Spectral grazing and forage intake monitoring device and uses thereof

Abstract

Described herein are devices, systems, and methods for determining forage intake, forage quality, and / or foraging behavior of an animal. The devices and systems of the present disclosure can also be used to determine other individual animal, herd, forage and other environmental characteristics. The device can be configured, in some embodiments, to take other measurements, such as location, speed, mastication amount, etc., which can be combined with the forage spectral data to provide further insight into grazing patterns, preferences, behaviors, and the like

Description

SPECTRAL GRAZING AND FORAGE INTAKE MONITORING DEVICE AND USES THEREOFCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 546,782, filed on November 1, 2023, the contents of which is incorporated by reference herein in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under Grant No. 2018-67007- 28452; 2019-67021-29007; and 2021-67021-34769 awarded by United States Department of Agriculture - National Institute of Food and Agriculture. The government has certain rights in the invention.TECHNICAL FIELD

[0003] The subject matter disclosed herein is generally directed to devices for monitoring grazing and forage intake of animals and uses thereof.BACKGROUND

[0004] Forage intake of pastured and other free-ranging grazing animals is challenging to monitor. Even more challenging is monitoring forage quality consumed by these grazing animals. Although equations to estimate forage intake and quality exist, they fall far short of being a reliable representation of forage intake and quality and do not always represent individual animal behaviors. As such, there exists a need for devices, systems, and methods for monitoring forage intake and quality of grazing animals.

[0005] Citation or identification of any document in this application is not an admission that such a document is available as prior art to the present invention.SUMMARY

[0006] Described in certain example embodiments herein is a head-mounted forage intake monitoring device comprising a spectrophotometer configured to detect spectra of an environment located at an optimized distance in front of the mouth of an animal during use thereby detecting one or more spectra of forage to be consumed by the animal.

[0007] Described in certain example embodiments herein is a head-mounted forage intake monitoring device comprising: a spectrophotometer; proximity sensor, and a headpiece; wherein the spectrophotometer and proximity sensor are coupled to or otherwise integrated with the headpiece such that, when worn by an animal during use, the spectrophotometer detects one or more spectra of an environment located at an optimized distance in front of the mouth of the animal during use thereby detecting spectra of forage to be consumed by the animal.

[0008] In certain example embodiments, the spectrophotometer detects visible light, ultraviolet light, infrared light, or any combination thereof.

[0009] In certain example embodiments, the headpiece comprises or is a harness, a halter, a mask, a headcollar, a headstall, a bonnet, a wrap, an ear cover, helmet, a collar, or any combination thereof.

[0010] In certain example embodiments, the spectral sensor is coupled or otherwise integrated with the headpiece such that the spectral sensor is positioned on or within immediate proximity to an ear of the animal or, more broadly, to the lateral aspect of the head.

[0011] In certain example embodiments the head-mounted forage intake monitoring device further comprises one or more additional sensors or measuring devices.

[0012] In certain example embodiments, the one or more additional sensors or detection devices selected from a gyroscope, an accelerometer, a temperature sensor, a photosensor, a chemical sensor, a humidity sensor, a proximity sensor, a global positioning system, pedometer, speedometer, a degree of freedom inertial measurement unit, a mastication tracking device, or any combination thereof.

[0013] In certain example embodiments, the head-mounted forage intake monitoring device further comprises a transmitter configured to transmit data to a receiver, wherein the transmitter is (a) coupled the spectral sensor, (b) the one or more additional sensors or detection devices, or (c) both (a) and (b).

[0014] In certain example embodiments, the animal is a domesticated animal or a wild animal.

[0015] In certain example embodiments, the animal is a horse, cattle, sheep, rhinoceros, elephant, zebra, yak, bison, donkey, mule, goat, deer, elk, moose, antelope, alpaca, llama, wildebeest, pronghorn, or reindeer.

[0016] Described in certain example embodiments herein is a computer-implemented method to determine forage intake, forage quality, and / or foraging behavior, the method comprising receiving forage spectral data from a head-mounted forage intake monitoring device of the present disclosure in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior from the received forage spectral data; executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non-transitory computer readable memory, or any combination thereof.

[0017] In certain example embodiments, the computer-implemented method further comprises receiving data from the one or more additional sensors or detection devices in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior of the animal from the received data from the one or more additional sensors or detection devices; and executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non- transitory computer readable memory, or any combination thereof.

[0018] Described in certain example embodiments herein is a non-transitory computer readable medium comprising computer-executable instructions recorded thereon for causing a computer to perform a computer-implemented method of the present disclosure.

