WO2020084414A1

WO2020084414A1 - Yield and market analytics

Info

Publication number: WO2020084414A1
Application number: PCT/IB2019/058886
Authority: WO
Inventors: Michael CABIGON; Jim SEETHRAM; Steven Splinter; Denis TASCHUK
Original assignee: Radient Technologies Innovations Inc.
Priority date: 2018-10-22
Filing date: 2019-10-17
Publication date: 2020-04-30

Abstract

Methods and apparatus consistent with the present disclosure may allow a computer to receive information regarding conditions of cannabis plant matter growing at different farms. Methods consistent with the present disclosure may allow a computing device to collect and correlate environmental data, market data, and plant growth data when estimates are made regarding product quality and value. These estimates may allow buyers of cannabis plant matter or concentrates extracted from cannabis plant matter to be sold to buyers based on terms acceptable to a particular grower and a particular buyer. Such terms may be based on data collected, organized, or estimated by computer systems that communicate with computers of both buyers and growers. Computing devices consistent with the present disclosure may allow buyers to commit to purchase contracts with growers and may provide a grower with the ability to receive payments or to send bills to buyers.

Description

YIELD AND MARKET ANALYTICS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present patent application claims priority benefit of U.S. provisional patent application number 62/749,079 filed October 22, 2018 and U.S. provisional patent application number 62/751,439 filed October 26, 2018, the disclosures of which are incorporated, herein, by reference.

BACKGROUND OF THE INVENTION

Field of Invention

[0002] The present disclosure is generally related to cannabis biomass quality tracking and supply hedging. More specifically, the invention relates to cannabis biomass and cannabis extract quality tracking and supply hedging to reduce supply chain risks and improve yield estimates.

Description of the Related Art

[0003] The term cannabis or "cannabis biomass" encompasses the Cannabis sativa plant and also variants thereof, including subspecies sativa, indica and ruderalis, cannabis cultivars, and cannabis chemovars (varieties characterised by chemical composition), which naturally contain different amounts of the individual cannabinoids, and also plants which are the result of genetic crosses. The term "cannabis biomass" is to be interpreted accordingly as encompassing plant material derived from one or more cannabis plants. Cannabis biomass may be farmed or grown outdoors in dedicated fields or indoors under controlled conditions, for example in green houses.

[0004] Historical delivery methods of cannabis have involved smoking ( e.g ., combusting) the dried cannabis plant material. Smoking results, however, in adverse effects on the respiratory system via the production of potentially toxic substances. In addition, smoking is an inefficient mechanism that delivers a variable mixture of active and inactive substances, many of which may be undesirable. Alternative delivery methods such as ingesting typically require extracts of the cannabis biomass (also known as cannabis concentrates or cannabis oils). Often cannabis is grown by "growers" or farmers and cannabis extracts and derivative products produced by "extractors". In some cases, the growers and extractors may be the same entity. In other cases, the extractors may be buyers of cannabis biomass from the growers in order to process the biomass into concentrates and other cannabis products.

[0005] Pricing for outdoor farming of cannabis biomass is generally lower than that for indoor cannabis farming - in general, indoor farms have more control over the grow conditions and biomass can be harvested more frequently and at optimal times. If outdoor cannabis farmers could estimate outdoor farm biomass yield and quality, buyers could buy outdoor cannabis biomass cheaper and reduce supply chain risks.

[0006] Outdoor cannabis farmers need a way to hedge their supply chain risks (e.g. weather variations) and buyers have a need to reroute the biomass from different sources based on grow conditions and protect themselves from adverse price moves. By hedging, a transaction may occur between growers and buyers at a price level for a cash market transaction that may not actually take place for several months but that can effectively protect them - buyers from adverse price moves and growers from variations in expected yield and quality of the cannabis crop.

[0007] What are needed are methods and apparatus that helps growers and buyers mitigate risks of variable cannabis prices based on supply and demand by providing a hedging platform that sets the price of cannabis ($/kg) based on a hedging model that considers grower, buyer and environment data.

SUMMARY OF THE CLAIMED INVENTION

[0008] The presently claimed invention relates to a method, a non-transitory computer readable storage medium, or an apparatus executing functions consistent with the present disclosure. A method consistent with the present disclosure may include identifying from received information a type of cannabis plant matter grown by a grower at a farm and identifying weather conditions associated with a location of the farm. After this information is received, analysis may be performed that identifies a grower metric and a weather metric and then an initial price for a type of cannabis plant matter growing at the farm may be received. Next, an adjusted price for the type of cannabis plant matter may be identified based on either the grower metric the weather metric, or both the grower and the weather metric. After this the method may validate that the adjusted price for the type of cannabis plant matter.

[0009] When the presently claimed invention is implemented as a non-transitory computer-readable storage medium, a processor executing instructions out of a memory may implement a method consistent with the present disclosure. Here again the method may include identifying from received information a type of cannabis plant matter grown by a grower at a farm and identifying weather conditions associated with a location of the farm. After this information is received, analysis may be performed that identifies a grower metric and a weather metric and then an initial price for a type of cannabis plant matter growing at the farm may be received. Next, an adjusted price for the type of cannabis plant matter may be identified based on either the grower metric the weather metric, or both the grower and the weather metric. After this the method may validate that the adjusted price for the type of cannabis plant matter.

[0010] An apparatus consistent with the present disclosure may include a memory and a processor that executes instruction out of the memory to may include identify from received information a type of cannabis plant matter grown by a grower at a farm and identifying weather conditions associated with a location of the farm. After this information is received, the processor may perform analysis that identifies a grower metric and a weather metric, and then an initial price for a type of cannabis plant matter growing at the farm may be received. Next, an adjusted price for the type of cannabis plant matter may be identified by the processor based on either the grower metric the weather metric, or both the grower and the weather metric. The processor may then validate the adjusted price for the type of cannabis plant matter based on received information.

