art.

[0024] All documents mentioned herein are hereby incorporated by reference in their entirety. References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the text. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context. Thus, the term “or” should generally be understood to mean “and / or” and so forth.

[0025] Recitation of ranges of values herein are not intended to be limiting, referring instead individually to any and all values falling within the range, unless otherwise indicated herein, and each separate value within such a range is incorporated into the specification as if it were individually recited herein. The words “about,” “approximately” or the like, when accompanying a numerical value, are to be construed as indicating a deviation as would be appreciated by one of ordinary skill in the art to operate satisfactorily for an intended purpose. Similarly, words of approximation such as “about,” “approximately,” or “substantially” when used in reference to physical characteristics, should be understood to contemplate a range of deviations that would be appreciated by one of ordinary skill in the art to operate satisfactorily for a corresponding use, function, purpose, or the like. Ranges of values and / or numeric values are provided herein as examples only, and do not constitute a limitation on the scope of the described embodiments. Where ranges of values are provided, they are also intended to includeeach value within the range as if set forth individually, unless expressly stated to the contrary. The use of any and all examples, or exemplary language (“e.g.,” “such as,” or the like) provided herein, is intended merely to better illuminate the embodiments and does not pose a limitation on the scope of the embodiments. No language in the specification should be construed as indicating any unclaimed element as essential to the practice of the embodiments.

[0026] In the following description, it is understood that terms such as “first,” “second,” “top,” “bottom,” “up,” “down,” and the like, are words of convenience and are not to be construed as limiting terms unless specifically stated to the contrary.

[0027] In general, the devices, systems, and methods disclosed herein relate to providing access, interaction, and / or customization related to certain information relevant to the production of a specific farm product. While some data related to farm products may currently be available at the point of purchase, this information may be limited, may not be relevant to the specific farm product being sold, and / or may use terminology that may be difficult for a consumer to fully understand (or may even be misleading). In many other cases, data relating to production of the farm product (such as data relating to environmental impact, energy efficiency, food safety, animal husbandry, labor practices, and so on) is simply unavailable. Some or all of these concerns and difficulties may be addressed by the present teachings, which may include a technology solution that ensures that a consumer of a farm product (and / or other persons, entities of interest, and so on) may have access to production data specific to the farm product, prior to purchasing and / or at other points in a life cycle of the farm product (e.g., before or after consumption of same). The present teachings may thus offer improved convenience and insight for concerned purchasers of farm products, increased customer satisfaction, a platform for consumer engagement, and / or a personalized user experience.

[0028] It shall be understood that, in some aspects of the present teachings, the “farm product” includes a plant, such as leafy greens and similar. Thus, the farm product will be understood to include plants such as fruits and vegetables that are grown for animal (e.g., human or otherwise) consumption, use, and / or enjoyment. However, it shall be understood that other types of farm products may also or instead be used herein in the systems and methods of the present teachings. By way of example, the farm product may include an agricultural commodity such as wheat, corn, soybeans, and the like; a species of livestock such as cattle, hogs, sheep, horses, poultry and the like; a product of such crops or livestock in its unmanufactured state such as ginned cotton, wool-clip, maple syrup, milk, eggs, and the like. Other examples of farm products are presented herein, such as with reference to the system of Fig. 1, and all such examples will be understood as being applicable to any of the systems and methods of the present teachings unless expressly stated to the contrary or otherwise clear from the context.

[0029] A brief overview of an implementation of the present teachings will now be described by way of example. Using a system that includes functionality of the present teachings, a farm product may be associated with a unique identifier (e.g., a tag having a quick response (QR) code). Production data for the farm product, or other data related thereto, may be stored in a database and associated with the unique identifier. A potential consumer interested in the farm product may scan the QR code (or otherwise retrieve the unique identifier), such as with a smartphone or other user device. In response, the system may transmit the associated production data to the user. In a more specific example, a consumer may be interested in learning more about the production of a specific head of lettuce that is tagged with a QR code (located on the packaging, for example). The consumer may scan the tag, and the system may transmit (e.g., via a web page) a variety of production data specific to the head of lettuce of interest. The production data may include a video (such as one or more time-lapse videos showing the planting, cultivation, and / or harvesting of the specific head of lettuce), certain characteristics of the specific head of lettuce (such as a plant cultivar, date of planting, date of harvesting, size, flavor characteristic, and so on), and / or data related to production of the specific head of lettuce (such as an energy input through the cultivation cycle, a feed or water input, a growing temperature profile, and the like). By way of example, the consumer may now be empowered to make an informed purchasing decision, and may have increased satisfaction in that decision.

[0030] The present teachings may also or instead include providing a user interface, such as a software application (“app”), a website, and / or other interface, that a user may use to access production information associated with one or more farm products. The user interface may enable increased user interaction with the production process, such as by allowing a user to submit feedback on the farm product, which may, in some cases, be used to adjust production parameters for the farm product. The user may be able to access production data through the user interface without the need to scan a product tag, such as by selecting a product from a list (e.g., a custom list associated with the user), from a graphical depiction of a farm, or similar. In some cases, user feedback may be used to train a model (such as a machine learning model) that may predict user preferences and / or provide automatic adjustment of production parameters and / or related farm devices. The present teachings may thus enable consumers to make informed choices, provide custom tailoring of product characteristics to match consumer preferences, and intelligently automate production methods.

[0031] In a more specific example, a user of the system may be a company that makes and sells frozen pizzas. The example user may place repeat orders for tomatoes with a farm, and may be able to connect with the farm over a data network by way of a user interface (such as anapp). The user may be able to provide credentials to access the system through a custom user interface that may show, for example, a history of orders placed by the user. The user may be able to select a farm product (e.g., the most recent order delivered), such as from a list. Upon selection, the user may be presented with certain production data specific to that order. The data may include, for example, a time lapse video of the order being harvested or cultivated, a graphical representation of one or more production parameters (such as one or more plots of ambient conditions over the time the plant was grown), product characteristics (such as appearance, flavor, texture, color, and / or size), and so on. The user may also, or instead, be able to provide feedback on a product (e.g., through an interactive feature of the user interface, such as a slider). For example, the user may provide feedback requesting that a future order include a sweeter tomato, a smaller tomato, a redder tomato, a tomato with more or less seeds, and so forth. This feedback can be provided through prompts on the user interface, such as prompts for the user to indicate a desired taste profile, appearance, texture, size, and so on. The prompts may include choices for a user to select, questions for a user to answer, and / or fields where a user can provide their own customized insights. This feedback may be scaled or otherwise transformed or the like, as may be desired for system processing. In some cases, this feedback may be used to adjust one or more production parameters, such as lighting, feeding, watering, and / or other ambient growing conditions for the plant. In this way, the user may be able to customize future orders to their preferences.

[0032] Aspects of the present teachings may thus allow users like food manufacturers to interact directly with farms via a digital platform to place orders, access detailed production data, and provide feedback for future customization. Some additional example use cases are described below.

[0033] Customizable farm-to-table delivery services — businesses and consumers may use an aspect of the present teachings to select specific produce from farms (e.g., local farms), view harvest data, and tailor orders based on preferences such as size, ripeness, or flavor profile. These platforms may include interactive features for feedback, allowing users to request changes for future products — such as requesting spicier peppers or sweeter corn.

[0034] Restaurant partnerships with farms — restaurants can use digital systems of the present teachings to place recurring orders, track delivery histories, and request specialized produce (e.g., microgreens grown under specific light conditions for enhanced flavor). Chefs can provide feedback on texture or taste, which farms can use to adjust cultivation methods for future crops.

[0035] Community Supported Agriculture (CSA) with customization — CSA members may log into an app or the like to customize their periodic (e.g., weekly) produce box or the like,swap items, and rate previous products. Feedback on quality or preferences (e.g., “prefer smaller zucchini” or “more red apples next time”) may be collected and used to inform planting and harvesting decisions.

[0036] Wholesale buyers and food hubs — grocery stores or food hubs may use platforms of the present teachings to manage bulk orders from multiple farms, track product characteristics, and provide feedback on shelf life, appearance, or consumer demand. This data may help farms refine their production for better alignment with market needs.

[0037] Example advantages of the present teachings may thus include enhanced transparency and trust: e.g., access to detailed production data (such as time-lapse harvest videos or environmental conditions) may increase confidence in product quality and sourcing. Another example advantage includes customization: e.g., the ability to provide granular feedback (e.g., taste, size, color, etc.) may enable buyers to receive products tailored to their exact needs, improving satisfaction and product consistency. Another example advantage includes convenience and sustainability: e.g., direct relationships with local producers may support sustainable sourcing and reduce food miles, benefiting the environment and local economies. Another example advantage includes direct market access for farms, demand forecasting (e.g., real-time feedback and order histories may enable better planning, reduce waste, and optimize production schedules), product improvement (a continuous feedback loop may help farms refine their offerings, enhancing quality and aligning with market trends), and so forth.

[0038] Moreover, implementations of the present teachings may be combined with any of the farming techniques described in IntT Pat. App. No. PCT / US2023 / 079981 (published as WO2024107936A2), which is incorporated by reference herein. That is, combining a user- driven feedback system with a highly adaptable farming setup — such as a vertical farming system where parameters can be precisely adjusted, even in specific zones — may unlock a wide range of powerful benefits for both producers and buyers. By way of example, this can benefit from hyper-localized environmental control, where a vertical farm can allow for precise adjustment of variables like light, temperature, humidity, and nutrient delivery in specific regions or even individual sections within a same row, column, or structure of a farm. This means different crops, or even different batches of the same crop, may be grown under conditions tailored to optimize yield, flavor, size, and / or nutritional content. For example, if a buyer requests tomatoes with higher sweetness or deeper color, the farm can adjust lighting spectra or nutrient concentrations just for that batch or section, delivering a truly customized product.

[0039] Combining the present teachings with hyper-customizable farms (e.g., vertical farms) may also provide enhanced product quality and consistency. That is, the ability to fine-tune conditions right up to harvest may allow farms to boost specific product qualities, such as increasing vitamin C or flavonoids, improving shelf life, and / or reducing defects without sacrificing yield. This can lead to more consistent, higher-quality produce that meets the exacting standards of food manufacturers, retailers, and consumers. Also, or instead, the present teachings may provide rapid response to market feedback, where buyers can use a digital system to request changes, and farms can quickly implement those preferences in specific zones, test results, and iterate, creating a fast feedback loop between customer demand and production. This agility may enable farms to stay ahead of market trends and build stronger relationships with buyers through responsive service. Also, or instead, the present teachings may provide scalability and flexibility — e.g., where modular, zone-based control systems can make it easy to scale operations or introduce new crops without major overhauls. Farms may thus adapt to changing market demands or expand capacity as needed, ensuring long-term viability.