[0019] Described in certain example embodiments herein is a forage intake monitoring system comprising the head-mounted forage intake monitoring device of the present disclosure; non- transitory computer-readable medium; and a processor configured to execute instructions stored on the non-transitory computer readable medium which, when executed, cause the processor to perform a computer-implemented method of the present disclosure.

[0020] Described in certain example embodiments herein is a method of monitoring forage intake comprising securing the head mounted forage intake monitoring device of the present disclosure to the animal; and allowing the animal to graze for a period of time thereby and obtaining spectral data of consumable forage by the spectrophotometer.

[0021] In certain example embodiments, the method of monitoring forage intake further comprises obtaining and / or transmitting, by or to a computing system or device, data from the one or more other sensors or detection devices.

[0022] In certain example embodiments, the method of monitoring forage intake further comprises determining one or more characteristics of forage intake and / or foraging behavior of the animal from the obtained spectral data and / or data from the one or more other sensors or detection devices.

[0023] These and other aspects, objects, features, and advantages of the example embodiments will become apparent to those having ordinary skill in the art upon consideration of the following detailed description of example embodiments.BRIEF DESCRIPTION OF THE DRAWINGS

[0024] An understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention may be utilized, and the accompanying drawings of which:

[0025] FIG. 1 is a depiction of contrast in spectrophotometer (Spectral A - W) wavelengths and inertial motion measurement values of pitch, yaw and roll (QI, Q2, and Q3, respectively) unit data readings between standing and grazing periods.

[0026] FIG. 2 shows RStudio ggplot results. This plot depicts the contrast in spectrophotometer data readings between standing and grazing periods.

[0027] FIG. 3 shows an embodiment of the spectral grazing and forage intake monitoring device shown on an equine.

[0028] FIG. 4 shows an embodiment of the spectral grazing and forage intake monitoring device shown on an equine.

[0029] FIG 5 illustrates detection of forage spectra of an environment located at an optimized distance in front of the mouth of an animal during use thereby detecting one or more spectra of forage to be consumed by the animal.

[0030] FIG. 6 is a block diagram depicting a system for determine forage intake, forage quality, and / or foraging behavior based in whole or in part on forage spectral data, in accordance with certain example embodiments.

[0031] FIG. 7 depicts a computing machine 2000 and a module 2050 in accordance with certain example embodiments.

[0032] FIGS. 8-9 show graphs demonstrating predicted spectral data versus observed lab analysis for NDF values by field and date, respectively. Correlation coefficient for NDF was 0.1907, indicating weak, positive correlation.

[0033] FIGS. 10-11 show graphs demonstrating predicted spectral data versus observed lab analysis for DM values by field and date, respectively. Correlation coefficient for DM was 0.3686, indicating a weak, positive correlation.

[0034] FIG. 12 demonstrates NDF to ADF ratio over date for each field.

[0035] FIGS. 13-14 show graphs demonstrating predicted spectral data versus observed lab analysis for ADF values by field and date, respectively. Correlation coefficient for ADF was 0.2342, indicating a weak, positive correlation.

[0036] FIGS. 15-16 show graphs demonstrating predicted spectral data versus observed lab analysis for CP values by field and date, respectively. Correlation coefficient for 0.3594 indicating a weak, positive correlation.

[0037] The figures herein are for illustrative purposes only and are not necessarily drawn to scale.DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS

[0038] Before the present disclosure is described in greater detail, it is to be understood that this disclosure is not limited to particular embodiments described, and as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0039] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, the preferred methods and materials are now described.

[0040] All publications and patents cited in this specification are cited to disclose and describe the methods and / or materials in connection with which the publications are cited. All such publications and patents are herein incorporated by references as if each individual publication or patent were specifically and individually indicated to be incorporated by reference. Such incorporation by reference is expressly limited to the methods and / or materials described in the cited publications and patents and does not extend to any lexicographical definitions from the cited publications and patents. Any lexicographical definition in the publications and patents cited that is not also expressly repeated in the instant application should not be treated as such and should not be read as defining any terms appearing in the accompanying claims. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the dates of publication provided could be different from the actual publication dates that may need to be independently confirmed.

[0041] As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present disclosure. Any recited method can be carried out in the order of events recited or in any other order that is logically possible.