BRIEF DESCRIPTIONS OF THE DRAWINGS

[0011] FIG. 1 illustrates an exemplary network environment in which a growth monitoring system may be implemented to identify yield estimates.

[0012] FIG. 2 is a flowchart illustrating an exemplary method for analyzing data regarding a cannabis farm.

[0013] FIG. 3 is a flowchart illustrating an exemplary method for correlating cannabis plant growth and quality data to pricing data.

[0014] FIG. 4 is a flowchart illustrating an exemplary method for adjusting pricing for cannabis plant matter

[0015] FIG. 5 illustrates a computing system that may be used to implement an embodiment of the present invention.

DETAILED DESCRIPTION

[0016] Methods and apparatus consistent with the present disclosure may allow a computer to received information regarding conditions of cannabis plant matter growing at different farms. Methods consistent with the present disclosure may allow a computing device to collect and correlate environmental data, market data, and plant growth data when estimates are made regarding product quality and value. These estimates may allow buyers of cannabis plant matter or concentrates extracted from cannabis plant matter to be sold to buyers based on terms acceptable to a particular grower and a particular buyer. Such terms may be based on data collected, organized, or estimated by computer systems that communicate with computers of both buyers and growers. Computing devices consistent with the present disclosure may allow buyers to commit to purchase contracts with growers and may provide a grower with the ability to receive payments or to send bills to buyers.

[0017] FIG. 1 illustrates an exemplary network environment in which a growth monitoring system may be implemented to identify yield estimates. As illustrated, FIG. 1 includes environment computer 105, grower computer 120, buyer computer 155, and hedging computer 175 that may communicate with each other via the cloud or Internet 150. Environment network computer 105 includes an application program interface (API) 110 and an environmental database 115. Environment computer 105 may receive environmental data requests from computers 120, 155, 175, or other computing devices via API 110. API 110 can be a web interface or could be an application program (APP) that is downloaded to a computing device from an APP store, such as the Apple APP store.

[0018] Grower computer 120 includes grower database 125, shipment database 130, and one or more program code modules that allow a processor at grower computer 120 to perform basic (base) functions of a receiving and evaluating sensor data, to perform communication functions, and to display items and receive input via a graphical user interface (GUI). The grower computer 120 of FIG. 1 may receive data farm sensors 140 and from cannabis sensors 145. Farm sensors 140 may be any sensor known in the art that can sense data associated with conditions or actions that occur on a farm. Examples of farm sensors 140 include sensors that measure ambient temperature, humidity in the air, soil moisture content, soil chemical levels, soil PH (e.g. an acid level or a base level), a volume of water provided to the farm, or an amount of rainfall. Cannabis sensors 145 may be sensors that measure data from which plant specific data can be derived. Examples of plant specific measured or derived data include plant height, plant biomass density, plant matter volume, average trichrome density, cannabinoid content, or cannabinoid mass per unit volume of plant matter, or cannabinoid mass per estimated plant mass.

[0019] The system of FIG. 1 tracks and stores farm data, cannabis plant data, and shipment volumes and estimates yield and product quality. A group of sensors help track farm growing conditions (e.g. temperature sensor, humidity sensor, etc.). In certain instances, these sensors may provide sensor data to a set of program code referred to as a grower base software module. A group of sensors that track cannabis plant growth (e.g. soil sensors, hyperspectral imaging sensors, etc.) and send data to the grower base software module. Grower database 125 may store shipment volume data for a specific farm during a growing season (e.g. bushels/day or kilograms/day). Grower database 125 may store data that updates continuously when tracking farm data, plant data, shipment data, and yield and quality estimates. Grower computer 120 may be a system that receives data from sensors and shipment database, calculates crop yield, identifies quality data, and updates the grower database 125. Additionally, grower computer 120 may update or augment information stored in grower database 125 using a user interface, such as a graphical user interface (GUI). Grower computer 120 may also allow the hedging computer 175 to access data stored at grower database 125. A communication interface and communication software may allow grower computer 120 to communicate with other computer using any communication interface wired or wireless (e.g. Bluetooth, Wi-Fi, NFC, cellular, Ethernet, or some other method) that allows grower computer 120, buyer computer, and hedging computer 175 network to communicate with each other. One or more of these computes may provide a GUI on a display that allows users to view and access the sensor, yield, and quality data for his crop. As such, any of the computers illustrated in FIG. 1 may communicate over the cloud or Internet 150 using any communication interface known in the art.

[0020] Optical sensors that may be used with the present invention include any type of camera or device that acquires images, senses reflected light, or senses an amount of light that passes through a sample of plant matter. Collected image data may be used to identify colors of trichomes when identifying or estimation a number or types of cannabinoids that may be included in plant material. Image data may be used to identify a height of different plants at the farm.

[0021] A processor executing program code at grower computer 120 may be able to track the growth rate of plants over time. Grower computer 120 may also control and measure a volume of water provided to a field of cannabis plant matter and soil moisture sensors in that field may provide data to grower computer 120 to determine optimal times to water the field of cannabis plants. As soon as the grower computer 120 determines that one or more of the soil moisture sensors have dropped below a threshold level, grower computer 120 may initiate one or more valves to open to provide water to one or more zones of the field.

