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Title:
CBG MODEL FOR MANAGEMENT OF GREENHOUSE OPERATIONS AND CANNABIS PRODUCTS MADE THEREWITH
Document Type and Number:
WIPO Patent Application WO/2021/062561
Kind Code:
A1
Abstract:
There is provided a method for cultivating and harvesting cannabis plants, comprising: cultivating a batch of a plurality of cannabis plants in a grow area, processing plant material from a portion of the plurality of cannabis plants in the batch to determine a cannabigerol (CBG) content in the plant material, wherein the processing is performed at a plurality of time points during the cultivating, harvesting the batch at a harvest time point, the harvest time point being determined at least in part based on the plant material having a prescribed CBG content relative to a predetermined CBG threshold at a predetermined time point.

Inventors:
ALSAYAR MAX (CA)
ELVIRA GEORGE (CA)
Application Number:
PCT/CA2020/051332
Publication Date:
April 08, 2021
Filing Date:
October 02, 2020
Export Citation:
Click for automatic bibliography generation   Help
Assignee:
HEXO OPERATIONS INC (CA)
International Classes:
A01H5/02; A01G7/00; A01G22/00; A01H1/04; A01H6/28; A23L33/105; A61K31/05; A61K31/352; A61K36/185; C07C39/19; C07C39/23; C07D311/80; G01N33/48
Other References:
PACIFICO ET AL.: "Time course of cannabinoid accumulation and chemotype development during the growth of Cannabis sativa L", EUPHYTICA, vol. 160, 18 August 2007 (2007-08-18), pages 231 - 240, XP019572552, ISSN: 1573-5060
JIKOMES ET AL.: "The cannabinoid content of legal cannabis in Washington state varies systematically across testing facilities and popular consumer products", SCIENTIFIC REPORTS, vol. 8, 14 March 2018 (2018-03-14), pages 1 - 18, XP055811967, ISSN: 2045-2322
RICIIINS ET AL.: "Accumulation ofbioactive metabolites in cultivated medical Cannabis", PLOS ONE, vol. 13, no. 7, 23 July 2018 (2018-07-23), pages 1 - 20, XP055811968, ISSN: 1932-6203
Attorney, Agent or Firm:
SMART & BIGGAR LLP (CA)
Download PDF:
Claims:
CLAIMS:

1. A method for cultivating and harvesting cannabis plants, comprising: cultivating a batch of a plurality of cannabis plants in a grow area, processing plant material from a portion of the plurality of cannabis plants in the batch to determine a cannabigerol (CBG) content in the plant material, wherein the processing is performed at a plurality of time points during the cultivating, harvesting the batch at a harvest time point, the harvest time point being determined at least in part based on the plant material having a prescribed CBG content relative to a predetermined CBG threshold at a predetermined time point.

2. A method for cultivating cannabis plants according to claim 1, wherein the prescribed CBG content is lower than the predetermined threshold at the predetermined time point.

3. A method for cultivating cannabis plants according to claim 1, wherein the prescribed CBG content is higher than the predetermined threshold at the predetermined time point.

4. A method for cultivating cannabis plants according to any one of claims 1 to 3, wherein the predetermined time point is at least 5 days prior to the harvest time point.

5. A method for cultivating cannabis plants according to claim 4, wherein the predetermined time point is at least 7 days prior to the harvest time point.

6. A method for cultivating cannabis plants according to claim 5, wherein the predetermined time point is at least 10 days prior to the harvest time point.

7. A method for cultivating cannabis plants according to claim 6, wherein the predetermined time point is at least 14 days prior to the harvest time point.

8. A method for cultivating cannabis plants according to any one of claims 1 to 7, wherein a cultivation period between a beginning of the cultivating and the harvest time point is reduced compared to a method for cultivating cannabis plants in which the harvest time point is not determined at least in part based on the plant material having the prescribed CBG content relative to the predetermined CBG threshold at the predetermined time point.

9. A method for cultivating cannabis plants according to claim 8, wherein the cultivation period is reduced by at least 7 days.

10. A method for cultivating cannabis plants according to claim 9, wherein the cultivation period is reduced by at least 14 days.

11. A method for cultivating cannabis plants according to any one of claims 1 to 10, further comprising comparing the CBG content in the plant material to a reference CBG content at the plurality of time points during the cultivating.

12. A method for cultivating cannabis plants according to claim 11, further comprising measuring a deviation between the CBG content in the plant material and the reference CBG content at the plurality of time points during the cultivating.

13. A method for cultivating cannabis plants according to claim 12, further comprising implementing a change of at least controllable cultivation parameter at least in part based on the measured deviation.

14. A method for cultivating cannabis plants according to claim 13, wherein the controllable cultivation parameter is lighting parameter.

15. A method for cultivating cannabis plants according to claim 14, wherein the lighting parameter in an artificial lighting parameter.

16. A method for cultivating cannabis plants according to claim 15, wherein the artificial lighting parameter in a lighting intensity.

17. A method for cultivating cannabis plants according to claim 15, wherein the artificial lighting parameter in a lighting cycle.

18. A cannabis product characterized in that, in a set of 100 units of the cannabis product, the set including at least a first subset of units obtained from a first batch of a plurality of cannabis plants of a single strain, and at least a second subset of units obtained from a second batch of a plurality of cannabis plants of the single strain, the first subset of units having a first average concentration of a cannabinoid and the second subset of units having a second average concentration of the cannabinoid, the first average concentration is within 25 % of the second average concentration.

19. A cannabis product according to claim 18, wherein the first average concentration is within 20% of the second average concentration.

20. A cannabis product according to claim 19, wherein the first average concentration is within 15% of the second average concentration.

21. A cannabis product according to claim 20, wherein the first average concentration is within 10% of the second average concentration.

22. A cannabis product according to claim 21, wherein the first average concentration is within 5% of the second average concentration.

23. A cannabis product according to any one of claims 18 to 22, wherein the cannabinoid includes tetrahydrocannabinol (THC).

24. A cannabis product according to claim 23, wherein the cannabinoid includes D-9- tetrahydrocannabinol.

25. A cannabis product according to any one of claims 18 to 24, wherein the cannabinoid includes cannabidiol (CBD).

26. A cannabis product according to any one of claims 18 to 25, wherein the cannabis product is a dried cannabis bud or flower.

27. A cannabis product according to any one of claims 18 to 26, wherein the cannabis product is a fresh cannabis bud or flower.

28. A set of 100 units of a cannabis product, the set including at least a first subset of units obtained from a first batch of a plurality of cannabis plants of a single strain, and at least a second subset of units obtained from a second batch of a plurality of cannabis plants of the single strain, the first subset of units having a first average concentration of a cannabinoid and the second subset of units having a second average concentration of the cannabinoid, the first average concentration being within 25 % of the second average concentration.

29. A set of 100 units of cannabis product according to claim 28, wherein the first average concentration is within 20% of the second average concentration.

30. A set of 100 units of cannabis product according to claim 29, wherein the first average concentration is within 15% of the second average concentration.

31. A set of 100 units of cannabis product according to claim 30, wherein the first average concentration is within 10% of the second average concentration.

32. A set of 100 units of cannabis product according to claim 31, wherein the first average concentration is within 5% of the second average concentration.

33. A set of 100 units of cannabis product according to any one of claims 28 to 32, wherein the cannabinoid includes tetrahydrocannabinol (THC).

34. A set of 100 units of cannabis product according to claim 33, wherein the cannabinoid includes D-9-tetrahydrocannabinol.

35. A set of 100 units of cannabis product according to any one of claims 28 to 34, wherein the cannabinoid includes cannabidiol (CBD).

36. A set of 100 units of cannabis product according to any one of claims 28 to 35, wherein the cannabis product is a dried cannabis bud or flower.

37. A set of 100 units of cannabis product according to any one of claims 28 to 36, wherein the cannabis product is a fresh cannabis bud or flower.

38. A cannabis product characterized in that, in a set of 100 units of the cannabis product, the set including at least a first subset of units obtained from a first batch of a plurality of cannabis plants of a single strain, and at least a second subset of units obtained from a second batch of a plurality of cannabis plants of the single strain, the first subset of units having a first average concentration of a cannabinoid and the second subset of units having a second average concentration of the cannabinoid, the first average concentration is within 25 % of the second average concentration, the first and second batch being obtained with the method of any one of claims 21 to 37.

39. A cannabis product according to claim 38, wherein the first average concentration is within 20% of the second average concentration.

40. A cannabis product according to claim 39, wherein the first average concentration is within 15% of the second average concentration.

41. A cannabis product according to claim 40, wherein the first average concentration is within 10% of the second average concentration.

42. A cannabis product according to claim 41, wherein the first average concentration is within 5% of the second average concentration.

43. A cannabis product according to any one of claims 38 to 42, wherein the cannabinoid includes tetrahydrocannabinol (THC).

44. A cannabis product according to claim 43, wherein the cannabinoid includes D-9- tetrahydrocannabinol.

45. A cannabis product according to any one of claims 38 to 44, wherein the cannabinoid includes cannabidiol (CBD).

46. A cannabis product according to any one of claims 38 to 45, wherein the cannabis product is a dried cannabis bud or flower.

47. A cannabis product according to any one of claims 38 to 45, wherein the cannabis product is a fresh cannabis bud or flower.