[0042] Where a range is expressed, a further aspect includes from the one particular value and / or to the other particular value. Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are alsoencompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure. For example, where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure, e.g., the phrase “x to y” includes the range from ‘x’ to ‘y’ as well as the range greater than ‘x’ and less than ‘y’. The range can also be expressed as an upper limit, e.g. ‘about x, y, z, or less’ and should be interpreted to include the specific ranges of ‘about x’, ‘about y’, and ‘about z’ as well as the ranges of Tess than x’, less than y’, and Tess than z’. Likewise, the phrase ‘about x, y, z, or greater’ should be interpreted to include the specific ranges of ‘about x’, ‘about y’, and ‘about z’ as well as the ranges of ‘greater than x’, greater than y’, and ‘greater than z’. In addition, the phrase “about ‘x’ to ‘y’”, where ‘x’ and ‘y’ are numerical values, includes “about ‘x’ to about ‘y’”.

[0043] It should be noted that ratios, concentrations, amounts, and other numerical data can be expressed herein in a range format. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. Ranges can be expressed herein as from “about” one particular value, and / or to “about” another particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms a further aspect. For example, if the value “about 10” is disclosed, then “10” is also disclosed.

[0044] It is to be understood that such a range format is used for convenience and brevity, and thus, should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or subranges encompassed within that range as if each numerical value and sub-range is explicitly recited. To illustrate, a numerical range of “about 0.1% to 5%” should be interpreted to include not only the explicitly recited values of about 0.1% to about 5%, but also include individual values (e.g., about 1%, about 2%, about 3%, and about 4%) and the sub-ranges (e.g., about 0.5% to about1.1%; about 5% to about 2.4%; about 0.5% to about 3.2%, and about 0.5% to about 4.4%, and other possible sub-ranges) within the indicated range.General Definitions

[0045] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0046] As used herein, the singular forms “a”, “an”, and “the” include both singular and plural referents unless the context clearly dictates otherwise.

[0047] As used herein, "about," "approximately," “substantially,” and the like, when used in connection with a measurable variable such as a parameter, an amount, a temporal duration, and the like, are meant to encompass variations of and from the specified value including those within experimental error (which can be determined by e.g. given data set, art accepted standard, and / or with e.g., a given confidence interval (e.g. 90%, 95%, or more confidence interval from the mean), such as variations of + / - 10% or less, + / -5% or less, + / -!% or less, and + / -0.1% or less of and from the specified value, insofar such variations are appropriate to perform in the disclosed invention. As used herein, the terms “about,” “approximate,” “at or about,” and “substantially” can mean that the amount or value in question can be the exact value or a value that provides equivalent results or effects as recited in the claims or taught herein. That is, it is understood that amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact, but may be approximate and / or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art such that equivalent results or effects are obtained. In some circumstances, the value that provides equivalent results or effects cannot be reasonably determined. In general, an amount, size, formulation, parameter or other quantity or characteristic is “about,” “approximate,” or “at or about” whether or not expressly stated to be such. It is understood that where “about,” “approximate,” or “at or about” is used before a quantitative value, the parameter also includes the specific quantitative value itself, unless specifically stated otherwise.

[0048] The term “optional” or “optionally” means that the subsequent described event, circumstance or substituent may or may not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.

[0049] The recitation of numerical ranges by endpoints includes all numbers and fractions subsumed within the respective ranges, as well as the recited endpoints.

[0050] The terms “subject,” “individual,” and “patient” are used interchangeably herein to refer to a vertebrate, preferably a mammal, more preferably a human. Mammals include, but are not limited to, murines, simians, humans, farm animals, sport animals, and pets. Tissues, cells and their progeny of a biological entity obtained in vivo or cultured in vitro are also encompassed.

[0051] Various embodiments are described hereinafter. It should be noted that the specific embodiments are not intended as an exhaustive description or as a limitation to the broader aspects discussed herein. One aspect described in conjunction with a particular embodiment is not necessarily limited to that embodiment and can be practiced with any other embodiment(s). Reference throughout this specification to “one embodiment”, “an embodiment,” “an example embodiment,” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” or “an example embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to a person skilled in the art from this disclosure, in one or more embodiments. Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention. For example, in the appended claims, any of the claimed embodiments can be used in any combination.

[0052] All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as though each individual publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.OVERVIEW