Similarly chemical or pH sensors may provide data to grower computer 120 such that grower computer can identify that portions of the field require fertilizer or that a soil pH is too acidic or too basic for optimal plant growth. After computer 120 identifies that a portion of the field needs more fertilizer or needs an application of an element that reduces or increases the soil pH, a message could be sent to workers to apply fertilizer or the soil pH modifying element to specific locations in the field. In certain instances, the fertilizer or soil pH modifying element may be provided by adding fertilizer or the element to water provided to the field. In other instances, drones or robots could be used to provide the fertilizer or the element to the field.

[0022] Cannabis sensors can include cameras, optical sensors, or density sensors. In some instances, the soil pH, moisture, or chemical sensors classified above as farm sensors may be classified as cannabis sensors. Data from one or more cameras may be used to identify the height of different plants in a field and may also be used to identify whether the plants appear to be filling in with leaf or flowering (bud) plant matter as expected. An analysis of this camera data may also be used to identify a general density of plant matter. For example, camera data could be used to classify plants into categories of high, medium, and low density. The assignment of a general density category to a plant may be a function of plant age, plant height, plant type, or other metrics. General density may also be identified based on identifying how much flowing matter or space on the plant that includes stem yet an insignificant amount of leaf or flower material. Optical sensors may collect spectral or hyperspectral data from plants growing in the field. From this sensor data, grower computer 120 may identify health metrics (e.g. high, medium, low) to assign to the plants or to identify a cannabinoid content included in the plants. The health metrics may be identified by identifying colors included in light reflected by the plants or colors of light shined through a plant biomass. The assignment of a health metric to a plant may also be a function of plant age, plant height, plant type, or other metric combined with spectral or hyperspectral data.

[0023] Grower computer 120 may store relevant sensor data or results of an analysis performed by grower computer 120 in grower database 125 or in shipment database 130 based on operation of the base set of program code functions of FIG. 1. The GUI program code functions of grower computer 120 of FIG. 1 could allow a user of grower computer 120 to view data stored in database 125 or database 130 and to receive user input. Grower computer 120 may be a networked system of computing devices that collect data. For example a user, a drone, or a robot may hold a mobile device that acquires image data and that wirelessly sends that image data to a computer where the image data is analyzed. Data stored in the shipment database 130 may include a number of bushels or kilograms (kg) of plant matter produced over a span of time (e.g. per day or per square acre or foot over a growing season), estimated crop yield, or actual crop yield.

[0024] Program code that allows grower computer 120 to communicated with other computers via cloud or internet 150 via a communication interface. Such a communication interface may be include a wired network connection or may be an interface that sends and receives data wirelessly. This communication interface may allow grower computer 120 to request and receive weather reports form environment computer 105, and may allow grower computer 120 to provide information to buyer computer 155 or hedging computer 175. Environment computer 105 may also provide weather data to buyer computer 155 or hedging computer 175 when contract for purchasing plant biomass are negotiated.

[0025] Program code that allows grower computer 120 to communicate with other computers via cloud or internet 150 via a communication interface. Such a communication interface may include a wired network connection or may be an interface that sends and receives data wirelessly. This communication interface may allow grower computer 120 to request and receive weather reports form environment computer 105, and may allow grower computer 120 to provide information to buyer computer 155 or hedging computer 175. Environment computer 105 may also provide weather data to buyer computer 155 or hedging computer 175 when contracts for purchasing plant biomass are negotiated. The computers included in FIG. 1 may communicate with environment computer 105 that accesses and stores weather data in environment database 115. Other computers in FIG. 1 may be configured to access to the data stored at environment database 115. This environment database may store weather forecasts and actual weather data (e.g. rain, sunshine, etc.). An application programming interface that allows the hedging network may allow hedging computer 175 to access to the data stored at environment computer 105.

Buyer computer 155 or hedging computer 175 may collect, track, and store cannabis market price in respective databases 160 and 185. These databases may store cannabis market prices that may vary based on yield, quality and baseline weather conditions.

[0026] Buyer computer 155 is illustrated as including market price database 160 and communication interface 165. Buyer computer may receive prices for cannabis biomass or other cannabis products from one or more sources and may provide this price data to grower computer 120 or the hedging computer 175 via communication interface 165. Buyer computer may also receive pricing information from hedging computer 175. Hedging computer 175 includes communication interface 180, hedging database 185, and program code 190 instructions that are executable by a processor for performing different functions. Program code 190 functions include base program code, hedging algorithm program code, contracts program code, and program code that identifies payment data and billing charges. The execution of program code by a processor at hedging computer 175 may identify a hedging metric by combining data received from a grower with weather forecast data. In instances where a first grower provides growing data that identifies that their plant matter will likely be considered higher quality than plant matter grown by a second grower, the execution of the hedging algorithm may provide an indication that the plant matter grown by the first grower's crop should be assigned a higher grower metric than a grower metric assigned to the second grower's crop. The hedging algorithm may also generate a weather metric based on weather conditions or forecasts affecting the farm of the first grower and the farm of the second grower. Good weather conditions may result in a higher weather metric and poor weather conditions may result in a lower weather metric.