[0040] Fig. 1 illustrates a system 100 for providing access to production data for a farm product 110, in accordance with a representative example. More particularly, the system 100 may be used for tagging the farm product 110 with a tag 112 that may be scanned or otherwise read by a user 101 to provide production data 172 specific to the farm product 110 for the user 101. In general, the system 100 may include a networked environment where a data network 102 interconnects a plurality of participating devices and / or users 101 in a communicating relationship. The participating devices may, for example, include any number of user devices 120, remote computing resources 130, databases 140, and other resources 150. Generally, the system 100 may be used for any of the implementations of the present teachings described herein. For example, the system 100 may be used for receiving data (e.g., a unique identifier 114) related to the tag 112, and transmitting certain production data 172 associated with the tagged farm product 110 from a remote computing resource 130 over the data network 102 and / or providing other output 160 to a user 101. More specifically, in the system 100, the farm product 110 may be associated with a unique identifier 114, and production data 172 specific to the farm product 110 may similarly be associated with the unique identifier 114. A user 101 may scan or otherwise access the tag 112 to retrieve the unique identifier 114, transmit the scan 122 or the unique identifier 114 over the data network 102 to a remote computing resource 130 for data retrieval (which may occur automatically), where the remote computing resource 130 may then provide output 160 to the user 101 over the data network 102. This entire process can be done relatively quickly, e.g., in near real-time (such as less than five minutes, less than one minute, mere seconds or shorter, etc.). Certain participants and aspects of the system 100 will now be described.

[0041] The user 101 may be associated with the user device 120 — e.g., such as where the user device 120 is a smartphone, tablet, computer, or similar. The user 101 may also or instead be associated with the farm product 110. For example, the user 101 may be a consumer, or potential consumer, of the farm product 110, the user may have ordered, or may be considering purchasing the farm product 110, the user may be a grower, farmer, or producer of the farm product, or similar. In some implementations the user 101 is associated with the food industry — e.g., the user 101 may be an entity (or an individual associated with such an entity) such as a restaurant, food distributer, food market, food product producer (e.g., a producer of packaged food items such as the frozen pizza example above or similar). The user 101 may also or instead include an individual otherwise interested in learning about the farm product 110 — e.g., a chef, a food service industry professional, a user or professional associated with selling farm products, someone purchasing farm products for another person, and so on. In some instances, the user 101 may not be human, but instead the user 101 may include a computing device, computer program, or the like — e.g., where the user 101 is a computer-program product comprising computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices (e.g., the user device 120) is configured to capture, create, edit, receive, and / or transmit a scan 122 or other data for processing as described herein for obtaining output 160 or the like.

[0042] In an aspect, the system 100 may be configured to associate different types of users (e.g., a first user, a second user, a third user, and so on) with one or more specific attributes. An example of an attribute may be a set of permissions to access the system 100 — such as to access information through the user interface 126, within the database 140, and so on. Examples of permission types include permission to edit, permission to view (without being able to edit), permission to comment, and permission to access. Other examples of attributes include a type or style of user interface, or features within a user interface; payment structures or interfaces; pricing structures; user authentication systems, related requirements, and so on; subscription options; and the like. As used herein, the terms “first user,” “second user,” and “third user” will be understood to refer to a type or class of user, where any type of user may include multiple specific individuals and / or one or more classes of individual. For example, the first user may be associated with the farm 170, such as a farmer, a grower, a manager of the farm, and so on, or may otherwise be associated with the production of the farm product 110. The first user may be associated with a first set of permissions, which may include one or more of: permission to edit the unique identifier 114 or to associate the unique identifier 114 with the farm product 110, permission to view all of the production data 172 associated with the farm 170, permission to edit at least a portion of the production data 172 (e.g., a portion associatedwith a specific farm product 110 or a specific region of the farm 170), and permission to edit one or more of the production parameters 174. In some aspects, e.g., where the first user is a system administrator, the first user may have permission to edit permissions for other users (e.g., the second user and the third user).

[0043] Continuing the above example, the second user may be a member of the general public, for example, a consumer, or potential consumer, of the farm product 110 who may be unaffiliated with the farm 170. The second user may have limited permissions, such as permission to view only a portion of the production data 172, and / or a prohibition from viewing some or all of the production parameters 174 (e.g., the production parameters 174 may be hidden from, or not displayed to, the second user). In one example, the second user may be a consumer shopping at a grocery store; this second user may be considering purchasing the farm product 110, may have scanned a tag 112 on the farm product 110, and / or transmitted the corresponding unique identifier 114 to the system 100. The second user may then be able to view a portion of the production data 172, such as a video 162 showing at least a portion of the production of that specific farm product 110. The second user may be able to take some actions within the platform, such as sending other data 118 (e.g., user feedback), reporting a current location of the user device 120, and so on. In an aspect, the second user may be provided with limited access to the system, such as single-use access. The user access, and the degree of limitation of access, may be associated with the farm product 110 (e.g., with the tag 112) and / or may be associated with the user (e.g., an IP address, a browser cookie, or the like).

[0044] Continuing the example, the third user may have a level of access to the system 100 that is somewhat higher than the access provided to the second user. The third user may include, for example, a consumer having a purchase contract (or similar) with the farm 170, such as a chef, a restaurant, a retail store, and so on. The third user may be associated with a specific farm product 110 or group of farm products 110 (e.g., a portion of a crop on the farm 170 that is being custom grown for the third user — which may in turn be associated with a specific location of the farm 170, one or more specific farm devices 176, and so on). The third user may have permissions such as: permission to view all of the production data 172 for the farm product 110 (e.g., the specific farm product 110 with which they are associated), permission to edit at least a portion of the one or more production parameters 174 for the farm product 110, permission to view at least a portion of the production data 172 for another farm product on the farm 170 (e.g., a farm product that is not being grown specifically for the third user, but which they may be interested in purchasing), and so on. For example, a third user (restaurant) may have a contract to purchase a certain quantity of lettuce from a first user (farm). The first user may also be a system administration who may configure the permissions for the third user; for example, thefirst user may associate one or more of the production parameters 174 with the third user (e.g., an amount of feed or light given to the lettuce being grown for the third user), and may provide the third user with permission to view and / or edit the associated production parameters 174.

[0045] The data network 102 may be any network(s) or internetwork(s) suitable for communicating data and information among participants in the system 100. This may include public networks such as the Internet, private networks, telecommunications networks such as the Public Switched Telephone Network or cellular networks using third generation (e.g., 3G or IMT-2000), fourth generation (e.g., LTE (E-UTRA) or WiMAX- Advanced (IEEE 802.16m)), fifth generation (e.g., 5G), and / or other technologies, as well as any of a variety of corporate area or local area networks and other switches, routers, hubs, gateways, and the like that might be used to carry data among participants in the system 100.

[0046] Each of the participants of the data network 102 may include a suitable network interface comprising, e.g., a network interface card, which term is used broadly herein to include any hardware (along with software, firmware, or the like to control operation of same) suitable for establishing and maintaining wired and / or wireless communications. The network interface card may include without limitation a wired Ethernet network interface card (“NIC”), a wireless 802.11 networking card, a wireless 802.11 USB device, or other hardware for wired or wireless local area networking. The network interface may also or instead include cellular network hardware, wide-area wireless network hardware or any other hardware for centralized, ad hoc, peer-to-peer, or other radio communications that might be used to connect to a network and carry data. In another aspect, the network interface may include a serial or USB port to directly connect to a local computing device such as a desktop computer that, in turn, provides more general network connectivity to the data network 102.

[0047] The farm product 110 may be any as described herein — e.g., a cultivated product such as wheat, corn, soybeans, an edible leafy green plant, a flowering plant, a root vegetable, a tuber plant, a vine, a cannabis and / or hemp plant, a tobacco plant, a cactus, a tree (e.g., a peach tree, an apple tree, an avocado tree, a tree cultivated for timber, and so on), a mushroom, and the like; livestock such as cattle, hogs, sheep, horses, poultry, fish, and similar used or produced in farming operations; fish from commercial fisheries, fishing operations, and the like (where parameters as described herein may include location, fuel usage, fishing methods, and the like); a product of a crop or livestock (e.g., an unmanufactured product), such as wool-clip, maple syrup, milk, eggs, cheese, and so on; and / or similar.

[0048] The farm product 110 may have one or more characteristics 116 associated therewith. Such characteristics 116 may include, by way of example, one or more of the following: a flavor (e.g., bitter, sour, sweet, earthy, fruity, nutty, pungent, spicy, floral, and soon), a texture (e.g., crunchy, hard / soft, liquid / solid, rough / smooth, creamy, crumbly, gritty, and so on), a mass or a size, an appearance (e.g., a color, a shape, a texture, and so on), a species or breed, and the like. One or more of the characteristics 116 of the farm product 110 may be stored by the system 100 (e.g., in the database 140) and may be accessible by one or more networked devices of the system (e.g., the user device 120, the remote computing resource 130, and so on). In one aspect, one or more characteristics 116 may be determined or provided at the point of production (e.g., a user associated with the farm 170 may enter data related to one or more of the characteristics 116 into the database 140, and / or such characteristics 116 may be automatically provided by a computing device, such as a measured size and / or weight of the farm product 110, a known species or breed of the farm product 110, and so on). In another aspect, one or more characteristics 116 of the farm product 110 may be subjective (e.g., a flavor characteristic) and feedback related to such characteristics 116 may be provided (e.g., as other data 118) by one or more users 101 who may be consumers of the farm product 110, or similar.

[0049] The tag 112 may be a device structurally configured to be directly attached to the farm product 110 or indirectly attached to the farm product 110, such as where the tag 112 may be applied to packaging of the farm product 110, included on a menu listing an item including the farm product 110, and the like. Alternatively, the tag 112 may be configured to be implanted within the farm product 110 (e.g., within the tissue of an animal — such as within a layer of skin of the animal). In general, the tag 112 may be readable, scannable, or similarly accessible to retrieve the unique identifier 114 associated with the farm product 110. The tag 112 may include one or more of a QR code, a barcode, an alpha-numeric identifier, an integrated circuit, a microchip, a nanochip, and the like. For example, the tag 112 may include a passive transponder (e.g., a radio transponder, such as in the case of a radio-frequency identification (RFID) tag). The tag 112 may, for example, be scannable by a camera 124 of the user device 120 which may output a scan 122 or may transmit data related to the unique identifier 114 associated with the tag 112 over the data network 102. The unique identifier 114 may be, for example, an alphabetic, alphanumeric, numeric code, a unique symbol, a URL, and so on.

[0050] The production data 172 associated with the farm product 110 may be specific to a particular farm product 110 that is, or that is intended to be, tagged (e.g., with the tag 112). In other words, specific production data 172 may relate to the production of a particular farm product 110, and may not merely be related to general production data which may or may not be applicable to the particular farm product 110 — such as general conditions of the farm 170, recommendations or best practices for producing the type of farm product, etc. (although, in some cases, general production data may be the same as, or similar to, specific production data). In response to receiving data related to the unique identifier 114, the system 100 may transmitthe production data 172 (e.g., from the remote computing resource 130 over the data network 102) for storage, presentation, or other similar uses. Production data may include some or all of: data related to preparations for production (e.g., preparing a substrate for growing a plant), cultivation data (such as one or more production parameters 174), sales data (such as price data, contract or pre-sales data, user data for a purchaser of the farm product), and so on.