[0053] Forage intake has been studied in a variety of different ways. Calculations of forage biomass yield have been utilized to estimate forage intake during a grazing event, as well as indigestible plant marker. Despite acceptable accuracy, these methods are impractical for everyday use. Other precision livestock feeding technologies, such as the Smarttag sensor (Nedap Smarttag Neck, Nedap Livestock Management, Groenlo, the Netherlands and the RumiWatch System (RWS) have been developed from cattle studies with the potential for applications in equine management. The EquiWatch System (EWS) was developed from the RWS to track horse eating behaviors and differences in mastication patterns among haylage, hay, and concentrate. Although these technologies have promise in monitoring grazing time, they may struggle to characterize feed intake and forage quality. Technologies such as near-infrared spectroscopy (NIRS), a relatively inexpensive, quick, noninvasive and nontoxic way to measure the energy content of fresh herbage, have been studied to explore live analysis of forage quality. The use of inertial measurement units (IMU) has also been studied in small ruminants for comparison of grazing behavior and forage intake . Although individual technologies and studies shed light on key components of equine grazing, such as forage preference, calculating forage biomass yield, determination of grazing versus non-grazing behavior, forage chemical composition, and mastication rate, there is no single unit of technology capable of measuring all of these characteristics concurrently to provide a precise measurement of the quantity and quality of forage being consumed in a grazing period. In addition, forage samples can be submitted for proximate analysis, but pasture forage is not a uniform distribution of nutrition and horses are known to be selective grazers. Therefore, being able to chronologically measure animal behavior within a grazing period and simultaneously perform live-analysis of forage nutritional composition would open the door for developing precision feeding

[0054] With that said, embodiments disclosed herein can provide forage intake sensing devices and systems that can collect spectral data from forage likely to be consumed by an animal and methods of determining forage behavior, forage intake, and other foraging characteristics. The devices and systems of the present disclosure can also be used to determine other individual animal, herd, forage and other environmental characteristics. These and Other compositions, compounds, methods, features, and advantages of the present disclosure will be or becomeapparent to one having ordinary skill in the art upon examination of the following drawings, detailed description, and examples. It is intended that all such additional compositions, compounds, methods, features, and advantages be included within this description, and be within the scope of the present disclosure.FORAGE INTAKE SENSING DEVICES AND SYSTEMS

[0055] Generally, described herein are forage intake sensing devices that can be worn on the head of an animal such that spectral data of forage in proximity to the mouth or other part of the anatomy (e.g., a trunk, nose, etc.) involved in tearing or otherwise removing forage from pasture, grassland, rangeland, etc. for consumption by the animal. This is forage likely to be consumed by the animal. The device can be configured, in some embodiments, to take other measurements, such as location, speed, mastication amount, etc., which can be combined with the forage spectral data to provide further insight into grazing patterns, preferences, behaviors, and the like. Without being bound by theory, the use of spectral data can provide information on forage type and forage quality, which can provide improved and more accurate assessments of foraging characteristics than currently available systems reliant on motion sensing. Embodiments of the device of the present disclosure also have benefit of being low powered so as to be practically and economically feasible, unlike video based approaches. As demonstrated in the Working Examples, by use of spectral sensing, the devices herein can provide an improvement of the error rate over motion sensing based devices. For example, motion based sensor devices have about a 26% whereas the devices of the present disclosure can achieve only about 5-8% error.

[0056] Described in certain example embodiments herein is a head-mounted forage intake monitoring device including a spectrophotometer configured to detect spectra of an environment located at an optimized distance (d, FIGS. 3-5) in front of the mouth of an animal during use thereby detecting one or more spectra of forage to be consumed by the animal. In certain example embodiments, described herein is a head-mounted forage intake monitoring device including a spectrophotometer; and a headpiece; wherein the spectrophotometer is coupled to or otherwise integrated with the headpiece such that, when worn by an animal during use, the spectrophotometer detects one or more spectra of an environment located at an optimized distance in front of the mouth of the animal during use thereby detecting spectra of forage to be consumed by the animal. In some embodiments, the optimized distance is 1 to 36 inches away from the nose or relevantanatomical part of the animal and covers the space between 45 degrees to the right and left of the midline of the head of the animal. See e.g., FIG. 5. In some embodiments, the head-mounted forage intake monitoring device further includes a proximity sensor. The proximity sensor can be coupled or otherwise integrated with the a head piece and / or spectrophotometer.

[0057] As used herein, “spectrophotometer” refers to any sensor, sensing device, or sensing system that is capable of detecting and / or measuring the presence and / or intensity of a wavelength or range of wavelengths of light. Spectrophotometer is used interchangeably with spectrometer herein. In general, spectrophotometers capture and measure light reflected by an object or scene in the form of a reflectance spectrum. The working output of a spectrophotometer is typically referred to as a spectrum or spectra. In some embodiments, the spectrophotometer is portable. In some embodiments, the spectrophotometer is a mini-spectrometers. In some embodiments, the spectrometer is a MEMS-based spectrometer. In some embodiments, the spectrometer is a Fourier transform infrared spectrometer (FITR)-based spectrometer. In some embodiments, the spectrophotometer is an ultra-compact spectrophotometer. In some embodiments, the ultracompact spectrophotometer is an MEMS-Fabry -Perot Interferometer. In some embodiments, the spectrometer is a hyperspectral sensor. In some embodiments, the spectrophotometer is a spectral sensor. In some embodiments, the spectrometer (including but not limited to spectral sensors) is a chip-based spectrometer. In some embodiments, the spectrometer is a Raman spectrometer.

