WO2022226601A1

WO2022226601A1 - Handheld fibre test device

Info

Publication number: WO2022226601A1
Application number: PCT/AU2022/050400
Authority: WO
Inventors: Vasiliki STAIKOPOULOS; Benjamin James PULLEN; Jamie Roy Sherrah; John Andrew KOZUB; Hayden FAULKNER
Original assignee: Bio-Optics Australia Pty Ltd
Priority date: 2021-04-30
Filing date: 2022-04-29
Publication date: 2022-11-03
Also published as: AU2022266850A1

Abstract

Apparatus for real-time measurement of animal fibre characteristics comprising: a housing, comprising: a sample window adapted to receive a sample of animal fibres; an optical imaging system, adapted to image animal fibres located on or adjacent the sample window; an imaging sensor to generate image data of the imaged animal fibres; a processor adapted to receive the image data and analyse the image data to determine at least one or more characteristics of the imaged animal fibres; a hinged portion hingedly fixed to the housing and resiliently biased into engagement with the housing, the hinged portion comprising: a light source adapted to illuminate sample animal fibres located on or adjacent the sample window. Also disclosed are methods for testing of animal fibres.

Description

HANDHELD FIBRE TEST DEVICE

Technical Field

[0001] The present invention relates to apparatus, device and methods for automated testing of fibres and in particular to apparatus, device and methods for automated testing of the characteristics and qualities of animal fibres.

[0002] The invention has been developed primarily for use in handheld apparatus, devices, and methods of usage and operation thereof for real-time automated testing of the characteristics and qualities of animal fibres including, for example, sheep wool, alpaca hair, angora (rabbit) hair, mohair (goat), or cashmere (goat) wool, either in the field or shearing shed for rapid fibre classification and will be described hereinafter with reference to this application. However, it will be appreciated that the invention is not limited to this particular field of use.

Background

[0003] Any discussion of the background art throughout the specification should in no way be considered as an admission that such background art is prior art, nor that such background art is widely known or forms part of the common general knowledge in the field in Australia or worldwide.

[0004] All references, including any patents or patent applications, cited in this specification are hereby incorporated by reference. No admission is made that any reference constitutes prior art. The discussion of the references states what their authors assert, and the applicants reserve the right to challenge the accuracy and pertinence of the cited documents. It will be clearly understood that, although a number of prior art publications are referred to herein, this reference does not constitute an admission that any of these documents forms part of the common general knowledge in the art, in Australia or in any other country.

[0005] The Australian wool market exports approximately $3.5 billion dollars per annum, and approximately 70-80% of the value of that wool is determined by the diameter of its fibre. The finer the fleece, the higher the price. Current industry practices include visual inspection of the fleece at shearing time (manual wool classing), which is subjective and inaccurate, or sending away samples for testing using either image based (OFDA2000) portable systems or laser-based systems (CSIRO Sirolan Laserscan). These external services are costly, time consuming and make the farmer wholly dependent on a third-party for their testing results.

[0006] Current on farm wool assessment procedures are subjective and out-dated. The current technological solution of dispatching samples to a third-party test facility is time consuming and has a lengthy delay (up to 6 weeks) to get results back to the grower. Furthermore, in the wool industry at least, growers currently accept that the fleece variability within each bale could result in a lower overall value associated following a core sample test and loss of overall revenue. Given the current wool market, a variation in assessed wool fibre diameter of only a few micrometres can easily result in hundreds of dollars difference per bale creating an ongoing loss potential at each shearing cycle.

[0007] Therefore, there exists an urgent need for apparatus and methods for rapid on-site testing of animal fibre quality to improve classification of animal fibres at the source and maximise grower’s efficiencies and profits.

[0008] Portable fibre testing devices are known, for example, the OFDA2000 available from BSC Electronics of Ardross, Western Australia and the Sirolan Laserscan developed by the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO). Both of these devices are prohibitively expensive for individual growers with the OFDA200 retailing (as at the time of filing this application) for about AUD$75,000 and the Sirolan Laserscan for about AUD$95,000+. Additionally, each of these devices have stringent sample preparation procedures which, if prepared incorrectly, can either result in inaccurate readings or fouling of the device. For example, for the OFDA2000, the wool sample to be clipped and prepared before being placed into the machine for testing. The Sirolan Laserscan, requires the samples to be cleaned of any greasy residue for accurate laser measurements. This technology can take between 10 minutes (OFDA2000) to 6 weeks (out-sourced Laserscan via the Australian wool testing authority (AWTA)) to get results.

[0009] Portability of the device is also important to ensure uptake of device usage. The OFDA2000 device is a benchtop device which may be operated from a dedicated briefcase-sized housing, but requires a connection to a computer device or laptop. Similarly, the Sirolan Laserscan is a large benchtop device requiring connection to a personal computer and typically requires two extra people to operate the testing workflow in a shearing shed environment.

[0010] A further animal fibre testing device is the Micron Meter available from FibreLux of Johannesburg, South Africa. The FibreLux unit is more compact than the OFDA2000 briefcase version and does not require a personal computer connection but still is too large to be operated single-handedly and still requires careful sample preparation including clipping a clean animal fibre sample, combing the animal fibres out to a preferred fibre density (i.e. sample not having too many or too few animal fibres) to be held in a sample holder which is inserted into the device.

[0011] Accordingly, there is a need for a simple to operate device which does not require any complex or time-consuming handling procedures which can provide growers with accurate fibre diameter readings in the field in real-time or near-real-time and which is able to accept dirty/greasy samples to allow farmers to sample multiple animals in a short amount of time making the process of wool testing much faster and more efficient than current methods.

Summary

[0012] It is an object of the present invention to overcome or ameliorate at least one or more of the disadvantages of the prior art, or to provide a useful alternative.