[0051] The farm product 110 may be produced (e.g., grown) in a facility, which is generally referred to herein as a farm (e.g., the farm 170 of the system 100). In some aspects, the farm 170 may include a user of the system, may include one or more resources of the system 100 (e.g., the remote computing resource 130, the database 140, one or more user devices 120, and so on), and may be connected to the data network 102. The farm 170 may be associated with one or more production parameters 174. In some cases, the one or more production parameters 174, and / or an attribute of the one or more production parameters 174, may be included in the production data 172.

[0052] In some aspects, a farm may be divided into multiple regions to facilitate organization, management, and customization of production processes. These regions may be defined in various ways, depending on the farm’s layout, production methods, and specific needs. A region may, for example, include a group of plant support apparatus or other planting systems. For example, in a greenhouse setting, a region may include multiple rows of hydroponic systems, vertical growing apparatus, or traditional planting beds. A grouping may be based on factors such as crop type, growth stage, or environmental conditions. In some aspects, a grouping may be selected by a user (e.g., where a region of a farm is leased or contracted to a buyer). In some cases, a region may be defined as a single plant support apparatus. This could be particularly useful in precision farming applications or when dealing with a specialized growing system. For instance, a large-scale vertical farming unit with multiple levels and sections may be considered a distinct region due to its unique environmental controls and crop management requirements. Furthermore, a region may be as granular as a portion of a plant support apparatus. In highly controlled environments, such as those using advanced internet of things (loT) sensors and automation, even a subsection of a growing system may be treated as a separate region. This level of specificity may allow for micro-adjustments to growing conditions, tailored to the needs of specific plant varieties or growth stages. The division of a farm into regions may enable more precise control over production parameters, facilitate targeted data collection, and allow for customized growing conditions within the same overall facility. This approach may support diverse crop production, experimental growing techniques, and efficient resource allocation across different areas of the farm.

[0053] Examples of the production parameters 174 include a daily light integral (DLI), a light intensity, a light spectra, a production duration (such as, in the case of the farm product 110 being a plant, a time from planting or seeding until harvesting; where the farm product 110 is an animal, a time from birth or from stocking until harvesting; where the farm product 110 is a product of a plant or animal, a production cycle — for example, a time between bean harvests from a coffee plant; and so on), a feed amount (e.g., plant fertilizer, animal feed, and the like), a feed composition, a water amount, a water savings amount (as compared to an industry standard or to a previous, or another, farm configuration), a temperature, a humidity, a barometric pressure or other pressure, a location within a farm, a treatment regimen, an energy usage, an efficiency (as related to an input such as power, feed, water, etc.), and so on. Production parameters may also or instead relate to plant types and / or seeding or planting arrangements; examples of such production parameters include plant spacing, seed density, row or column spacing, planting depth, expected germination rate, desired final plant density, choice of direct seed sowing or transplanting of seedlings, growing medium parameters (e.g., size or type, saturation, dry back percentage, density, and the like), method of propagation for clonally propagated plants, spatial or temporal arrangement of different species (e.g., considerations of plant height, growth rate, or resource competition), timing of planting or seeding in relation to external events (e.g., seasonal changes, market demands, or coordinated harvest schedules), and the like. For plants grown in vertical systems, for example, production parameters may include the number of plants per vertical column or tray. Crop rotation schedules and companion planting arrangements may also be considered production parameters related to plant types and layouts. The selection of plant varieties or cultivars for specific growing conditions or desired characteristics may be included as a production parameter. This may involve choosing diseaseresistant varieties, high-yield cultivars, or plants bred for particular flavor profiles or nutritional content.

[0054] Some, or all, of the one or more production parameters 174 may be adjustable within the farm 170, such as by adjusting one or more farm devices 176. For example, the DLI may be adjustable by adjusting farm devices 176, such as timers that control lights, power components, and so on. In some aspects, one or more of the production parameters 174 may be adjusted, e.g., by a controller 178, in response to feedback provided by a user of the system 100. For example, a user 101 may consume a farm product 110, such as an edible leafy green plant (e.g., lettuce). A tag 112 may be located on packaging of the lettuce, and the user 101 may scan the tag 112 (e.g., with a camera 124 of their user device 120) to learn more about how their food was produced. A user interface 126 may accept input from the user 101, such as feedback related to a characteristic 116 of the lettuce. The user 101 may, for example, input feedback thatthe flavor of the lettuce was bitter. The feedback may be transmitted via the data network 102 to the farm 170 where the lettuce was produced. In response to the feedback (or, e.g., in response to aggregated feedback from a plurality of users, which can be parsed in any productive manner such as by geographic region, culture, user type, and so on), one or more production parameters 174 may be adjusted, e.g., by the controller 178, to change the characteristics 116 of current or future crops related to the farm product 110.

[0055] The farm devices 176 that may be adjustable by the system 100 may include one or more of lighting devices, systems, or components thereof (such as lighting sources, timers, dimmers, positioning devices, and so on); apparatus for growing or harvesting plants (e.g., positioning devices for adjusting location of plants, automated harvesting devices, vehicles, etc.); devices and systems for harvesting animal products, such as machines for egg harvesting or milking cows; irrigation systems or components thereof, such as timers, valves, flow regulators, and the like; heating, cooling, or ventilation systems or components thereof, such as heaters, fans, louvers, or timers; and cameras and related motion control devices for providing the output 160 (such as a video 162 or the like). Each of these components — either individually or collectively with one or more other components — may facilitate some of the advantages of the present disclosure such as increased plant yield, cost savings, space savings, transparency in the growing / harvesting processes, reduced waste, ease of use, and so on. In a specific example, a vertical growing system such as that described in IntT Pat. App. Pub. No. WO2024107936A2, where the entire contents of the foregoing is hereby incorporated by reference herein, may include one or more farm devices 176 such as a plant support apparatus, a lighting apparatus, an irrigation system, and a climate-control system such as those described therein. In one case, an individual plant support apparatus or a portion thereof (e.g., one side of the plant support apparatus), may be adjusted in response to feedback received — for example, the position of the plant support apparatus relative to one or more lighting apparatus may be adjusted. In another example, a mount for camera may be adjustable in response to user feedback (such as a request, though the user interface 126, to zoom in, pan, tilt, or the like so that the user may access different views of the farm product 110). In another example, a user may submit a change request for a specific region of the farm to switch from growing romaine lettuce to butter lettuce. The user interface may allow for selecting the new cultivar, adjusting planting parameters, and monitoring the crop’s progress. In response to the change request, the system may automatically modify various growing conditions such as lighting, irrigation, nutrients, temperature, and air circulation to optimize the cultivation of butter lettuce. As the growing cycle continues, the system may make further dynamic adjustments based on real-time sensor data to potentially improve crop quality and yield.

[0056] The user devices 120 may include any devices within the system 100 operated by one or more users 101 for practicing the techniques as contemplated herein. The user devices 120 may thus be coupled to the data network 102. Specifically, the user devices 120 may include any device for capturing a scan 122 (e.g., a camera lens, a structured light scanner, a laser scanner, a time-of-flight (ToF) camera, a photogrammetry system, a handheld scanner, and the like) — or otherwise creating, preparing, editing, or receiving the scan 122 — and, in some instances, transmitting the scan 122 (e.g., over the data network 102). To this end, the user device 120 may include a camera 124 or the like, or the user device 120 may otherwise be in communication with a camera 124 or the like. In a preferred implementation, the user device 120 includes a smartphone or the like having an internal camera 124, processing capability, and access to the data network 102, all in one device. The user device 120 may also or instead include any device for receiving output 160 responsive to transmitting the scan 122 (or performing another action in the system 100) over the data network 102, e.g., displaying such output 160 on a display 128 featuring a graphical user interface 126 or the like. Similarly, the user device 120 may include any device for creating, preparing, editing, receiving, and / or transmitting (e.g., over the data network 102) other data or files in the system 100, such as other data 118 (e.g., other data 118 related to one or more of the user 101, the user device 120, or the scan 122), production data 172, and so on. The user device 120 may also or instead include any device for managing, monitoring, or otherwise interacting with tools, platforms, and devices included in the systems and techniques contemplated herein. The user device 120 may be coupled to the data network 102, e.g., for interaction with one or more other participants in the system 100. It will also be understood that all or part of the functionality of the system 100 described herein may be performed on the user device 120 (or another component of the system 100) without a connection to the data network 102 — by way of example, a closed network native application on a smartphone may be utilized, whereby functionality can run in a closed environment.

[0057] By way of further example, the user devices 120 may include one or more desktop computers, laptop computers, network computers, tablets, mobile devices, portable digital assistants, messaging devices, cellular phones, smartphones, portable media or entertainment devices, or any other computing devices that can participate in the system 100 as contemplated herein. As discussed above, the user devices 120 may include any form of mobile device, such as any wireless, battery-powered device, that might be used to interact with the networked system 100. It will also be appreciated that one of the user devices 120 may coordinate related functions (e.g., performing processing of the scan 122 and adjusting one ormore production parameters 174, and the like) as they are performed by another entity such as one of the remote computing resources 130 or other resources 150.

[0058] A user device 120 may be associated with a location, e.g., a location of the user 101 associated therewith. In some cases, this location data may be used by the system 100. By way of example, when a user 101 uses the user device 120 or similar to scan the tag 112 or otherwise interact with the farm product 110 or its data (or the system 100 more generally), such as to monitor growth of a plant that the user 101 has purchased or is contemplating purchasing, the system 100 may use the location to calculate the distance to the farm, which can, for example, be used to determine and / or emphasize how locally grown produce is. The location data of one or more users 101 that interact with a component of the system 100 may also or instead be used for other purposes, such as to gather insights into geographic preferences for farm products 110 and / or attributes thereof.

[0059] Each user device 120 may generally provide a user interface 126. The user interface 126 may be maintained by a locally-executing application on one of the user devices 120 that receives data from, for example, the remote computing resources 130 or other resources 150. In other examples, the user interface 126 may be remotely served and presented on one of the user devices 120, such as where a remote computing resource 130 or other resource 150 includes a web server that provides information through one or more web pages or the like that can be displayed within a web browser or similar client executing on one of the user devices 120. The user interface 126 may in general create a suitable visual presentation for user interaction on a display device of one of the user devices 120, and provide for receiving any suitable form of user input including, e.g., input from a keyboard, mouse, touchpad, touch screen, hand gesture, or other use input device(s).

[0060] The remote computing resources 130 may include, or otherwise be in communication with, a processor 132 and a memory 134, where the memory 134 stores code executable by the processor 132 to perform various techniques of the present teachings. More specifically, a remote computing resource 130 may be coupled to the data network 102 and accessible to the user device 120 through the data network 102, where the remote computing resource 130 includes a processor 132 and a memory 134, where the memory 134 stores code executable by the processor 132 to perform the steps of a method according to the present teachings — such as any of the methods or techniques described herein. However, it will be understood that such a processor 132 and a memory 134 may also or instead be located on the user device 120 or another component of the system 100.

