GB2594524A - Systems and methods for monitoring pollination activity - Google Patents

Systems and methods for monitoring pollination activity Download PDF

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Publication number
GB2594524A
GB2594524A GB2006508.2A GB202006508A GB2594524A GB 2594524 A GB2594524 A GB 2594524A GB 202006508 A GB202006508 A GB 202006508A GB 2594524 A GB2594524 A GB 2594524A
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GB
United Kingdom
Prior art keywords
pollen
image
characteristic
pollinators
wavelength
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
GB2006508.2A
Other versions
GB2594524B (en
GB202006508D0 (en
Inventor
Edwards Murphy Fiona
Whelan Padraig
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Apisprotect Ltd
Original Assignee
Apisprotect Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Apisprotect Ltd filed Critical Apisprotect Ltd
Priority to GB2006508.2A priority Critical patent/GB2594524B/en
Publication of GB202006508D0 publication Critical patent/GB202006508D0/en
Priority to PCT/EP2021/060552 priority patent/WO2021219486A1/en
Publication of GB2594524A publication Critical patent/GB2594524A/en
Application granted granted Critical
Publication of GB2594524B publication Critical patent/GB2594524B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/60Type of objects
    • G06V20/69Microscopic objects, e.g. biological cells or cellular parts
    • AHUMAN NECESSITIES
    • A01AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01KANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K55/00Bee-smokers; Bee-keepers' accessories, e.g. veils
    • AHUMAN NECESSITIES
    • A01AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01KANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K29/00Other apparatus for animal husbandry
    • A01K29/005Monitoring or measuring activity, e.g. detecting heat or mating
    • G01N15/1433
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/22Matching criteria, e.g. proximity measures
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/20Image preprocessing
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/40Extraction of image or video features
    • G06V10/56Extraction of image or video features relating to colour
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/40Extraction of image or video features
    • G06V10/60Extraction of image or video features relating to illumination properties, e.g. using a reflectance or lighting model
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G01N15/01
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10Investigating individual particles
    • G01N15/14Electro-optical investigation, e.g. flow cytometers
    • G01N2015/1486Counting the particles

Abstract

A method of monitoring pollination activity comprising capturing an image 210, analysing the image 220, detecting if the image contains at least one pollinator and at least one pollen element 230, determining a characteristic of the pollen element 240 and comparing the characteristic to one or more predefined characteristics to determine the source of the pollen element (310, figure 3). The image may be captured near a hive. The pollen element may be detected by determining if the image contains a pollen basket or scopa having pollen disposed thereon. The pollen characteristic may comprise one or more colours, wavelengths, or a dimension. The identified source may be a type of plant or a geographical location. The number of pollinators meeting a characteristic may be counted and a user notified in dependence on a predetermined threshold number. Data relating to the number of pollen baskets, pollinators, and pollen elements may be recorded in a memory and a report comprising one or more of the data may be produced. A system (100, figure 1) comprising an image capturing device (130, figure 1) for capturing an image and one or more processors (110, figure 1), optionally utilising machine learning algorithms, is also claimed.