[0013] Disclosed herein are apparatus and devices including a portable digital microscope system capable of capturing images of raw animal fibre (e.g. wool) samples and measuring the mean diameter and standard deviation of the animal fibre samples.

[0014] In a particular embodiment, the portable digital microscope system includes:

(i) a microscope unit;

(ii) a controlling unit;

(iii) a display unit;

(iv) a RFID unit;

(v) a camera interface;

(vi) a light output device; and

(vii) a communications interface (e.g. Wi-Fi/internet/SIM card).

[0015] The hand-held device of the embodiments disclosed herein is configured for measuring wool diameter (micron) in real-time by capturing an image of the fibres (using specialised digital optics) on the animal and using proprietary algorithms, providing immediate fibre-diameter readings. This device allows wool growers to determine the value of their wool instantly, improve farming practices and increase revenue by accurate sorting of fleece. It will also open new previously unexplored areas, for testing at point-of-sale, pre-shearing and potential for tracking provenance.

[0016] One embodiment provides a system configured for performing a method as described herein.

[0017] According to a first aspect of the present invention, there is provided an apparatus for real-time measurement of animal fibre characteristics. The apparatus may comprise a housing. The housing may comprise a sample window adapted to receive a sample of animal fibres. The housing may further comprise an optical imaging system, adapted to image animal fibres located on or adjacent the sample window. The housing may further comprise an imaging sensor to generate image data of the imaged animal fibres. The housing may further comprise a processor adapted to receive the image data and analyse the image data to determine at least one or more characteristics of the imaged animal fibres. The apparatus may further comprise a hinged portion hingedly fixed to the housing and resiliently biased into engagement with the housing. The hinged portion may comprise a light source adapted to illuminate sample animal fibres located on or adjacent the sample window.

[0018] According to a particular arrangement of the first aspect, there is provided an apparatus for real-time measurement of animal fibre characteristics comprising: a housing, comprising: a sample window adapted to receive a sample of animal fibres; an optical imaging system, adapted to image animal fibres located on or adjacent the sample window; an imaging sensor to generate image data of the imaged animal fibres; and a processor adapted to receive the image data and analyse the image data to determine at least one or more characteristics of the imaged animal fibres; a hinged portion hingedly fixed to the housing and resiliently biased into engagement with the housing, the hinged portion comprising: a light source adapted to illuminate sample animal fibres located on or adjacent the sample window.

[0019] The housing may be adapted to be hand-held.

[0020] The optical system may comprise first and second imaging lenses adapted for imaging sample animal fibres located on or adjacent the sample window onto the image sensor. The optical system may be a microscope imaging arrangement.

[0021] At least one or more characteristics of the imaged animal fibres may be selected from the group comprising: fibre diameter; mean fibre diameter; standard deviation of fibre diameter; coefficient of variance; comfort factor; colour; yield; point of break; and tensile strength.

[0022] The apparatus may further comprise a first communications module adapted to communicate identifying information relating to the animal from which a fibre sample for test is obtained. The first communications module may be an RFID reader, telecommunications module (e.g. SIM card), Wi-Fi module, Bluetooth module, or the like. [0023] The apparatus may further comprise a second communications module adapted for communicating image analysis data generated by the processor to a user and/or database.

[0024] According to a second aspect of the present invention, there is provided a method of real-time measurement of animal fibre characteristics. The method may comprise the step of providing an apparatus as disclosed in the first aspect for accepting a sample of animal fibres. The method may further comprise the step of clamping the animal fibres adjacent the sample window and clamping the fibres against the housing with the hinged portion. The method may further comprise the step of illuminating the animal fibre sample with light from the light source of the hinged portion. The method may further comprise the step of imaging the illuminated sample animal fibres with the optical system on to an image sensor. The method may further comprise the step of receiving image data from the image sensor in a processor. The method may further comprise the step of using a processor, analysing the image date to determine at least one or more characteristics of the imaged sample animal fibres.

[0025] According to a particular arrangement of the second aspect, there is provided a method of real-time measurement of animal fibre characteristics comprising the steps of: providing an apparatus as claimed in Claim 1 for accepting a sample of animal fibres; clamping the animal fibres adjacent the sample window and clamping the fibres against the housing with the hinged portion; illuminating the animal fibre sample with light from the light source of the hinged portion; imaging the illuminated sample animal fibres with the optical system on to an image sensor; receiving image data from the image sensor in a processor; and using a processor, analysing the image date to determine at least one or more characteristics of the imaged sample animal fibres.

[0026] The method may further comprise the step of identifying an animal prior to extracting a fibre sample from the animal including the step of using an RFID reader housed within the apparatus of the first aspect, reading an identification token attached to the animal, and storing identification data with one or more sample images obtained and analysed by the apparatus.

[0027] The method may further comprise the step of communicating identified image analysis data generated by the processor to a user and or database.

Brief Description of the Drawings

[0028] Notwithstanding any other forms which may fall within the scope of the present invention, a preferred embodiment/preferred embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings in which: Figures 1A to 1C show example arrangements of a first embodiment of a handheld apparatus for real-time measurement of one or more characteristics of animal fibres;

Figures 1 D to 1 E show example arrangements of a second embodiment of a handheld apparatus for real-time measurement of one or more characteristics of animal fibres;

Figure 2 shows a schematic depiction of optical components contained within the housing of the apparatus of Figures 1 A to 1 E;

Figure 3 shows a particular arrangement of the optical components of Figure 2;

Figure 4 shows a schematic depiction of the internal components of the apparatus of

Figures 1A to 1 E;

Figure s shows a graphical depiction of the usage workflow of the apparatus of

Figures 1A to 1 E;

Figure 6 shows a collection of synthetic images of straight fibres used to train the neural network model for determining characteristics of imaged animal fibres;

Figure 7 shows a collection of generated synthetic images of curved fibres used to train the neural network model for determining characteristics of imaged animal fibres; and

Figure 8 shows a collection of training results validating the neural network analysis model.

