Abstract:
Agricultural systems have increased in complexity to allow farmers to control the environmental factors impacting a crop. By analyzing data from a plurality, and preferably a large number, of operations, a particular objective for a particular plan may be developed for a particular crop. The equipment at a particular site may then monitor the crop and be controlled by a device, such as an on-site hub, to operate equipment in a manner associated with the particular plan and objective.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    The present application claims the benefit of Provisional Patent Application No. 62/360,376, filed on Jul. 9, 2016, and is incorporated herein by reference in its entirety. 
     
    
     FIELD OF THE DISCLOSURE 
       [0002]    The present disclosure is generally directed toward a feedback loop and more specifically a feedback and control loop for agricultural systems. 
       BACKGROUND 
       [0003]    Historically, the scientific method has been applied in order to define a hypothesis and then employ investigation, data collection and analysis of data to prove the hypothesis. In the advent of increasingly available data processing capabilities, the underlying principle that offers a unique approach to a targeted application is that, rather than defining the anticipated hypothesis, all available data is collected and the analysis seeks to uncover all relevant correlations. This allows the data to tell the story without the encumbrance of pre-conceived notions. 
       SUMMARY 
       [0004]    With a bounty of publicly available statistical algorithms to identify implicit correlations between multi-dimensional data sets, the proposed system and method utilizes these publicly available statistical algorithms and applies them in a way that allows for the establishment of an optimal grow plan based on discovered correlations. More specifically, a grow plan will be presented to the farmer, and more importantly to the farmer&#39;s monitoring and control system, the ideal parameters for each of the key environmental variables based on the identified correlations with desired outcomes. 
         [0005]    It is with respect to the above issues and other problems that the embodiments presented herein were contemplated. In particular, embodiments of the present disclosure are directed toward the concepts of a controlled environment agriculture and systems for controlling the same. 
         [0006]    Embodiments of the present disclosure employ a combination of data collection, device control and underlying data analytics to help farmers cultivate crops in an optimized approach, targeting both quality, yield and growth cycle duration. 
         [0007]    The process employs the use of a flexible data collection scheme and an information technology architecture that allows for facility-based data storage and cloud-based data analysis. The cloud-based analysis, which identifies correlations between environmental variables and crop-specific and variety-specific quality and yield impacts, helps deliver a value added service to farmers as a time-specified scheme for managing environmental variables with the goal of achieving a specific objective function: maximum yield, maximum quality, minimum growth cycle duration, or a combination. As a further benefit, greater operating margins may be realized via better operational efficiencies and/or better crop results. 
         [0008]    In one embodiment, a server is disclosed, comprising: a processor, comprising logic circuitry and a memory; a communication interface; and wherein the processor: receives a first signal from a plant monitoring component monitoring at least one dynamic attribute of a plant; accesses a growth plan associated with an objective and crop; determines the first signal indicates an out-of-tolerance state from the growth plan; selects a first control component configured to impact the at least one dynamic attribute monitored by the plant monitoring component; selects a first modification signal in accordance with a first rule, wherein the first rule is selected in accordance with the objective and crop; and signals the first control component with the first modification signal to cause the first control component, upon receiving the first modification signal, to alter operation in accordance with the first modification signal and to cause the dynamic attribute to be in compliance with the growth plan. 
         [0009]    In another embodiment, a method is disclosed comprising: receiving a first signal from a plant monitoring component monitoring at least one dynamic attribute of a plant; accessing a growth plan associated with an objective and crop; determining the first signal indicates an out-of-tolerance state from the growth plan; selecting a first control component configured to impact the at least one dynamic attribute monitored by the plant monitoring component; selecting a first modification signal in accordance with a first rule, wherein the first rule is selected in accordance with the objective and crop; and signaling the first control component with the first modification signal to cause the first control component, upon receiving the first modification signal, to alter operation in accordance with the first modification signal and to cause the dynamic attribute to be in compliance with the growth plan. 
         [0010]    In another embodiment, a system is disclosed comprising: a first server; the first server comprising: a first processor, comprising logic circuitry and memory; a first communication interface; and a second server, the second server comprising: a second processor, comprising logic circuitry and memory; a second communication interface; and wherein the first processor, analyzes a plurality of observations of prior crops comprising dynamic attributes and a result attribute and derives a growth plan comprising at least one rule; wherein the second processor: receives a first signal from a plant monitoring component monitoring at least one dynamic attribute of a plant; accesses a growth plan associated with an objective and crop; determines the first signal indicates an out-of-tolerance state from the growth plan; selects a first control component configured to impact the at least one dynamic attribute monitored by the plant monitoring component; selects a first modification signal in accordance with the first rule; and signals the first control component with the first modification signal to cause the first control component, upon receiving the first modification signal, to alter operation in accordance with the first modification signal and to cause the dynamic attribute to be in compliance with the growth plan. 
         [0011]    The phrases “at least one,” “one or more,” and “and/or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B, and C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B, or C,” and “A, B, and/or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B, and C together. 
         [0012]    The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more,” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising,” “including,” and “having” can be used interchangeably. 
         [0013]    The term “automatic” and variations thereof, as used herein, refers to any process or operation done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material.” 
         [0014]    The term “computer-readable medium,” as used herein, refers to any tangible storage that participates in providing instructions to a processor for execution. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, NVRAM, or magnetic or optical disks. Volatile media includes dynamic memory, such as main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, magneto-optical medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, a solid-state medium like a memory card, any other memory chip or cartridge, or any other medium from which a computer can read. When the computer-readable media is configured as a database, it is to be understood that the database may be any type of database, such as relational, hierarchical, object-oriented, and/or the like. Accordingly, the disclosure is considered to include a tangible storage medium and prior art-recognized equivalents and successor media, in which the software implementations of the present disclosure are stored. 
         [0015]    While machine-executable instructions may be stored and executed locally to a particular machine (e.g., personal computer, mobile computing device, laptop, etc.), it should be appreciated that the storage of data and/or instructions and/or the execution of at least a portion of the instructions may be provided via connectivity to a remote data storage and/or processing device or collection of devices, commonly known as “the cloud,” but may include a public, private, dedicated, shared and/or other service bureau, computing service, and/or “server farm,” including but not limited to commercial cloud providers (e.g., Amazon Web Services). 
         [0016]    The terms “determine,” “calculate,” and “compute,” and variations thereof, as used herein, are used interchangeably and include any type of methodology, process, mathematical operation, or technique. 
         [0017]    The term “module,” as used herein, refers to any known or later-developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and software that is capable of performing the functionality associated with that element. Also, while the disclosure is described in terms of exemplary embodiments, it should be appreciated that other aspects of the disclosure can be separately claimed. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0018]    The present disclosure is described in conjunction with the appended figures: 
           [0019]      FIG. 1  depicts a first system in accordance with embodiments of the present disclosure; 
           [0020]      FIG. 2  depicts a first data flow in accordance with embodiments of the present disclosure; 
           [0021]      FIG. 3  depicts a second data flow in accordance with embodiments of the present disclosure; 
           [0022]      FIG. 4  depicts a growth plan development model in accordance with embodiments of the present disclosure; 
           [0023]      FIGS. 5A-5C  depict a system in accordance with embodiments of the present disclosure; 
           [0024]      FIG. 6  depicts a system model in accordance with embodiments of the present disclosure; 
           [0025]      FIG. 7  depicts a command model in accordance with embodiments of the present disclosure; 
           [0026]      FIG. 8  depicts a first process in accordance with embodiments of the present disclosure; 
           [0027]      FIG. 9  depicts a second process in accordance with embodiments of the present disclosure; 
           [0028]      FIG. 10  depicts a rule structure in accordance with embodiments of the present disclosure; 
           [0029]      FIG. 11  depicts a graphical representation of a data model in accordance with embodiments of the present disclosure; and 
           [0030]      FIG. 12  depicts a third data flow in accordance with embodiments of the present disclosure. 
       