48. A set of 100 units of cannabis product, the set including at least a first subset of units obtained from a first batch of a plurality of cannabis plants of a single strain, and at least a second subset of units obtained from a second batch of a plurality of cannabis plants of the single strain, the first subset of units having a first average concentration of a cannabinoid and the second subset of units having a second average concentration of the cannabinoid, the first average concentration being within 25 wt.% of the second average concentration, the first and second batch being obtained with the method of any one of claims 21 to 37.

49. A set of 100 units of cannabis product according to claim 48, wherein the first average concentration is within 20% of the second average concentration.

50. A set of 100 units of cannabis product according to claim 49, wherein the first average concentration is within 15% of the second average concentration.

51. A set of 100 units of cannabis product according to claim 50, wherein the first average concentration is within 10% of the second average concentration.

52. A set of 100 units of cannabis product according to claim 51, wherein the first average concentration is within 5% of the second average concentration.

53. A set of 100 units of cannabis product according to any one of claims 48 to 52, wherein the cannabinoid includes tetrahydrocannabinol (THC).

54. A set of 100 units of cannabis product according to claim 53, wherein the cannabinoid includes D-9-tetrahydrocannabinol.

55. A set of 100 units of cannabis product according to any one of claims 48 to 54, wherein the cannabinoid includes cannabidiol (CBD).

56. A set of 100 units of cannabis product according to any one of claims 48 to 55, wherein the cannabis product is a dried cannabis bud or flower.

57. A set of 100 units of cannabis product according to any one of claims 48 to 55, wherein the cannabis product is a fresh cannabis bud or flower.

Description:
CBG MODEL FOR MANAGEMENT OF GREENHOUSE OPERATIONS AND CANNABIS PRODUCTS MADE THEREWITH

CROSS-REFERENCE TO RELATED APPLICATION

[0001] The present application claims the benefit of U.S. provisional patent application serial no. 62/909,353 filed on October 2, 2019. The contents of the above-referenced document are incorporated herein by reference in their entirety.

TECHNICAL FIELD

[0002] This application generally relates to the field of large scale cultivation of cannabis plants and, more specifically, to methods and systems for management of greenhouse operations as well as cannabis plants cultivated using such methods and systems and cannabis products made using such methods and systems.

BACKGROUND

[0003] Large scale cultivation of cannabis plants requires high electrical energy consumption, soil and feeding material, space allocation in ever size-increasing greenhouse installations, etc. Typically, large scale licensed producers cultivate numerous cannabis plant strains, each having specific cannabinoid profiles so as to be able to meet market demand. As licensed producers increase their production capacity, increasing operational costs are also expected as, for example, energy consumption (stemming primarily from lighting requirements) represents about 90% of operational costs of cannabis products production.

[0004] The cannabis plant can grow in different environments, but the characteristics of the plant make crop normalization extremely challenging. There exists a significant number of different species and strains of cannabis plants, each with their own specific genetics. Crop amount and potency are predominantly determined by genetics, but also by environmental conditions given the plant’s extreme sensitivity to temperature, humidity, and light exposure, among other factors. Current scientific knowledge is inconclusive on which specific species or strains are suited for the large scale growth environment and on what would be ideal environmental conditions. Existing knowledge about cannabis growth primarily comes from anecdotal knowledge, and does not apply to large growth operations since open scientific research was illegal in many countries until recent years.

[0005] Further, cultivation strategies applied to small scale environments cannot be directly applied to large scale environments as large scale growing environments require stricter control mechanisms due to the high density of plants (to maximize the use of floor space). The cannabis plant goes through many stages that have different lighting or humidity requirements (e.g. the vegetative stage requires a long daytime and short nighttime) . However, if there are large variations in the actual moment as to when the plants reach specific stages, scheduled maturation time can be delayed, impacting overall yield. As such, assessing the maturity or ripeness of cannabis plants and a suitable harvest time for plants remains essentially based on visual inspection and, as such, is greatly reliant upon the experience / knowledge of the operator in charge of deciding when the plants ought to be harvested.

[0006] In light of the foregoing, there remains a need to provide methods and systems for management of cannabis production / greenhouse operations that alleviate at least some of the problems discussed above, and specifically that reduce the operational costs of cannabis production while maintaining consistent and optimal cannabinoid profiles across all the produced plants, even as the scale of cultivation environments increase towards large scale cultivation.

SUMMARY

[0007] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key aspects or essential aspects of the claimed subject matter.

[0008] In accordance with a first aspect, there is provided a cannabis product. The cannabis product is characterized in that, in a set of 100 units of the cannabis product, the set including at least a first subset of units obtained from a first batch of a plurality of cannabis plants of a single strain, and at least a second subset of units obtained from a second batch of a plurality of cannabis plants of the single strain, the first subset of units having a first average concentration of a cannabinoid and the second subset of units having a second average concentration of the cannabinoid, the first average concentration is within 25 % of the second average concentration. [0009] In accordance with a second aspect, there is provided a set of 100 units of cannabis product. The set including at least a first subset of units obtained from a first batch of a plurality of cannabis plants of a single strain and at least a second subset of units obtained from a second batch of a plurality of cannabis plants of the single strain. The first subset of units has a first average concentration of a cannabinoid and the second subset of units has a second average concentration of the cannabinoid. The first average concentration is within 25 wt% of the second average concentration.

[0010] In accordance with a third aspect, there is provided a method for cultivating cannabis plants. The method comprises cultivating a batch of a plurality of cannabis plants in a grow area, processing plant material from a portion of the plurality of cannabis plants in the batch to determine a content of cannabigerol (CBG) in the plant material and harvesting the batch at a harvest time point. The processing is performed at a plurality of time points during the cultivating. The harvest time point is determined at least in part based on the plant material having a prescribed CBG content relative to a predetermined CBG threshold at a predetermined time point.

[0011] In accordance with a fourth aspect, there is provided a cannabis product. The cannabis product is characterized in that, in a set of 100 units of the cannabis product, the set including at least a first subset of units obtained from a first batch of a plurality of cannabis plants of a single strain, and at least a second subset of units obtained from a second batch of a plurality of cannabis plants of the single strain, the first subset of units having a first average concentration of a cannabinoid and the second subset of units having a second average concentration of the cannabinoid, the first average concentration is within 25 % of the second average concentration, the first and second batch being obtained in accordance with the third aspect.

[0012] In accordance with a fifth aspect, there is provided a set of 100 units of cannabis product. The set includes at least a first subset of units obtained from a first batch of a plurality of cannabis plants of a single strain and at least a second subset of units obtained from a second batch of a plurality of cannabis plants of the single strain. The first subset of units has a first average concentration of a cannabinoid and the second subset of units has a second average concentration of the cannabinoid. The first average concentration is within 25 wt% of the second average concentration, the first and second batch being obtained in accordance with the third aspect.

[0013] All features of exemplary embodiments which are described in this disclosure and are not mutually exclusive can be combined with one another. Elements of one embodiment can be utilized in the other embodiments without further mention. Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments in conjunction with the accompanying Figures.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] A detailed description of specific exemplary embodiments is provided herein below with reference to the accompanying drawings in which:

[0015] Fig. 1 shows a flow chart of a process for producing cannabis products in accordance with a non-limiting embodiment;

[0016] Fig. 2 shows a flow chart of a process for deriving a CBG model and using the derived CBG model to uniformize and/ or optimize cultivation of cannabis plants in accordance with a non-limiting embodiment;

[0017] Fig. 3 show a CBG model curve for the NLxBB cannabis strain between the 3 rd and the 8 th week of cultivation and correlation with TF1C content in accordance with one embodiment; and

[0018] Fig. 4 shows a cannabis control system for controlling cultivation of cannabis plants in accordance with one embodiment.

[0019] In the drawings, exemplary embodiments are illustrated by way of example. It is to be expressly understood that the description and drawings are only for the purpose of illustrating certain embodiments and are an aid for understanding. They are not intended to be a definition of the limits of the invention.

DETAILED DESCRIPTION

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by a person of ordinary skill in the art to which the present invention pertains. As used herein, and unless stated otherwise or required otherwise by context, each of the following terms shall have the definition set forth below.

[0021] For the purpose of this specification, the expressions “cannabidiol” or “CBD” are generally understood to refer to one or more of the following compounds, and, unless a particular other stereoisomer or stereoisomers are specified, includes the compound “A2-cannabidiol.” These compounds are: (1) A5-cannabidiol (2-(6-isopropenyl-3-methyl-5-cyclohexen-l-yl)-5-pentyl-l,3- benzenediol); (2) A4-cannabidiol (2-(6-isopropenyl-3-methyl-4-cyclohexen-l-yl)-5-pentyl-l,3- benzenediol); (3) A3-cannabidiol (2-(6-isopropenyl-3-methyl-3-cyclohexen-l-yl)-5-pentyl-l,3- benzenediol); (4) A3,7-cannabidiol (2-(6-isopropenyl-3-methylenecyclohex-l-yl)-5-pentyl-l,3- benzenediol); (5) A2-cannabidiol (2-(6-isopropenyl-3-methyl-2-cyclohexen-l-yl)-5-pentyl-l,3- benzenediol); (6) DI-cannabidiol (2-(6-isopropenyl-3-methyl-l-cyclohexen-l-yl)-5-pentyl-l,3- benzenediol); and (7) Dό-cannabidiol (2-(6-isopropenyl-3-methyl-6-cyclohexen-l-yl)-5-pentyl-l,3- benzenediol).