[0061] The remote computing resources 130 may also or instead include data storage, a network interface, and / or other processing circuitry. In the following description, where thefunctions or configuration of a remote computing resource 130 are described, this is intended to include corresponding functions or configuration (e.g., by programming) of a processor 132 of the remote computing resource 130, or in communication with the remote computing resource 130. In general, the remote computing resources 130 (or one or more processors 132 thereof or in communication therewith) may perform a variety of processing tasks related to processing a scan 122 of a tag 112, and further assessment related thereto, as discussed herein. For example, the remote computing resources 130 may manage information received from one or more of the user devices 120 (e.g., the scan 122, other data 118, production data 172, and so on), and provide related supporting functions such as parsing or segmentation of the scan 122, data lookup or processing related to the scan 122, performing calculations, identifying various properties related to the unique identifier 114, applying one or more models 138 and / or algorithms to the other data 118, and / or, retrieving and / or analyzing information from a database 140 and / or the memory 134, providing an output 160, communicating with other resources 150 and the participants in the system 100, storing data, and the like. The remote computing resources 130 may also or instead include backend algorithms that react to actions performed by a user 101 at one or more of the user devices 120. These backend algorithms may also or instead be located elsewhere in the system 100.

[0062] The remote computing resources 130 may also or instead include a web server or similar front end that facilitates web-based access by the user devices 120 to the capabilities of the remote computing resource 130 or other components of the system 100. A remote computing resource 130 may also or instead communicate with other resources 150 in order to obtain information for providing to a user 101 through a user interface 126 on the user device 120. Where the user 101 specifies certain feedback or otherwise, this information may be used by a remote computing resource 130 (and any associated models 138) to access other resources 150. Additional processing may be usefully performed in this context, such as recommending adjustment to certain production parameters 174 used in relation to a farm 170 and / or one or more farm devices 176.

[0063] A remote computing resource 130 may also or instead maintain, or otherwise be in communication with, a database 140 of data 142, and optionally with an interface for users 101 at the user devices 120 to utilize the data 142 of such a database 140. Thus, in one aspect, a remote computing resource 130 may include a database 140 of data 142, and the remote computing resource 130 may act as a server that provides a platform for selecting and using such data 142, and / or providing supporting services related thereto. The database 140 may be a local database of the remote computing resource 130, or a remote database to the remote computingresource 130 or another participant in the system 100. Thus, the database 140 may include a cloud-based database or the like.

[0064] The data 142 included in the database 140 or otherwise in the system 100 may include one or more of: data related to the farm product 110 (e.g., the unique identifier 114 and / or one or more characteristics 116), other data 118 (e.g., user feedback), production data 172, and one or more production parameters 174. By way of example, other data 118 may include a location of the user 101 and / or farm product 110, a time and date of the scan 122, application usage information, other information pertaining to the user 101, feedback provided by the user 101 (or a population of users), and so on.

[0065] As set forth in more detail below, other data 118 may be obtained from the user 101 or otherwise, e.g., retrieved from a database 140 or third party. For example, the other data 118 may, at least in part, be formed from a user 101 or third party answering questions on a questionnaire presented to the user 101 on the user interface 126 of the user device 120. The other data 118 may be used at least in part to provide certain output 160 to the user 101, and may thus be used in the processing and analyses described herein — e.g., to customize recommendations made to the farm 170. In this manner, the other data 118 may include information related to at least one of user characteristics or user preferences. The other data 118 may also or instead include user input regarding an importance of certain information contained within the other data 118 for the user 101, where this importance may be used in weighting of the other data 118 in analyses described herein. Other weighting is also or instead possible, including weighting that does not require or account for user preferences. It will be understood that other data 118 may be used for analyses related to one or more of: importance of one or more characteristics 116 of the farm product 110, history of data provided by the user 101, making recommendations to the farm 170 based on data from a plurality of users 101 or a user 101 having a specific status, and the like.

[0066] In some aspects, a database 140 of the system 100 includes other data 118 for a population of users 101. Such other data 118 for a population of users 101 may be used, at least in part, in one or more of the analyses described herein. For example, if other data 118 specific to a first user is incomplete, other data 118 from the population of users 101 may be used to fill the gap — e.g., where other data 118 from one or more similarly situated users is used, and / or where an average, mean, median, or the like is used, which can be taken from an entire population or a subset thereof. Other data 118 for a population of users 101 may also or instead be used to customize a farm product 110 — e.g., by controlling one or more production parameters 174 — for the population of users 101, or other similarly-situated users. In this manner, patterns or tendencies for particular users 101 or user groups may be uncovered orinferred using the present teachings. For example, users 101 including individuals or entities associated with high-end restaurants may have certain preferences for certain farm products 110, which may differ from individuals or entities associated with frozen-food products that contain the same farm products 110. By way of another example, users 101 in Asia may generally have different tendencies for preferences of characteristics 116 for certain farm products 110 than users 101 in South America. In a similar fashion, certain farm products 110 used for a particular purpose may have certain characteristics 116 that are more or less important than those same farm products 110 used for another purpose — e.g., the importance of appearance of a fruit or vegetable used in a recipe where it is to be chopped and placed within a soup is likely less than that of the appearance of the same fruit or vegetable used in a recipe where it is to be plated in whole or similar. So, not only may a population of users 101 be used in the present teachings, but also use-cases may be used in the present teachings. In this manner, other data 118 in the present teachings may include a wealth of useful data related to consumer characteristics, consumer preferences, relative importance given to one or more characteristics 116 of a farm product 110, and otherwise for one or more users 101.

[0067] The data 142 stored in a database 140 of the system 100 may also or instead include reference information for use by the remote computing resource 130 for providing the output 160. For example, this data 142 may include historical data such as feedback information provided for one or more farm products 110 (e.g., from the same user 101 or a different user). The data 142 may also or instead include one or more models 138, e.g., for retrieval and use by the remote computing resource 130 or another participant for processing and analyzing information to generate the output 160. The data 142 may also or instead include a number of correlations and / or associations between one or more pieces of information.

[0068] To this end, in some aspects, the database 140 may be a farm product database including data 142 such as a plurality of characteristics 116 and / or categories of production data 172 for each of a plurality of farm products 110. By way of example, such characteristics 116 may include information related to at least one of the following for each of a plurality of farm products 110: taste characteristics, appearance characteristics, common production requirements (e.g., light, water, feed, and so on), size, shape, mass / weight, and the like. Such data 142 may be created, added, removed, and / or revised in any of a number of ways, including automatically by data scraping from information provided by one or more farms 170 or the like (e.g., web scraping to extract information from websites using automated tools or scripts to gather data from web pages), and / or manually by administrators, and so on.

[0069] The correlations and / or associations between one or more pieces of information used herein, which may included in the data 142, may use machine learning techniques. In thismanner, vast datasets including specific growing parameters for each crop may be paired with corresponding customer feedback or known results related to, e.g., taste and aesthetics. Machine learning algorithms may analyze this information to identify complex correlations between particular environmental settings and the resulting sensory or aesthetic qualities. For example, a model might discover that a specific light spectrum combined with a certain humidity level and nutrient mix consistently produces tomatoes rated higher for sweetness and visual appeal. These models can then predict how adjustments to these parameters will likely impact future harvests. This data-driven approach may enable an iterative cycle of improvement. Based on the machine learning insights, a farm may fine-tune its growing “recipes” — e.g., adjusting light intensity, CO2 levels, temperature, water, spacing, or nutrient balances — to steer crops towards the characteristics customers prefer. New feedback on subsequent batches may further refine the machine learning models, creating a continuous loop of optimization. This may not only lead to farm products that better meet consumer desires in terms of flavor and appearance, but may also allow for more efficient resource use and potentially tailored production for specific market segments as described herein.

[0070] By way of example, a latent space may be used in the system 100 for smart farms to optimize crop characteristics based on customer feedback by creating a compressed, lower-dimensional representation of both the numerous growing parameters (e.g., light, humidity, watering, spacing, nutrients, and so forth) and complex customer feedback, e.g., on taste and appearance. In this manner, an encoder model may be used to map the detailed cultivation settings and the quantified end-user feedback into distinct points within the abstract latent space. Machine learning algorithms may then learn the intricate relationships between these encoded representations, figuring out how a specific signature of growing conditions in its latent form translates to a particular sensory profile (or the like) in its corresponding latent space. This learned mapping may allow a farm to predict the likely sensory outcome of novel parameter combinations. More powerfully, the farm may define a desired taste or appearance profile as a target point in the sensory latent space, and then use a decoder to translate this back, identifying the optimal point in the growing parameter latent space and, subsequently, the specific, real-world settings needed to achieve that outcome. This approach may facilitate dimensionality reduction, uncover hidden influential features, enable the generation of new growing recipes through interpolation or exploration within the latent space, and ultimately allow for more nuanced control and creative tailoring of crops to meet or even anticipate consumer preferences.

[0071] A remote computing resource 130 may also or instead be configured to manage access to certain content (e.g., for a particular user 101). In one aspect, a remote computingresource 130 may manage access to a component of the system 100 by a user device 120 according to input from a user 101.

[0072] Thus, and as described throughout the present disclosure, a remote computing resource 130 coupled to the data network 102 and accessible to the user device 120 through the data network 102 may include a processor 132 and a memory 134, where the memory 134 stores code executable by the processor 132 to perform the steps of any of the methods described herein. Also or instead, the user device 120 itself may include a processor 132 and a memory 134, where the memory 134 stores code executable by the processor 132 to perform the steps of any of the methods described herein. By way of example, such a method may include one or more of the following: associating a farm product 110 with a unique identifier 114, associating production data 172 specific to the farm product 110 with the unique identifier 114, the production data 172 including at least a video 162 of production specific to the farm product 110 (e.g., a time-lapse video, a stop motion video, a video at increased speed, and the like), tagging the farm product 110 with a tag 112 having the unique identifier 114, receiving data (e.g., a scan 122 or other data 118, where the data is related to the unique identifier 114 associated with the tag 112) over the data network 102 from a user device 120, and / or transmitting production data 172 associated with the unique identifier 114 from the remote computing resource 130 over the data network 102. In another aspect, a system 100 disclosed herein may include: associating a farm product 110 with a unique identifier 114, the farm product 110 having one or more characteristics 116 and being produced under one or more production parameters 174; associating production data 172 specific to the farm product 110 with the unique identifier 114, the production data 172 including at least information related to one of the one or more production parameters 174; providing a user interface 126 for accessing at least a portion of the production data 172 from a remote computing resource 130 over a data network 102; receiving, over the data network 102 from a user device 120, data related to the unique identifier 114 associated with the farm product 110; providing, responsive to receiving the data related to the unique identifier 114, at least a portion of the production data 172 associated with the unique identifier 114, from the remote computing resource 130 over the data network 102; receiving, from the user interface 126, feedback associated with one or more of the characteristics 116 of the farm product 110; and adjusting one or more of the production parameters 174 responsive to the feedback. In some aspects, the processor 132 executes, at least in part, on the user device 120. In some aspects, the processor 132 executes, at least in part, on a remote computing resource 130 (e.g., a remote server) coupled to the user device 120 through the data network 102.