Definitions

[0029] The following definitions are provided as general definitions and should in no way limit the scope of the present invention to those terms alone, but are put forth for a better understanding of the following description.

[0030] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the invention belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. For the purposes of the present invention, additional terms are defined below. Furthermore, all definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms unless there is doubt as to the meaning of a particular term, in which case the common dictionary definition and/or common usage of the term will prevail. [0031] For the purposes of the present invention, the following terms are defined below.

[0032] The articles “a” and “an” are used herein to refer to one or to more than one (i.e. to at least one) of the grammatical object of the article. By way of example, “an element” refers to one element or more than one element.

[0033] The term “about” is used herein to refer to quantities that vary by as much as 30%, preferably by as much as 20%, and more preferably by as much as 10% to a reference quantity. The use of the word ‘about’ to qualify a number is merely an express indication that the number is not to be construed as a precise value.

[0034] Throughout this specification, unless the context requires otherwise, the words “comprise”, “comprises” and “comprising” will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements.

[0035] Any one of the terms: “including” or “which includes” or “that includes” as used herein is also an open term that also means including at least the elements/features that follow the term, but not excluding others. Thus, “including” is synonymous with and means “comprising”.

[0036] In the claims, as well as in the summary above and the description below, all transitional phrases such as “comprising”, “including”, “carrying”, “having”, “containing”, “involving”, “holding”, “composed of”, and the like are to be understood to be open-ended, i.e. to mean “including but not limited to”. Only the transitional phrases “consisting of” and “consisting essentially of” alone shall be closed or semi-closed transitional phrases, respectively.

[0037] The term, “real-time”, for example “displaying real-time data”, refers to the display of the data without intentional delay, given the processing limitations of the system and the time required to accurately measure the data. Similarly, a process occurring “in real time” refers to operation of the process without intentional delay or in which some kind of operation occurs simultaneously (or nearly simultaneously) with when it is happening.

[0038] The term, “near-real-time”, for example “obtaining real-time or near-real-time data” refers to the obtaining of data either without intentional delay (“real-time”) or as close to real-time as practically possible (i.e. with a small, but minimal, amount of delay whether intentional or not within the constraints and processing limitations of the of the system for obtaining and recording or transmitting the data.

[0039] Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, preferred methods and materials are described. It will be appreciated that the methods, apparatus and systems described herein may be implemented in a variety of ways and for a variety of purposes. The description here is by way of example only.

[0040] The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.

[0041] In this respect, various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g. a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory medium or tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above.

[0042] The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.

[0043] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0044] Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. Flowever, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

[0045] Also, various inventive concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

[0046] The phrase “and/or”, as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e. elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e. “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

[0047] As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e. the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items . Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of”, or, when used in the claims, “consisting of” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other, but not both”) when preceded by terms of exclusivity, such as “either”, “one of”, “only one of”, or “exactly one of”. “Consisting essentially of”, when used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0048] As used herein in the specification and in the claims, the phrase “at least one”, in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B”, or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

[0049] For the purpose of this specification, where method steps are described in sequence, the sequence does not necessarily mean that the steps are to be carried out in chronological order in that sequence, unless there is no other logical manner of interpreting the sequence.

[0050] In addition, where features or aspects of the invention are described in terms of Markush groups, those skilled in the art will recognise that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.

Detailed Description

[0051] It should be noted in the following description that like or the same reference numerals in different embodiments denote the same or similar features.

[0052] Referring to the Figures, disclosed herein is a hand-held device 100 adapted for measurement of the diameter or wool or other animal fibres/hair in real-time by capturing an image of the wool (or other animal fibres) fibres using specialised digital optics and using proprietary algorithms, providing an instant mean and standard deviation (average) diameter (size/micron) reading of the imaged animal fibres (e.g. sheep wool) to determine the fibre quality. The device 100 has the significant advantage that the wool fibres can be tested on the animal without any requirement for clipping the wool/animal fibres or to do any preparation of the animal fibre sample for use with device 100. Device 100 provides a further significant advantage in that it allows wool growers to determine the quality of their wool/animal fibres on their livestock instantly and in real-time, thus improving farming practices and increasing revenues by accurate sorting of fleece/fibres. The following description of the features and operation of device 100 is described in the specific context of the measurement of wool fibres from sheep, however, nothing in this specification precludes the device 100 from also being utilised to measure the diameter of alternate animal fibres as would be appreciated by the skilled addressee including, for example (but not limited to): alpaca hair; angora (rabbit) hair; mohair (goat); or cashmere (goat) wool, and the like.

[0053] Figures 1 A to 1C show a first example embodiment of device 100 adapted for single-hand-held operation for the testing of animal fibres including sheep wool. Device 100 includes a housing 101 adapted to provide a secure grip on device 100 by a human operator. Device 100 includes selector controls 103 and a display screen 105. Device 100 comprises a hinged portion 110 connected to a distal end of device 100 by hinge means 111. In particular arrangements hinge means 111 may be a torsion spring hinge mechanism. Torsion spring hinge mechanism 111 is preferably resiliently biased to close the hinged portion 110 towards device housing 101.