    
    
     DETAILED DESCRIPTION 
       [0031]    The ensuing description provides embodiments only and is not intended to limit the scope, applicability, or configuration of the claims. Rather, the ensuing description will provide those skilled in the art with an enabling description for implementing the embodiments. It will be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the appended claims. 
         [0032]    Any reference in the description comprising an element number, without a subelement identifier when a subelement identifier exists in the figures, when used in the plural, is intended to reference any two or more elements with a like element number. When such a reference is made in the singular form, it is intended to reference one of the elements with the like element number without limitation to a specific one of the elements. Any explicit usage herein to the contrary or providing further qualification or identification shall take precedence. 
         [0033]    The exemplary systems and methods of this disclosure will also be described in relation to analysis software, modules, and associated analysis hardware. However, to avoid unnecessarily obscuring the present disclosure, the following description omits well-known structures, components, and devices that may be shown in block diagram form and are well known or are otherwise summarized. 
         [0034]    For purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the present disclosure. It should be appreciated, however, that the present disclosure may be practiced in a variety of ways beyond the specific details set forth herein. 
         [0035]    This disclosure comprises of a multi-step process to produce and deliver a facility specific, crop-specific Grow Plan for indoor agriculture operations. This plan, which can be optimized to a specific objective function of maximum yield, maximum quality, minimum growth cycle duration, or a combination of those factors, can be locally monitored and managed at each facility. 
         [0036]      FIG. 1  depicts first system  100  in accordance with embodiments of the present disclosure. In one embodiment, plant  102  is monitored by plant monitoring component  104 . Plant  102  may be a single plant, plurality of plants, variety of plants, agricultural crop, or a plurality of agricultural crops. Plant monitoring component  104  comprises one or more devices and/or components each monitoring at least one dynamic attribute of plant  102 . Dynamic attribute may be an attribute of the plant itself (e.g., size, water consumption, maturity, etc.). Dynamic attribute of plant  102  may comprise an environmental factor experienced by plant  102  is subject (e.g., temperature, CO2 level, lighting, humidity, insect population, etc.). In another embodiment, plant monitoring component  104  may monitor indirect and/or aggregate attributes of plant  102 , including but not limited to electrical power consumption associated with control component  106  and/or other components. In yet another embodiment, plant monitoring component  104  may comprise an input from an automated and/or manual entry system (e.g., a computer terminal operable to receive plant height, weight, infestation, and/or other observations of plant  102 ). In another embodiment, a dynamic attribute of plant  102  may comprise a location, such as when a location is a proxy for an environmental state (e.g., plant  102  while in location A is subject to a particular temperature and/or other environmental conditions, plant  102  while in location B is subject to a different temperature and/or other environmental conditions, etc.). In addition to dynamic attributes, plant  102  may have one or more static attributes (e.g., date planted, type, identifier, etc.) and may remain fixed or, at least, changed infrequently. Static attribute may change (e.g., correct a data entry error, enter omitted information, record a rare event, etc.), but as they are generally changed with a directed action (e.g., destroy infested plants, harvest a crop, destructive analysis of a particular plant, etc.), monitoring is generally unwarranted and, instead, may be modified via data entry to plant monitoring component  104 . 
         [0037]    In another embodiment, control component  106  comprises one or more devices operable to affect at least one dynamic attribute of plant  102 . Control component  106  may comprise, but is not limited to, water pump/valve, heat/cooling unit control, humidity control, CO2 control, air circulation, etc. Additionally, control component  106  may comprise an on-site alarm, such as to alert personnel and/or other systems of current dynamic attribute requiring intervention. For example, a soil moisture meter may indicate a dynamic attribute that, unless remedied promptly, may be damaging or fatal to plant  102 . An alarm may identify the issue, such as to cause a human to replace the soil moisture meter if faulty, tend to a plugged irrigation component, etc. 
         [0038]    In another embodiment, hub  108  is located on-site, such as to be, co-located with plant  102 , connected to plant monitoring component  104  with an on-site network, and/or connected to control component  106  within an on-site network. In one embodiment, hub  108  executes instructions to process signals from plant monitoring component  104  and provide modification signals to control component  106 . For example, if plant monitoring component  104  comprises a thermometer and a temperature is out of tolerance, hub  108  may execute instructions that cause control component  106 , comprising a heating/cooling component, to execute an operation to cause the temperature to remediate the out-of-tolerance temperature. As a benefit of hub  108  being co-located with one or both of plant monitoring component  104  and control component  106 , connectivity via external network  110  need not be continually maintained in order to maintain the dynamic attributes of plant  102 . 
         [0039]    In another embodiment, hub  108  executes instructions to access, via network  110 , server  112 . Server  112  may be a dedicated and/or shared server, such as a distributed computing platform (“cloud”). In one embodiment, server  112  gathers data from a plurality of hubs, of which hub  108  may comprise one of the plurality of hubs, which may comprise respective plant monitoring components and/or control components associated with other farms and/or prior growing cycles. Server  112  may then analyze the data from the plurality of hubs to determine a best practice for growing plant  102 . In a further embodiment, the best practice for growing plant  102  may be selected, in part, in accordance with an objective. For example, one farmer operating a particular farm may be subject to a water shortage. Accordingly, the objective may be to get plant  102  to maturity with as little water as possible even at the expense of additional electricity. Another objective may be to reduce cycle time, to that plant  102  can go from seed (or other origin, such as a cutting, graft, etc.) to maturity whereby plant  102  may be harvested and the process may be repeated and thereby maximize the number and/or quality of harvests within a given timeframe. Another farmer operating another farm may be contractually obligated to harvest plant  102  at a particular date and, accordingly, willing to use more water and/or other resources to satisfy the obligation. Objectives may be constant and remain unchanged, at least through a growing cycle, or change, such as to mitigate an outbreak of insects or mold midway through a growing cycle. Server  112  may leverage a larger data set, from multiple hubs and/or growing cycles, to determine a particular rule or set of rules to control component  106  and the dynamic attributes of plant  102  and to comply, or at least better comply, with provided objective. As a result, trial-and-error modifications to the dynamic attribute of plant  102  may be reduced or eliminated in favor of modifications based on a determined cause and effect for a particular crop (e.g., type/species of plant  102 ) and objective. 