[0027] Program code functions 190 at hedging computer 175 may allow hedging computer to execute program functions that identify or update cannabis prices based on calculations performed by a hedging algorithm. Other program code functions that may be performed at hedging computer 175 include executing instructions consistent with functions of a hedging base software module that uses data stored at the hedging database 185. A processor at hedging computer 175 may execute program code of the hedging algorithm to set up a contract price for growers and buyers, may execute program code of a contracts software module when contract prices for cannabis plant matter or concentrates are negotiated, and that executes program code consistent with a payment/billing software module. The hedging computer may identify good or bad weather conditions based on information known to provide risks to growing plants. Good weather conditions may be associated with preferred temperature ranges, preferred air humidity ranges, sunny weather, or a lack of high winds. Bad weather may be associated with temperatures that are above or below high or low temperature thresholds, high humidity levels, rain greater than at threshold amount, or high winds. As such, the hedging algorithm at hedging computer 175 may generate a hedging metric that is a function of a grower metric and a weather metric. A good grower metric combined with a good weather metric may result in hedging metrics that increases the value of a crop produced by a grower. Bad metrics may also affect a projected value of a crop. After the hedging computer 175 identifies a value for a particular crop, that value may be shared with buyer computer 155 and with grower computer 120 when a buyer and a grower negotiate a price to by the grower's crop. In certain instances, contracts between the buyer and the grower may be received and stored in the hedging database 185 of FIG. 1. Hedging database 185 may also store grower metrics, weather metrics, and hedging metrics for later use.

[0028] Program code at a hedging computer 175 may identify a grower metric, a weather metric, a pricing metric (that may vary for each grower). These metrics may be used by hedging computer 175 when a buyer data respectively set a final contract price to purchase plant matter or cannabis concentrates from a grower. This process may also include identifying a hedging metric that may be a factor that increases or decreases a price negotiated between a buyer and a grower. Hedging computer 175 may also include program code that for the buyer and grower to make agreements when a contract price is calculated by the hedging base module at hedging computer 175. Hedging computer may then send price and quality information to grower computer 120 and to buyer computer 155 when a grower and a buyer negotiate terms of a purchase contract. The payments/billing program code at hedging computer 175 may be used to generate and manage invoices payments at the end of a growing season based on the actual volume of cannabis involved in a transaction between a buyer and a grower. [0029] FIG. 2 is a flowchart illustrating an exemplary method for analyzing data regarding a cannabis farm. Functioning of instructions consistent with a grower base software module will now be explained with reference to FIG.2. The steps of FIG. 2 may include receiving continuously new farm sensor data, cannabis plant sensor data, and shipment volume data from appropriate databases for a given grower. FIG. 2 begins with step 210 where farm data is received at a grower computer, such as grower computer 120 of FIG. 1. This farm data may be received from a set of farm sensors that measure one or more of ambient temperatures, humidity in the air, soil moisture content, soil chemical levels, soil pH (e.g. an acid level or a base level), a volume of water provided to cannabis plants, or an amount of rainfall. The farm data may also identify a type of cannabis plant biomass or may identify plant height, average light hours per day, average humidity, or normalized yield. The sensor data collected overtime may be used to identify preferred conditions for growing cannabis plants. The farm data received in step 210 may also include data received from cannabis sensors or data that was the result of an analysis that used cannabis sensor data or a combination of cannabis sensor data and farm sensor data. Cannabis sensors may be sensors that measure data from which plant specific data can be derived. Examples of plant specific data include plant height, plant biomass, plant biomass density, plant matter volume, average trichome density, cannabinoid content, cannabinoid mass per unit volume of plant matter, or cannabinoid mass per estimated plant mass.

[0030] The farm data received in step 210 may be stored in a grower database in step 220 of FIG. 2. Next, in step 230 of FIG. 2 an estimated crop quality, an estimated growth rate, or a yield may be estimated. Such estimates may be based on a stage of a life cycle of cannabis plants. Life cycle stages of cannabis plants may include seed, sprout, seedling, juvenile from seed, adolescent from seed, adult from seed, adult from seed and seed producing, clone, juvenile clone, adolescent clone, adult clone, and mother of clones. Alternatively, the lifecycle of cannabis plants may be classified as germination, seedling, vegetative from seed, flowering from seed, juvenile clone, vegetative clone, and flowering clone. Each one of these life cycles may be associated with specific metrics or parameters for identifying a quality, a growth rate, or a projected yield. The yield analysis may also consider effect of plant diseases, accidental plant damage, plant rot, root rot, mold, humidity, soil pH, or consider any factor discussed below in respect to table 1. Operation of program code at grower computer 120 may cause data to be stored at grower database 125 of FIG. 1. This stored data may include data from sensors and may include yield and quality estimates projections.

Yield projections may estimate a volume or mass of cannabis plant matter from a farm and may include a projected yield/acre farmed. An estimated yield/acre may include weighing a mass of cannabis biomass in a representative area and then performing calculations that extrapolate the weight/area to yield/acre. In certain instances, the grower computer may identify a total mass of cannabinoids that may be extracted from a field of cannabis. Such an identification may be based on historical extraction data received at grower computer 120. In such instances, historical extraction data may be received from a computer that belongs to an extractor that creates cannabis concentrates from materials extracted from cannabis plant material.

[0031] Data collected over time or expected data may be organized in a data structure like table 1 below that may be used to cross-reference and track the growth of specific plants as plants grow through their life cycle stages. The life cycle stages included in table 1 are germination, seedling, vegetative, and flowering. The metrics included in table 1 are life cycle stage time, temperature range, humidity, soil moisture, soil chemical content, soil pH, fertilizer applied, volume of water applied per unit time, rain volume per unit time, height/length, plant biomass, biomass density, plant matter volume, trichome density, cannabinoid content, and growth rate. Initially certain cells of table 1 may be populated with estimates that may include preferred baseline durations of plant life cycle, preferred temperature ranges, a preferred humidity, and other preferred baseline metrics. As time goes on, table 1 may also be populated with actual measured or interpolated data. Over time, best case metrics may be identified. For example, best case metrics may include temperatures, volumes of water, soil moisture content, and soil chemical content that result in fastest plant growth. Plant growth may be measure by a length of time that a particular plant has stayed in a particular growth stage, a plant height/length, a total amount of actual or estimated plant biomass, a plant matter volume, a biomass density, a trichome density, a cannabinoid content, or a growth rate (mass, density, or height change per unit time). Each different stage of a plant s lifecycle may be associated with different preferred metrics over time and these identified preferred metrics may replace metrics that were originally used as baseline metrics. As such, plant growth can be characterized and then optimized to maximize cannabinoid yield over time.