[0054] Hinged portion 110 is adapted to be operated one-handed by a user’s hand. The user holds the housing 101 of device 100 such that their thumb is oriented to depress a proximal edge 112 of hinged portion 110. When depressed, as depicted in Figure 1C, hinged portion 110 articulates about hinge 111 to expose sample window 130 of device 100 adapted to receive an animal fibre sample for test. In use, the user opens hinged portion 110 to expose the sample window 130, places or captures an animal fibre sample over window 130 and the user then releases hinged portion 110 such that the animal fibre sample is clamped against sample window 130 by hinged portion 110.

[0055] Particular arrangements of device 100 optionally include teethed portions 107 to assist in retaining a sample of animal fibres in position when clamped against sample window 130 by hinged portion 110. Teethed portions 107 also act to space out the animal fibres of the sample to assist in maintaining an optimal sample thickness fortesting. Teeth portions 107 may optionally be provided as complementary removable plates 109 as shown in inset of Figure 1 B to allow the plates to be easily replaced to provide a clean surface for accurate measurement of the animal fibres, or alternatively removed for cleaning. Such removable plates include the teeth portions 107 and is optically transparent adapted to cover sample window 130 of housing 101 and optical window 135 of hinged portion 110 to keep optically transparent windows 130 and 135 clean of animal fibres, oils and environmental contaminants such as dust. In practice, two complementary removable sample plates 109a and 109b are provided where sample plate 109a is adapted to be attached to one side of the sample area e.g. to hinged portion 110 to protect optical window 135 and sample plate 109b is adapted to be attached to the other side of the sample area e.g. to housing 101 to protect sample window 130. Removable and replaceable complementary sample plates 109a and 109b including complementary teeth portions 107 provide a significant advantage to operation of device 100/200 in the field or shed environment where conducting large numbers of measurements of animal fibres may be likely to leave dirt, grime or greasy residue on the sample window 130 and or optical window 135 of hinged portion 110. Removable sample plates 109a and 109b are easy to clean or be substituted for new, clean plates 109a and 109b to ensure the sample window 130 and optical window 135 remain clean as degradation of the optical transmission of one or both of windows 130 or 135 has a significant impact on the amount of light available for illuminating an animal fibre sample and for imaging of the animal fibre sample onto the image sensor 150.

[0056] Figures 1 D and 1 E show a further example embodiment 200 of an animal fibre testing device. Like reference signs of Figures 1 D and 1 E refer to like components as described with reference to Figures 1 A to 1C.

[0057] Figure 2 shows a schematic depiction of optical components contained within housing 101 of device 100, including a light source 131 within hinged portion 110. Light source 131 may be selected from the group of an incandescent bulb, LED light source, or laser. In particular arrangements a white light bulb or LED source may be used, or alternatively, light having a particular wavelength range may be used. For example, a possible light source may be an LED emitting light generally in the green region of the optical spectrum, e.g. with a wavelength emission centred on or about 525 nm such as product no. LED525L available from Thorlabs of Newton, New Jersey, United States. Selection of a limited wavelength range of emission is advantageous as improved resolution in the imaging may be achieved as compared with a white light source. The optical power output 131a provided by light source 131 may be in the range of about 1 to 20 mW and may be optionally in the range of about 5 mW ± 2 mW. Light source 131 may be chosen on the basis of the output wavelength where a light source with shorter wavelength may be chosen to provide increase optical resolution to resolve individual animal fibres of the sample under test. In particular arrangements, hinged portion 110 may be a modular component adapted to be easily removed from fibre test device 100 and replaced with a further hinged portion comprising a light source 131 with different optical characteristics, such as a particular output wavelength advantageously suited for measurement of particular animal fibres. Thus it is envisaged that a plurality of such modular hinged portions 110 are provided, each hinged portion particular optimised for testing of a particular type of animal fibre.

[0058] Light source 131 is preferably powered from a battery 133 housed within hinged portion 110. Hinged portion 110 includes an optical window 135 to permit light generated by light source 131 in use to illuminate sample window 130 located within housing 101 of device 100. An optical diffuser (not shown) may optionally be provided in front of sample window 130 (i.e. intermediate optical source 131 and sample window 130) to distribute the light from source 131 evenly across the sample plate 130 and the animal fibres under test, and also to minimise or substantially eliminate bright spots in the illumination of sample plate 130 and the animal fibres under test.

[0059] In use, an animal fibre sample located on sample window 130 is imaged by an objective lens 139. In particular arrangements, the objective lens may provide a magnification of at least 10-times and have a numerical aperture of about 0.25-0.30. Alternative objective lens 139 may be selected on the basis of the desired optical resolution and the physical distances between the sample window 130 and image sensor 150 such of a camera 151 in the housing 101 of devicel 00, as would be readily appreciated by the skilled addressee.

[0060] A tube lens 141 is provided to focus light imaged from sample window 130 by objective 139 onto image sensor 150. Tube lens 141 in particular arrangements may be an infinity corrected achromatic lens with a focal length of about 50 mm. In particular arrangements, the optical components of device 100 comprise an infinity-corrected optical system, where light 140 from an animal fibre sample located at sample window 130 passes through objective lens 139 which does not form an image and thus the light enters as an infinity-parallel beam in the tube lens 141 which forms an intermediate image on image sensor (or camera) 150.