         [0040]    Hub  108  may comprise at least one microprocessor (“processor”), memory, bus, and optionally storage media or other hardware. Hub  108  further comprises a network interface hardware and instructions to be executed by the processor, such as to enable connectivity to server  112  via network  110 , plant monitoring component  104 , and control component  106  and to execute instructions based upon inputs from plant monitoring component  104  and providing control signals to control component  106 . 
         [0041]    It should be appreciated that the monitoring-controlling feedback loop monitored and controlled by hub  108  receiving dynamic attributes from plant monitoring component  104  and issuing signals to control operations of control component  106  may comprise a plurality of homogeneous signals (e.g., increment water pump  1  output) or a plurality of heterogeneous signals (e.g., increment water pump  1  output, start water pump  2 , increment water pump  2  output, etc.). The signal, which may be determined in accordance with the capabilities of a particular control component  106 , may be uniform for any out-of-tolerance state. For example, a low-temperature state indicated by plant monitoring component  104  may cause hub  108  to issue a signal (e.g., “ON”) to a heater embodiment of control component  106 . The degree to which the temperature is out-of-tolerance may have no impact on the content or target of the signal. In another embodiment, the degree to which the attribute is out-of-tolerance may vary the content and/or target of the signal. For example, a small heater may be signaled to increment output, when the deviation is small and another heater may be activated when the deviation is larger. Therefore, and in one embodiment, hub  108  generates a signal to control component  106  to cause an out-of-tolerance state to be remedied within a previously determined timeframe, which may be selected in accordance with the performance of control component  106  and/or sensitivity or delay of plant monitoring component  104  to avoid overshooting the correction. For example, increasing water controlled by a pump or valve may not be detectable by a soil moisture meter for thirty minutes. Accordingly, hub  108  may signal control component  106  (e.g., pump or valve) to apply additional water for one minute and, even though plant monitoring component  104  (e.g., soil moisture monitor) may still indicate dry soil, take no action unless dry soil is still indicated at some point after the thirty minutes. 
         [0042]      FIG. 2  depicts data flow  200  in accordance with embodiments of the present disclosure. In one embodiment hub  108  may provide primary data collection from a variety of data sources (e.g., one or more of plant monitoring component  104 ), which may include, but are not limited to, temperature, humidity, lighting, water, CO2 levels, imaging, etc. In another embodiment, hub  108 , may comprise a device-flexible hub, and further comprise an input-output device with sufficient data storage, provided by the memory and/or storage media, and processing capabilities, provided by the processor, for managing the input (e.g., signals from plant monitoring component  104 ) and business rules (e.g., accessed from server  112 ) and the issuance of control signals out to intelligent relays to exercise control of control component  106 . 
         [0043]    In another embodiment, hub  108  receives data from environmental sensor  202 , energy sensor  208 , facility  214 , crop  204 , control  216 , optical  206 , and human-machine interface (HMI  212 ). Crop  204  may be a data source, such as a record providing identification of a particular plant species or variety comprising a crop. Facility  214  may be a data source, such as record or component providing facility information (e.g., equipment, operational limitations, etc.) 
         [0044]      FIG. 3  depicts data flow  300  in accordance with embodiments of the present disclosure. In one embodiment, data collected at the facility, such as by hub  108  at each of a plurality of sites may store data on-site at the facility (e.g., memory and/or storage device of hub  108  and/or a storage device accessible to hub  108 , etc.). In another embodiment, data may be ported to the analytics cloud, such as comprising server  112 . Porting may occur periodically or in response to an event, such as the state of plant monitoring component  104  or an ad hoc request. Server  112  may then process and store the data in database  312  gathered from a plurality of facility interfaces  302 . Data scrubbing  308  may categorize or otherwise annotate the data to populate records stored in data warehouse  310 , such as comprising database  312 . For example, a record of a particular plant monitoring component  104  may provide a light level value to a record and the record annotated from a static value, such as the plant variety. 
         [0045]    In one embodiment, data warehouse  310  is a dynamic data store and may further incorporate feedback loop data inputs. The primary inputs are environmental variables and data collected from plant monitoring component  104 , such as optical sensors that monitor crop growth patterns. More static data, such as facility attributes, crop attributes, hardware attributes are may also be inputs. Finally, feedback on crop quality, a more qualitative assessment is provided, such as at harvest; specifically, quality variables will be input to the cloud environment at time of harvest. 
         [0046]    Server  112  may execute analytics engine  314  to process the data stored in data warehouse  310 , such as to identify correlations between the productivity of the crop, the quality of the crop, the environmental, facility, hardware, and crop attributes. For example, server  112  may execute a multi-dimensional correlation assessment. A multiple dimension correlation assessment can be a processor-intensive task, a preliminary assessment of the initial data sets may be provided to identify the key variables that are subject to further analysis in order to derive correlations. Once the smaller population of deterministic variables have been identified, algorithms for deriving correlations may be employed on the data sets in order to derive useful and meaningful relationships. The effectiveness of the correlation may be determined by applying primary filters to the full set of variables to isolate correlation inspection to reduce processing requirements and complex n-dimension analysis. Correlations will continue to emerge as the data sets become more expansive, both in terms of the number of farming facilities and the diversity of crops, farming objective, and the particular dynamic attributes of a crop. 
         [0047]      FIG. 4  depicts growth plan development model  400  in accordance with embodiments of the present disclosure. In one embodiment, development model  400  depicts the creation of individual facility optimized grow plan  404  to be ported to hub  108  that resides at the subject facility. Facility data interface  302 , such as providing data gathered from hub  108  and/or other site-specific data, is collected at data warehouse  310  comprising database  312 . A processor, such as from server  112 , then creates an optimization scheme  402  from the data within data warehouse  310  (e.g., dynamic and static attributes of plurality of other plants, optimization strategies, etc.). Optimization scheme engine  402 , which may derive insights from the correlations previously or concurrently identified, develops an optimal grow plan, per the desired objective function, and passes the grow plan down to hub  108 . This grow plan is used by the hub to establish the environmental parameters within which the grow environment must be operated throughout the grow cycle. 
         [0048]    Table 1 provides examples of functions, which may comprise at least a portion of growth plan  404 ; descriptions, input data provided by plant monitoring equipment and/or other components; and outputs provided by control component  106  and/or other components. 
         [0000]    
       