[0058] In some embodiments the spectrophotometer detects ultraviolet (UV) light, visible light, infrared light, or any combination thereof. In some embodiments, the spectrometer is capable of hyperspectral imaging. In some embodiments, the spectrophotometer detects from about 410 nm to about 940 nm, such as 410 nm, to / or 411 nm, 412 nm, 413 nm, 414 nm, 415 nm, 416 nm, 417 nm, 418 nm, 419 nm, 420 nm, 421 nm, 422 nm, 423 nm, 424 nm, 425 nm, 426 nm, 427 nm,428 nm, 429 nm, 430 nm, 431 nm, 432 nm, 433 nm, 434 nm, 435 nm, 436 nm, 437 nm, 438 nm,439 nm, 440 nm, 441 nm, 442 nm, 443 nm, 444 nm, 445 nm, 446 nm, 447 nm, 448 nm, 449 nm,450 nm, 451 nm, 452 nm, 453 nm, 454 nm, 455 nm, 456 nm, 457 nm, 458 nm, 459 nm, 460 nm,461 nm, 462 nm, 463 nm, 464 nm, 465 nm, 466 nm, 467 nm, 468 nm, 469 nm, 470 nm, 471 nm,472 nm, 473 nm, 474 nm, 475 nm, 476 nm, 477 nm, 478 nm, 479 nm, 480 nm, 481 nm, 482 nm,483 nm, 484 nm, 485 nm, 486 nm, 487 nm, 488 nm, 489 nm, 490 nm, 491 nm, 492 nm, 493 nm,494 nm, 495 nm, 496 nm, 497 nm, 498 nm, 499 nm, 500 nm, 501 nm, 502 nm, 503 nm, 504 nm,nm, 506 nm, 507 nm, 508 nm, 509 nm, 510 nm, 511 nm, 512 nm, 513 nm, 514 nm, 515 nm nm, 517 nm, 518 nm, 519 nm, 520 nm, 521 nm, 522 nm, 523 nm, 524 nm, 525 nm, 526 nm nm, 528 nm, 529 nm, 530 nm, 531 nm, 532 nm, 533 nm, 534 nm, 535 nm, 536 nm, 537 nm nm, 539 nm, 540 nm, 541 nm, 542 nm, 543 nm, 544 nm, 545 nm, 546 nm, 547 nm, 548 nm nm, 550 nm, 551 nm, 552 nm, 553 nm, 554 nm, 555 nm, 556 nm, 557 nm, 558 nm, 559 nm nm, 561 nm, 562 nm, 563 nm, 564 nm, 565 nm, 566 nm, 567 nm, 568 nm, 569 nm, 570 nm nm, 572 nm, 573 nm, 574 nm, 575 nm, 576 nm, 577 nm, 578 nm, 579 nm, 580 nm, 581 nm nm, 583 nm, 584 nm, 585 nm, 586 nm, 587 nm, 588 nm, 589 nm, 590 nm, 591 nm, 592 nm nm, 594 nm, 595 nm, 596 nm, 597 nm, 598 nm, 599 nm, 600 nm, 601 nm, 602 nm, 603 nm nm, 605 nm, 606 nm, 607 nm, 608 nm, 609 nm, 610 nm, 611 nm, 612 nm, 613 nm, 614 nm nm, 616 nm, 617 nm, 618 nm, 619 nm, 620 nm, 621 nm, 622 nm, 623 nm, 624 nm, 625 nm nm, 627 nm, 628 nm, 629 nm, 630 nm, 631 nm, 632 nm, 633 nm, 634 nm, 635 nm, 636 nm nm, 638 nm, 639 nm, 640 nm, 641 nm, 642 nm, 643 nm, 644 nm, 645 nm, 646 nm, 647 nm nm, 649 nm, 650 nm, 651 nm, 652 nm, 653 nm, 654 nm, 655 nm, 656 nm, 