[0022] For the purpose of this specification, the expression “cannabinoid” means a compound such as cannabigerolic acid (CBGa), cannabigerol (CBG), cannabigerol monomethylether (CBGm), cannabigerovarin (CBGv), cannabichromene (CBC), cannabichromevarin (CBCV), cannabidiol

(CBD), cannabidiol monomethylether (CBDM), cannabidiol-C4 (CBD-C4), cannabidivarin (CBDV), cannabidiorcol (CBD-C1), delta-9-tetrahydrocannabinol (D9-TH£), delta-9-tetrahydrocannabinolic acid A (THCA-A), delta-9-tetrahydrocannabionolic acid B (THCA-B), delta-9-tetrahydrocannabinolic acid-C4 (THCA-C4), delta-9-tetrahydrocannabinol-C4, delta-9 -tetrahydrocannabivarin (THCV), delta-9-tetrahydrocannabiorcol (THC-C1), delta-7-cis-iso tetrahydrocannabivarin, delta-8- tetrahydrocannabinol (Dd-THC), cannabicyclol (CBL), cannabicyclovarin (CBLV), cannabielsoin

(CBE), cannabinol (CBN), cannabinol methylether (CBNM), cannabinol-C4 (CBN-C4), cannabivarin (CBV), cannabinol-C2 (CBN-C2), cannabiorcol (CBN-C1), cannabinodiol (CBND), cannabinodivarin (CBVD), cannabitriol (CBT), 10-ethoxy-9hydroxy-delta-6a-tetrahydrocannabinol, 8,9-dihydroxy- delta-6a-tetrahydrocannabinol, cannabitriolvarin (CBTV), ethoxy-cannabitriolvarin (CBTVE), dehydrocannabifuran (DCBF), cannabifuran (CBF), cannabichromanon (CBCN), cannabicitran (CBT), 10-oxo-delta-6a-tetrahydrocannabionol (OTHC), delta-9-cis-tetrahydrocannabinol (cis-THC), 3,4,5,6-tetrahydro-7-hydroxy-alpha-alpha-2-trimethyl-9-n-pro pyl-2, 6-methano-2H-l-benzoxocin-5- methanol (OH-iso-HHCV), cannabiripsol (CBR), trihydroxy-delta-9-tetrahydrocannabinol (triOH- THC), cannabinol propyl variant (CBNV), and derivatives thereof. As used herein, CBG is used to refer to any one of CBG, CBGa, CBGm, CBGv or any combination thereof.

[0023] For the purpose of this specification, the expression “cannabis product” could mean any goods that are produced from cannabis or hemp, which include plants, plant material, oils, resins, drinks, food additives, edibles, creams, aerosol sprays and vaporization substances, for example. These cannabis products could be used for medical and/or recreational purposes. Cannabis products could include active substances such as cannabinoids. However, the cannabis products described herein might not always include an active substance.

[0024] For the purpose of this specification, the expression “large scale” in the context of cannabis greenhouse refers to cultivation of cannabis plant on an industrial scale, e.g., at least 500,000 cannabis plants a year.

[0025] For illustrative purposes, specific example embodiments will be explained in greater detail below in conjunction with the Figures. It should be appreciated, however, that the present disclosure provides many applicable concepts that can be embodied in any of a wide variety of specific contexts. The specific embodiments discussed are merely illustrative and do not limit the scope of the present disclosure. For example, embodiments could include additional, different, or fewer features than shown in the drawings. In the flow diagrams illustrated in the accompanying figures, a rectangle generally annotates a step, apparatus, device, location or operation, and a pentagon generally annotates an input, product or output.

[0026] In accordance with a first non-limiting embodiment there is provided cultivation management systems and methods to reduce cannabis greenhouse operational costs, while controlling and/ or uniformizing and/ or optimizing at least one desirable trait in a cannabis plants cultivated using such systems and methods or in cannabis products made from cannabis plants processed using such systems and methods, as further described below.

Cannabis production process overview

[0027] Fig. 1 is a flow diagram illustrating an exemplary process 100 for producing cannabis products in accordance with one non-limiting embodiment. Process 100 comprises a first step 102 of cultivating a batch of cannabis plants. A “batch” refers to a group or set of cannabis plants. The batch of cannabis plants may be cultivated in a grow area, the grow area being defined as an area of a given surface area that is being used to cultivate the cannabis plants of the batch. The grow area could be provided in greenhouses (i.e., in at least partially-sealed ambient environment structures) or in any other suitable structure that is conducive to the cultivation of cannabis plants. In some non-limiting examples, the grow area may be comprised of a plurality of sub-areas wherein the sub-areas are contiguous areas, but the sub-areas could also be non-contiguous (i.e., non-adjacent) in other non-limiting examples. That is, grow areas that include a plurality of non-adjacent areas are also possible. In other non-limiting examples, the greenhouse comprising the grow area may have a cultivation space of between about 5,000 square feet and about 35,000 square feet, however any other suitable dimension is possible in other non-limiting examples.

[0028] The cannabis plants of the batch of cannabis plants cultivated in the grow area may be from the same type of seeds or from the same mother plant, a mother plant being defined as a plant grown for the purpose of taking cuttings or offsets in order to grow more of the same plant. In other non limiting examples, the grow area may be used to cultivate cannabis plants from a plurality of different seed types and/ or a plurality of different mother plants.

[0029] It is appreciated that the step 102 may consist of a variety of distinct stages of development of cannabis plants. Without wishing to be bound by any theory, such stages may notably include the germination phase, the seedling phase, the vegetative phase and the flowering phase. Each one of those stages may be triggered and/ or supported and/ or terminated by various cultivation conditions, as further described below, such that the cultivation conditions need not be identical over the entire cultivation period of the cannabis plants. The cultivation period of the cannabis plants may be defined as the period starting from the beginning of the germination phase from seeds or the beginning of the cloning phase from cuttings up to the end of the flowering stage (i.e., pre -harvest stage), or as the period starting from the beginning of seedling phase up to the end of the flowering stage (i.e., pre harvest stage), depending on the cultivation protocols chosen. Cuttings as used herein refers to any part of the plant, including but not limited to stems, stems including leaves, plant silk material, root material but not including rooting parts.

[0030] In this non-limiting embodiment, the expression of one cannabinoid precursor in the plants of the batch may be monitored during the cultivation period to derive a model (e.g., a CBG model) that can be used to identify an optimal harvest time at which the plants of the batch ought to be harvested so as to achieve desirable traits for the cannabis plants being harvested at step 104 and/or uniformize at least some traits of the plants being harvested at step 104 and/ or reduce the operational costs of the cultivating step 102. In parallel, the cultivation conditions may be controlled within a grow area at step 102 so as to provide uniform and/or optimized cultivation conditions during the cultivation period, or during a subset of the cultivation period, for all plants of the batch of cannabis plants within the grow area so as to achieve desirable traits for the cannabis plants being harvested at step 104 and/ or uniformize at least some traits of the plants being harvested at step 104 and/ or reduce the operational costs of the cultivating step 102, as further described below.

[0031] The cultivation conditions comprise various controllable cultivation parameters that can be organized as distinct cultivation regimen. It is appreciated that the cultivation parameters may be identical or substantially identical over an entire greenhouse (that is, over a plurality of distinct grow areas), or they may be different between distinct grow areas in other non-limiting examples. Furthermore, other controllable parameters of the cannabis products production process 100 that are not specifically related to the cultivating step 102, and for example that relate to post-harvest processing step 106, may also be controlled in other non-limiting examples. Any other suitable parameter relating to cannabis products production processes, such as but not limited to the process 100, including any production step thereof, may be also controlled in other embodiments. In yet further non-limiting examples, a given set of controllable cultivation parameters may be specific to a batch of cannabis plants, to a strain of cannabis plant or both.

[0032] In a first non-limiting example, at step 102 the cannabis plants within a specified grow area are exposed to light to sustain at least a vegetative phase and a flowering phase. A first set of controllable cultivation parameters may therefore be related to a lighting regimen of the cannabis plants during the cultivating step 102. Such lighting regimen comprises, but is not limited to, lighting parameters such as a lighting intensity (which may be normalized to a specified unit surface area), a lighting length of exposure, a lighting exposure cycle, a lighting spectrum, a lighting source and the likes. For example, the lighting regimen may comprise a specified lighting intensity that is applied over a pre-determined period of time which is repeated over a 24-hour time period, with or without alternating sources (i.e., natural, artificial). Since cannabis plants may also require, depending on their current growth phase, a pre-determined period of time over the course of a 24-hour period during which the cannabis plants are deprived or substantially deprived of light, it is appreciated that any controllable parameter relating directly or indirectly to light deprivation also falls within the scope of the lighting regimen above. For example, during the vegetative phase, a lighting regimen comprising 24-hours light cycles may be used to trigger root development during at least 3 weeks, while during the flowering phase a lighting regimen comprising 12-hours light cycles may be used during at least 6 to 8 weeks. [0033] It will be readily appreciated that, in the context of cultivation performed in greenhouses, the lighting source may be natural (i.e., sun) and/ or artificial (i.e., halogen lamps, etc.). As such, the lighting regimen discussed above may include both lighting from natural sources (natural lighting regimen) and artificial sources (artificial lighting regimen), the artificial lighting regimen being controllable and generally used to supplement the natural lighting regimen. As such, the artificial lighting regimen is reliant on the seasons during which cultivation occurs, with more supplementation being generally needed during winter to maintain a suitable lighting regimen, as further described below.