         
               
               
               
               
             
               
             
               
               
               
               
             
               
             
               
               
               
               
             
               
             
               
               
               
               
             
               
             
               
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Function 
                 Description 
                 Input Data 
                 Output 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Alerting 
               
             
          
           
               
                 Peak Demand 
                 Alerts the growers via 
                 The grower should be 
                 Alert. Yellow 
               
               
                   
                 alert when electricity 
                 able to define the 
                 when approaching 
               
               
                   
                 consumption is within a 
                 parameters - in terms of 
                 target, red when at 
               
               
                   
                 certain percentage of a 
                 the percentage within 
                 or behind target 
               
               
                   
                 peak demand mark 
                 target to drive an alert 
               
               
                   
                   
                 and/or the peak demand 
               
               
                   
                   
                 target. The peak demand 
               
               
                   
                   
                 target can either be 
               
               
                   
                   
                 derived from previously 
               
               
                   
                   
                 monitored data or from 
               
               
                   
                   
                 peak demand as noted 
               
               
                   
                   
                 on electricity bill, if 
               
               
                   
                   
                 applicable, can be pulled 
               
               
                   
                   
                 from electronic bill, but 
               
               
                   
                   
                 manual is fine for 
               
               
                   
                   
                 starters. Current 
               
               
                   
                   
                 building monitoring will 
               
               
                   
                   
                 inform the business rule 
               
               
                   
                   
                 with current demand 
               
               
                   
                   
                 value on 5-minute 
               
               
                   
                   
                 intervals. Could trigger 
               
               
                   
                   
                 one-minute intervals 
               
               
                   
                   
                 after yellow alert is 
               
               
                   
                   
                 triggered, 
               
               
                 Low Voltage 
                 Monitor and alert grower 
                 Voltage parameters can 
                 Alerts as above; 
               
               
                   
                 when voltage levels are 
                 be configured, with 
                 yellow when 
               
               
                   
                 out of acceptable bounds 
                 industry standard range 
                 approaching limit, 
               
               
                   
                   
                 as default. Building 
                 red when at or 
               
               
                   
                   
                 monitoring derives 
                 beyond limit. 
               
               
                   
                   
                 voltage values at breaker 
               
               
                   
                   
                 and informs business 
               
               
                   
                   
                 rule analyzer. 
               
               
                   
                   
                 Constraints should be 
               
               
                   
                   
                 put on range a user can 
               
               
                   
                   
                 define. 
               