657 nm, 658 nm nm, 660 nm, 661 nm, 662 nm, 663 nm, 664 nm, 665 nm, 666 nm, 667 nm, 668 nm, 669 nm nm, 671 nm, 672 nm, 673 nm, 674 nm, 675 nm, 676 nm, 677 nm, 678 nm, 679 nm, 680 nm nm, 682 nm, 683 nm, 684 nm, 685 nm, 686 nm, 687 nm, 688 nm, 689 nm, 690 nm, 691 nm nm, 693 nm, 694 nm, 695 nm, 696 nm, 697 nm, 698 nm, 699 nm, 700 nm, 701 nm, 702 nm nm, 704 nm, 705 nm, 706 nm, 707 nm, 708 nm, 709 nm, 710 nm, 711 nm, 712 nm, 713 nm nm, 715 nm, 716 nm, 717 nm, 718 nm, 719 nm, 720 nm, 721 nm, 722 nm, 723 nm, 724 nm nm, 726 nm, 727 nm, 728 nm, 729 nm, 730 nm, 731 nm, 732 nm, 733 nm, 734 nm, 735 nm nm, 737 nm, 738 nm, 739 nm, 740 nm, 741 nm, 742 nm, 743 nm, 744 nm, 745 nm, 746 nm nm, 748 nm, 749 nm, 750 nm, 751 nm, 752 nm, 753 nm, 754 nm, 755 nm, 756 nm, 757 nm nm, 759 nm, 760 nm, 761 nm, 762 nm, 763 nm, 764 nm, 765 nm, 766 nm, 767 nm, 768 nm nm, 770 nm, 771 nm, 772 nm, 773 nm, 774 nm, 775 nm, 776 nm, 777 nm, 778 nm, 779 nm nm, 781 nm, 782 nm, 783 nm, 784 nm, 785 nm, 786 nm, 787 nm, 788 nm, 789 nm, 790 nm nm, 792 nm, 793 nm, 794 nm, 795 nm, 796 nm, 797 nm, 798 nm, 799 nm, 800 nm, 801 nm nm, 803 nm, 804 nm, 805 nm, 806 nm, 807 nm, 808 nm, 809 nm, 810 nm, 811 nm, 812 nm nm, 814 nm, 815 nm, 816 nm, 817 nm, 818 nm, 819 nm, 820 nm, 821 nm, 822 nm, 823 nm nm, 825 nm, 826 nm, 827 nm, 828 nm, 829 nm, 830 nm, 831 nm, 832 nm, 833 nm, 834 nm835 nm, 836 nm, 837 nm, 838 nm, 839 nm, 840 nm, 841 nm, 842 nm, 843 nm, 844 nm, 845 nm,846 nm, 847 nm, 848 nm, 849 nm, 850 nm, 851 nm, 852 nm, 853 nm, 854 nm, 855 nm, 856 nm,857 nm, 858 nm, 859 nm, 860 nm, 861 nm, 862 nm, 863 nm, 864 nm, 865 nm, 866 nm, 867 nm,868 nm, 869 nm, 870 nm, 871 nm, 872 nm, 873 nm, 874 nm, 875 nm, 876 nm, 877 nm, 878 nm,879 nm, 880 nm, 881 nm, 882 nm, 883 nm, 884 nm, 885 nm, 886 nm, 887 nm, 888 nm, 889 nm,890 nm, 891 nm, 892 nm, 893 nm, 894 nm, 895 nm, 896 nm, 897 nm, 898 nm, 899 nm, 900 nm,901 nm, 902 nm, 903 nm, 904 nm, 905 nm, 906 nm, 907 nm, 908 nm, 909 nm, 910 nm, 911 nm,912 nm, 913 nm, 914 nm, 915 nm, 916 nm, 917 nm, 918 nm, 919 nm, 920 nm, 921 nm, 922 nm,923 nm, 924 nm, 925 nm, 926 nm, 927 nm, 928 nm, 929 nm, 930 nm, 931 nm, 932 nm, 933 nm,934 nm, 935 nm, 936 nm, 937 nm, 938 nm, 939 nm, 940 nm, or any range of values therein.