[0073] As discussed herein, the systems 100 and techniques of the present teachings may include and utilize one or more models 138 that are configured and programmed to perform certain tasks to assist with the various methods described herein. In some aspects, one or more of the models 138 may be trained using data (e.g., such as feedback) from a population of users, which can be classified, analyzed, tagged, and / or processed for such purposes. By way of example, a model 138 may recognize patterns between one or more characteristics 116 of a farm product 110, one or more production parameters 174, user preferences as provided in other data 118, and similar. Such patterns may be used in creating or predicting user groups, user behaviors, farm product groups, and so on. In one example, a model may recognize a pattern of users having a specific attribute (e.g., being located in the same geographic area) to share a preference for a certain bitterness level of leafy green vegetables. The model may predict that other users having the attribute will have similar preferences. This prediction may be used by the system to, for example, recommend one or more farm products 110 that the user may like, adjust one or more production parameters 174 of farm products 110 being produced for a specific user group, and so on.

[0074] The present teachings may include one or more of supervised machine learning and unsupervised machine learning. Supervised machine learning may find an association between data (e.g., feature vectors X) and a corresponding label (y, which can be categorical or continuous) so that the computer can learn an algorithm, f, that maps the input to the output (e.g., y = f(X)). Two further subgroups can include classification and regression problems, where the supervised machine-learning model is trained to predict categorical data and continuous data, respectively. Some examples of models include: support vector machines, stochastic gradient descent, k nearest neighbors, decision trees, neural networks, and so on.

[0075] Unsupervised machine learning may assume a similar structure to supervised machine learning, except that no training labels y may be used. These models may attempt to learn the underlying structure or distribution of the data to learn more about its behavior. Some example of tasks here are clustering and associations (e.g., Apriori algorithms for association rule learning). Semi-supervised approaches may occur when practitioners feed in a partial list of labeled training data. This typically increases accuracy as a result of using labeled data, but allows for practitioners to minimize cost (e.g., time and monetary to gather labeled data).

[0076] The other resources 150 may include any resources that can be usefully employed in the devices, systems, and methods as described herein. For example, these other resources 150 may include without limitation other data networks, human actors (e.g., programmers, researchers, annotators, editors, analysts, and so forth), sensors (e.g., audio or visual sensors), data mining tools, computational tools, data monitoring tools, algorithms, and soforth. The other resources 150 may also or instead include any other software or hardware resources that may be usefully employed in the networked applications as contemplated herein. For example, the other resources 150 may include payment processing servers or platforms used to authorize payment for access, content, or option / feature purchases, or otherwise. In another aspect, the other resources 150 may include certificate servers or other security resources for third-party verification of identity, encryption or decryption of data, and so forth. In another aspect, the other resources 150 may include a desktop computer or the like co-located (e.g., on the same local area network with, or directly coupled to through a serial or USB cable) with one of the user devices 120 or remote computing resources 130. In this case, the other resource 150 may provide supplemental functions for the user device 120 and / or remote computing resource 130. Other resources 150 may also or instead include supplemental resources such as cameras, scanners, input devices, and so forth.

[0077] The other resources 150 may also or instead include one or more web servers that provide web-based access to and from any of the other participants in the system 100. While depicted as a separate network entity, it will be readily appreciated that the other resources 150 (e.g., a web server) may also or instead be logically and / or physically associated with one of the other devices described herein, and may, for example, include or provide a user interface for web access to a remote computing resource 130 or a database 140 in a manner that permits user interaction through the data network 102, e.g., from a user device 120.

[0078] It will be understood that the participants in the system 100 may include any hardware or software to perform various functions as described herein. For example, one or more of the user device 120 and the other resources 150 may include a memory 134 and a processor 132.

[0079] The various components of the networked system 100 described above may be arranged and configured to support the techniques, processes, and methods described herein in a variety of ways. For example, in one aspect, a user device 120 connects through the data network 102 to a server (e.g., that is part of one or more of the remote computing resource 130 or other resources 150) that performs a variety of tasks related to receiving, processing, and / or analyzing a scan 122 of a tag 112, or other data 118, to provide output 160 to a user 101. For example, the remote computing resource 130 may include a server that hosts a website (and / or a mobile application or application programming interface) that runs a platform for processing a scan 122 and other data 118. More specifically, a user 101 associated with the user device 120 and having appropriate permissions for using the system 100 may use the user device 120 to transmit a scan 122 or other data 118 over the data network 102 to the remote computing resource 130. The remote computing resource 130 may receive the scan 122 or other data 118from the user 101 over the data network 102 for processing thereof, where a result of the processing may include the output 160.

[0080] The output 160 may include one or more of the following: an identification of the farm product 110 (e.g., a species, the unique identifier 114 associated with the farm product 110, etc.); information related to the identified farm product 110 (e.g., a location on the farm 170, a planting date, a birth date, an anticipated harvest date, an order number, and so on); one or more characteristics 116 of the farm product 110 (e.g., an actual, estimated, or predicted size, color, texture, and the like); a video 162 (such as a live stream or a time-lapse showing the farm product 110 over a least a portion of a production period); a time-series plot 164 (e.g., a timeseries plot 164 of one or more production parameters 174, such as an amount of feed provided daily, ambient conditions over the time of cultivation, a visualization of a change in appearance such as a size or color change); other information related to one or more production parameters 174 (e.g., a DLI, a production duration, etc.); other data 118, such as feedback received from one or more users 101; and the like. In some cases, data included in the output 160 may be relative or approximate rather than including absolute or actual values; for example, the output 160 may show an increase in size rather than an absolute size, a water or energy savings compared to a prior production cycle rather than an absolute efficiency, and so on. The output 160 may also or instead include a metric related to one or more characteristics 116 of the farm product 110. The output 160 may be programmatically configured for presentation on the display 128 (and more specifically, the graphical user interface 126) of the user device 120. In some instances the output 160 includes prompts and / or queries to generate further output 160, such as prompts related to user preferences to determine whether to make adjustments to one or more production parameters 174.

[0081] In an aspect, many of the techniques of the present teachings are performed by the remote computing resource 130. For example, the remote computing resource 130 may include an analysis engine (or otherwise a processor 132) configured by computer-executable code to analyze the scan 122 and / or the other data 118, a recommendation engine (or otherwise a processor 132) configured by computer-executable code to provide a recommendation for the user 101 or otherwise in the system 100, and so on. However, it will be understood that some of the features and functionality described with reference to the remote computing resource 130 may also or instead be performed by another participant in the system 100.

[0082] Fig. 2 is flow chart of a method 200 for providing access to production data for a farm product (e.g., production data 172 for a farm product 110 as discussed with respect to Fig. 1), in accordance with a representative example. The method 200 may be implemented on aplatform such as one supported by any of the systems described herein, such as the system 100 of Fig. 1.

[0083] As shown in step 202, the method 200 may include associating a farm product with a unique identifier (e.g., the farm product 110 may be associated with unique identifier 114 as per Fig. 1). Associating may include a physical association, such as tagging, labeling, marking, etc. the farm product with the unique identifier, and / or may include a digital association, such as an association saved in a database (e.g., a database 140). For example, a farm product may be associated with a unique identifier by associating the unique identifier with a specific location of production of the farm product (e.g., in the case of a vertical growing system, a plant support apparatus, or portion thereof, where a plant may be produced). The farm product may be any as described herein, for example, a plant such as: an edible leafy green plant (e.g., lettuce, spinach, cabbage, endive, arugula, fennel, watercress, and so on), flowering plants (e.g., lavender, quinoa, hops, etc.), potatoes or other root vegetables and / or tuber plants (e.g., beets, carrots, yams, sweet potatoes, etc.), a vine (i.e., plants with a growth habit of trailing or scandent stems, lianas, or runners, such as grapes, tomatoes, and the like), a hemp plant, a tobacco plant, a cactus, mushrooms or other fungi, and so on; livestock such as chicken, pigs, cows, fish, rabbits, crustaceans, sheep, and so on; an unmanufactured plant or animal product, such as ginned cotton, wool-clip, maple syrup, milk, and eggs; or a manufactured plant-based or animal-based product, such as wine, coffee, preserves, butter, leather, and so on.

[0084] As shown in step 204, the method 200 may include associating production data specific to the farm product with the unique identifier (e.g., as described with respect to the system 100 of Fig. 1, production data 172 specific to a farm product 110 may be associated with a unique identifier 114). The production data may include, or may be related to, one or more production parameters, which may or may not be related to a farm where the farm product is produced (e.g., the farm 170 of system 100). The production data may include, for example, a video of production specific to the farm product (e.g., the video 162 of the system 100), where the video may include one or more of: a live stream; a time-lapse showing the farm product over a least a portion of a production period (e.g., a time-lapse of a plant taken over the growing period of the plant, a time-lapse of the harvest of an unmanufactured animal product — such as wool, honey, eggs, and so on); a video of the raising or slaughtering of a farmed animal; and the like. In some instances, the production data is navigable by a user. For example, a video may be navigable by the user — e.g., to change views, which can include video control functionality such as zooming, recording, screen capturing, and so on, and / or navigating to different and / or related feeds, for example to view a similar crop having different growing parameters (e.g., where a user can view video of growth of a lettuce intended to be sweet, and the user can view video ofgrowth of a lettuce intended to be bitter, and where production parameters themselves may be viewable on a user interface). In this manner, the present teachings may facilitate a user experience where the user has full or partial transparency into a production process. The production data may also or instead include a data plot, such as a time-series plot of one or more production parameters (e.g., the time-series plot 164 of the system 100). The production data may also or instead include any suitable presentation of any of the relevant data as described herein.

[0085] As shown in step 206, the method 200 may include tagging the farm product with a tag having the unique identifier (e.g., as described with respect to the system 100 of Fig. 1, the farm product 110 may be tagged with a tag 112 having the unique identifier 114). In some cases, the farm product may be directly tagged; for example, in the case of livestock a tag such as a microchip or nanochip may be injected into the animal’s skin, an external tag, band, or similar may be applied to the animal’s ear, foot, or other body part, and so on. In other cases the farm product may be indirectly tagged, such as by tagging a container, packaging, menu, instore display or similar, inserting a tag into substrate that holds a plant, and so on. The tag may include one or more of: a QR code, a barcode, an alpha-numeric identifier, a microchip, and an integrated circuit, any of which may themselves include, or be used to retrieve, the unique identifier. Tagging the farm product may also, or instead, include receiving a tagged the farm product — e.g., a first farm that finishes a plant that was propagated at a second farm may receive the plant after it has already been tagged, while not physically performing the tagging themselves.

[0086] As shown in step 208, the method 200 may include receiving data related to the unique identifier associated with the tag. For example, with reference to the system 100 of Fig. 1, data related to the unique identifier 114 associated with the tag 112, such as a scan 122, other data 118, or the unique identifier 114 itself, may be transmitted over the data network 102 by the user device 120. In some instances, images or other content regarding the farm product may be included within this data that is transmitted for processing. The data may be received by another networked system component, such as the remote computing resource 130, a user device associated with the farm 170, or another resource of the system 100 described above.