[0061] Figure 3 shows a particular arrangement of the optical components located within housing 101 , including sample window 130 comprising a glass plate or cover slip; objective lens 139 receiving light 140 from animal fibres located at sample window 130; tube lens 141 and image sensor (camera) 150 mounted on a heat sink 155. In this particular example arrangement, the optical system length is about 200 mm from the sample window 130 to the rear of the image sensor heat sink 155 thus housing 101 of device 100 in this presently described example arrangement would need to be at least about 200 mm long. Of course, different selection of the optical components within housing 101 for imaging the animal fibre sample on sample window 130 would lead to respective changes in the optical component separation distances and thus would affect the total size of housing 101 as would be appreciated by the skilled addressee. For example a particular fibre test device 100 may include a higher magnification objective lens 139 for increased resolution in the imagine of the animal fibres on sample window 130 which would lead to a larger device 101. As indicated the position of tube lens 141 with respect to objective 139 is adapted to be variable to provide optimal optical imaging of an animal fibre sample located at sample window 130. As would be appreciated by the skilled addressee, an objective lens 139 having different optical properties may be used which would necessarily require modification of the spacing between remaining components of the optical system 200 of device 100. Alternatively, in particular arrangements, objective lens 139 and tube lens 141 may be modular, for example a user may be able to swap out objective lens 139 and complementary tube lens 141 within housing 101 for a new combination of objective lens 139 and complementary tube lens 141 to provide a different magnification of the animal fibres on the sample window 130 which can be advantageous for testing of different quality (grade) of animal fibres, e.g. coarse or fine fibres where a higher magnification may be desirable.

[0062] Figure 4 shows a schematic outline of the complete components of device 100 including optical system 200 described above. In particular arrangements of device 100, the device includes an RFID reader module 201 to read RFID tags of livestock to enable association of fibre measurements with livestock individuals. [0063] Referring to Figure 4, a processor 203 is included within housing 101 to integrate the various components of device 100 including image sensor 150, display 105, RFID module 201 ; light source 131 , and a communication module 209. Communication module 209 may comprise, for example, a telecommunications modem able to accept a telecommunication SIM card (e.g. for communication via an available telecommunication network such as a GSM, CDMA, 1 G, 2G, 3G, 4G, or 5G network, or the like) and enable integration of the processor 203 and memory 207. Memory 207 is adapted to store operating software instructions for processor 203 for operation of the components of device 100 including: interfacing with RFID module 201 and communication modem 209; initiating a sample measurement of animal fibres located on sample window 130; receiving image data from image sensor 150; and analysing the image sensor data to determine the diameter and optionally, other properties, of the sample under test. Memory 207 is also utilised to store the image data and image analysis result data for subsequent communication to a centralised database. Flousing 101 also includes a power input (charging) port 211 , which may be, for example, a USB port or similar power input port for charging batteries 215 and 133. Input port 211 provides power to internal power supply 213 to provide power to device 100 and also to charge an internal battery 215 within housing 101 and also battery 133 within hinged portion 110, for mobile operation of device 100.

[0064] In further arrangements, input port 211 may further be a communications port to enable control of processor 203 for tethered operation of device 100, or access to device memory 207 to download recorded measurement data from device 100.

[0065] Figure s (panels A-F) outline the usage workflow of device 100 of determining the diameter of a sample of wool from a sheep 501 , comprising the steps of:

^■ (Panel A) — RFID module 201 of Device 100 is activated to scan the RFID tag 502 of sheep 501. Animal ID data is then stored in memory 207 as a scanned RFID tag record;

^■ (Panel B) — A mid-flank side sample location 503 on animal 501 is selected for measurement;

^■ (Panel C) — A selection of animal fibres 505 is selected from selected sample site 503 of animal 501 (in the present example wool fibres of sheep 501) and separated from remaining fibres. User holding device 100 opens hinged portion 110 to admit selected fibres 505;

^■ (Panel D) — Selected fibres 505 are clamped between hinged portion 110 and against sample window 130; ^■ (Panel E) — Fibres 505 are illuminated by with light generated by light source 131 and imaged by the optical system depicted in Figure 2 onto image sensor 150; and

^■ (Panel F) — Received image is analysed by processor 203 to determine, at least, the diameter (mean and standard deviation) of fibres 505 and the result of the image processing including the measured diameter of fibres 505 is displayed to the user on display 105. Measured fibre characteristics are associated with the scanned RFID tag record and stored in memory 207.

Image Processing and Analysis

[0066] As discussed above, handheld measurement device 100 is able to scan animal fibres directly on the animal with minimal or zero sample preparation. The animal fibres are imaged by optical microscope system 200 housed within housing 101 of device 100 onto a high-resolution imaging sensor 150 and the captured image is analysed by onboard processor 203 to determine the characteristics of the fibres under test including mean and standard deviation of the fibre diameter. Other characteristics may also be determined by the image processing as would be appreciated and with appropriate modifications to the optical system and or image processing analysis associated with an improved device 100.

[0067] The images generated by image sensor 150 are generated with pre-specified distribution parameters. The thickness distribution parameters are used as ground truth. Flowever, this is problematic because it may not reflect the actual image content. For example, consider generation of an image of fibres with mean thickness of 20 pixels, standard deviation 5 pixels, and where the image contains a single fibre whose thickness is sampled from that distribution at 24 pixels. The ground truth mean thickness of 20 pixels is very different from the actual image content of 24 pixels.

[0068] To overcome this, dense ground truth is computed from generated synthetic images. For each image, as an image of each fibre is drawn, its width is sampled along the fibre. These samples are assembled for all fibres in the image, and the ground truth mean and standard deviation of thickness is computed from the samples. The dense ground truth method more closely reflects the laser measurement method of chopping up fibres and sampling them.

[0069] To assist with development of a fibre thickness analysis model for analysis of sheep wool, a dataset of synthetic animal fibre images was generated to be as similar as possible to real sample images of sheep wool. Using this method any number of images can be generated randomly with the desired properties. This was done to control the factor of fibre thickness distribution. [0070] The image processing used for measurement of the fibre thickness from images generated by sensor 150 utilise optical prediction methods. Traditionally such methods are not robust and difficult to automate; their limitations are normally required to be overcome by the addition of human interactions such as manual annotation or parameter selection. However, integration of the optical prediction method with deep neural networks enables automation of the prediction algorithm. This involves training the neural network on a large data set of input-output pairs, namely wool sample images and the corresponding ground truth thickness measurements.