Table 1: Cannabis Lifecycle vs. Farm/Plant Metrics [0032] After estimates are made in step 230, determination step 240 may identify whether any metrics or parameters should be updated. In certain instances, a grower may provide updated metrics or parameters to program code that generates the estimates. For example, a grower may indicate that he provided his plants with an additional volume of water or with additional fertilizer. Alternatively, program code may be developed that automatically changes a parameter used in an equation that forecasts future plant growth. For example, if an estimate of plant growth after a fertilizer application does not correspond to actual plant growth measurement data, a parameter in the plant growth forecasting equation could be changed such that the equation would generate growth rate estimates that corresponded better to the actual measurement data. Such a parameter change may be identified by re-running estimates using data recorded from an earlier time. For example, if a plant was forecast to grow in a week by six inches in height after a fertilizer application and the plant actually grew eight inches in height, a parameter in the growth rate forecasting equation could be increased to a value where the re-running of the last growth rate forecast would provide an eight inch height growth forecast instead of the six inch growth forecast. When determination step identifies that growth estimates should be updated, program flow may move from step 240 to step 250 of FIG. 2 where the estimates may be updated. Next, those updated estimates may be stored in a database in step 260 of FIG. 2. When

determination step 240 of FIG. 2 identifies that estimates should not be updated, program flow may move from step 240 to step 260 where estimates made in step 230 may be stored in the database. After step 260, the estimates may be sent to computing devices that are external to a growing monitoring system or computer in step 270 of FIG. 2. For example, when the growing monitoring system 105 of FIG. 1 makes a plant quality, growth rate, or yield estimate, that estimate may be sent to edible MFG computer 145 or extraction computer 125. The data sent in step 270 may be used to identify an estimated market price for a grower's plants at harvest time or to update a price that was estimated earlier.

[0033] The quality of the crop may be evaluated in a variety of metrics known in the art, including inter alia purity or potency of a compound in a resulting extract. Such evaluation ma include sampling and subjecting the biomass to various, chemical, mechanical, or optical tests.

[0034] Metrics associated with temperature, humidity, soil moisture, soil pH, rain volumes per unit time, fog density, biomass density, plant biomass, or trichome density may be measured or be estimated from sensor data. Some of these factors, such as temperature, humidity in the air, soil moisture, soil pH, rain volume, and fog density may be measured directly using a particular type of sensor. Other of these factors, such as biomass density, plant biomass, or trichome density may be estimated by data collected by other sensors. An amount of biomass may be estimated from image data. Cannabis plants in a vegetative state typically have clusters of leafs that are attached to a single stem at a base portion of what may be referred to as a "leaf cluster." Such leaf clusters typically include an odd number of individual leafs that draw nutrients through the single stem that attaches them to a branch or a main stem of a cannabis plant. A plant in a vegetative state that has leaf clusters separated by a centimeter could be assigned a "low leaf plant density metric," vegetative plants with clusters of leafs that are separated by less than a centimeter could be assigned a "medium leaf plant density metric," and vegetative plants with overlapping leafs on a plant that light does not readily shine through may be assigned a "high leaf plant density metric." Similarly, flower material could be assigned different metrics based on a trichome density. Such a trichome density could correspond to a count of trichomes in an image combined with an estimated volume of a flower or number of flowers. Trichome densities could also correspond to a total length along a stem that is covered with plant flowers (buds).

[0035] Table 2 illustrates other growth data that may be stored in a grower database, such as the cannabis growth database 115 of FIG. 1. Table 2 includes a first column that identifies biomass type and a plant lot reference number, a second column that identifies a number of days included in current growth cycle, a third column that identifies an amount of water provided to a given plant lot number, and a fourth column that identifies an estimated plant density. Table 2 also includes different columns that identify soil pH, average ambient temperature in degrees Celsius (C), numbers of average hours of light provided to the plants, average ambient humidity levels, and normalized yield estimates.

Table 2: Cannabis Growth Data

[0036] The types of biomass included in table 2 include cannabis sativa, hemp, and cannabis indicia. Further, the different lots of cannabis sativa have been provided lot numbers of 101 and 104, that the different lots of hemp has been provided lot numbers of 102 and 105, and that the lot of cannabis indicia has been provided lot number 103. Each of the plants in each of these different lots of plants have been growing a different number of days. The data of table 2 also indicates that as the plants age, they grow taller, and require more water. This data also indicates that as the plants age their density also tends to increase and that after the plants age, they may be provided less light. The 23 hours of light indicated in the first row of table 2 may be the result of a grower providing artificial light to the plants of lot 101. Furthermore, the shorter light cycles may be the result the removal of light or the tenting of plants. From the density data or other data, a processor at a computer may calculate an estimated normalized yield. As the plant matures, this yield estimate may increase. In certain instances, the yield estimate may increase with plant density. [0037] A particular type of biomass or cannabinoids included in that biomass may be identified based on inputs that were originally received at a grower computer, by accessing test results stored at the grower computer, or by accessing test data stored at a computer of a test lab. Various strains of cannabis along with concentrations of compounds of interest, which may be measured using any variety of analytical testing devices. Testers that may have originally acquired this analytical test data may use any analytical test method known in the art including, yet not limited to an optical tester, a high performance liquid chromatograph (HPLC), an ultra-high performance liquid chromatograph (UHPLC), a gas chromatograph (GC), a spectral tester, other types of chromatographies, or mass spectrometers.