[0087] As shown in step 210, the method 200 may include transmitting production data, such as any described herein, associated with the unique identifier. For example, production data associated with the unique identifier may be transmitted in response to receiving the data related to the unique identifier (e.g., from the user device 120 of the system 100). The production data (e.g., production data 172) may be transmitted, for example, from a remote computing resource 130 over the data network 102.

[0088] Fig. 3 is flow chart of a method 300 for receiving feedback related to production data for a farm product, in accordance with a representative example. It will be understood that, similar to the method 200 described above and any other methods described herein, the method 300 of Fig. 3 may be implemented on a platform such as one supported by any of the systems described herein, such as the system 100 of Fig. 1. For example, the method 300 may include providing production data 172 for a farm product 110 as discussed with respect to Fig. 1. Further, it will be understood that many of the steps and features described herein with reference to other figures or examples — e.g., many of the features of the method 200 of Fig. 2 — may also or instead be included in a method 300 described below.

[0089] As shown in step 302, the method 300 may include associating a farm product with a unique identifier. The step 302 may, in some aspects, include features described with respect to the step 202 of method 200. For example, the farm product, or packaging thereof, may be tagged, labeled, marked, or similarly physically associated with the unique identifier, and / or the farm product may be digitally associated with the unique identified (e.g., in a database). The step 302 may also include tagging the farm product with a tag having the unique identifier, as described, for example, with reference to the step 206 of the method 200.

[0090] As shown in step 304, the method 300 may include associating production data specific to the farm product with the unique identifier. The step 304 may, in some aspects, include features described with respect to the step 204 of method 200. For example, and referring to the system 100 of Fig. 1, the production data 172 may include one or more of: a video 162 of production, one or more production parameters 174, and / or a time-series plot 164. The production data 172 may include at least information related to one or more of the production parameters 174, such as a DLI, a light intensity, a light spectra, a production duration, a feed amount, a feed composition, a water amount, a water savings amount, a temperature, a humidity, a location within a farm, a treatment regimen, an energy usage, and an efficiency.

[0091] As shown in step 306, the method 300 may include providing a user interface for accessing the production data, or a portion of the production data, or other data associated with the specific farm product having the unique identifier. For example, and with reference to Fig. 1, a user interface 126 may be provided on a user device 120 for accessing at least a portion of the production data 172 from a remote computing resource 130 over a data network 102.

[0092] As shown in step 308, the method 300 may include receiving data related to the unique identifier associated with the farm product. The step 308 may, in some aspects, include features described with respect to the step 208 of method 200. For example, and referring to the system 100 of Fig. 1, data related to the unique identifier 114 associated with the farmproduct 110 — such as a scan 122 of a tag 112, other data 118, or the unique identifier 114 itself — may be transmitted over the data network 102 by the user device 120. In another example, the received data may be related to a user 101 of the system 100, such as a user name, an account number, location data, or the like. The data related to the user 101 may be included in a user profile that is accessible by the system (e.g., the user profile may be stored in the database 140), and the user profile may be associated with one or more farm products 110. Such farm products 110 may be selectable by the user 101, such as by selecting from the user interface 126, and the received data may include the selection made by the user of a particular farm product 110.

[0093] As shown in step 310, the method 300 may include providing production data, or a portion thereof, associated with the unique identifier. This step 310 may, in some aspects, include features described with respect to the step 210 of method 200. For example, and referring to the system 100 of Fig. 1, production data 172 associated with the unique identifier 114 may be provided, for example, by the remote computing resource 130 over the data network 102, in response to receiving data (e.g., a selection from the user interface 126) related to the unique identifier 114. The production data may be provided to the user via the user interface (such as the user interface 126 of the system 100).

[0094] As shown in step 312, the method 300 may include receiving feedback associated with one or more of the characteristics of the farm product. In some instances, the feedback is a result of presenting queries or prompts for user input. For example, the system 100 of Fig. 1 may receive feedback (such as other data 118) from the user 101 through the user interface 126. The feedback may be associated with one or more of the characteristics 116 of the farm product 110. For example, the user interface 126 may provide one or more interactive elements that a user may use to provide feedback, such as a slider, a spinner, a point scale, an input field, and so on. The user interface 126 may, for example, include an interactive sliding scale where a user may rate one or more characteristics 116 of the farm product 110 — a rating for bitterness may thus include a slider that the user may position between “bitter” and “sweet”. The user interface 126 may, for example, include a point scale where bitterness is ranked on a scale of 0 points (least bitter) to 10 points (most bitter). The feedback may also, or instead, relate to a user preference for the farm product (e.g., the interface may present a question such as “How would you prefer this lettuce?” and the user may submit feedback by positioning a slider between “more bitter” and “more sweet”). In some aspects, a large language model LLM may be used at least in part to process user input and feedback.

[0095] As shown in step 314, the method 300 may include storing feedback in a training data set. The feedback may be associated with one or more of the characteristics of thefarm product (such as a characteristic 116 of the farm product 110, as described with reference to the system 100 of Fig. 1). The training data set may be used to train a machine learning model (e.g., the model 138). The model may, for example, include one or more algorithms that evolve in response to changes in the training data set (e.g., as more feedback is stored in the training data set, the model outputs may change, improve in accuracy, become more nuanced, and so on).

[0096] As shown in step 316, the method 300 may include training a model to predict consumer preferences using the training data set. For example, and referring to the system 100 of Fig. 1, the model 138 may be trained to predict preferences of a user 101 based on feedback (e.g., other data 118) provided by the user or a plurality of users. In one aspect, the model may predict preferences of a first user based on previous feedback provided by that user. Output of the model may be used, for example, to recommend other products to that user. In another aspect, the model may also, or instead, recognize or consider patterns among a plurality or distinct group of users — such as users having a shared preference or a shared attribute (e.g., users in the same or similar geographic location, users of a similar age or other demographic, users of a similar means, users in similar industries or trades, and so forth). Thus, the model may predict preferences for a first user based on feedback received from other users in the group of users.

[0097] As shown in step 318, the method 300 may include adjusting one or more of the production parameters in response to the feedback. For example, in the system 100 of Fig. 1, one or more of the production parameters 174 may be adjusted, such as by adjusting one or more farm devices 176, based on feedback (e.g., other data 118) provided by one or more users 101. In some cases, one or more of the production parameters may be automatically adjusted (e.g., by the processor 132, the other resources 150, or similar) in response to receiving the feedback. Certain criteria may be in place to permit an automatic adjustment to occur; for example, only specific types of users may be permitted to make adjustments, and / or adjustments may only occur when a threshold is met (e.g., a predetermined quantity of feedback is received or a predetermined difference is detected between a predicted parameter and a parameter as reported by feedback), and the like. In an aspect, information related to an adjustment may be visible in the user interface 126 (e.g., as described in step 310). For example, a production parameter after adjusting, before adjusting, or both may be displayed (e.g., as a time-series plot). The adjustments to one or more production parameters 174 may be displayed in conjunction with the video 162 of the related farm product 110.

[0098] In one example, a user may be a repeat purchaser of a farm product (e.g., a restaurant that frequently orders lettuce from a farm). The user may submit feedback on a firstorder of lettuce — for example, the user may prefer the lettuce to be more bitter and have more purple color. Based on this feedback, production parameters may be adjusted for other orders of lettuce (e.g., future orders, and / or orders currently being cultivated) for the same user or different users (e.g., other users grouped by a similar user attribute). For example, when the feedback received is associated with flavor (e.g., bitter / sweet), adjusting one or more of the production parameters may include changing an amount of nitrogen delivered (such as by adjusting a dosing level in a feed supply, adjusting a quantity of feed delivered, or similar) or the adjustment may include increasing a DLI (e.g., by adjusting a timer connected to a lighting source). In another example, when the feedback received is associated with color, adjusting one or more of the production parameters may include changing the temperature (e.g., more ventilation may be provided to decrease the temperature to cultivate a lettuce having more purple coloration — this adjustment may include adjusting thermostats, timers, fans, louvers, and so on). It will be understood that many examples are also or instead possible, i.e., where feedback is associated with a specific attribute that can be adjusted through a change in specific production parameters, all of which are intended to be included herein.

[0099] In another aspect, the user providing feedback may be a grower, farmer, farm manager, or similar. The user may provide feedback based upon an anticipated need, in order to induce an automatic adjustment from the system. For example, a grower may anticipate a seasonal change or a short-term weather change. In one case, if cloudy conditions are expected, the user may provide feedback that the plants need to receive more light. The system may automatically adjust related production parameters and / or farm devices in response to this feedback. In another case, the user may expect that seasonal temperatures will impact the flavor of the farm product, and may provide feedback to reflect this. In some aspects, the user interface may allow certain users to directly adjust one or more production parameters or control one or more farm devices.

[0100] Fig. 4 is flow chart of a method 400 of adjusting production parameters for a region of a farm, in accordance with a representative example. It will be understood that, similar to the method 200 described above and any other methods described herein, the method 400 of Fig. 4 may be implemented on a platform such as one supported by any of the systems described herein, such as the system 100 of Fig. 1. For example, the method 400 may include providing production data 172 for a farm product 110 as discussed with respect to Fig. 1. Further, it will be understood that many of the steps and features described herein with reference to other figures or examples — e.g., many of the features of the method 300 of Fig. 3 — may also or instead be included in a method 400 described below. Moreover, it will be understood that many of the steps and features described herein with reference to this and other figures or examples may beperformed using a computer program product comprising computer-executable code or computer-usable code that, when executing on one or more computing devices, performs any and / or all of the steps thereof.

[0101] As shown in step 402, the method 400 may include associating a region of a farm with a unique identifier. The step 402 may, in some aspects, include features described with respect to the step 302 of method 300, where aspects of step 302 related to a farm product may apply, mutatis mutandis, to a region of the farm. The region of the farm may include one or more plant support apparatus, or may include a portion of a plant support apparatus. The region of the farm may have, or be associated with, one or more production parameters such as one or more of: a plant spacing, a crop yield, a number of seeds per unit area, a type of seed, a daily light integral (DLI), a light intensity, a light spectra, a production duration, a feed amount, a feed composition, a water amount, a water savings amount, a temperature, a humidity, a location within the farm, a treatment regimen, an energy usage, and an efficiency.

[0102] As shown in step 404, the method 400 may include associating production data specific to the region of the farm with the unique identifier. The step 404 may, in some aspects, include features described with respect to the step 204 of method 200. For example, and referring to the system 100 of Fig. 1, the production data 172 may include one or more of: a video 162 of production, one or more production parameters 174, and / or a time-series plot 164. The production data 172 may include at least information related to one or more of the production parameters 174.

[0103] As shown in step 406, the method 400 may include providing a user interface for accessing the production data, or a portion of the production data, or other data associated with the specific farm product having the unique identifier. For example, and with reference to Fig. 1, a user interface 126 may be provided on a user device 120 for accessing at least a portion of the production data 172 from a remote computing resource 130 over a data network 102. The user interface may be configured to receive the change request associated with one or more of the plant type and the production parameters through one of a slider, a spinner, a point scale, a menu, and a text field.