Description

Making such a determination collectively for a colony may be useful in evaluating pollination optimisation; such embodiments are best presented in relation to FIGs. 4-8.
FIG. 4 provides an exemplary method 400 substantially similar to the method 300 of FIG. 3, but further comprising determining 410 a number of pollinators in the captured image having a pollen element with a characteristic which substantially corresponds to a predefined characteristic. For example, and as presented in FIG. 5, it may be desirable to determine 510 the colour(s) or wavelength of pollen in the pollen basket(s) of pollinator(s) in the captured image, compare 520 the determined colour(s) or wavelength(s) of the pollen in the pollen basket(s) to one or more predefined colours or wavelengths to determine the source of the pollen element, and then determine 530 a number of pollinators having a pollen element in the captured image with pollen of a colour or wavelength which substantially corresponds to a predefined colour, wavelength, or which falls within a range of wavelengths. It will be understood that in certain embodiments when the determined wavelength is compared with more than one predefined wavelength, the determined wavelength may be compared with a range of wavelengths. That is, whether the determined wavelength of pollen falls between a first predefined wavelength and a second predefined wavelength. What is more, the process of comparison may be repeated iteratively as desired. This would facilitate calculation of a percentage of pollinators foraging from a particular type of plant, and therefore possibly a percentage of pollinators foraging from a grower's land. As elucidated above, such determinations are crucial in evaluating pollination optimisation. If the percentage of pollinators with a target pollen colour or target wavelength is less than a predefined threshold, this would indicate that the pollinators are not working as they should and grower intervention may be required. For example, intervention may comprise one or more of: replacing a colony or colonies, adding a further colony or colonies, identifying the existence of competing foragers, or simply removing pollinators if the flowering season has ended. If the percentage of pollinators (e.g.) with pollen baskets containing a target pollen colour or target wavelength is greater than a predefined threshold, this would indicate to the grower that the pollinators are working well. A target wavelength may comprise a single pollen wavelength, or any pollen wavelength falling within a predefined range of wavelengths. When the target wavelength comprises a single pollen wavelength, the comparison to a predefined wavelength may be made to within a predefined margin, i.e. plus or minus
GB2006508.2A 2020-05-01 2020-05-01 Systems and methods for monitoring pollination activity Active GB2594524B (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
GB2006508.2A GB2594524B (en) 2020-05-01 2020-05-01 Systems and methods for monitoring pollination activity
PCT/EP2021/060552 WO2021219486A1 (en) 2020-05-01 2021-04-22 Systems and methods for monitoring pollination activity

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
GB2006508.2A GB2594524B (en) 2020-05-01 2020-05-01 Systems and methods for monitoring pollination activity

Publications (3)

Publication Number Publication Date
GB202006508D0 GB202006508D0 (en) 2020-06-17
GB2594524A true GB2594524A (en) 2021-11-03
GB2594524B GB2594524B (en) 2022-04-27

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
GB2006508.2A Active GB2594524B (en) 2020-05-01 2020-05-01 Systems and methods for monitoring pollination activity

Country Status (2)

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GB (1) GB2594524B (en)
WO (1) WO2021219486A1 (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117495947A (en) * 2023-10-23 2024-02-02 佛山市天下谷科技有限公司 Pineapple flower-dropping method, electronic equipment and computer readable storage medium

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019236843A1 (en) * 2018-06-06 2019-12-12 Monsanto Technology Llc Systems and methods for distinguishing fertile plant specimens from sterile plant specimens

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150123801A1 (en) * 2013-11-01 2015-05-07 Eltopia Communications, LLC Monitoring the state of a beehive
CN108872096A (en) * 2018-07-11 2018-11-23 中国农业科学院蜜蜂研究所 A kind of multifarious measurement method of honeybee herborization pollen

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019236843A1 (en) * 2018-06-06 2019-12-12 Monsanto Technology Llc Systems and methods for distinguishing fertile plant specimens from sterile plant specimens

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
C. Yang and J. Collins, "Deep Learning for Pollen Sac Detection and Measurement on Honeybee Monitoring Video," 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ), Dunedin, New Zealand, 2019, pp. 1-6, doi: 10.1109/IVCNZ48456.2019.8961011 *
F. E. Murphy, M. Magno, L. O'Leary, K. Troy, P. Whelan and E. M. Popovici, "Big brother for bees (3B) Energy neutral platform for remote monitoring of beehive imagery and sound," 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI), Gallipoli, 2015, pp. 106-111, doi: 10.110 *
J. Marstaller, F. Tausch and S. Stock, "DeepBees - Building and Scaling Convolutional Neuronal Nets For Fast and Large-Scale Visual Monitoring of Bee Hives," 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2019, pp. 271-278, doi: 10.1109/ICCVW.2019.0 *

Also Published As

Publication number Publication date
WO2021219486A1 (en) 2021-11-04
GB2594524B (en) 2022-04-27
GB202006508D0 (en) 2020-06-17

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