[0059] As used herein “headpiece” refers to any device, covering, strapping, collar, veil, shield, hat, garment, apparel, attire, clothes, helmet, hood, and / or the like to be placed on or around, secured to or around, attached on or around, or otherwise worn on or around the head or portion thereof of an animal. In some embodiments, the headpiece comprises or is a harness, a halter, a mask, a headcollar, a headstall, a bonnet, a wrap, an ear cover, a helmet, a collar, or any combination thereof. In some embodiments, the spectral sensor is coupled or otherwise integrated with the headpiece such that the spectral sensor is positioned on, in, or within immediate proximity to an ear of the animal when worn.

[0060] In addition to the spectral sensor, the head-mounted forage intake monitoring device further comprises one or more additional sensors or measuring devices or components. In some embodiments, the one or more additional sensors or measuring devices selected from a gyroscope, an accelerometer, a temperature sensor, a photosensor, a chemical sensor, a humidity sensor, a proximity sensor, a global positioning system, pedometer, speedometer, a degree of freedom inertial measurement unit, a mastication tracking device, or any combination thereof. In some embodiments, the spectral sensor is coupled to and / or in communication with one or more additional sensors or measuring devices. In some embodiments, the spectral sensor is electrically coupled and / or in electrical communication with one or more additional sensors or measuring devices. In some embodiments, the spectral sensor is optically coupled and / or in optical communication with one or more additional sensors or measuring devices. In some embodiments, the spectral sensor is wirelessly coupled and / or in wireless communication with one or moreadditional sensors or measuring devices. One or more of the one or more additional sensors or measuring devices can be coupled to (e.g., physically, electrically, optically, wirelessly, or otherwise) and / or in communication with (e.g., physically, electrically, optically, wirelessly, or otherwise one or more additional sensors or measuring devices.

[0061] The head-mounted forage intake monitoring device of the present disclosure can also include one or more power sources. In some embodiments, one or more of the one or more power sources are rechargeable. In some embodiments, one or more of the power sources are not rechargeable. In some embodiments, one or more of the power sources are solar. In some embodiments, one or more of the one or more power sources are configured to connect to a power grid to recharge, such as when not in use. In some embodiments, the power source is one or more batteries. The spectral sensor and / or one or more additional sensors or measuring devices can be coupled (e.g., electrically) to the power source.

[0062] The head-mounted forage intake monitoring device of the present disclosure can also include one or more other components, devices, and / or the like capable of and / or configured to receive and transmit data, signals, and / or the like; devices, systems, and components capable of executing and / or storing processing logic. Such devices include, without limitation, processors, memory, transmitters, receivers, and / or the like. In some embodiments, the head-mounted forage intake monitoring device further comprising a transmitter configured to transmit data to a receiver, wherein the transmitter is (a) coupled the spectral sensor, (b) the one or more additional sensors or detection devices, or (c) both (a) and (b). These component(s) can be coupled to (e.g., electrically, wirelessly, optically, etc.) and / or in communication (e.g., electrical, wireless, optical, etc.) with the spectral sensor, one or more additional sensors and / or measuring devices, and / or power source. These component(s) can also be coupled or be configured to couple to one or more other sensors, computing systems or components (e.g., memory, processors, transmitters, receivers, etc.) that are not directly part of the head-mounted forage intake monitoring device. In such a case, the headmounted forage intake monitoring device and other component(s) can be considered a system.

[0063] The head-mounted forage intake monitoring device can be specifically designed, oriented, shaped, or otherwise fitted to one or more animals. Further, the head-mounted forage intake monitoring device can be made in different sizes (e.g., small, medium, large, extra-large, etc.) to accommodate different ranges of head sizes even within the same animal species. Forexample, animals with similar head shapes, such as horses and donkeys, could use the same headmounted forage intake monitoring device so long as the spectral sensor can detect forage at the optimized distance in front of the mouth of an animal when in use. Additionally, it will be appreciated that the appropriate fit and size of the head-mounted forage intake monitoring device is such that spectral sensor detects forage at the optimized distance in front of the mouth of the animal when in use. Any suitable material or combination of materials can be used to form the head-mounted forage intake device and form a base to which the electronic or other components can be attached, integrated or otherwise coupled to. Exemplary suitable materials include, textiles, polymers, silicone, neoprene, plastics, rubbers, leather, and / or the like, and combinations of such materials. The headpiece can have one or more components that can be coupled together and form the base of the head piece that the one or more other components, such as the spectral sensor, one or more additional sensors or measuring devices, power sources, processors, transmitters, receivers, etc. can be attached or otherwise coupled. The base of the headpiece can be made of an of the suitable materials or combination thereof previously mentioned. In some embodiments, the base sets forth the shape and size of the headpiece. The base can be formed so as to provide one or more other functions such as protection against pests or injury. For example, in some embodiments, the base is formed such that it is a mask that can prevent flies from contacting the head or part thereof or protect from sunlight. In other non-limiting examples, the base is formed so as to protect the head from physical damage, such as during animal transport or in a hospital setting. Other suitable forms, such as collars, hats, etc. will be immediately appreciated by one of ordinary skill in the art in view of the disclosure herein.