               
                 Equipment 
                 Alert on anomalous 
                 Options include: 1. 
                 Alert indicating 
               
               
                 Performance 
                 performance of HVAC 
                 Publicly-available 
                 that the equipment 
               
               
                   
                 or Dehumidifier Unit. 
                 baseline information for 
                 in question is 
               
               
                   
                   
                 devices; 2) Monitored 
                 operating in a way 
               
               
                   
                   
                 the devices for several 
                 that is outside of 
               
               
                   
                   
                 months, normalized for 
                 expected 
               
               
                   
                   
                 weather, to determine 
                 conditions, This 
               
               
                   
                   
                 standard operating load 
                 may trigger a call 
               
               
                   
                   
                 shape for device. Then 
                 to a service 
               
               
                   
                   
                 an analysis would need 
                 professional to 
               
               
                   
                   
                 to be applied against that 
                 check out unit. 
               
               
                   
                   
                 “normalized” load shape 
               
               
                   
                   
                 to flag deviations 
               
               
                   
                   
                 Weather may be at least 
               
               
                   
                   
                 one factor. 
               
             
          
           
               
                 Economics 
               
             
          
           
               
                 Running 
                 Offers growers run time 
                 15-minute interval 
                 On-demand 
               
               
                 Electricity Costs 
                 electricity bill economic 
                 starting at the beginning 
                 feature to allow adhoc 
               
               
                   
                 estimates based on 
                 of the billing period 
                 calculations to 
               
               
                   
                 consumption dating back 
                 should be passed into a 
                 be presented. The 
               
               
                   
                 to beginning of new bill 
                 rate calculator (can 
                 calculation result 
               
               
                   
                 period 
                 license or develop). That 
                 should have 
               
               
                   
                   
                 calculator will then 
                 date/time stamp 
               
               
                   
                   
                 assess the usage relative 
                 below to indicate 
               
               
                   
                   
                 to the rules of the 
                 when the calc. 
               
               
                   
                   
                 customer tariff in order 
                 was last run 
               
               
                   
                   
                 to determine their 
               
               
                   
                   
                 running totals for that 
               
               
                   
                   
                 bill period. The user 
               
               
                   
                   
                 record must include 
               
               
                   
                   
                 their current electricity 
               
               
                   
                   
                 rate in order for our 
               
               
                   
                   
                 back end to know which 
               
               
                   
                   
                 rate within the rate 
               
               
                   
                   
                 engine is proper running 
               
               
                   
                   
                 electricity bill estimator 
               
               
                   
                   
                 based on fixed charges 
               
               
                   
                   
                 and bill-period-start-to- 
               
               
                   
                   
                 current hour calculation. 
               
             
          
           
               
                 Forecasting 
               
             
          
           
               
                 Next Day 
                 Predict the following 
                 Data inputs include: 
                 Load shape for 
               
               
                 Consumption 
                 day, week electricity 
                 Historical demand data 
                 facility, and by 
               
               
                   
                 consumption and costs 
                 by whole building and 
                 device, for the 
               
               
                   
                   
                 device, weather data, 
                 next 24-hour 
               
               
                   
                   
                 consumption normalized 
                 period. 
               
               
                   
                   
                 t weather data, future 
                 Additionally, the 
               
               
                   
                   
                 weather forecasts (may 
                 user will be 
               
               
                   
                   
                 come from a weather 
                 presented with the 
               
               
                   
                   
                 service in the form of 
                 anticipated 
               
               
                   
                   
                 predicted or historical 
                 economics. 
               
               
                   
                   
                 data); finally, 
               
               
                   
                   
                 anticipated production 
               
               
                   
                   
                 and operational 
               
               
                   
                   
                 schedules are needed to 
               
               
                   
                   
                 inform this forecast. 
               
               
                   
                   
                 These inputs then 
               
               
                   
                   
                 applied to the rate 
               
               
                   
                   
                 calculator for the 
               
               
                   
                   
                 economic calculation. 
               
               
                 Next Month 
                 Determine the next 
                 Data inputs include: 
                 Growers will be 
               
               
                 Energy Cost 
                 month electricity costs 
                 Historical demand data 
                 presented with 
               
               
                 Projection 
                 based on weather 
                 by whole building and 
                 projected hourly, 
               
               
                   
                 forecasts and planned 
                 device, weather data, 
                 daily load shapes 
               
               
                   
                 production schedule 
                 consumption normalized 
                 and monthly 
               
               
                   
                   
                 t weather data, future 
                 projected kW and 
               
               
                   
                   
                 weather forecasts or 
                 kWh bar charts 
               
               
                   
                   
                 historical weather data 
                 with the 
               
               
                   
                   
                 (from a weather service 
                 associated cost 
               
               
                   
                   
                 in the form of predicted 
                 implications based 
               
               
                   
                   
                 or historical data); 
                 on current rate 
               
               
                   
                   
                 finally, anticipated 
                 schedule 
               
               
                   
                   
                 production and 
               
               
                   
                   
                 operational schedules 
               
               
                   
                   
                 are needed to inform this 
               
               
                   
                   
                 forecast. These inputs 
               
               
                   
                   
                 are then applied to the 
               
               
                   
                   
                 rate calculator for the 
               
               
                   
                   
                 economic calculation. 
               