[0104] The user interface may provide various interactive methods for selecting a region of a farm. For example, the interface may display a graphical map or layout of the farm, allowing users to click or tap on specific areas to select a region. The map may be zoomable and / or pannable, enabling users to easily navigate larger farm layouts. In some implementations, the interface may offer a dropdown menu or searchable list of predefined farm regions, such as “Plant Support Apparatus 1-10”, “Greenhouse 1”, “North Field”, or “Hydroponic Section B”. Users may also have the option to input custom region names or identifiers. The interface mayinclude a hierarchical selection system, where users first choose a broad area (e.g., “Indoor Growing Facilities”) and then narrow down to specific regions within that area. This approach may be particularly useful for large or complex farm layouts. In some cases, the user interface may incorporate augmented reality (AR) features, allowing users to point their device’s camera at different areas of the farm to select regions in real-time. This may be especially helpful for on-site selection and management. The interface may also provide the ability to select multiple regions simultaneously, either by clicking and dragging to create a selection box or by using multi-select options. This feature may allow users to compare data or apply changes across several regions at once.

[0105] For farms with vertical growing systems, the interface may offer a 3D representation of the growing space, enabling users to select specific levels or sections within a vertical structure (e.g., “Column X, Lower Quadrant”). Users may be able to rotate and explore this 3D model to make precise selections. In some implementations, the interface may allow users to define custom regions by drawing polygons or freeform shapes on a map of the farm. This flexibility may be useful for selecting irregularly shaped areas or creating temporary zones for specific projects or experiments. The user interface may also or instead incorporate timebased selection features, allowing users to choose not only spatial regions but also temporal ranges. This may be useful for analyzing or adjusting production parameters over specific growing cycles or seasons.

[0106] In some implementations, the user interface may provide options for selecting plant types or specifying seed spacing within the chosen farm region. Users may access a dropdown menu or searchable database of available plant varieties, allowing them to choose specific crops or cultivars for cultivation. The interface may display relevant information for each plant type, such as optimal growing conditions, expected yield, and nutritional content. For seed spacing, the user interface may offer interactive tools that allow users to specify planting density and arrangement. This may include a grid-based system where users can define row spacing and seed placement, or a more freeform tool for creating custom planting patterns. The interface may provide visual feedback, such as a preview of the planting layout overlaid on the selected farm region. In some cases, the system may suggest optimal spacing based on the selected plant type and growing conditions. Users may be able to adjust these suggestions using, e.g., sliders or input fields to fine-tune the planting density. The interface may also allow users to save custom planting templates for future use or apply consistent spacing across multiple selected regions. For vertical farming systems, the plant selection and spacing tools may be integrated with the 3D representation of the growing space. Users may be able to specify different plant types and spacing for each level or section of the vertical structure, optimizingspace utilization and crop diversity. The user interface may also provide real-time feedback on how changes to plant type and spacing may affect other production parameters, such as water usage, nutrient requirements, and expected yield. This information may help users make informed decisions about crop selection and planting strategies for different regions of the farm.

[0107] As shown in step 408, the method 400 may include receiving data related to the unique identifier associated with the region of the farm. The step 408 may, in some aspects, include features described with respect to the step 208 of method 200. For example, and referring to the system 100 of Fig. 1, data related to the unique identifier 114 associated with the farm product 110 — such as a scan 122 of a tag 112, other data 118, or the unique identifier 114 itself — may be transmitted over the data network 102 by the user device 120. In another example, the received data may be related to a user 101 of the system 100, such as a user name, an account number, location data, or the like. The data related to the user 101 may be included in a user profile that is accessible by the system (e.g., the user profile may be stored in the database 140), and the user profile may be associated with one or more regions of the farm 170. Such regions of the farm 170 may be selectable by the user 101, such as by selecting from the user interface 126, and the received data may include the selection made by the user of a particular farm product 110.

[0108] As shown in step 410, the method 400 may include providing production data, or a portion thereof, associated with the unique identifier. This step 410 may, in some aspects, include features described with respect to the step 210 of method 200, or step 310 of method 300. For example, and referring to the system 100 of Fig. 1, production data 172 associated with the unique identifier 114 may be provided, for example, by the remote computing resource 130 over the data network 102, in response to receiving data (e.g., a selection from the user interface 126) related to the unique identifier 114. The production data may be provided to the user via the user interface (such as the user interface 126 of the system 100).

[0109] As shown in step 412, the method 400 may include receiving (e.g., from the user interface) a change request associated with one or more of the plant type and the production parameters. The change request may be associated with one or more characteristics of the farm product. For example, the system 100 of Fig. 1 may receive the change request (such as other data 118) from the user 101 through the user interface 126. The change request may also, or instead, be associated with one or more of the characteristics 116 of the farm product 110.

[0110] As shown in step 414, the method 400 may include adjusting one or more of the production parameters in response to the change request. For example, in the system 100 of Fig. 1, one or more of the production parameters 174 may be adjusted, such as by adjusting one or more farm devices 176, based on feedback (e.g., other data 118) provided by one or more users101. In some cases, one or more of the production parameters may be automatically adjusted (e.g., by the processor 132, the other resources 150, or similar) in response to receiving the change request. Adjusting one or more of the plant type and the production parameters may include an adjustment to one or more of lighting, feed, water, soil, and an environmental condition. This step may also or instead involve implementing changes to the farm's operations based on the received change request. For example, if the change request involves switching to a different plant type, the system may initiate processes to prepare the region for the new crop. If the change request involves adjusting production parameters, the system may modify settings on various farm devices to achieve the desired conditions. The step 414 may further include automatically adjusting one or more of the production parameters responsive to receiving the change request. This automatic adjustment may, for example, be carried out by a control system that interprets the change request and translates it into specific actions to be taken by various farm devices or systems.[OHl] In an example, a third user (e.g., as described in the previous example) associated with a restaurant chain may submit a change request through the user interface for a specific region of the farm dedicated to growing lettuce for their establishments. The change request may involve switching from a current cultivar of romaine lettuce to a new cultivar of butter lettuce that the restaurant wants to feature in an upcoming seasonal menu. The user interface may allow the third user to select the specific farm region, view current production data, and / or choose the new butter lettuce cultivar from a dropdown menu of available plant types. The system may further display information about the new cultivar, including its optimal growing conditions and expected yield.

[0112] Additionally, the change request may include adjusting the seed spacing to optimize growth for the new cultivar. The user interface may, for example, provide a grid-based tool where the third user can specify a tighter spacing for the butter lettuce plants compared to the previous romaine lettuce configuration. For example, the user may adjust the row spacing from 30 cm to 25 cm and the in-row plant spacing from 25 cm to 20 cm to achieve a higher plant density suitable for the compact growth habit of butter lettuce. The system may then process this change request, updating the production parameters for the specified region to accommodate the new cultivar and spacing requirements. This may involve adjustments to irrigation schedules, nutrient delivery, and lighting conditions to suit the needs of the butter lettuce. The third user may be able to monitor the implementation of these changes and track the progress of the new crop through the user interface, potentially including access to live video feeds or time-lapse recordings of the plants’ growth.

[0113] In response to the change request for switching from romaine lettuce to butter lettuce, the system may automatically adjust various growing parameters to optimize conditions for the new crop. For example, the system may modify the lighting schedule to provide a daily light integral (DLI) more suitable for butter lettuce. This may involve adjusting the intensity, duration, or spectral composition of artificial lighting in indoor growing environments. The irrigation system may be reconfigured to accommodate the water requirements of butter lettuce. This may include altering the frequency and volume of watering cycles, as butter lettuce may have different moisture needs compared to romaine lettuce. The system may also adjust the humidity levels in the growing environment to create optimal conditions for the new crop. Nutrient delivery may be automatically modified to match the specific nutritional needs of butter lettuce. The system may adjust the composition and concentration of the nutrient solution, potentially increasing certain minerals or reducing others based on the cultivar's requirements. The pH levels of the nutrient solution may also be fine-tuned to ensure optimal nutrient uptake for butter lettuce. Temperature controls may be adjusted to maintain the ideal growing conditions for butter lettuce. This may involve modifying heating and cooling systems to maintain a temperature range that promotes faster growth and better flavor development in the new crop. The system may also adjust air circulation patterns within the growing area to prevent issues like tipburn, which butter lettuce can be susceptible to under certain conditions. This may include modifying fan speeds or changing the positioning of air circulation devices.

[0114] As the growing cycle progresses, the system may continue to make dynamic adjustments based on real-time data collected from sensors throughout the growing area. These ongoing adjustments may help optimize growth, manage resource usage efficiently, and potentially improve the quality and yield of the butter lettuce crop. By providing these capabilities, the method 400 may enable more dynamic and responsive management of farm regions, allowing for rapid adjustments to production strategies based on user input, market demands, or changing environmental conditions.

[0115] The above systems, devices, methods, processes, and the like may be realized in hardware, software, or any combination of these suitable for a particular application. The hardware may include a general -purpose computer and / or dedicated computing device. This includes realization in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices or processing circuitry, along with internal and / or external memory. This may also, or instead, include one or more application specific integrated circuits, programmable gate arrays, programmable array logic components, or any other device or devices that may be configured to process electronic signals. It will further be appreciated that a realization of the processes ordevices described above may include computer-executable code created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways. At the same time, processing may be distributed across devices such as the various systems described above, or all of the functionalities may be integrated into a dedicated, standalone device or other hardware. In another aspect, means for performing the steps associated with the processes described above may include any of the hardware and / or software described above. All such permutations and combinations are intended to fall within the scope of the present disclosure.

[0116] Embodiments disclosed herein may include computer program products comprising computer-executable code or computer-usable code that, when executing on one or more computing devices, performs any and / or all of the steps thereof. The code may be stored in a non-transitory fashion in a computer memory, which may be a memory from which the program executes (such as random-access memory associated with a processor), or a storage device such as a disk drive, flash memory or any other optical, electromagnetic, magnetic, infrared, or other device or combination of devices. In another aspect, any of the systems and methods described above may be embodied in any suitable transmission or propagation medium carrying computer-executable code and / or any inputs or outputs from same.

[0117] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings.

[0118] Unless the context clearly requires otherwise, throughout the description, the words “comprise,” “comprising,” “include,” “including,” and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in a sense of “including, but not limited to.” Additionally, the words “herein,” “hereunder,” “above,” “below,” and words of similar import refer to this application as a whole and not to any particular portions of this application.

[0119] It will be appreciated that the devices, systems, and methods described above are set forth by way of example and not of limitation. For example, regarding the methods provided above, absent an explicit indication to the contrary, the disclosed steps may bemodified, supplemented, omitted, and / or re-ordered without departing from the scope of this disclosure. Numerous variations, additions, omissions, and other modifications will be apparent to one of ordinary skill in the art. In addition, the order or presentation of method steps in the description and drawings above is not intended to require this order of performing the recited steps unless a particular order is expressly required or otherwise clear from the context.