[0034] A single lighting regimen may be used over the entirety of the vegetative and flowering phases in some examples, however in other non-limiting examples various lighting regimen may be used between the vegetative and flowering phases, or even between specific sub-phases of the vegetative and flowering phases. Similarly, a single lighting regimen may be used over the entire grow area or over the entire greenhouse, however in other non-limiting examples various lighting regimen may be used between specific sub-areas of a given grow area or between distinct grow areas of the greenhouse.

[0035] Any other cultivation regimen that is relevant to cannabis plants cultivation at step 102, as well as to cannabis products production generally, may also be used in other non-limiting examples such as, but not limited to, the temperature regimen (i.e., temperature and temperature cycle, etc.), the water irrigation regimen (i.e., overall water volume, water flow, water cycle, etc.), the humidity regimen (i.e., humidity and humidity cycle, etc.), the fertilizer regimen (type of fertilizer used, concentration and strength of fertilizer, fertilizer addition cycle, etc.), the carbon dioxide (CO2) regimen (i.e., CO2 amount and cycle, etc.), the air flow regimen (air flow cycle, etc.) and the likes. Any other suitable set of controllable parameters may be possible in other embodiments.

[0036] The cannabis plants in the batch are then harvested at step 104 and then subjected to various post-harvest processing stages at step 106, as notably described in Canadian patents application nos. CA 3,048,298 and CA 3,033,404, the content of which is incorporated therein by reference. For example, cannabis plants harvested may be separated in distinct plant parts (e.g., flowers or buds, trims, etc.) and then subjected to a dehydration process. In the context of the present disclosure, dried cannabis plant parts, such as dried cannabis flowers, exhibit a moisture content of between about 10 wt% and about 15 wt%. Cannabis plant parts which have not been subjected to a dehydration process post-harvesting are referred to as fresh plant parts, such as fresh cannabis flowers, and exhibit a moisture content of between about 15 wt% and about 80 wt%. In some examples, the post-harvest processing stages may include treatments to control and/or eliminate pests and/or diseases that may be present in the cannabis plants being harvested at step 104, such as but not limited to treatment with pesticides, beneficial insects and the likes.

[0037] The timing of the harvesting step 104 may or may not coincide with the beginning or the end of any one of the growth stages of the cannabis plants in the batch. The timing of the harvesting step 104 may also be optimized using the various controllable cultivation parameters, as further described below. A plurality of different batches of cannabis plants may be cultivated at step 102 and/ or harvested at step 104 in parallel or sequentially, each within distinct grow areas. For example, a greenhouse could be divided into a plurality of distinct grow areas, each grow area being used to cultivate a respective batch of cannabis plants. Each one of the batches of cannabis plants could then be used for producing different cannabis products. Multiple cannabis products could be produced from a single batch of cannabis plants. Different batches of cannabis plants could also or instead be combined to produce a single cannabis product.

[0038] In this non-limiting embodiment, various traits of the batch of cannabis plants being cultivated in the grow area at step 102, or the cannabis products obtained therefrom, may be dependent upon the controllable cultivation conditions described above. Said differently, the controllable cultivation conditions described above may be controlled such that various traits of the batch of cannabis plants being cultivated at step 102 that are desirable may be obtained. These traits include, but are not limited to: an optimal harvest time of the cannabis plants, a plant mass, a plant density, a plant height, a quantity of total cannabinoids including but not limited to CBG and/ or THC and/ or CBD, a yield of THC and/ or CBD per weight of the cannabis plants, a yield of cannabis plants per surface area of the grow area of the respective batch of cannabis plants, plant mass at harvest per overall irradiance during the cultivation period, CBG content during the cultivation cycle, yield THC/ CBD per plant per overall irradiance over cultivation period, terpene profile, and the likes. These traits of the batch of cannabis plants may also be used to optimize and/ or uniformize the cultivation of cannabis plants using a CBG model, as further described below.

[0039] With further reference to Fig. 2, a non-limiting example of a process 200 for optimizing and/or uniformizing cultivation of cannabis plants using a CBG model, notably during step 102 of process 100 above, is shown. CBG is a chemical precursor to cannabinoids such as THC and CBD - in fact, CBGa is the precursor to THCa and CBDa, the decarboxylated forms of these cannabinoids (i.e., CBG, THC and CBD) being only produced following decarboxylation, for example through a heat treatment. In most cannabis strains, CBG is present in small concentrations (i.e., less than 1% wt). Surprisingly, the present inventor has found that CBG content is representative of THC and CBD production in the plants, the CBG content decreasing as the THC and CBD content increase. This change in CBG content can in turn be correlated with an optimal harvest time for the plant. As mentioned above, CBG as used herein refers to any one of CBG, CBGa, CBGm and CBGv or any combination thereof.

[0040] The process for deriving a CBG model will first be described. Any batch of cannabis plants from which samples will be obtained to derive a CBG model, as well as any data related thereto (e.g., cultivation conditions, grow area and the likes), will be referred to as reference batch and reference data. This is to be distinguished from test batches and test data, which relate to batches of cannabis plants, as well as data related thereto, the cultivation of which will be controlled and/ or optimized and/ or uniformized using the reference data and the CBG content reference curve from the reference batch, as further described below.

[0041] In a first step 202 at least one sample from respective cannabis plants within a reference batch of cannabis plants is obtained, the reference batch of cannabis plants being cultivated at step 102 of process 100 above within a specified grow area under specified cultivation conditions, the at least one sample being representative of at least a subset of the reference batch of cannabis plants. In one non-limiting example, the cannabis plants constituting the subset of the reference batch of cannabis plants may be randomly selected within the reference batch to ensure statistical significance of the respective sample. The at least one sample from respective cannabis plants may include, for example, at least a part of a flower, trim, or waste of the cannabis plant or it may also include any other suitable part of the plant in other examples. Step 202 may be repeated any suitable number of time at various time points during the cultivation period. In one non-limiting example, at least one sample may be obtained every 2 weeks during the cultivation period, every week during the cultivation period, several times a week during the cultivation period, every day during the cultivation period or even multiple times a day during the cultivation period to derive temporal data.

[0042] Various traits of the at least one sample may be also measured at step 202 in some non limiting examples, specifically those that do not require any processing. These include but are not limited to the plant mass and/or plant density that may also be recorded at the various time points. These may also include qualitative features such as but not limited to whether the flowering has started, the size and appearance of flowers and the likes.

[0043] The process 200 further comprises a step 204 of processing the at least one sample obtained at step 202 in order to measure and quantify at least expression levels of CBG in the at least one sample (i.e., a content of CBG in the at least one sample). In one non-limiting example, measuring and quantifying expression levels of CBG (i.e., measuring the content of CBG in the cannabis plant or within parts of the cannabis plants) may be performed using high pressure liquid chromatography (HPLC), such as with the Agilent InfmityLab Poroshell 120 SB-C18 column (Agilent Technologies, USA). Any other suitable analytical technique may be used in other non-limiting examples. It is appreciated that, since a plurality of samples may be processed at various time points during the cultivation period, statistically-significant data related to CBG content may be obtained. That is, for a given time point during the cultivation period of the cannabis plants, an average CBG content, as well as expected standard deviation of CBG content, may be obtained.

[0044] In other non-limiting examples, other traits of the at least one sample may also be measured at step 204 at the various time points, such as but not limited to a THC content, a CBD content, a yield of THC per weight of the at least one sample, a yield of CBD per weight of the at least one sample and the likes.

[0045] At step 206, data comprising information relating to the at least one sample from the reference batch (e.g., batch number, cannabis strain, grow area, CBG content, cultivation conditions (e.g., day of cultivation at the relevant time point, lighting regimen, etc.), THC and/ or CBD content, processing date(s), and the likes) may be stored, for example on a computer readable memory support. It is appreciated that the CBG content obtained at step 204 is correlated at least to: (1) the specific strain of cannabis plants; and (2) the specific cultivation conditions used to cultivate the cannabis plants in the specific grow area. In this non-limiting example, the CBG content obtained at step 204 is not correlated to the specific grow area itself where the batch is cultivated, at least because the determination of the specific cultivation conditions in one grow area could be used to reproduce the same environment (i.e., the same cultivation conditions) in a distinct grow area. In other words, for example, a change in the strain of the cannabis plants being cultivated may result in a change in CBG content in the plants under the same cultivations conditions. Given that the cultivation conditions may also change over the course of the cultivation period, at least information pertaining to the cultivation conditions ought to be recorded for every one of the at least one sample at each relevant time point. Information pertaining to the specific strain of cannabis plants being cultivated does not change over the cultivation period.

[0046] At step 208, the data stored in the database at step 206 may be used to derive a CBG model (i.e., a curve representative of CBG content = f (time)) for the reference batch. The CBG model is therefore also correlated at least to: (1) the specific strain of cannabis plants; and (2) the specific cultivation conditions used to cultivate the cannabis plants. Because each time point of the CBG model may be linked to a plurality of samples obtained at step 202, the CBG model embodies statistically significant data. That is, for a given time point, the CBG model may provide an average CBG content as well as standard deviation for the CBG content in some non-limiting examples.