[0038] In operation, hedging computer 175 of FIG. 1 may store data in hedging database that cross references different growers with growth data and hedging algorithm metrics that may be used to negotiate contracts between respective growers and different buyers. For example, operation of program code may allow a first grower, grower 001 to obtain estimates that identify a total cannabinoids concentration in a cannabis plant biomass of grower 001 is 15% by mass. In such an instance data stored in hedging database 185 may also identify that grower 001 should expect a yield of 350 kg/acre. Data stored in hedging database 185 may identify that a second grower, grower 002 may obtain a total cannabinoids concentration in their biomass of 20% with 250 kg/acre. Hedging database data may also identify a third grower, grower 003 who may obtain a total cannabinoids concentration of 10% with 300 kg/acre. In certain instances, a respective grower may be allowed to access and view the collected and estimated data and adjust the parameters manually by means of a GUI in step 250 of FIG. 2. Here as in step 260 of FIG. 2, yield and quality data for given grower may be stored in a hedging database. For example, grower 001 may obtain 350 kg of biomass/acre of farmed land and 15% for the total cannabinoid concentration. Grower 002 may obtain 250 kg of biomass/acre of farmed land and 20% for the total cannabinoid concentration. Grower 003 may obtain 300 kg of biomass/acre of farmed land and 10% for the total cannabinoid concentration. This data may then be available when contract negotiations are performed between a grower and a buyer. Data stored a hedging database may include data organized in the tabular format of table 3. After step 260, the estimates stored in step 260 may be sent to other computing devices, such as grower computer 120 or buyer computer 155 of FIG. 1. [0039] FIG. 3 is a flowchart illustrating an exemplary method for correlating cannabis plant growth and quality data to pricing data. Functioning of program code consistent with a hedging base software module will now be explained with reference to FIG. 3. FIG. 3 begins with a first step where weather and growing data are received and stored in a hedging database, such as hedging database 185 of FIG. 1. Next, step 320 may retrieve a base price for a given quality of cannabis plant matter from a base price market price database 160 at the buyer computer 155 of FIG. 1. Instructions consistent with a hedging algorithm may be initiated in step 330 of FIG. 3. Program flow may that move to determination step 340 that may identify whether the base price for a batch of cannabis plant biomass should be updated. When determination step 340 identifies that the base price should be updated at step 340, program flow may move to step 350 where the base price may be updated based on execution of the hedging algorithm program code. Operation of the hedging algorithm program code may compare current market conditions when identifying a hedging metric, may review growth data when identifying a growth metric, and may review weather data when identifying a weather metric. These metrics along with current base price information may cause a processor to identify that a current base price should for high quality cannabis indica should be increased or decreased based on current market, growing, or weather conditions. For example, when a demand for cannabis indica is high and a current supply is low, prices for cannabis indica plant matter may be increased. In other instances, prices may be lowered, for example when supply exceeds demand or when weather threatens the quality of cannabis plant matter at a particular farm. Program flow may flow to step 360 of FIG. 3 from step 350, or may flow directly from step 340 to step 360. Method may proceed directly from step 340 to step 360 when determination step 340 identifies that the base price should not be updated. A set of program code that allows data to be shared and/or contracts to be negotiated between a buyer and a grower may be initiated in step 360. This may allow a buyer computer to receive information from a hedging computer or from a grower computer. After step 360, program flow moves to step 370 where a set of program code that performs functions consistent with receiving payments or with sending bills may be initiated. This may allow for growers to bill and receive payment from a buyer as contracts are fulfilled. [0040] Table 3 illustrates data that may be stored at a hedging database. Table 3 includes three different lot number of cannabis plants 100, 200, & 300 grown by three different growers, grower 001, grower 002, and grower 003. Table 3 also cross references growth times, average temperatures, root health metrics, kilograms (kg) per acre yield, quality (e.g. cannabinoid mass as a percentage of plant mass), grower metrics, temperature ranges, weather metrics, prices per tonne of plant matter, and adjusted prices per kg of the plant matter.

[0041] FIG. 4 is a flowchart illustrating an exemplary method for adjusting pricing for cannabis plant matter. The steps of FIG. 4 may be performed by a set of program code that when executed by a processor makes calculations consistent with a hedging algorithm. Step 410 of FIG. 4 is a step where data stored at a hedging database may be accessed. From this data a grower metric may be identified in step 420, a weather metric may be identified in step 430, and a hedging metric may be identified in step 440. The grower metric may be a quality measurement of the ability of the grower to produce high quality cannabis biomass. Growers able to track root health and harvest at optimal times (e.g. using hyperspectral imaging, etc.) will likely collect a higher yield/quality per growing season. The data of table 3 identifies that grower 001 produced a total cannabinoids concentration of 15% with 350 kg/acre on average, grower 002 may obtain a total cannabinoids concentration of 20% with 250 kg/acre on average, and grower 003 may obtain a total cannabinoids concentration of 10% with 300 kg/acre on average. Therefore, the grower metric will be medium for grower 001 because the quality of the biomass is average even though it showed the highest yield of the three growers. Grower metric should be high for grower 002 because the quality of the cannabis biomass is the highest even though the yield was the lowest. Grower metric for grower 003 is low because the quality is very low even though the yield is average. In this example, the quality of the cannabis plant matter varies based on a cannabinoid

concentration included in each respective set of plant matter, where a 20% is considered high quality, a cannabinoid concentration less than 20% and greater than 10% may correspond to a medium quality, and a cannabinoid concentration less than or equal to 10% may be considered low quality. These concentration values may be based on a mass of cannabinoids included in a mass of dry cannabis plant matter.