               
                 Annual Energy 
                 Determine the next 12- 
                 Same as above 
                 Growers will be 
               
               
                 Cost Projection 
                 months of electricity 
                   
                 presented with 
               
               
                   
                 costs based on weather 
                   
                 projected hourly, 
               
               
                   
                 forecasts and planned 
                   
                 daily load shapes 
               
               
                   
                 production schedule 
                   
                 and monthly 
               
               
                   
                   
                   
                 projected kW and 
               
               
                   
                   
                   
                 kWh bar charts 
               
               
                   
                   
                   
                 with the 
               
               
                   
                   
                   
                 associated cost 
               
               
                   
                   
                   
                 implications based 
               
               
                   
                   
                   
                 on current rate 
               
               
                   
                   
                   
                 schedule. Further, 
               
               
                   
                   
                   
                 they will be 
               
               
                   
                   
                   
                 presented with an 
               
               
                   
                   
                   
                 annualized roll-up 
               
               
                   
                   
                   
                 of the costs and 
               
               
                   
                   
                   
                 consumption. This 
               
               
                   
                   
                   
                 can be presented 
               
               
                   
                   
                   
                 as a comparison 
               
               
                   
                   
                   
                 with previous 
               
               
                   
                   
                   
                 years as available. 
               
             
          
           
               
                 Analytics &amp; Optimization 
               
             
          
           
               
                 Yield Max 
                 Determine the 
                 Monitored data for all 
                 Grower will be 
               
               
                 Assessment 
                 relationships/correlations 
                 end sues and input data 
                 presented with the 
               
               
                   
                 between environmental 
                 on yield (per crop) with 
                 discrete impacts 
               
               
                   
                 parameters and yield on 
                 correlations explicitly 
                 on yield for each 
               
               
                   
                 a per strain basis 
                 drawn between the 
                 of the primary 
               
               
                   
                   
                 monitored data and the 
                 environmental 
               
               
                   
                   
                 yield. Further, data on 
                 parameters 
               
               
                   
                   
                 the strain, and 
               
               
                   
                   
                 equipment attributes for 
               
               
                   
                   
                 each crop will be needed 
               
               
                   
                   
                 to be input to identify 
               
               
                   
                   
                 additional potential 
               
               
                   
                   
                 correlations. 
               
               
                 Quality Max 
                 Determine the 
                 Monitored data for all 
                 Grower will be 
               
               
                 Assessment 
                 relationships/correlations 
                 end sues and input data 
                 presented with the 
               
               
                   
                 between environmental 
                 on quality (per crop) 
                 discrete impacts 
               
               
                   
                 parameters and quality 
                 with correlations 
                 on quality for each 
               
               
                   
                 on a per strain basis 
                 explicitly drawn 
                 of the primary 
               
               
                   
                   
                 between the monitored 
                 environmental 
               
               
                   
                   
                 data and the quality. 
                 parameters 
               
               
                   
                   
                 Quality may be assessed 
               
               
                   
                   
                 through optical sensors 
               
               
                   
                   
                 capable of tracking 
               
               
                   
                   
                 color, size and other 
               
               
                   
                   
                 quality attributes as 
               
               
                   
                   
                 defined by the grower. 
               
               
                   
                   
                 Further, data on the 
               
               
                   
                   
                 strain, and equipment 
               
               
                   
                   
                 attributes for each crop 
               
               
                   
                   
                 will be needed to be 
               
               
                   
                   
                 input to identify 
               
               
                   
                   
                 additional potential 
               
               
                   
                   
                 correlations. 
               
               
                 Yield/Quality/Cost 
                 Allow for adjustments to 
                 Outputs from 
                 Grower - and 
               
               
                 Optimization 
                 an objective function to 
                 correlations above, in 
                 more specifically 
               
               
                   
                 allow growers to 
                 addition to desired strain 
                 the growers 
               
               
                   
                 optimize to any of three 
                 will be required to set 
                 equipment and 
               
               
                   
                 features: cost, yield and 
                 the function. 
                 interface - will be 
               
               
                   
                 quality 
                 Furthermore, the grower 
                 presented with an 
               
               
                   
                   
                 will need to define the 
                 actionable grow 
               
               
                   
                   
                 relative weight of each 
                 plan - or 
               
               
                   
                   
                 optimization attribute., 
                 operating 
               
               
                   
                   
                 meaning determining the 
                 parameters - for 
               
               
                   
                   
                 weight of cost vs. yield 
                 all end uses 
               
               
                   
                   
                 vs. quality. 
                 relative to a 
               
               
                   
                   
                   
                 desired strain and 
               
               
                   
                   
                   
                 optimizing 
               
               
                   
                   
                   
                 function 
               
               
                   
               
             
          
         
       
     