[0047] At this step, the model may also be correlated to other traits of the cannabis plants from the reference batch that have been acquired either at step 202 or 204. In one non-limiting example, for a given time point of the CBG model, a correlation may be established between a CBG content and any one of a plant mass, a plant density, a quantity of THC, an amount of CBD, a yield of THC, a yield of CBD or any other suitable trait in other non-limiting examples.

[0048] An exemplary CBG model for the cannabis strain NLxBB is shown in Fig. 3. In this non limiting example, the CBG model obtained based on a measured CBG content after 3, 4, 5, 6, 7, 8 and 10 weeks of cultivation. The CBG model curve depicted in Fig. 3 may be obtained by polynomial regression, or any other suitable regression in other non-limiting examples.

[0049] In one non-limiting example, using such correlation, a CBG threshold for the reference batch may then be identified at step 210, the CBG threshold being defined as the lowest CBG content measured after which at least the content of THC and/ or CBD in the plant does not substantially increase. For example, it may be found that the CBG content in the cannabis plants of the reference batch has dropped from 1% after 2 weeks to 0.6% after 6 weeks of cultivation. In the meantime, after the CBG content reaches 0.6%, the content of THC and/or CBD does not increase by more than 10% over the course of the next 2 weeks in the cannabis plants of the reference batch. The CBG threshold as defined above may therefore be an indicator of the optimal harvest time for the reference batch of cannabis plants cultivated in the grow area, the optimal harvest time being identified at step 212. In this example, the optimal harvest time maximizes the THC and/or CBD content while at the same time decreasing the operational costs of the cultivation. This is so because the cultivation period may be shortened, the weeks following the CBG threshold not correlating with a substantial increase of the THC and/ or CBD content. In some non-limiting examples, using prediction from the CBG model and the identification of a suitable CBG threshold the cultivation period may be reduced when compared to the cultivation period without the use of the CBG model by at least one week and in some cases at least 2 weeks.

[0050] While a non-substantial increase has been characterized in the example above as an increase of less than 10% of the THC and/or CBD content over the course of 2 weeks, it will be readily appreciated that whether or not an increase is considered substantial in the circumstances may be defined in any suitable manner — in some non-limiting examples this may require a consideration of the operational costs of the cultivation. In other words, an increase in the THC and/ or CBD content may not be considered substantial if it does not outweigh the operational costs of the cultivation during the corresponding timeframe. This can be done, for example, by considering the overall irradiance over the cultivation period (which can be directly correlated to the operational costs of the cultivation), for example using parameters such as a plant mass at harvest per overall irradiance during the cultivation period or a yield of THC/ CBD per plant per overall irradiance over cultivation period. It is further appreciated that the CBG threshold may be based on any other suitable parameter in other non-limiting examples beyond the content of THC and/ or CBD (e.g., yield of THC and/ or CBD, the content of any other cannabinoid, terpene profile and the likes). In light of the foregoing, the optimal harvest time for the reference batch corresponds to the time during the cultivation period at which the CBG threshold is reached. It is appreciated that the optimal harvest time identified at step 212 is also correlated a least to: (1) the specific strain of cannabis plants; and (2) the specific cultivation conditions used to cultivate the cannabis plants. Reference data regarding the CBG threshold and the optimal harvest time is then stored in a database at step 214. In yet further non-limiting examples, the CBG threshold may also be determined factoring the overall mass of the plants that are to be harvested — it will be readily appreciated that even if the THC and/ or CBD content in the plants has reached a satisfactory level, the plants themselves may not have achieved an overall size and/or density conducive to harvesting from an economical perspective. That is, the content of THC and/ or CBD in the plants may be satisfactory, but there would not be a satisfactory overall quantity of THC and/ or CBD harvested. In yet further non-limiting examples, any other suitable cannabinoid may be used as indicator for the identification of the CBG threshold. [0051] In further non-limiting examples, the CBG threshold may be defined in any other suitable manner. For example, the particular strain of cannabis being cultivated may have a CBG content that peaks at 2% after 5 weeks from 1% after 2 weeks and then drops back to 1% after 8 weeks. In such an instance, the CBG threshold may be defined as the highest CBG content reached during the cultivation period. Such a threshold may be of interest for the production and harvest of cannabis plants with high CBG content, as well as cannabis products derived therefrom with high CBG content.

[0052] The derivation of the CBG model for the reference batch, as well as the identification of the CBG threshold and optimal harvest time at steps 208, 210 and 212, respectively, may all be used for example to predict how a test batch of cannabis plants ought to grow in a grow area, to predict when a test batch of cannabis plants ought to be harvested, as well as to identify any deviation in the expected cultivation of a test batch of cannabis plants when compared to a reference batch of cannabis plants, as further described below. Steps 202 to 214 may therefore be repeated, at various time points, for various reference batches and reference cultivation conditions such that a database of CBG models may be derived in other non-limiting examples, each one of the CBG model being correlated to a reference batch of cannabis plants and reference cultivation conditions.

[0053] In order to predict how a test batch of cannabis plants ought to grow under specific cultivation conditions, to predict when a test batch of cannabis plants ought to be harvested, as well as to identify any deviation in the expected cultivation of a test batch of cannabis plants when compared to a reference batch of cannabis plants, steps 202 to 206 are first repeated at various time points during the cultivation of a test batch of cannabis plants, as described above. At step 216, data regarding the test batch of cannabis plants, as well as data regarding test cultivation conditions, including grow area, is compared to reference data stored in the database of CBG models at step 214 to identify at step 218 at least one CBG model with reference data that matches the test data. In one non-limiting example, the reference and test data may be considered to match when the strains of cannabis plants are the same, when the grow area is the same and when the cultivation conditions are the same. In other non-limiting examples, a match may be found when the strains of cannabis plants are the same, when the grow areas are different and when the test cultivation conditions are different, but still within a pre-determined range from the reference cultivation conditions, the pre-determined range being defined in any suitable manner (e.g., a lighting intensity that does not vary by more than 10%, etc.). Any suitable matching algorithm may be used in other embodiments. It is also appreciated that the steps 216 and 218 may not need to be repeated at each time point, assuming that the cultivation conditions for the test batch and the reference batch do not change. In the event that the cultivation conditions for at least one of the test batch and the reference batch change and that such change is beyond the acceptable pre-determined range of variation for the comparison at step 216, steps 216 and 218 may be repeated at each relevant time point in an attempt to seek to identify a new CBG model with a better match than the previously identified CBG model.

[0054] At step 220, the CBG content measured at step 204 for the test batch at a given time point is compared to the expected CBG content at this time as it is derived from the CBG model identified at step 218. It is appreciated that the statistical significance of the data obtained for the reference batches is helpful at this stage in assessing whether the CBG content deviates or not from the expected CBG content. In one non-limiting example, the comparison may include determining whether the CBG content measured at step 204 for the test batch falls within the standard deviation established with the CBG model.

[0055] When no deviation is detected at step 222, the CBG model may be used to predict how the test batch is going to grow, the premise being that, the cannabis strain and the cultivation conditions being at least substantially identical between the test batch and the reference batch, the test batch of cannabis plants ought to grow in a manner similar or substantially similar to the growth of the reference batch of the cannabis plants. It is appreciated that such prediction may notably be confirmed at each and every time point for which: (1) test data is acquired; and (2) reference data is available. In one non-limiting example, the optimal harvest time identified for the reference batch at step 212 may be used to identify when the test batch ought to be harvested at step 224. For example, it may be that the test data acquired after 2, 4 and 6 weeks confirms that the CBG content in the test batch conforms (in a statistically-significant manner) to the CBG model identified at step 218 — for example, assuming the CBG model gave a CBG threshold of 0.15 after 8 weeks, the test batch may then be harvested at the 8 th week of the cultivation period at step 224. In the non-limiting examples in which a correlation has been established between a CBG content in the reference batch and a plant mass, a plant density, a content of THC, a content of CBD, a yield of THC, a yield of CBD and the likes for the reference batch, this additional data may also be used to determine when the plant ought to be harvested. It is appreciated that, by using the CBG model, harvest may be performed without reliance on visual examination of the cannabis plants in the test batch, may be anticipated weeks in advance as long the cultivation of the test batch is in line with that of the reference batch in terms of CBG content, and may also be used to uniformize at least one trait of the cannabis plants being harvested between distinct test batches and in different grow areas, as further described below.

[0056] By controlling the harvest time of the test batch based on the CBG model obtained for the reference batch, and by repeating such control over a plurality of test batches, it is appreciated that at least some traits of the cannabis plants being harvested may be uniformized. That is, in one non limiting example, when harvesting according to the process 200, a variation in a content in at least a subset of cannabis plants of at least one of an average content of THC or an average content of CBD between a first batch and a second batch may be less than about 25%, in some cases less than about 20%, in some cases less than about 15%, in some cases less than about 10%, in some cases less than about 5% and in some cases even less. In other non-limiting examples, when harvesting according to the process 200, a variation in a content in at least a subset of cannabis products of at least one of an average content of THC or an average content of CBD between products obtained from a first test batch of cannabis plants and products obtained from a second test batch of cannabis plants may be less than about 25%, in some cases less than about 20%, in some cases less than about 15%, in some cases less than about 10%, in some cases less than about 5% and in some cases even less.

[0057] The variation in the content in at least a subset of cannabis products of at least one of an average content of THC or an average content of CBD between the first batch and the second batch may be measured as follows:

1. 100 units of a cannabis product are provided — cannabis product as used herein includes cannabis buds and flowers, either fresh or dried, as may be obtained from cannabis plants. One unit of cannabis product correspond to either one bud or one flower.

2. Among the 100 units of the cannabis product provided, two randomized batches of units are identified (i.e., the first batch and the second batch). The randomization method may be any suitable method. The two randomized batches may have any suitable batch size, as long as the size of the batch is consistent with the generation of statistically-significant THC or CBD content data, as described below. For example, the first batch may include 50 buds randomly selected and the second batch may also include 50 buds randomly selected. In other non-limiting examples, the size of the first batch and the second batch need not be identical — for examples the first batch may include 30 buds randomly selected and the second batch may include 20 buds randomly selected.

3. The THC or CBD content is then measured for each unit of each randomized batch to generate an average THC or CBD content for each one of the first batch and the second batch. The measurement of the THC or CBD content may be performed using high pressure liquid chromatography (HPLC), such as with the Agilent InfinityLab Poroshell 120 SB-C18 column (Agilent Technologies, USA).

4. Different windows of variation around the average THC or CBD content for the first batch are then calculated. For example, assuming an average of 22 wt% of THC for the first batch, a variation of 25% around such average corresponds to a THC content between 16.5 wt% and 27.5 wt%, a variation of 20% around such average corresponds to a THC content between 17.6 wt% and 26.4 wt%, a variation of 15% around such average corresponds to a THC content between 18.7 wt% and 25.5 wt%, etc.

5. The average THC or CBD content of the second batch (in wt%) is then compared to the above windows of variation around the average THC or CBD content for the first batch to determine within which window it falls. For example, if the average THC or CBD content for the second batch falls within the window of variation of 10% around the average THC or CBD content for the first batch, the variation in the content of THC or CBD between the first batch and the second batch is less than about 10%.

[0058] Any other suitable trait of the cannabis plants being cultivated may be uniformized in other embodiments, such as but not limited to a content in any other cannabinoid, a yield of THC and/ or CBD per weight of the cannabis plants, a yield of cannabis plants per surface area of the grow area of the respective batch of cannabis plants, a plant mass at harvest per overall irradiance during the cultivation period, a yield of THC/ CBD per plant per overall irradiance over cultivation period, a terpene profile and the likes.

[0059] It will be readily appreciated that, using the CBG model, low variability in terms of THC and/ or CBD content as described above may be obtained between various test batches cultivated: (i) in the same grow area with the same cultivation conditions at different time, (ii) in the same grow areas with distinct cultivation conditions, (ii) in distinct grow areas with the same cultivation conditions or (iv) in distinct grow areas with distinct cultivation conditions. This is reliant at least in part upon the breadth of CBG models available at step 218 as well as how well such CBG models may be matched to the CBG content of the test batches. In other non-limiting examples, the variability between various test batches in the content of any other cannabinoid, as well as the variability between various test batches in any other trait of the cannabis plants and the cannabis products produced therefrom, such as but not limited to a yield of THC and/ or CBD per weight of the cannabis plants, a yield of cannabis plants per surface area of the grow area of the respective batch of cannabis plants, a plant mass at harvest per overall irradiance during the cultivation period, a yield of THC/ CBD per plant per overall irradiance over cultivation period, a terpene profile and the likes, may be reduced or eliminated using the CBG model and the process 200. Therefore, when the test cultivation conditions substantially conform to the reference cultivation conditions (i.e. the entire set of operating, controllable parameters within all relevant cultivation regimen), an operator may rely on the CBG model to determine an optimal harvest time that can maximize either the CBG content in the cannabis plants at the time of harvest, and/ or THC/ CBD content, and/ or the yield of THC/ CBD, as well as any other suitable trait in other embodiments.

[0060] When a deviation is detected at step 222, in an optional non-limiting example an assessment may be performed as to whether the deviation is simply related to a temporal lag in the cultivation of the cannabis plants. That is, in some non-limiting examples, the cultivation of the test batch may, for one reason or another, exhibit a CBG content profile that lags one week behind that of the CBG model identified at step 218 for the reference batch. Yet, factoring this lag of one week into consideration, the CBG content profile of the test batch matches that of the reference batch. In such a case, identifying a new CBG model, instead of keeping the current CBG model that has been identified, may actually decrease the accuracy of the predictions set forth by the process 200, notably in terms of the time at which the test batch ought to be harvested. An algorithm may therefore be used at step 226 to determine whether a deviation is merely the result of a lag in the CBG content profile compared to the CBG model identified at step 218. In the case a lag is indeed identified, an optimal harvest time may still be computed factoring in the lag at step 224. It is appreciated that this algorithm ought to function in an iterative manner, the existence of such lag becoming more accurately identifiable as the number of time points at which the at least one sample of test batch is obtained at step 202 increases. Alternatively, when a deviation is detected at step 222, a control mechanism may be established in an attempt to steer a CBG content profile towards a given CGB model, as further described below.

[0061] In this non-limiting embodiment, with further reference to Fig. 4, the cultivation parameters described above are controllable and they may have an impact on various traits of the batch of cannabis plants being cultivated at step 102 or the cannabis products obtained therefrom when they are modulated, in particular because they may impact the biosynthesis process of THC and CBD, the main commercial cannabinoids in the current cannabis industry. As discussed above, such traits may include, but are not limited to: a quantity / expression profile of CBG, a quantity of THC and/or CBD, a yield of THC and/ or CBD per weight of the cannabis plants, total cannabinoids in the cannabis plants, a yield of cannabis plants per surface area of the grow area of the respective batch of cannabis plants, a height and a density of the cannabis plants, a terpene profile and the likes.

[0062] In one non-limiting embodiment, control of the controllable cultivation parameters may be established via a cannabis cultivation control system 400. The cannabis cultivation control system 400 may implemented on a network-based computer platform, but other types of implementation such as an implementation using one or more stand-alone computers can be used.

[0063] In this embodiment, the cannabis cultivation control system 400 comprises a server 402 which performs data processing functions and manages access to a database 404. The database 404 notably stores the reference data and test data described above. The database 404 may be connected directly to the server 402, however other arrangements are also possible. For instance, the database 404 may be placed at any suitable location, as long as it can be accessed by the necessary network devices to read the data or write data to it. The database 404 may comprise a single information storage unit or several information storage units in other examples. The control system 400 may communicate with at least one workstation 406 at which an operator may interact with the control system 400. The at least one workstation 406 may be a desktop unit, a mobile device such as a Personal Digital Assistant (PDA), a laptop computer and the likes. The at least one workstation 406 communicates with the server 402 over communication links 408. The communication links 408 may be wireline or wireless. It is appreciated that, in this non-limiting embodiment, the process 200 may be implemented on the cannabis cultivation control system 400 and test and reference data may be saved in the database 404. [0064] The cannabis cultivation control system 400 also comprises at least one controller 410 that is connected and communicates at least with a plurality of control subsystems 41¾ ambient environment sensors 414, server 402 and the database 404. In one non-limiting example, the controller 410 may receive inputs from the ambient environment sensors 414 and may output commands to various control systems 41 ¾ as further described below.

[0065] The multiple control subsystems 412 t may comprise subsystems each related to one of the cultivation regimen described above and each configured to monitor and control their respective environmental element within the ambient environment. That is, in some non-limiting examples, there are a lighting subsystem 412i, a temperature subsystem 412 , a humidity subsystem 412 3 , a carbon dioxide subsystem 412 4 , an air flow subsystem 412s and the likes. Any other suitable control subsystem 412i is possible in other non-limiting examples which could be customized to the end-users needs.

[0066] In a first mode of operation, the control subsystems 412 t are configured to maintain a specified cultivation regimen over the entire cultivation period. An operator may therefore define a set of cultivation conditions via the workstation 406, the set of cultivation conditions being stored in the database 404. The control system 400 may then constantly (or at periodic intervals) monitors the ambient parameter via the environment sensors 414 and ensures that the data acquired by the environment sensors for each one of the control subsystems 412 t matches the data for that particular control subsystems 412 t as stored in the database 404. In this first mode of operation, the control system 400 is therefore configured to maintain the status quo in the greenhouse or grow area, such that cultivation conditions defined by the operator remain substantially constant over the entire cultivation period, or during a specific subset of the cultivation, for instance when cultivation conditions change between the vegetative and the flowering phase of the growth of cannabis plants.

[0067] When a deviation is detected at step 222 and it has been optionally determined that such deviation is not the result of a temporal lag at step 226, the control system 400 may then switch to a second mode of operation 300 as shown in Fig. 2. In this non-limiting embodiment, at step 302 a modification to at least one parameter of the set of cultivation parameters to eliminate and / or reduce the deviation is computed. This computation may notably be based on the correlation between the CBG content, the various traits of the cannabis plants (i.e., a THC content, a CBD content, a yield of THC per weight of the at least one sample, a yield of CBD per weight of the at least one sample, etc.) and a given set of cultivation conditions via the database of CBG models. For example, a first CBG model may have been obtained and saved for a given cannabis strain, in a given grow area under a first lighting regimen. In parallel, a second CBG model may have been obtained and saved for the same strain, in the same grow area under a second lighting regimen. All other cultivation regimen being equal, the control system 400 may compute how the change in the lighting regimen impacted the CBG model, i.e. how the change in the lighting regimen modified the CBG content in the cannabis plants over time. Using this computed effect, a mathematical relationship may then be established between a modification to the lighting regimen and a modification to the CBG content in the cannabis plants over time. For example, it may have been found that switching from 22-hour light cycles to 23- hour light cycles between the 3 rd and 5 th week of the cultivation period leads to an increase in CBG content of about 20% at weeks 4 and 5 for a particular strain in a particular grow area, the remaining parameters of the cultivation regimen being the same. Assuming a deviation has been found at step 222 to the effect that the CBG content measured in the test batch is 20% below the expected CBG content derive from the CBG model, the control system 400 may then instruct the lighting subsystem 412i to increase the lighting cycles from 22 hours to 23 hours for 2 weeks at step 304. The control system may act automatically, or in the alternative a notification may be made to the operator via the workstation 406 such that the operator may or may not decide to act on the suggested modification to the lighting regimen. It is appreciated that, in other non-limiting examples, mathematical correlations may be established between CBG content and a variety of cultivation parameters such that, as deviations are detected, the cannabis cultivation control system 400 may suggest more than one possible modification to a variety of cultivation regimen in an attempt to reduce or eliminate the deviation. At step 306, the updated test data pertaining to the modified cultivation conditions is then stored in the database before returning to step 202.

[0068] In the context of the present disclosure, a given cultivation regimen may be optimized to improve substantially any trait any/ or characteristic of the cannabis plants being harvested. In one non-limiting example, the lighting regimen may be optimized to ensure that a yield of the cannabis plants being harvested is at least 300 g/ m 2 of grow area. In this example, the optimal (and controlled) lighting regimen may be defined as a lighting regime that delivers between about 20 and about 60 pmol/ m 2 / s of light to the grow area (specifically, per unit surface of the grow area and per unit time of the grow area), in some cases between about 30 and about 60 pmol/m 2 /s, in some cases between about 35 and about 55 pmol/m 2 /s. [0069] In other non-limiting embodiments, a given cultivation regimen may be controlled using the CBG model (e.g., via steps 302 and 304 above) such that the cannabis plants cultivated at step 102 or the cannabis products produced therefrom after the post-harvest processing steps exhibit desired traits. In one non-limiting example, the lighting regimen may be controlled for a grow area during the vegetative phase such that the batch of cannabis plants within the grow area reaches a pre-determined mass per plant and/ or a pre-determined density per plant before a beginning of the flowering phase or before the cannabis plants substantially enter in the flowering phase, the beginning of the flowering phase being defined as the beginning of the phase during which the plants produces reproductive structures such as flowers or buds or blooms or blossoms. In another non-limiting example, the lighting regimen may be controlled for a grow area during the vegetative phase such that the batch of cannabis plants within the grow area exhibits a pre-determined harvest yield (e.g., a density per square foot of grow area). In some non-limiting examples, the lighting regimen may be controlled for the grow area during the vegetative phase such that the batch of cannabis plants within the grow area reaches a density of at least 300 g per square foot (comprising at least 180 g per square foot of flowers) at harvest, in some cases a density of at least 400 g per square foot (comprising at least 240 g per square foot of flowers) at harvest, in some cases a density of at least 500 g per square foot (comprising at least 300 g per square foot of flowers) at harvest and in some cases even more. At step 202 above, plant mass and/ or plant density and/ or plant height data may be measured at each time point for the reference batch and the test batch along with qualitative data. As such, the cannabis cultivation control system 400 may also correlate plant mass and/ or plant density data with the cultivation conditions up to the flowering phase. In this non-limiting example, the control system 400 may therefore be used to trigger the flowering phase when the cannabis plants have reached a desired mass and/or density and/ or height. The cannabis cultivation control system 400 may also be used to uniformize the mass and/ or density and/ or height of the cannabis plants when the flowering phase is triggered between various batches and various row areas, using the data accumulated in the CBG model database. It is appreciated that, using the cannabis cultivation control system 400, the cultivation of cannabis may advantageously be decoupled from natural day-night cycle variations as well as seasonal variations.

[0070] The lighting regimen may therefore be optimized to reduce operational costs while uniformizing at least some traits of the cannabis plants produced up to harvest. For example, a baseline lighting regimen may first be characterized and then correlated to baseline operational costs (which stem primarily from the lighting regimen), the baseline lighting regimen comprising a baseline lighting intensity and baseline lighting cycles for the vegetative and flowering phases for a given grow area. It may be found that reducing the baseline lighting cycle for the vegetative phase from 24-hour cycles to 22 -hour cycles during the last 2 weeks of the vegetative phase does not substantially impact the mass per plant and/ or the density per plant before a beginning of the flowering phase — for example, the mass per plant and/ or the density per-plant may not decrease by more than 5% with 22 -hour cycles during the last 2 weeks of the vegetative phase when compared to 24-hour cycles during the last 2 weeks of the vegetative phase. Whether or not a decrease of no more than 5% of the mass per plant and/ or the density per-plant during that time period is acceptable ought to take into consideration the cost savings of reducing the lighting regimen from 24-hour cycles to 22 -hour cycles for a 2-week period, which can notably be computed by measuring the amount of time of lighting savings. For example, over the course of 2 weeks (14 days), the cost savings of reducing the lighting regimen from 24-hour cycles to 22-hour cycles are equivalent to 14 x 2 = 28 hours of lighting at the prescribed (baseline) lighting intensity. It is possible to convert this figure into a monetary one by using the costs of lighting the grow area at the prescribed lighting intensity for one hour. An accounting exercise may then be performed to determine whether the proposed reduction of the baseline lighting cycle for 2 weeks makes economic sense, for instance, by comparing the monetary figure of the cost savings to an expected loss of revenue based on a decrease in the mass of the plant or the density of the plant pre-flowering phase, and ultimately the quantity of cannabis plants harvested. Any other suitable calculation may be performed based on the lighting exposure cycle, lighting intensity and the likes, as well as based on any other cultivation regimen or combination of cultivation regimen.

[0071] It is appreciated that by uniformly controlling the lighting regimen over the grow area or over the entire greenhouse, the desired traits and/or features and/or characteristics may also be uniformized in all the cannabis plants cultivated within the grow area or within the greenhouse. That is, the lighting regimen may also be controlled such that cannabis plants in distinct grow areas (e.g. in large greenhouses, etc.) exhibit uniform desired traits and/or features and/or characteristics over all plants of the batch of cannabis plants. In other examples, for example in instance in which genetic differences between the various strains of cannabis plants that may be cultivated in different grow areas mean that the cannabis plants in different grow areas will respond differently to a single lighting regimen, a plurality of lighting regimen may be used to obtain uniform desired traits and/or features and/ or characteristics in all the cannabis plants cultivated within the distinct grow areas, or within the greenhouse, despite the genetic differences between the strains of cannabis plants being cultivated. Said differently, the lighting regimen may be controlled such that the desired traits and/ or features and/ or characteristics are identical or substantially identical between all cannabis plants being grown in a greenhouse, irrespective of whether different strains are being cultivated in the greenhouse. This also allows cannabis plants cultivation to be performed year-round, independent of natural day-night cycle variations.

EXAMPLES

[0072] The examples provided below are for illustrative purposes only and are not meant to limit the scope of the systems and methods described herein.

[0073] A mathematical simulation of small scale cultivation of cannabis plants with a specified set of cultivation parameters (i.e., with a specified cultivation regimen) was developed so as to predict the correlation between an increased cultivation scale (i.e., an increase in the size of the grow area or in the number of grow areas within a given greenhouse) and cultivation parameters and how to improve a quantity and quality of the cannabinoid yield. Quantity is used herein to refer generally to the weight of cannabis plants, or to the weight of a specific part of cannabis plants (e.g., flowers/buds) while quality is used herein to refer to the cannabinoid yield in the plants (e.g., the yield of THC and/or CBD), the terpene profile, the visual aesthetics of the plants and/or flowers/buds and the likes. Vegetative propagation approaches were investigated to scale rapidly and consistently by minimizing variations in the plants due to their different strains and genetics. This necessitated first the investigation of the impact of various controllable cultivation conditions (e.g., lighting regimen, CO2 regimen and the likes) on the cultivation of cannabis plants and the cannabinoid profile of the cannabis plants cultivated on a small cultivation scale (i.e., a grow area of about 5000 square feet comprising about 300 cannabis plants) to produce deterministic results that could then be extrapolated to larger cultivation scales (i.e., grow areas of anywhere between about 10000 square feet and 250,000 square feet and even larger).

[0074] A baseline simulation model was established, in which cultivation conditions are critical because they impact the biosynthesis of THC and CBD. These cultivation conditions are also currently limited by existing cultivation infrastructures (e.g., greenhouse sizes, grow areas sizes, etc.) as well as constraints imposed by the seasonal environmental conditions that affect greenhouse cultivation. The effects of various lighting regimen on the growth of the cannabis plants and on the resulting total mass of the cannabis for the area allocated (biomass) was studied first. Specifically, a controlled lighting regimen was used to modulate flowering of the cannabis plants by limiting the amount of time needed for a plant to naturally flower and produce flowers/buds.

Example 1

[0075] In this example, the simulation of small scale cultivation process design was refined by breaking down the cultivation and harvest process into objects in a dynamic model that can adapt to different cultivation methods. This is done by adding the CBG data collection as the harvest trigger and by breaking down the cultivation and harvest into batches. The correlation between the CBG content, and variations of such content over time during the light cycle was also studied.

[0076] Plants of each growing strain were organized into a batch. The list of plants was assigned a random number and a representative sample size was selected. A random selection methodology was used to determine how many plants of each batch should make up the sample. These plants were sampled at the start of flowering stage, then the tests were repeated every week. The collected samples were dried in a scientific oven (since freshly-cut cannabis flowers start to degrade if they are not dried immediately), the drying data was collected as part of the drying-experiment. The samples were then sent to a third-party laboratory for testing and the resulting data showed different moisture content for the different strains. Calculations were then made and a Harvest Lab spreadsheet was built to run the repeated calculations. The increase and decrease of the CBG content enable one to determine the baseline data for the simulation model. This data was tied-in to previous natural growing results to identify the effects of the new environmental changes on the cultivation. Other biometric data may also be collected at the harvest point (e.g., bud weights, visual appearance, etc.) The wet and dry cannabis flower weight is collected and compared across batches. Also, qualitative notes are made, including but not limited to the size of the flowers, shape of the flowers and terpene profile.

[0077] The new harvest method includes black-out curtains that are used to control the 12-hr cycle of day light. This, combined with measuring the CBG content, enables one to establish a relationship between the day light cycle and the CBG content. The hypothesis was that THC and CBD content in the cannabis plants were affected by the day-light cycle, and that the target of 12-hour day length for a period of 8-10 weeks is sufficient to produce the optimum yields of THC and/or CBD. It was unknown how each strain would behave in the 12-hour cycle, how the plants would grow in the short vegetative stage (bio-mass weight), and how the CBG content would evolve during the flowering stage. The 12-hour cycle was used in conjunction with artificial lighting and light measurements, following plans developed in Example 1. The controlled light cycle was found to be a key component for the cultivation process since it allowed control over the growing stages of the cannabis plants. Using the controlled lighting regimen it is this possible to trigger the flowering stage when the plants have reached the desired bio-mass. This is measured when the plants reach the optimal targeted height and achieve a good vegetative bulk. This also allows cannabis growth to be performed year round, independent of the natural day-night cycle variations.

[0078] The Watt-per-square -meter is an important factor in yield ratio calculation. The effect of harvest timeline on the potency of the cannabinoid yield was uncovered. The data collected on the effect of vegetative cycle length and the flowering cycle length on the yield is used in the herein described simulation model and allows one to better project yield goals for current and future harvests of cannabis. This baseline investigation was critical since there were too many genetic variations between strains of cannabis plants to investigate and there was a need to understand their growing behaviour without any bias.

[0079] In this example, operating conditions are critical because they affect the biosynthesis of THC and CBD. There was a need to understand how to produce the highest bio-mass of cannabis plants using the smallest area occupied by the plant, while balancing the amount of THC and CBD produced from CBG, and its sensitivity to the environment. The focus was specifically on lighting conditions to study the effects of lighting variations on the growth of the cannabis plants and on the resulting total mass of the cannabis for the area allocated (biomass).

[0080] In this example, the CBG content was measured for different plant batches at different stages of growth. Data on a number of strains was collected which included THC only plants and THC:CBD plants. It was found that THC and THC:CBD plants produced CBG during their first weeks of flowering. The concentrations of CBG were different for each strain of plants. These tests were repeated once a week for the same sample size in and a trend in CBG concentrations was established. It was found that before any jump in THC concentration, the plants would increase the CBG concentration first. As the plants approach their peak levels of THC or THC:CBD, the CBG concentration would level off and not experience any further jumps. For example, with reference to Fig. 3, the THC content in the cannabis strain NLxBB tested increased from 11 wt% to 25 wt% between the 4 th and 6 th week of cultivation while, during the same time period, the CBG content decreased from 2 wt% to 1 wt%. Then, the THC content only increases from 25 wt% to 27 wt% between the 6 th and 8 th week of cultivation, that is an increase in THC content of 8% over 2 weeks. In this example, the CBG threshold is 1 wt% and after such threshold has been reached, the content of THC and/ or CBD does not increase by more than 10% over the course of the next 2 weeks.

[0081] In another example, the CBG content was measured for different plant batches of the NLxBB cannabis strain at different stages of growth. Relevant data is provided in Table 1 below. In this example, the THC content in the cannabis strain NLxBB tested increased from 9.77 wt% to 23.91 wt% between the 4 th and 8 th week of cultivation, with the CBG content decreasing from 1.02 wt% to 0.83 wt% between the 7 th and 8 th week of cultivation. The CBG threshold is defined in this example at 0.83% since (1) after week 8 the content of THC does not significantly increase in the plants and (2) before week 8 the overall quantity of THC to be harvested from the plants would not be large enough to make economic sense.

Table 1: CBG, THC and CBD content in the NLxBB strain between week 3 and week 8 of cultivation.

[0082] In another example, the CBG contents were measured for different plant batches of the SS cannabis strain at different stages of growth. Relevant data is provided in Table 3 below. In this example, the THC content in the cannabis strain SS tested increased from 5.17 wt% to 6.23 wt% between the 6 th and 8 th week of cultivation, the CBD content increased from 8.37 wt% to 10.59 wt% over the same period of time while the CBG content decreased from 1.60 wt% to 0.97 wt% also over the same period of time. CBG threshold is defined in this example at 0.97% since (1) after week 8 the content of THC and/or CBD does not significantly increase in the plants and (2) before week 8 the overall quantity of THC and/ or CBD to be harvested from the plants would not be large enough to make economic sense

Table 2: CBG, THC and CBD content in the SS strain between week 6 and week 8 of cultivation.

Example 2

[0083] The indicator used to monitor the effects of new environmental factors (light cycle and CO2 levels) is the harvest yield (grams of dry product per plant), with a target of 300-500g per plant. The optimal quantity of light needed (in micro mol/ m 2 / s — the amount of light hitting a surface that is in the range of 400-700 nanometers) over a given period of time (e.g., a month) to achieve a certain yield of cannabis plants was also identified, as shown below. The light quantity was measured using an Apogee Quantum SQ-214-SS sensor. Cannabis plants (of the NLxBB strain) were grown as batches within two separate grow areas and the effect of different light quantities (in terms of month average per second) on the yield of the cannabis plants at harvest was assessed.

Table 3: Yield of cannabis plants for various average light quantities.

[0084] In this example, the optimal light quantity was between 35 and 55 micro mol/m 2 /s because it resulted in a yield of at least 300 g/ m 2 of buds. A light quantity of 21.28 pmol/ m 2 / s was not sufficient to reach a target yield threshold of at least 300 g/m 2 of buds.

Example 3

[0085] In this example, two distinct batches of cannabis plants were grown in similar, or substantially similar cultivation conditions. The first batch comprised 50 plants, was grown in lighting conditions that were not optimized as per Example 1 and without performing any measurement of CBG content in the plants such that the plants were not harvested at a time determined at least in part based a CBG content in the plants. The plants of the first batch were harvested in October. The second batch comprised 57 plants, was grown in lighting conditions that were optimized as per Example 1 and using the CBG model to identify harvest time. The plants of the second batch were harvested in September.

Table 4: Yield of cannabis plants for optimized (i.e., optimal lighting, use of CBG model — Batch 2) and non-optimized (non-optimal lighting, no use of CBG model — Batch 1).

[0086] The yield (for the buds and trims) of the batch cultivated and harvested according to optimized lighting conditions and using the CBG model was increased compared to the batch that was not cultivated and harvested according to the CBG model, with the Batch 2 exhibiting an increase in the yield compared to batch 1 of 129.5%. It was found from statistical analyses, that the optimal quantity of light needed was generally between 30-50 pmol/ m 2 / s (micromole per square meter per second - amount of light hitting a surface that is in the range of 400-700 nanometers). On average, the harvest yields were below the target of 300g/plant. This led to targeting smaller plants sizes with denser biomass on them

[0087] Since larger growing area have more losses/gains in temperature and humidity, resulting in more process noise and unpredictability compared to smaller greenhouses, the herein described monitoring system takes into consideration different external noise factors when analyzing the data and greenhouse operational parameters.

[0088] Note that titles or subtitles may be used throughout the present disclosure for convenience of a reader, but in no way these should limit the scope of the invention. Moreover, certain theories may be proposed and disclosed herein; however, in no way they, whether they are right or wrong, should limit the scope of the invention so long as the invention is practiced according to the present disclosure without regard for any particular theory or scheme of action.

[0089] All references cited throughout the specification are hereby incorporated by reference in their entirety for all purposes.

[0090] It will be understood by those of skill in the art that throughout the present specification, the term “a” used before a term encompasses embodiments containing one or more to what the term refers. It will also be understood by those of skill in the art that throughout the present specification, the term “comprising”, which is synonymous with “including,” “containing,” or “characterized by,” is inclusive or open-ended and does not exclude additional, un-recited elements or method steps.

[0091] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In the case of conflict, the present document, including definitions will control.

[0092] As used in the present disclosure, the terms “around”, “about” or “approximately” shall generally mean within the error margin generally accepted in the art. Hence, numerical quantities given herein generally include such error margin such that the terms “around”, “about” or “approximately” can be inferred if not expressly stated.

[0093] Although various embodiments of the disclosure have been described and illustrated, it will be apparent to those skilled in the art in light of the present description that numerous modifications and variations can be made. The scope of the invention is defined more particularly in the appended claims.