[0071] The following factors were incorporated into the synthetic dataset to determine which of these would increase the error rate:

1 . brightness distribution;

2. fibre count distribution;

3. fibre curvature;

4. fibre edge brightness; and

5. different layers with blur to simulate depth of field.

[0072] An example of a collection of synthetic images of straight fibres having variations in the count, brightness and thickness variation of the fibres in each image is shown in Figure 6. Increasing the complexity of the model generation, a further collection of synthetic images having curvature and brightness variations is also generated as shown for example in Figure 7.

[0073] In the present arrangement, to generate an analysis module from the synthetic image data, a Resnet 50 architecture was used. ResNet-50, available in MATLAB® is a convolutional neural network that is 50 layers deep which includes a pre-trained module capable of identifying common objects from new image. In the present model, two outputs of the Resnet 50 architecture are used, one output for the mean fibre thickness and the second for the standard deviation of the fibre thickness in the analysed image. A synthetic data set is used for training the model. Once trained, the model is proofed on further synthetic image data using a direct regression network before usage on real image data from device 100.

[0074] A database of sample images generated from a prototype of device 100 was used in these experiments. Image augmentation was used to address the very small data set size including image resizing, cropping, rotation, hue-saturation variations, image flips and per-channel normalisations. Care is taken not to change the scale of the images during augmentation since this would interfere with thickness measurements.

[0075] Training results are shown in Figure 8, from left to right:

(a) the MSE loss versus iterations; (b) average error of the mean thickness;

(c) scatter plot of predicted mean thickness versus ground truth ;

(d) average error of the standard deviation of thickness; and

(e) scatter plot of predicted standard deviation of thickness versus ground truth.

[0076] The scatter plots of Figure 8 show that the above regression model predictions are accurate to within +/- a pixel or so over the range of values for image data obtained from real data sets.

[0077] The image processing procedure described above is aimed at determining the mean and standard deviation of fibre widths for an image directly by posing it as a regression problem. In this circumstance a regression model is utilised, where the input is a single still image, and the outputs are two values, one for the mean width and one for the standard deviation. This is a complex task and requires the model to be able to determine spatial distances within an image, something that current models aren’t really designed to do, however the process was found to be fast in practice.

[0078] In an alternative image processing procedure, a segmentation model, the process first attempts to determine what pixels of the input image belong to a particular fibre, and then directly measures the widths of individual fibres based on the identified pixels. The first stage is carried out by an instance segmentation model, which shares many similarities with the regression model described above, however it aims to predict different outputs - a set of pixel masks instead of two discrete values. Compared to the regression model above, the segmentation model is effective at its task, despite being slightly larger and slower. The second stage of the segmentation model implements optimised pixel distance measuring algorithms to sample many distance measures per fibre, which are combined to determine the mean and standard deviations for a single image.

[0079] Both the regression and segmentation models are data driven (deep) machine learning models, and utilised in the fully supervised learning regime, meaning they both require many annotated data points to learn from. As it is incredibly time consuming and expensive to annotate wool images with ground truth, and that at least tens of thousands of examples were likely required to train such models sufficiently, ‘fake’ wool images were generated to train the image processing models. The wool image generation algorithm used to generate the fake wool images is able to generate completely random, but realistic wool-like images.

[0080] In summary the process is broken down into 4 stages:

1 Generating synthetic wool training dataset, including the steps of: (a) Generating background, noise blotches, other noise, and fibre texture layer images by hand;

(b) Generating random number of fibre masks, using line generators;

(c) Using fibre masks with fibre texture images to generate texture fibre layers;

(d) Using multiple fibre masks to generate fibre gradients on fibre cross-section; and

(e) Layering the fibre layers and the background and noise layers into a full image. Training an image segmentation model on the wool dataset to obtain a trained model, including the steps of:

(a) Defining the model, currently a modified ResNet-50 with a Feature Pyramid Network (FPN) before a Bounding Box Head Network and Mask Feature Head Network, but changes regularly based on experimentation;

(b) Initialising the model with pre-trained weights; and

(c) Training the model in an iterative training loop including loss back propagation, and updating the model with each iteration until the model overfits. In the present example the model was looped through approximately 2,000,000 iterations using a batch size less than 10, however, as would be appreciated, the number of iterations and batch size will change with each particular model. Using the trained model to determine which pixels belong to which fibre given a real-world wool image, including the steps of:

(a) Loading real world wool image into memory;

(b) Evenly tiling each wool image into non-overlapping tiles suitable for handling memory restrictions of the device 100; and

(c) Iteratively passing the image tiles through the model to generate mask tiles. Using the pixel label predictions as input to the optimised distance measuring and statistics algorithm to generate mean and standard deviations, including the steps of:

(a) Finding all pixels within a fibre mask;

(b) Randomly sampling these points one by one;

(c) For each point, spiralling out until a pixel is found not in a mask, and assigning this as an edge point;

(d) From the edge point, looking in direction back towards the starting point until finding another non-mask pixel, assigning this as the other edge point;

(e) Calculating the distance between the two edge point pixels using the well-known Euclidean distance algorithm;

(f) Scaling the calculated distance by a pixel to micron factor, and by a threshold scaling factor adapted to make up for consistent errors across all masks to counteract for such consistent errors in which the model masks consistently wider or thinner than actual fibre widths - the threshold scaling factor shifts the entire resulting distribution left or right to better align with the ground truth distribution. In the present model arrangement, the scaling factor is between about 1 and 1 .1 , so only a very slight shift is necessary in the present model arrangement indicating the strength of the synthetic training data used to train the model. The scaling factor is calculated by averaging width distances between the results from the image processing procedure described above and the ground truth test set;