         [0049]      FIGS. 5A-5C  depict system  500  in accordance with embodiments of the present disclosure. In one embodiment, system  500  depicts the dynamic attributes of plant  102  that may be continuously or periodically monitored throughout the grow cycle and the business rules maintained by hub  108  to assess the dynamic attributes and their compliance, or deviation, from expected attribute values of a grow plan, such as grow plan  404 . Signals are provided to control component  106  to maintain environmental variables within desired parameters. Should a state exist whereby parameters are outside of a provided window, hub  108  issues alerts to facility operators to indicate deviations from optimal parameters, such as an alarm feature of control component  106  to solicit intervention by human operators and/or other systems. For example, should plant monitoring component  104  detect a temperature state that varies by one degree, as determined by hub  108  and grow plan  404 , no action may be triggered. If plant monitoring component  104  detect a temperature state that varies by five degrees, as determined by hub  108  and grow plan  404 , control component  106  may be provided with a signal such as to discontinue heat, initiate cooling, etc. If plant monitoring component  104  detect a temperature state that varies by ten degrees, as determined by hub  108  and grow plan  404 , an alert may be provided to human operators (e.g., text message, email, audible alarm, visual alarm, etc.) and/or automated system (e.g., signal actuated windows to open, discontinue lighting and/or other heat-generating components, etc.). 
         [0050]    In one embodiment, computer  502 , such as laptop  502 A, smart phone  502 B, and/or other computing device configured to access network  504  and devices attached thereto, is utilized to access services provided by networked components, such as server  112 —which, as described above, may be a single processor, single server, plurality of processors, plurality of servers, and/or a static or dynamic set of processors and/or servers. In one embodiment, server  112  comprises a plurality of servers commonly referred to as a “cloud.” Access to the cloud, may comprise first accessing elastic load balancer  506  gating access to public subnet  508  whereby an elastic IP  510  is assigned and perimeter security  512  gates access to authorized IP addresses to private subnet  514 . 
         [0051]    Private subnet  514  may comprise, front end  516  providing web services and API  518  provides middleware. Middleware/API services may be provided by Sailes.js, Swagger, waterline, Blueprint IP and comprise an API gateway. API  518  accesses ETL  524  customer database  526 , data analytics  528 . Data pipeline  520  may comprise batch-processing  522  of embedded template library (ETL)  524 . API  518  and/or customer database  526  may then access data analytics  528  (e.g., Amazon EMR Kenisis, etc.). 
         [0052]    Batch processing  522  may receive data from third-party API  530 , which may include but is not limited to, Curb  532 , GoElectric  534 , Encycle  536 , Stem  538 , etc. Additionally, on-demand services  540  may comprise Genebility  542 , Curb  544  and receive data from API  518  and be accessed by computer  502 A. 
         [0053]      FIG. 6  depicts system model  600  in accordance with embodiments of the present disclosure. In one embodiment, system model  600  provides an alternative view illustrated by system model  500 . Front-end  516  provides web server services, such as by Vue.js, Axios, Highcharts, D3, Vuecharts, Quasar, VueRouter, Vuex, etc. API  518  may provide middleware services (e.g., Sails.js, Swagger, Waterline, Blueprint API, etc.) and comprise API gateway  604  to provide services (e.g., login, client lookup, kilowatt lookup, circuit lookup, third-party authorization, etc.). Batch processing  522  calls ETL  524 . Customer database  526  may comprise storage database  606  accessed by a mogodb interface and access data analytics  528 . 
         [0054]      FIG. 7  depicts command model  700  in accordance with embodiments of the present disclosure. Command model  700  illustrates data structures and primary keys (“PK”) and foreign keys (“FK”) and their relationship, illustrated using a crow&#39;s-foot notation. In one embodiment, customer login  702  comprises data elements utilized by a customer, modeled by customer  704 . Customer  704  may have one or more circuits  708  for a particular control component  106  or plant monitoring component  104 . Power usage is modelled by circuit usage  706  and located physically and/or logically by circuit location  710 . A provider of a circuit is modeled by vendor  712  which may have vendor login  714 . 
         [0055]      FIG. 8  depicts process  800  in accordance with embodiments of the present disclosure. In one embodiment, step  802  receives site data (e.g., circuit information, equipment, operational parameters of equipment, access protocols, etc.). Step  804  receives crop data (e.g., cultivar, genus, species, etc.). Step  806  receives one or more objectives (e.g., conservation of water, shortest time to maturity, etc.). Objectives may be prioritized or aggregated, such as to balance two priorities (e.g., reduce electricity usage and shorten time to maturity, etc.). 
         [0056]    Step  808  then generates growth plan  808 , such as by server  112  producing grow plan  404 . Which then may be presented in a human-readable report and/or stored in storage  812 , such as a memory and/or media accessible by hub  108 . Hub  108  may then be configured to operate, and therefore operate control component  106 , according to the growth plan  808 . 
         [0057]    Step  808  may receive crop identifiers received in step  804 . Step  808  may access records (not show) associated with attributes of the crop (e.g., plant A requires light level B, plant C requires light level D, etc.). Additionally or alternatively, step  804  may receive attributes of crop data (e.g., water requirements, light requirements, temperature requirements, etc.). As a benefit, a new plant  102  may be similar to another plan whereby the step  808  may generate the growth plan based on the similarities and differences from a growth plan associated with the other plant. As a benefit of process  800 , hub  108  may be provided with instructions to gather data, from plant monitoring component  104 , process the data with respect to grow plan  404 , and provide signals to control component  106  to operate a facility and subject plant  102  to dynamic attributes associated with the objective. 
         [0058]      FIG. 9  depicts process  900  in accordance with embodiments of the present disclosure. In one embodiment, process  900  acquires sensor data  902 , such as from plant monitoring component  104 , at a subject facility. Step  904  stores the sensor data for analysis at step  906 , such as by hub  108 . Alternatively, analysis  906  may comprise additional sensor data  914  from one or more additional facilities and their respective hub  108 . Additional and/or alternative data sources  912  may be accessed, such as to provide historic and/or alternative sources of sensor data and/or other data. Alternative data sources  912  may further comprise known results (e.g., data collected at harvest) from a particular dynamic attribute of a particular plant. In another embodiment, step  904  may comprise facility and/or plan information, such as the particular plant  102 , attributes of plant  102 , or equipment, parameters, use, or other attribute of plant monitoring component  104  and/or control component  106 . 