[0042] The weather metric identified in step 430 may be a quality measurement for the weather conditions. "High" indicates that the weather conditions are optimal for crop growth and therefore, the best production can be expected from the grower production cycle. As an example, if the temperature is higher than 75 F, the rainfall is higher than 3 inches and the sunshine is above 70%, the weather metric will be high meaning that optimal growing conditions are expected for the grow cycle. Estimating the total hedging metric in step 440 of FIG. 4 include combining the grower and weather metrics. A grower like grower 002 that is likely to produce high quality biomass and it is likely to experience the best weather conditions for its crop, as such grower 002 is likely to experience a surge in demand for their crop, so the hedging metric will be high for grower 002. When grower 003 is not likely to obtain a high quality product and whose farm will go through unfavorable weather conditions, will not experience a surge in demand, so the hedging metric for grower 003 will be low. Grower 002 may be assigned a medium hedging metric because the weather conditions will not be optimal but not totally unfavorable and in regular circumstances the grower is able to obtain a medium quality crop. Hedging metrics may also be include factors that track supply and demand for certain type of plant matter a previously discussed.

[0043] Next in step 450 a base price for cannabis plant matter may be received and this data may be received from the buyer computer 155 of FIG. 1. In certain instances, this base price may be a current market price for cannabis plant matter. In an example consistent with the data of table 3, the expected market price for a kg of cannabis for a given season may be $15.00/ kg of high quality biomass (cannabinoid concentration higher than 20%). $12.50/kg of medium quality biomass (cannabinoid concentration between 10% and 20%) and $10.00/kg of low quality biomass (cannabinoid concentration lower than 10%). After step 450, determination step 460 may identify whether the base price for received in step 405 should be updated, when yes program flow may move to step 470 where an adjusted base price may be identified. The adjusted base price for the transaction between buyer and grower may be identified according to a hedging algorithm. In an example, when the hedging metric is high and the market price is $15.00/kg of high quality biomass, the price could be set or bid at $12.50/kg of biomass so that buyer can minimize risks from weather variations or issue with the farm running operations that may lead to lower yields and/or lower quality plant material. On the opposite side, the buyer may identify that they can buy cheaper biomass in advance of harvest season. As such, future negative or positive forecasts may cause bid prices to be set as hedge against variations in future biomass market variations. From these bid prices growers may enter into futures contracts with buyers. When determination step 460 identifies that the base price should not be updated or after step 470, program flow may move to step 480 where metrics and cannabis plant prices may be stored in a hedging database.

[0044] FIG. 5 illustrates a computing system that may be used to implement an embodiment of the present invention. The computing system 500 of FIG. 5 includes one or more processors 510 and main memory 520. Main memory 520 stores, in part, instructions and data for execution by processor 510. Main memory 520 can store the executable code when in operation. The system 500 of FIG. 5 further includes a mass storage device 530, portable storage medium drive(s) 540, output devices 550, user input devices 560, a graphics display 570, peripheral devices 580, and network interface 595.

[0045] The components shown in FIG. 5 are depicted as being connected via a single bus 590. However, the components may be connected through one or more data transport means. For example, processor unit 510 and main memory 520 may be connected via a local microprocessor bus, and the mass storage device 530, peripheral device(s) 580, portable storage device 540, and display system 570 may be connected via one or more input/output (I/O) buses.

[0046] Mass storage device 530, which may be implemented with a magnetic disk drive or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit 510. Mass storage device 530 can store the system software for implementing embodiments of the present invention for purposes of loading that software into main memory 520.

[0047] Portable storage device 540 operates in conjunction with a portable non- volatile storage medium, such as a FLASH memory, compact disk or Digital video disc, to input and output data and code to and from the computer system 500 of FIG. 5. The system software for implementing embodiments of the present invention may be stored on such a portable medium and input to the computer system 500 via the portable storage device 540.

[0048] Input devices 560 provide a portion of a user interface. Input devices 560 may include an alpha-numeric keypad, such as a keyboard, for inputting alpha- numeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys. Additionally, the system 500 as shown in FIG. 5 includes output devices 550. Examples of suitable output devices include speakers, printers, network interfaces, and monitors. [0049] Display system 570 may include a liquid crystal display (LCD), a plasma display, an organic light-emitting diode (OLED) display, an electronic ink display, a projector-based display, a holographic display, or another suitable display device. Display system 570 receives textual and graphical information, and processes the information for output to the display device. The display system 570 may include multiple-touch touchscreen input capabilities, such as capacitive touch detection, resistive touch detection, surface acoustic wave touch detection, or infrared touch detection. Such touchscreen input capabilities may or may not allow for variable pressure or force detection.

[0050] Peripherals 580 may include any type of computer support device to add additional functionality to the computer system. For example, peripheral device(s) 580 may include a modem or a router.

[0051] Network interface 595 may include any form of computer interface of a computer, whether that be a wired network or a wireless interface. As such, network interface 595 may be an Ethernet network interface, a BlueT ooth™ wireless interface, an 802.11 interface, or a cellular phone interface.