[0120] The method steps of the implementations described herein are intended to include any suitable method of causing such method steps to be performed, consistent with the patentability of the following claims, unless a different meaning is expressly provided or otherwise clear from the context. So, for example performing the step of X includes any suitable method for causing another party such as a remote user, a remote processing resource (e.g., a server or cloud computer) or a machine to perform the step of X. Similarly, performing steps X, Y, and Z may include any method of directing or controlling any combination of such other individuals or resources to perform steps X, Y, and Z to obtain the benefit of such steps. Thus, method steps of the implementations described herein are intended to include any suitable method of causing one or more other parties or entities to perform the steps, consistent with the patentability of the following claims, unless a different meaning is expressly provided or otherwise clear from the context. Such parties or entities need not be under the direction or control of any other party or entity, and need not be located within a particular jurisdiction.

[0121] While particular embodiments have been shown and described, it will be apparent to those skilled in the art that various changes and modifications in form and details may be made therein without departing from the spirit and scope of this disclosure and are intended to form a part of the invention as defined by the following claims, which are to be interpreted in the broadest sense allowable by law.

Claims

CLAIMSWhat is claimed is:

1. A computer program product comprising computer executable code embodied in a non- transitory computer readable medium that, when executing on one or more computing devices, performs the steps of: associating a farm product with a unique identifier, the farm product having one or more characteristics and being produced under one or more production parameters; associating production data specific to the farm product with the unique identifier, the production data including at least information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the farm product; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, feedback associated with the one or more characteristics of the farm product; and adjusting the one or more production parameters responsive to the feedback.

2. The computer program product of claim 1, wherein the production data includes one or more of: a video of production, the one or more production parameters, and a time-series plot.

3. The computer program product of claim 1, wherein the one or more production parameters include one or more of: a daily light integral (DLI), a light intensity, a light spectra, a production duration, a feed amount, a feed composition, a water amount, a water savings amount, a temperature, a humidity, a location within a farm, a treatment regimen, an energy usage, and an efficiency.

4. The computer program product of claim 1, wherein the one or more characteristics of the farm product include one or more of: a flavor, a texture, a mass, and an appearance.

5. The computer program product of claim 1, wherein the user interface is configured to receive the feedback associated with the one or more characteristics of the farm product through one of a slider, a spinner, a point scale, and a text field.

6. The computer program product of claim 1, wherein the feedback received is associated with the one or more characteristics of the farm product, and adjusting the one or more production parameters includes an adjustment to one or more of lighting, feed, water, soil, and an environmental condition.

7. The computer program product of claim 1, wherein the feedback received is associated with flavor, and adjusting the one or more production parameters includes changing an amount of nitrogen delivered.

8. The computer program product of claim 1, further comprising code that, when executing on the one or more computing devices, performs the step of: automatically adjusting the one or more production parameters responsive to receiving the feedback.

9. The computer program product of claim 1, further comprising code that, when executing on the one or more computing devices, performs the steps of: storing the feedback associated with the one or more characteristics of the farm product in a training data set; training a model to predict consumer preferences using the training data set; and adjusting the one or more production parameters responsive to a model prediction.

10. The computer program product of claim 9, wherein the feedback is associated with one or more of a geographic location, a demographic, a user type, and a use type for the farm product.

11. A method of receiving feedback related to production data for a farm product, the method comprising: associating a farm product with a unique identifier, the farm product having one or more characteristics and being produced under one or more production parameters; associating production data specific to the farm product with the unique identifier, the production data including at least information related to one of the one or more production parameters;providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the farm product; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, feedback associated with the one or more characteristics of the farm product; and adjusting at least one of the one or more production parameters responsive to the feedback.

12. The method of claim 11, wherein the production data includes one or more of: a video of production, the one or more production parameters, and a time-series plot.

13. The method of claim 11, wherein the one or more production parameters include one or more of: a daily light integral (DLI), a light intensity, a light spectra, a production duration, a feed amount, a feed composition, a water amount, a water savings amount, a temperature, a humidity, a location within a farm, a treatment regimen, an energy usage, and an efficiency.

14. The method of claim 11, wherein the one or more characteristics of the farm product include one or more of: a flavor, a texture, a mass, and an appearance.

15. The method of claim 11, wherein the user interface is configured to receive the feedback associated with the one or more characteristics of the farm product through one of a slider, a spinner, a point scale, and a text field.

16. The method of claim 11, wherein the feedback received is associated with the one or more characteristics of the farm product, and adjusting the one or more production parameters includes an adjustment to one or more of lighting, feed, water, soil, and an environmental condition.

17. The method of claim 11, wherein the feedback received is associated with flavor, and adjusting the one or more production parameters includes changing an amount of nitrogen delivered.

18. The method of claim 11, the method further including: automatically adjusting the one or more production parameters responsive to receiving the feedback.

19. The method of claim 11, further comprising: storing the feedback associated with the one or more characteristics of the farm product in a training data set; training a model to predict consumer preferences using the training data set; and adjusting the one or more production parameters responsive to a model prediction.

20. The method of claim 19, wherein the feedback is associated with one or more of a geographic location, a demographic, a user type, and a use type for the farm product.

21. A system for receiving feedback related to production data for a farm product, the system comprising: a data network; a database configured to store production data for a plurality of farm products; and a remote computing resource coupled to the data network and the database, the remote computing resource including a processor and a memory, the memory storing code executable by the processor to perform the steps of: associating a farm product with a unique identifier, the farm product having one or more characteristics and being produced under one or more production parameters; associating production data specific to the farm product with the unique identifier, the production data including at least information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the farm product; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, feedback associated with the one or more characteristics of the farm product; andadjusting at least one of the one or more production parameters responsive to the feedback.

22. A method of adjusting production parameters for a region of a farm, the method comprising: associating a region of a farm with a unique identifier, the region having one or more production parameters; associating production data specific to the region with the unique identifier, the production data including at least a plant type and information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the region; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, a change request associated with one or more of the plant type and the production parameters; and adjusting one or more of the plant type and the production parameters responsive to the change request.

23. The method of claim 22, wherein the one or more production parameters include one or more of: a plant spacing, a crop yield, a number of seeds per unit area, a daily light integral (DLI), a light intensity, a light spectra, a production duration, a feed amount, a feed composition, a water amount, a water savings amount, a temperature, a humidity, a location within the farm, a treatment regimen, an energy usage, and an efficiency.

24. The method of claim 22, further comprising automatically adjusting one or more of the production parameters responsive to receiving the change request.

25. The method of claim 22, wherein the region of the farm includes one or more plant support apparatus.

26. The method of claim 22, wherein the region of the farm includes a portion of a plant support apparatus.

27. The method of claim 22, wherein the production data includes one or more of: a video of production, the one or more production parameters, and a time-series plot.

28. The method of claim 22, wherein the user interface is configured to receive the change request associated with one or more of the plant type and the production parameters through one of a slider, a spinner, a point scale, a menu, and a text field.

29. The method of claim 22, wherein the change request received is associated with one or more characteristics of a farm product, and adjusting one or more of the plant type and the production parameters includes an adjustment to one or more of lighting, feed, water, soil, and an environmental condition.

30. A computer program product comprising computer executable code embodied in a non- transitory computer readable medium that, when executing on one or more computing devices, performs the steps of: associating a region of a farm with a unique identifier, the region having one or more production parameters; associating production data specific to the region with the unique identifier, the production data including at least a plant type and information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the region; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, a change request associated with one or more of the plant type and the production parameters; and adjusting one or more of the plant type and the production parameters responsive to the change request.

31. A system for adjusting production parameters for a region of a farm, the system comprising: a data network; a database configured to store production data; and a remote computing resource coupled to the data network and the database, the remote computing resource including a processor and a memory, the memory storing code executable by the processor to perform the steps of: associating a region of a farm with a unique identifier, the region having one or more production parameters; associating production data specific to the region with the unique identifier, the production data including at least a plant type and information related to one of the one or more production parameters; providing a user interface for accessing at least a portion of the production data from a remote computing resource over a data network; receiving, over the data network from a user device, data related to the unique identifier associated with the region; providing, responsive to receiving the data related to the unique identifier, at least a portion of the production data associated with the unique identifier, from the remote computing resource over the data network; receiving, from the user interface, a change request associated with one or more of the plant type and the production parameters; and adjusting one or more of the plant type and the production parameters responsive to the change request.

32. A method of providing access to production data for a farm product, the method comprising: associating the farm product with a unique identifier; associating production data specific to the farm product with the unique identifier, the production data including at least a video of production specific to the farm product; tagging the farm product with a tag having the unique identifier; receiving, over a data network from a user device, data related to the unique identifier associated with the tag; and transmitting, responsive to receiving the data related to the unique identifier, the production data associated with the unique identifier, from a remote computing resource over the data network.

33. The method of claim 32, wherein the video includes a time-lapse of a least a portion of the production of the farm product.

34. The method of claim 33, wherein the farm product is a plant and the video includes a time-lapse showing the plant over at least a portion of a growing period.

35. The method of claim 33, wherein the farm product is an unmanufactured animal product and the video includes a time-lapse of a harvest of the unmanufactured animal product.

36. The method of claim 32, wherein the tag includes one or more of: a quick response (QR) code, a barcode, an alpha-numeric identifier, a microchip, a radio frequency identification tag, and an integrated circuit.

37. The method of claim 32, wherein the production data includes an attribute of one or more production parameters.

38. The method of claim 32, wherein the production data includes a time-series plot of one or more production parameters.

39. The method of claim 32, wherein the farm product is one of an edible leafy green plant, a flowering plant, a root vegetable, a tuber plant, a vine, a hemp plant, a tobacco plant, a cactus, and a mushroom.

40. The method of claim 39, wherein the farm product is one of lettuce, spinach, beets, cabbage, quinoa, endive, carrots, arugula, fennel, and watercress.

41. A computer program product comprising computer executable code embodied in a non- transitory computer readable medium that, when executing on one or more computing devices, performs the steps of: associating a farm product with a unique identifier; associating production data specific to the farm product with the unique identifier, the production data including at least a video of production specific to the farm product; tagging the farm product with a tag having the unique identifier;receiving, over a data network from a user device, data related to the unique identifier associated with the tag; and transmitting, responsive to receiving the data related to the unique identifier, the production data associated with the unique identifier, from a remote computing resource over the data network.

42. A system for providing access to production data for a farm product, the system comprising: a data network; a database configured to store production data for a plurality of farm products; and a remote computing resource coupled to the data network and the database, the remote computing resource including a processor and a memory, the memory storing code executable by the processor to perform the steps of: associating a farm product with a unique identifier; associating the production data specific to the farm product with the unique identifier, the production data including at least a video of production specific to the farm product; tagging the farm product with a tag having the unique identifier; receiving, over a data network from a user device, data related to the unique identifier associated with the tag; and transmitting, responsive to receiving the data related to the unique identifier, the production data associated with the unique identifier, from a remote computing resource over the data network.

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