(g) Repeating for a predetermined number of fibre points to get a set of distances per fibre mask;

(h) Removing outlier distances on a per fibre basis;

(i) Calculating the mean and standard deviation of the fibre width across each of the fibres and across each of the tiles; and

(j) Calculating the mean and standard deviation of fibre widths across the entire image. [0081] As would be readily appreciated by the skilled addressee, the regression and segmentation image processing procedures are executed by program software code adapted to be executed by a computer processor. In preferred arrangements, the image processing training steps are executed by a computer processor to provide a trained image processing model and this trained image processing model is stored in memory 207 of device 100 to be executed by processor 203 of device 100 to analyse actual animal fibre samples place on sample window 130 as required. As would be appreciated by the skilled addressee, the actual software code for either training the image processing models or analysing real wool samples may be implemented in numerous different variations in the actual software code without marked or significant modification of the image analysis process or the output from the software analysis of images of real wool fibres obtained by use of device 100 to analyse real animal fibre samples placed on sample window 130 In further arrangements, the model training steps may be modified to provide an optimum trained image processing model adapted to particular types of animal fibres. Many such models may be stored in memory 207 of device 100 to be selected on demand and executed by processor 203 of device 100 to analyse particular types of animal fibre samples. Alternatively particular trained models designed for analysis of particular types of animal fibres may be uploaded to memory 207 of device 100 on demand to executed by processor 203 of device 100 as required.

Interpretation

In Accordance With

[0082] As described herein, ‘in accordance with’ may also mean ‘as a function of’ and is not necessarily limited to the integers specified in relation thereto.

Composite Items

[0083] As described herein, ‘a computer implemented method’ should not necessarily be inferred as being performed by a single computing device such that the steps of the method may be performed by more than one cooperating computing devices.

Database

[0084] In the context of this document, the term “database” and its derivatives may be used to describe a single database, a set of databases, a system of databases or the like. The system of databases may comprise a set of databases wherein the set of databases may be stored on a single implementation or span across multiple implementations. The term “database” is also not limited to refer to a certain database format rather may refer to any database format. For example, database formats may include MySQL, MySQLi , XML or the like. Communication Protocol

[0085] The invention may be embodied using devices conforming to other network standards and for other applications, including, for example cellular, or WLAN standards and other wireless standards. Communication protocols that can be accommodated include, but are not limited to, IEEE 802.11 wireless LANs and links, wireless Ethernet, Bluetooth, NFC, and the like. Additionally, cellular communications methods may be incorporated into the device for remote connectivity including, for example GSM, CDMA, EDGE, 3G, 4G, 5G, and the like.

[0086] In the context of this document, the term wireless and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated devices do not contain any wires, although in some embodiments they might not. In the context of this document, the term “wired” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a solid medium. The term does not imply that the associated devices are coupled by electrically conductive wires.

Processes

[0087] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as “processing”, “computing”, “calculating”, “determining”, “analysing” or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities into other data similarly represented as physical quantities.

Processor

[0088] In a similar manner, the term “processor” may refer to any device or portion of a device that processes electronic data, e.g. from registers and/or memory to transform that electronic data into other electronic data that, e.g. may be stored in registers and/or memory. A “computer” or a “computing device” or a “computing machine” or a “computing platform” may include one or more processors.

[0089] The methodologies described herein are, in one embodiment, performable by one or more processors that accept computer-readable (also called machine-readable) code containing a set of instructions that when executed by one or more of the processors carry out at least one of the methods described herein. Any processor capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken are included. Thus, one example is a typical processing system that includes one or more processors. The processing system further may include a memory subsystem including main RAM and/or a static RAM, and/or ROM.

Implementation

[0090] It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e. computer) system executing instructions (computer-readable code) stored in memory storage. It will also be understood that the invention is not limited to any particular implementation or programming technique and that the invention may be implemented using any appropriate techniques for implementing the functionality described herein. The invention is not limited to any particular programming language or operating system.

Means for Carrying out a Method or Function

[0091] Furthermore, some of the embodiments are described herein as a method or combination of elements of a method that can be implemented by a processor or a processor device, computer system, or by other means of carrying out the function. Thus, a processor with the necessary instructions for carrying out such a method or element of a method forms a means for carrying out the method or element of a method. Furthermore, an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the invention.

Connected

[0092] Similarly, it is to be noticed that the term connected, when used in the claims, should not be interpreted as being limitative to direct connections only. Thus, the scope of the expression a device A connected to a device B should not be limited to devices or systems wherein an output of device A is directly connected to an input of device B. It means that there exists a path between an output of A and an input of B which may be a path including other devices or means. “Connected” may mean that two or more elements are either in direct physical or electrical contact, or that two or more elements are not in direct contact with each other but yet still co-operate or interact with each other.

Embodiments

[0093] Reference throughout this specification to “one embodiment”, “an embodiment”, “one arrangement” or “an arrangement” means that a particular feature, structure or characteristic described in connection with the embodiment/arrangement is included in at least one embodiment/arrangement of the present invention. Thus, appearances of the phrases “in one embodiment/arrangement” or “in an embodiment/arrangement” in various places throughout this specification are not necessarily all referring to the same embodiment/arrangement, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to one of ordinary skill in the art from this disclosure, in one or more embodiments/arrangements.

[0094] Similarly it should be appreciated that in the above description of example embodiments/arrangements of the invention, various features of the invention are sometimes grouped together in a single embodiment/arrangement, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment/arrangement. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment/arrangement of this invention.