[0052] The components contained in the computer system 500 of FIG. 5 are those typically found in computer systems that may be suitable for use with embodiments of the present invention and are intended to represent a broad category of such computer components that are well known in the art. Thus, the computer system 500 of FIG. 5 can be a personal computer, a hand held computing device, a telephone ("smart" or otherwise), a mobile computing device, a workstation, a server (on a server rack or otherwise), a minicomputer, a mainframe computer, a tablet computing device, a wearable device (such as a watch, a ring, a pair of glasses, or another type of jewelry/clothing/accessory ), a video game console (portable or otherwise), an e-book reader, a media player device (portable or otherwise), a vehicle-based computer, some combination thereof, or any other computing device. The computer can also include different bus configurations, networked platforms, multi-processor platforms, etc. The computer system 500 may in some cases be a virtual computer system executed by another computer system. Various operating systems can be used including Unix, Linux, Windows, Macintosh OS, Palm OS, Android, iOS, and other suitable operating systems.

[0053] The present invention may be implemented in an application that may be operable using a variety of devices. Non-transitory computer-readable storage media refer to any medium or media that participate in providing instructions to a central processing unit (CPU) for execution. Such media can take many forms, including, but not limited to, non-volatile and volatile media such as optical or magnetic disks and dynamic memory, respectively. Common forms of non- transitory computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM disk, digital video disk (DVD), any other optical medium, RAM, PROM, EPROM, a FLASH EPROM, and any other memory chip or cartridge.

[0054] The accompanying drawings illustrate various embodiments of systems, methods, and embodiments of various other aspects of the disclosure. Any person with ordinary skills in the art will appreciate that the illustrated element boundaries (e.g. boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. It may be that in some examples one element may be designed as multiple elements or that multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Furthermore, elements may not be drawn to scale. Non-limiting and non-exhaustive descriptions are described with reference to the following drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating principles.

[0055] While various flow diagrams provided and described above may show a particular order of operations performed by certain embodiments of the invention, it should be understood that such order is exemplary (e.g., alternative embodiments can perform the operations in a different order, combine certain operations, overlap certain operations, etc.).

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A method for correlating growth data to market data, the method comprising:

receiving growth data regarding a current set of cannabis plants, the cannabis plants of a type and grown by an identified grower at a growth location under a set of conditions; receiving price data associated with past sets of the same type of cannabis plant; and analyzing the received growth data to identify one or more metrics based on the conditions at the growth location;

identifying an adjusted price for the current set of cannabis plants, wherein the received price is adjusted by an adjustment based on at least one of the metrics.

2. The method of claim 1, further comprising:

receiving price data regarding at least a portion of the current set of cannabis plants; comparing the received price data to the adjusted price; and

validating the adjusted price based on the comparison.

3. The method of claim 1, wherein the metrics are indicative of a weather condition, and further comprising identifying that the weather condition corresponds to weather pattern data associated with decreasing quality of the type of cannabis plants grown at the growth location, wherein the adjusted price is lower than the received price based on the weather pattern data being associated with the decreased quality.

4. The method of claim 1, wherein the metrics are specific to the grower associated with historical yield data, wherein identifying the adjusted price is further based on the historical yield data associated with the grower.

5. The method of claim 1, further comprising receiving supply and demand data for the type of cannabis plant matter, wherein the adjusted price is further based on at least one of supply and demand.

6. The method of claim 1, further comprising identifying a yield estimate based on the received growth data, wherein identifying the adjusted price is further based on the yield estimate.

7. The method of claim 6, wherein the yield estimate identifies an estimated mass of the cannabis plants in the current set available for harvest.

8. The method of claim 6, wherein yield estimate identifies an estimated mass of cannabinoid extract available for extraction from the current set of cannabis plants.

9. The method of claim 1, further comprising identifying a growth rate of the current set of cannabis plants, wherein the adjusted price is further based on the identified growth rate.

10. A system for correlating growth data to market data, the system comprising:

one or more sensors that detect growth data regarding a current set of cannabis plants, the cannabis plants of a type and grown by an identified grower at a growth location under a set of conditions;

a communication network interface that receives price data associated with past sets of the same type of cannabis plant; and

a processor that executes instructions stored in memory, wherein the processor executes the instructions to:

analyze the received growth data to identify one or more metrics based on the conditions at the growth location, and

identify an adjusted price for the current set of cannabis plants, wherein the received price is adjusted by an adjustment based on at least one of the metrics.

11. The system of claim 10, wherein the communication network interface further receives price data regarding at least a portion of the current set of cannabis plants; and wherein the processor further:

compares the received price data to the adjusted price; and

validates the adjusted price based on the comparison.

12. The system of claim 10, wherein the metrics are indicative of a weather condition, and wherein the processor further identifies that the weather condition corresponds to weather pattern data associated with decreasing quality of the type of cannabis plants grown at the growth location, wherein the adjusted price is lower than the received price based on the weather pattern data being associated with the decreased quality.

13. The system of claim 10, wherein the metrics are specific to the grower associated with historical yield data, wherein the processor identifies the adjusted price further based on the historical yield data associated with the grower.,

14. The system of claim 10, wherein the communication network interface further receives supply and demand data for the type of cannabis plant matter, wherein the adjusted price is further based on at least one of supply and demand.

15. The system of claim 10, wherein the processor further identifies a yield estimate based on the received growth data, wherein the adjusted price is further based on the yield estimate.

16. The system of claim 15, wherein the yield estimate identifies an estimated mass of the cannabis plants in the current set available for harvest.

17. The system of claim 15, wherein yield estimate identifies an estimated mass of cannabinoid extract available for extraction from the current set of cannabis plants.

18. The system of claim 10, wherein the processor further identifies a growth rate of the current set of cannabis plants, wherein the adjusted price is further based on the identified growth rate.

19. A non-transitory, computer-readable storage medium, having embodied thereon a program executable by a processor to perform a method for correlating growth data to market data, the method comprising: