IP Library Granted Patent US 12,361,343
Granted Patent B2
US 12,361,343 · App. 18/165,821 · Granted Jul 15, 2025

Automated placement of plant varieties for optimum performance within a grow space subject to environmental condition variability

Inventors: Sudnya Diamos (Menlo Park, CA); A Samuel Pottinger (Emeryville, CA); Zachary Duncan Swafford (Woodside, CA)
Assignee: MJNN LLC
G06Q10/04G06Q10/06313A01G9/00
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Quick Facts
Patent No.
US 12,361,343
App. No.
18/165,821
Granted
Jul 15, 2025
Kind
B2
Abstract

Systems, methods and computer-readable media are provided for determining optimum placement, within a grow space, of plants of different varieties. Sets of reference environmental setpoints each correspond to a desired predicted performance of a respective plant variety. Environmental condition metrics at different positions within the grow space are determined. An allocation of the plants is determined among different volumetric regions within the grow space based on predicted performance of the plant varieties as a function of position within the grow space.

Claims (37)

1. One or more non-transitory computer-readable media storing instructions for allocating placement, within a grow space, of plants of one or more varieties of a plurality of plant varieties including a first variety and a second variety different from the first variety, wherein the instructions, when executed by one or more computing devices, cause at least one of the one or more computing devices to:

a. based on an allocation of the plants of the one or more plant varieties among different volumetric regions within the grow space, wherein the different regions include a first region and a second region different from the first region:

i. cause movement of a first receptacle support of a plurality of receptacle supports to the first region; and

ii. cause movement of a second receptacle support of the plurality of receptacle supports to the second region,

b. wherein the first and second receptacle supports respectively correspond to first plants of the first variety and second plants of the second variety that are respectively allocated to the first region and the second region, and

c. wherein the allocation is based on predicted performance of the one or more plant varieties as a function of one or more environmental conditions at different positions within the grow space.

2. The one or more computer-readable media of claim 1 , wherein each of the different regions encompasses one or more receptacle supports of the plurality of receptacle supports, and each receptacle support is operable to support a plurality of the plants.

3. The one or more computer-readable media of claim 1 , wherein each region of the different volumetric regions corresponds to a set of environmental conditions.

4. The one or more computer-readable media of claim 1 , wherein the first receptacle support is also operable to support plants of another plant variety.

5. The one or more computer-readable media of claim 1 , wherein the instructions, when executed, cause movement to the first region of a third receptacle support of the plurality of receptacle supports, which is operable to support first plants of the first plant variety allocated to the first region.

6. The one or more computer-readable media of claim 1 , wherein, in response to a change in environmental conditions, the allocation of the plants of the one or more plant varieties among the different regions based on predicted performance of the one or more plant varieties within the grow space is changed.

7. The one or more computer-readable media of claim 6 , storing further instructions that, when executed, cause movement from the first region to a third region of the first receptacle support supporting the first plants of the first plant variety that were allocated to the first region during a first allocation and then reallocated to the third region based upon the changed allocation.

8. The one or more computer-readable media of claim 1 , wherein the plurality of receptacle supports comprises a plurality of grow towers.

9. The one or more computer-readable media of claim 1 , wherein the allocation is based at least in part upon the Hungarian assignment algorithm.

10. The one or more computer-readable media of claim 1 , wherein the one or more environmental conditions correspond to at least one of temperature, relative humidity, or air flow.

11. The one or more computer-readable media of claim 1 , wherein the predicted performance relates to at least one of growth rate, harvest weight yield, or energy cost.

12. The one or more computer-readable media of claim 1 , wherein the environmental conditions are based at least in part upon sensor data.

13. The one or more computer-readable media of claim 1 , wherein each of the one or more sets of reference environmental setpoints is determined by an optimization algorithm.

14. The one or more computer-readable media of claim 13 , wherein the optimization algorithm uses machine learning.

15. The one or more computer-readable media of claim 1 , wherein the grow space has a controlled agricultural environment.

16. The one or more computer-readable media of claim 1 , wherein the allocation is based at least in part upon maximization of overall predicted performance of the plants.

17. A method for determining placement, within a grow space, of plants of one or more varieties of a plurality of plant varieties including a first variety and a second variety different from the first variety, the method comprising:

a. accessing, by a processor, environmental condition metrics at different positions within the grow space; and

b. determining, by a processor, an allocation of the plants of the one or more plant varieties among different volumetric regions within the grow space based on predicted performance of the one or more plant varieties as a function of the environmental condition metrics at different positions within the grow space, wherein the different regions include a first region and a second region different from the first region, wherein based on the allocation:

i. a first receptacle support of a plurality of receptacle supports is moved to the first region, wherein the first receptacle support supports first plants of the first variety that are allocated to the first region, and

ii. a second receptacle support of the plurality of receptacle supports is moved to the second region, wherein the second receptacle support supports second plants of the second variety that are allocated to the second region.

18. A system for determining allocation, within a grow space, of plants of one or more varieties of a plurality of plant varieties including a first variety and a second variety different from the first variety, the system comprising:

one or more memories storing instructions;

one or more processors, operatively coupled to the one or more memories, that execute the instructions to cause the system to:

a. access environmental condition metrics at different positions within the grow space; and

b. determine an allocation of the plants of the one or more plant varieties among different volumetric regions within the grow space based on predicted performance of the one or more plant varieties as a function of the environmental condition metrics at different positions within the grow space, wherein the different regions include a first region and a second region different from the first region, wherein based on the allocation:

i. a first receptacle support of a plurality of receptacle supports is moved to the first region, wherein the first receptacle support corresponds to first plants of the first variety that are allocated to the first region, and

ii. a second receptacle support of the plurality of receptacle supports is moved to the second region, wherein the second receptacle support corresponds to second plants of the second variety that are allocated to the second region, wherein each of the different regions encompasses one or more receptacle supports of the plurality of receptacle supports, and each receptacle support is operable to support a plurality of the plants.

19. The one or more non-transitory computer-readable media of claim 1 , wherein: each set of one or more sets of one or more reference environmental setpoints corresponds to a desired predicted performance of a respective plant variety of the plurality of varieties, and

the allocation is further based on a relationship between the one or more environmental conditions at different positions and the one or more sets of one or more reference environmental setpoints.

20. The method of claim 17 , further comprising accessing, by a processor, one or more sets of one or more reference environmental setpoints, wherein each of the one or more sets corresponds to a desired predicted performance of a respective plant variety of the plurality of varieties, and the allocation is further based on the one or more sets of one or more reference environmental setpoints.

21. The system of claim 18 , wherein the one or more memories comprise instructions to cause the system to access one or more sets of one or more reference environmental setpoints, wherein each of the one or more sets corresponds to a desired predicted performance of a respective plant variety of the plurality of varieties, and the allocation is further based on the one or more sets of one or more reference environmental setpoints.

Assignments (3)
TERMINATION AND RELEASE OF SECURITY INTEREST IN PATENTS AT REEL 070364/FRAME 0725 Recorded May 30, 2025
From: ONE MADISON GROUP – PLENTY II, LLC, AS COLLATERAL AGENT
To: MJNN LLC
Reel/Frame 071468/0487 →
SECURITY INTEREST Recorded Feb 28, 2025
From: MJNN LLC
To: ONE MADISON GROUP – PLENTY II, LLC, AS COLLATERAL AGENT
Reel/Frame 070364/0725 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 26, 2025
From: DIAMOS, SUDNYA; POTTINGER, A. SAMUEL; SWAFFORD, ZACHARY DUNCAN
To: MJNN LLC
Reel/Frame 070334/0230 →
Continuity (3)
Continuation 16861984 · Apr 29, 2020
Provisional Application 62842034 · May 2, 2019
Related Publication 20230186187A1 · Jun 15, 2023
References Cited (122)
US 4015366A · Hall, III · 1977 [cited by applicant]
US 4195441A · Baldwin · 1980 [cited by applicant]
US 4255897A · Ruthner · 1981 [cited by applicant]
US RE31023E · Hall, III · 1982 [cited by applicant]
US 4569150A · Carlson et al. · 1986 [cited by applicant]
US 4856227A · Oglevee et al. · 1989 [cited by applicant]
US 4926585A · Dreschel · 1990 [cited by applicant]
US 4992942A · Bauerle et al. · 1991 [cited by applicant]
US 5392611A · Assaf et al. · 1995 [cited by applicant]
US 5617672A · Garrett · 1997 [cited by applicant]
US 5841883A · Kono et al. · 1998 [cited by applicant]
US 5870302A · Oliver · 1999 [cited by applicant]
US 6065245A · Seawright · 2000 [cited by applicant]
US 6397162B1 · Ton · 2002 [cited by applicant]
US 7206772B2 · Tolley · 2007 [cited by applicant]
US 7617057B2 · May et al. · 2009 [cited by applicant]
US 7987632B2 · May et al. · 2011 [cited by applicant]
US 9030549B2 · Redden · 2015 [cited by applicant]
US 9064173B2 · Redden · 2015 [cited by applicant]
US 9095554B2 · Lewis et al. · 2015 [cited by applicant]
US 9370164B2 · Lewis et al. · 2016 [cited by applicant]
US 9565812B2 · Wilson · 2017 [cited by applicant]
US 9642317B2 · Lewis et al. · 2017 [cited by applicant]
US 9658201B2 · Redden et al. · 2017 [cited by applicant]
US 9717171B2 · Redden et al. · 2017 [cited by applicant]
US 9756771B2 · Redden · 2017 [cited by applicant]
US 9785886B1 · Andoni et al. · 2017 [cited by applicant]
US 9955552B2 · Ashdown et al. · 2018 [cited by applicant]
US 10008035B1 · Redden et al. · 2018 [cited by applicant]
US 10034435B2 · Helene et al. · 2018 [cited by applicant]
US 10165734B1 · Shelor et al. · 2019 [cited by applicant]
US 10602678B2 · Schurter et al. · 2020 [cited by applicant]
US 10980187B2 · Conrad · 2021 [cited by examiner]
US 10990875B2 · Panda · 2021 [cited by examiner]
US 11096337B1 · Wilson et al. · 2021 [cited by applicant]
US 11219169B2 · Zapalac · 2022 [cited by applicant]
US 11483981B1 · Lo et al. · 2022 [cited by applicant]
US 20030188477A1 · Pasternak et al. · 2003 [cited by applicant]
US 20040112077A1 · Forkosh et al. · 2004 [cited by applicant]
US 20040194371A1 · Kinnis · 2004 [cited by applicant]
US 20080148630A1 · Ryan et al. · 2008 [cited by applicant]
US 20080271367A1 · Huhta-Koivisto et al. · 2008 [cited by applicant]
US 20090199470A1 · Capen et al. · 2009 [cited by applicant]
US 20110120002A1 · Pettibone · 2011 [cited by examiner]
US 20120277117A1 · Zayed et al. · 2012 [cited by applicant]
US 20140007502A1 · Rueegg · 2014 [cited by applicant]
US 20140298511A1 · Lewis et al. · 2014 [cited by applicant]
US 20150027040A1 · Redden · 2015 [cited by applicant]
US 20150027041A1 · Redden · 2015 [cited by applicant]
US 20150027043A1 · Redden · 2015 [cited by applicant]
US 20150027044A1 · Redden · 2015 [cited by applicant]
US 20150223409A1 · Abahusayn · 2015 [cited by applicant]
US 20150282440A1 · Shelor · 2015 [cited by applicant]
US 20150342133A1 · Nakajima · 2015 [cited by examiner]
US 20150366154A1 · Lewis et al. · 2015 [cited by applicant]
US 20160000020A1 · Sugimoto · 2016 [cited by applicant]
US 20160148104A1 · Itzhaky et al. · 2016 [cited by applicant]
US 20160239709A1 · Shriver · 2016 [cited by examiner]
US 20160255778A1 · Redden et al. · 2016 [cited by applicant]
US 20170039425A1 · Itzhaky et al. · 2017 [cited by applicant]
US 20170105358A1 · Wilson · 2017 [cited by applicant]
US 20170146226A1 · Storey et al. · 2017 [cited by applicant]
US 20170161560A1 · Itzhaky et al. · 2017 [cited by applicant]
US 20170202170A1 · Lewis et al. · 2017 [cited by applicant]
US 20170206415A1 · Redden · 2017 [cited by applicant]
US 20170219711A1 · Redden et al. · 2017 [cited by applicant]
US 20170259079A1 · Grajcar et al. · 2017 [cited by applicant]
US 20170290260A1 · Redden et al. · 2017 [cited by applicant]
US 20170332544A1 · Conrad et al. · 2017 [cited by applicant]
US 20180014471A1 · Jensen et al. · 2018 [cited by applicant]
US 20180014485A1 · Whitcher · 2018 [cited by examiner]
US 20180014486A1 · Creechley et al. · 2018 [cited by applicant]
US 20180064055A1 · Lewis et al. · 2018 [cited by applicant]
US 20180116094A1 · Redden · 2018 [cited by applicant]
US 20180132441A1 · Harker · 2018 [cited by examiner]
US 20180206416A1 · Zimmerman et al. · 2018 [cited by applicant]
US 20180295783A1 · Alexander · 2018 [cited by examiner]
US 20190116739A1 · Lys et al. · 2019 [cited by applicant]
US 20190133052A1 · Carson · 2019 [cited by applicant]
US 20190170718A1 · Miresmailli · 2019 [cited by examiner]
US 20190259108A1 · Bongartz · 2019 [cited by examiner]
US 20200000045A1 · Shelor et al. · 2020 [cited by applicant]
US 20200110933A1 · Gionet, Jr. et al. · 2020 [cited by applicant]
US 20200236862A1 · Lys et al. · 2020 [cited by applicant]
AU 2018282639A1 · 2020 [cited by examiner]
CN 106134851A · 2016 [cited by applicant]
EP 0275712A1 · 1988 [cited by applicant]
EP 0361534A2 · 1990 [cited by applicant]
EP 0476929A2 · 1992 [cited by applicant]
EP 3366114A1 · 2018 [cited by applicant]
FI 108698 · 1999 [cited by applicant]
KR 20130041702A · 2013 [cited by applicant]
KR 20160028439A · 2016 [cited by applicant]
WO 9008460A1 · 1990 [cited by applicant]
WO 2017162254A1 · 2017 [cited by applicant]
WO 2019240572A1 · 2019 [cited by applicant]
Martha J. Carney, Patricia Venetucci, Esther Gesick, LED Lighting in Controlled Environment Agriculture, Outsourced Innovation, LLC, 2015. (Year: 2015). [cited by examiner]
“Sklearn.ensemble.AdaBoostClassifier.” (http://lijiancheng0614.github.io/scikit-learn/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) 2010-2016; 6 pages. [cited by applicant]
Carney et al. LED Lighting in Controlled Environment Agriculture, Outsourced Innovation, LLC, Aug. 2015, 50 pages. [cited by applicant]
Dahl et al., GPU-Based Deep Learning Inference: A Performance and Power Analysis, NVidia Whitepaper, Nov. 2015; 12 pages. [cited by applicant]
Hardaker, J.B, The Assignment Technique: Some Agricultural Applications of a Simple Optimizing Procedure, 12 pages. [cited by applicant]
Murphy, Kevin. “A Brief Introduction to Reinforcement Learning.” A Brief Introduction to Reinforcement Learning, University of British Columbia, 1998, www.cs.ubc.ca/˜murphyk/Bayes/pomdp.html; 1 page. [cited by applicant]
Nvidia, GPU-Based Deep Learning Inference: A performance and Power Analysis, Nov. 2015, 12 pages. [cited by applicant]
PCT International Search Report and Written Opinion mailed Jan. 28, 2020; in patent application No. PCT/US2019/055064, 9 pgs. [cited by applicant]
Riddle Patricia J., “Genetic Algorithms.” Computer Science 760, University of Auckland, 2012, www.cs.auckland.ac.nz/courses/compsci709s2c/lectures/Pat.d/760-GAs.pdf; 54 pages. [cited by applicant]
Shiffman, Daniel, The Nature of Code; The Magic Book Project, Chapter 10, 2012; 40 pages. [cited by applicant]
Shiffman, Daniel, The Nature of Code; The Magic Book Project, Chapter 9, 2012; 60 pages. [cited by applicant]
“Greenhouse.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/greenhouse. Accessed Jan. 11, 2023. (Year: 2023). [cited by applicant]
“Soil Mechanics.” Encyclopedia Britannica, Encyclopedia Britannica, Inc., 2016, https://www.britannica.com/science/soil-mechanics. (Year: 2016). [cited by applicant]
10 tips on Greenhouse Temperature Control for Gardeners. Greenhouse Coverings. (2022, April 5). https://farmplasticsupply.com/blog/greenhouse-coverings/ 1 0-ti ps-on-g reen house-temperature-control-for -gardeners#: - :… [cited by applicant]
DeKorne, Clayton. “Latent Cooling Loads.” JLC Online, Nov. 24, 2020, https://www.jlconline.com/how-to/hvac/latent-cooling-loads_o#:-:text=When%20water%20evaporates%20or%20condenses,attractions%20between%20molecules%20ar… [cited by applicant]
Evapotranspiration and the Water Cycle Completed. United States Geological Survey, Jun. 12, 2018, www.usgs.gov/special-topics/water-science-school/science/evapotranspiration-and-water-cycle#overview. (Year: 2018). [cited by applicant]
Final Office Action mailed Jan. 30, 2024 in U.S. Appl. No. 17/215,318, 38 pages. [cited by applicant]
Final Office Action mailed Jul. 3, 2023 in U.S. Appl. No. 17/281,000, 23 pages. [cited by applicant]
Fung, Canis. “What Is the Difference between Latent and Sensible Loads in Radiant Cooling?”, Mar. 9, 2018, https://www.linkedin.com/pulse/what-difference-between-latent-sensible-loads-radiant-canis-fung#:-:text=Regardin… [cited by applicant]
Morey, Richard D.; Romeijn, Jan-Willem; Rouder, Jeffrey N. (2016). “The philosophy of Bayes factors and the quantification of statistical evidence”. Journal of Mathematical Psychology. 72: 6-18. doi: 10.1016/j .jmp.2015… [cited by applicant]
Mukherjee, Nikhilesh. “Thermodynamic System and Control Volume: A Short Note.” Linked In, https://www.linkedin.com/pulse/thermodynamic-system-control-volume-short-note-nikhilesh-mukherjee?trk=pulse-article#:-:text=the%2… [cited by applicant]
Non-Final Office Action mailed Aug. 4, 2023 in U.S. Appl. No. 17/215,318, 31 pages. [cited by applicant]
Non-Final Office Action mailed Jan. 16, 2024 in U.S. Appl. No. 17/281,000, 29 pages. [cited by applicant]
Non-Final Office Action mailed Jan. 27, 2023 in U.S. Appl. No. 17/281,000, 31 pages. [cited by applicant]
Nyberg, Roger G., et al. “Inter-Rater Reliability in Determining the Types of Vegetation on Railway Trackbeds.” Lecture Notes in Computer Science, 2015, pp. 379-390., https://doi.org/10.1007/978-3-319-26187-4_36. (Year:… [cited by applicant]
Reducing Humidity in the Greenhouse. Center for Agriculture, Food, and the Environment, Mar. 30, 2017, https://ag.umass.edu/greenhouse-floriculture/fact-sheets/reducing-humidity-in-greenhouse#:-:text=Puddling%20water%20… [cited by applicant]