         [0059]    Analysis step  906  may access objective  812  and/or rules associated with one or more dynamic variables measured by plant monitoring component  104  and/or controlled by control component  106 . Step  908  may generate a report, such as to notify an on-site operator or other personnel or system of the current state of a facility, crop, or plant  102 . Step  908  may be generated periodically, in response to an event (e.g., out-of-tolerance state, daily, etc.), or on demand. As a further option, step  908  may be omitted, such as when no action or out-of-tolerance sate exists. If needed, step  910  generates control signals, such as to cause control component  106  to alter an operational parameter to cause plant  102  to be subject to a dynamic attribute, as measured by plant monitoring component  104 , associated with grow plan  404 . Process  900  may loop back to step  902  to continually monitor and, when necessary, adjust the dynamic attributes of plant  102 . 
         [0060]    In another embodiment, analysis step  906  may determine the correlation between at least one dynamic attribute and at least one result attribute. A result attribute is determined by harvesting the crop or, in the case of perennial crops, fruit is picked or otherwise harvested. For example, plant  102  of a prior crop may have produced a particular yield at the conclusion of the growth cycle and, was subject to a particular growth plan  404  or dynamic attribute (e.g., moisture, lighting, temperature, etc.). With sufficient data, such as from alternative data sources  912 , a determination within an acceptable confidence may be made that associates a particular result attribute(s) as being caused by a particular dynamic attribute(s). For example, a particular lighting and watering dynamic attribute produced a mature plant that was harvestable fifteen days sooner than a different lighting and watering dynamic attribute. Such a result may then be associated with a particular objective, such as when shorted time to maturity is desired, then the particular lighting and watering dynamic attributes should be utilized. Conversely, if another objective is desired, then the particular lighting and watering dynamic attribute may be omitted or de-emphasized in favor of dynamic attributes correlated with the other objective. 
         [0061]      FIG. 10  depicts rule structure  1000  in accordance with embodiments of the present disclosure. In one embodiment, one or more rules  1002  are associated with grow plan  404 . Rules  1002  may execute specific instructions necessary for a particular plant  102  to be subject to a dynamic attribute associated with the growth plan. For example, an objective such as “conserve water” may mean one level of water for one type of plant  102  and a different level of water for a different plant  102  and, in order to provide/restrict water, a signal must be formulated and provided to a particular control component  106  (e.g., turn off a pump, open a valve, etc.). Accordingly, rules  1002  may access attributes of control component  106  via site records  1004 , crop data  1006  identifies attributes, acceptable ranges of attributes, and/or effects of dynamic attributes on a particular crop. Objective  1008  provides rules to comply, within the acceptable limits of a particular crop, and may be implemented by the available equipment. 
         [0062]      FIG. 11  depicts graphic  1100  providing a graphical representation of a data model, such as at least a portion of one grow plan  404 , in accordance with embodiments of the present disclosure. In one embodiment, graphic  1100  provides window  1102  of operational parameters bounded by selected limits of a particular growth plan  404 . For example, high and low limits on temperature axis  1106 , electricity usage access  1108 , and water usage  1104  provide limits to window  1102 . While one three axes are illustrated in graphic  1100  to avoid unnecessarily complicating the figure, other numbers of axes may be utilized. In one embodiment, a single axis is provided, such as temperature whereby all other variables are either fixed or outside the influence of control component  106 . In other embodiments, three or more axes are utilized, such as when control component  106  is able to influence additional operational parameters. 
         [0063]    Growth plan  404  captures an objective of a farming operation for a particular facility and for a particular plan  102 . Plant monitoring equipment  104  monitors plant  102  and, if an attribute is outside of window  1102 , control component  106  operates to return the attribute to be within window  1102 . If control component  106  fails to return the attribute to be within window  1102 , control component  1102  may trigger an alarm or take other action to indicate that a correction, beyond the ability of control component  106 , is required. As a benefit of growth plan  404 , trade-offs may be made, but kept within window  1102 . For example, a particular facility may be able to purchase electricity at lower rates, such as during the night. Growth plan  404  may execute control component  106  in a manner that utilizes more electricity during such hours, and less electricity during peak-rate hours. For example, pumps may operate, more lights utilized, or more intense lighting utilized during times of discounted electric rates. Accordingly, the current state of a facility, and attribute of plant  102 , may be fluid within window  1102  without deviating from a particular growth plan  404 . 
         [0064]      FIG. 12  depicts data flow  1200  in accordance with embodiments of the present disclosure. Data flow  1200  utilizes a dumb hub  1202  to enable data connectivity, via network  110 , to server  112 . Dumb hub  1202  may provide limited or omitted processing functionality beyond enabling connectivity between server  112  and one or more of environmental sensor  202 , energy sensor  208 , facility  214 , crop  204 , control  216 , optical  206 , and HMI  212 . Dumb hub  1202  may optionally provide at least some network security (e.g., firewall). In embodiments where dumb hub  1202  is limited to facilitating connectivity and omits other processing features, dumb hub  1202  may be embodied as a USB hub, switch, router, wireless repeater, or the like. Dumb hub  1202  may also facilitate communications between two or more of environmental sensor  202 , energy sensor  208 , facility  214 , crop  204 , control  216 , optical  206 , and HMI  212 . 
         [0065]    In another embodiment, each of environmental sensor  202 , energy sensor  208 , facility  214 , crop  204 , control  216 , optical  206 , and HMI  212  communicates directly with server  112  via  110  and optionally with other components. In such an embodiment, hub  108  and dumb hub  1202  may be entirely omitted from data flow  1200 . 
         [0066]    In the foregoing description, for the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate embodiments, the methods may be performed in a different order than that described. It should also be appreciated that the methods described above may be performed by hardware components or may be embodied in sequences of machine-executable instructions, which may be used to cause a machine, such as a general-purpose or special-purpose processor (GPU or CPU), or logic circuits programmed with the instructions to perform the methods (FPGA). These machine-executable instructions may be stored on one or more machine-readable mediums, such as CD-ROMs or other type of optical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable mediums suitable for storing electronic instructions. Alternatively, the methods may be performed by a combination of hardware and software. 
         [0067]    Specific details were given in the description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits may be shown in block diagrams in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments. 
         [0068]    Also, it is noted that the embodiments were described as a process, which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function. 
         [0069]    Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine-readable medium, such as a storage medium. A processor(s) may perform the necessary tasks. A code segment may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc. 
         [0070]    While illustrative embodiments of the disclosure have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art.