[0095] Furthermore, while some embodiments/arrangements described herein include some but not other features included in other embodiments/arrangements, combinations of features of different embodiments/arrangements are meant to be within the scope of the invention, and form different embodiments/arrangements, as would be understood by those in the art. For example, in the following claims, any of the claimed embodiments/arrangements can be used in any combination.

Specific Details

[0096] In the description provided herein, numerous specific details are set forth. Flowever, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Terminology

[0097] In describing the preferred embodiment of the invention illustrated in the drawings, specific terminology will be resorted to for the sake of clarity. Flowever, the invention is not intended to be limited to the specific terms so selected, and it is to be understood that each specific term includes all technical equivalents which operate in a similar manner to accomplish a similar technical purpose. Terms such as “forward”, “rearward”, “radially”, “peripherally”, “upwardly”, “downwardly”, and the like are used as words of convenience to provide reference points and are not to be construed as limiting terms. Different Instances of Objects

[0098] As used herein, unless otherwise specified the use of the ordinal adjectives “first”, “second”, “third”, etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

Comprising and Including

[0099] In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word “comprise” or variations such as “comprises” or “comprising” are used in an inclusive sense, i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention.

[0100] Any one of the terms: “including” or “which includes” or “that includes” as used herein is also an open term that also means “including at least” the elements/features that follow the term, but not excluding others. Thus, including is synonymous with and means comprising.

Scope of Invention

[0101] Thus, while there has been described what are believed to be the preferred arrangements of the invention, those skilled in the art will recognize that other and further modifications may be made thereto without departing from the spirit of the invention, and it is intended to claim all such changes and modifications as fall within the scope of the invention . Functionality may be added or deleted from the block diagrams and operations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present invention.

[0102] Although the invention has been described with reference to specific examples, it will be appreciated by those skilled in the art that the invention may be embodied in many other forms.

Industrial Applicability

[0103] It is apparent from the above, that the arrangements described are applicable to the mobile device industries, specifically for methods and systems for distributing digital media via mobile devices.

[0104] It will be appreciated that the methods/apparatus/devices/systems described/illustrated above at least substantially provide an apparatus, device and methods for automated testing of the characteristics and qualities of animal fibres. [0105] The device and methods described herein, and/or shown in the drawings, are presented by way of example only and are not limiting as to the scope of the invention. Unless otherwise specifically stated, individual aspects and components of the device and methods may be modified, or may have been substituted therefore known equivalents, or as yet unknown substitutes such as may be developed in the future or such as may be found to be acceptable substitutes in the future. The device and methods described herein may also be modified for a variety of applications while remaining within the scope and spirit of the claimed invention, since the range of potential applications is great, and since it is intended that the present device and methods be adaptable to many such variations.

Claims

THE CLAIMS DEFINING THE INVENTION ARE AS FOLLOWS:

1 . Apparatus for real-time measurement of animal fibre characteristics comprising: a housing, comprising: a sample window adapted to receive a sample of animal fibres; an optical imaging system, adapted to image animal fibres located on or adjacent the sample window; an imaging sensor to generate image data of the imaged animal fibres; and a processor adapted to receive the image data and analyse the image data to determine at least one or more characteristics of the imaged animal fibres; a hinged portion hingedly fixed to the housing and resiliently biased into engagement with the housing, the hinged portion comprising: a light source adapted to illuminate sample animal fibres located on or adjacent the sample window.

2. Apparatus as claimed in Claim 1 , wherein the housing is adapted to be hand-held.

3. Apparatus as claimed in either Claim 1 or Claim 2, wherein the optical system comprises: first and second imaging lenses adapted for imaging sample animal fibres located on or adjacent the sample window onto the image sensor.

4. Apparatus as claimed in Claim 3, wherein the optical system is a microscope imaging arrangement.

5. Apparatus as claimed in any one of Claims 1 to 4, wherein the at least one or more characteristics of the imaged animal fibres is selected from the group comprising: fibre diameter; mean fibre diameter; standard deviation of fibre diameter; coefficient of variance; comfort factor; colour; yield; point of break; and tensile strength.

6. Apparatus as claimed in any one of the preceding claims, further comprising a first communications module adapted to communicate identifying information relating to the animal from which a fibre sample for test is obtained.

7. Apparatus as claimed in Claim 3, wherein the first communications module is an RFID reader.

8. Apparatus as claimed in any one of the preceding claims, further comprising a second communications module adapted for communicating image analysis data generated by the processor to a user and/or database.

9. A method of real-time measurement of animal fibre characteristics comprising the steps of: providing an apparatus as claimed in any one of Claims 1 to 8 for accepting a sample of animal fibres; clamping the animal fibres adjacent the sample window and clamping the fibres against the housing with the hinged portion; illuminating the animal fibre sample with light from the light source of the hinged portion ; imaging the illuminated sample animal fibres with the optical system on to an image sensor; receiving image data from the image sensor in a processor; and using the processor, analysing the image date to determine at least one or more characteristics of the imaged sample animal fibres.

10. A method according to Claim 9, wherein the processor analyses the image data using a neural network process.

11. A method according to either Claim 9 or Claim 10, wherein the at least one or more characteristics of the imaged animal fibres is selected from the group comprising: fibre diameter; mean fibre diameter; standard deviation of fibre diameter; coefficient of variance; comfort factor; colour; yield; point of break; and tensile strength.

12. A method according to any one of Claims 9 to 11 , further comprising the steps of: identifying an animal prior to extracting a fibre sample from the animal including the step of using an RFID reader housed within the apparatus of the first aspect; reading an identification token attached to the animal; and storing identification data with one or more sample images obtained and analysed by the apparatus.

13. A method according to any one of Claims 9 to 12, further comprising the step of communicating identified image analysis data generated by the processor to a user and or database.