[0064] The head-mounted forage intake monitoring device of the present disclosure can be configured for use with an animal. In some embodiments, the animal is a domesticated animal or a wild animal. In some embodiments, the animal is an animal that grazes grass or other forage. In some embodiments, the animal is a horse, cattle, sheep, rhinoceros, elephant, zebra, yak, bison, donkey, mule, goat, deer, elk, moose, antelope, alpaca, llama, wildebeest, pronghorn, or reindeer. Other animals in which this device would be useful will be appreciated by those of ordinary skill in the art. Such animals are within the scope of the present disclosure.SYSTEMS AND METHODS OF DETERMING FORAGING AND / ORENVIRONEMENTAL CHARACTERISTICS

[0065] The head-mounted forage intake monitoring device of the present disclosure can be used to collect spectral data, and in some embodiments, other data or information that can be used to determine or determine the probability of foraging characteristics of an animal. Exemplary foraging characteristics include, but are not limited to, forage intake, forage quality, and / or foraging behavior. It will be appreciated, that the spectral data of the forage likely to be consumed as well as data from one or more additional sensors or measuring devices can also be used by a computer-implemented system to determine characteristics regarding the environment in which the animal is foraging in. For example, the season, time of day, drought level, forage health, presence of predators or other herd members and / or herd size), etc. could be determined.

[0066] Described in some embodiments herein is a computer-implemented method to determine forage intake, forage quality, and / or foraging behavior that includes receiving forage spectral data from a head-mounted forage intake monitoring device of the present disclosure described in greater detail elsewhere herein in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior from the received forage spectral data; and executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non- transitory computer readable memory, or any combination thereof.

[0067] The method can further include receiving data from the one or more additional sensors or detection devices in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior of the animal from the received data from the one or more additional sensors or detection devices; executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non-transitory computer readable memory, or any combination thereof.

[0068] The present disclosure also includes computer program products, that can include a non-transitory computer-executable and / or readable storage medium and / or device havingcomputer-readable program instructions embodied thereon that when executed by a computer cause the computer to detect, calculate, and / or determine one or more characteristics of forage intake, forage quality, and / or foraging behavior from forage spectral data received from a headmounted forage intake monitoring device of the present disclosure described in greater detail elsewhere herein; the computer-executable program instructions comprising: computer-executable program instruction to differentiate spectral data associated with foraging from non-foraging; to differentiate spectral data between forage types; and / or differentiate spectral data between different forage qualities. In some embodiments, the computer program product also includes computerexecutable program instructions to identify from data from one or more other sensors one or more locomotion patterns or behaviors of the animal; mastication activity of the animal; biologic status or characteristic of an animal (e.g., temperature, ovulation / heat status, etc.); and / or an environmental condition.

[0069] Also described in several exemplary embodiments herein are non-transitory computer readable medium including computer-executable instructions recorded thereon for causing a computer to perform a method that includes receiving forage spectral data from a head-mounted forage intake monitoring device of the present disclosure described in greater detail elsewhere herein in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior from the received forage spectral data; executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non-transitory computer readable memory, or any combination thereof. In some embodiments, the method further includes receiving data from the one or more additional sensors or detection devices in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior of the animal from the received data from the one or more additional sensors or detection devices; and executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non-transitory computer readable memory, or any combination thereof.

[0070] Also described in several example embodiments herein is a forage intake monitoring system that includes a head-mounted forage intake monitoring device of the present disclosure described in greater detail elsewhere herein; non-transitory computer-readable medium; and a processor configured to execute instructions stored on the non-transitory computer readable medium which, when executed, cause the processor to perform the method that includes receiving forage spectral data from a head-mounted forage intake monitoring device of the present disclosure described in greater detail elsewhere herein in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior from the received forage spectral data; executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non-transitory computer readable memory, or any combination thereof. In some embodiments, the method further includes receiving data from the one or more additional sensors or detection devices in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior of the animal from the received data from the one or more additional sensors or detection devices; and executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non- transitory computer readable memory, or any combination thereof.Example System Architectures

[0071] FIG. 6 is a block diagram depicting a system for determine forage intake, forage quality, and / or foraging behavior based in whole or in part on forage spectral data, in accordance with certain example embodiments. As depicted in FIG. 1, the system 100 includes networked devices 110, 120 that are configured to communicate with one another via one or more networks 105. In some embodiments, a user associated with a user device 120 must install a user interface application 121 and / or make a feature selection to obtain the benefit of the techniques described herein.

[0072] Each network 105 includes a wired or wireless telecommunication means by which networked devices (including devices 110 and 120) can exchange data. For example, each network105 can include a local area network (“LAN”), a wide area network (“WAN”), an intranet, Internet, a mobile telephone or cellular network, a wireless network, optical network, or any combination thereof. Throughout the discussion of example embodiments, it should be understood that the terms “data” and “information” are used interchangeably herein to refer to text, images, audio, video, or any other form of information that can exist in a computer-based environment.

[0073] Each networked device 110 and 120 includes a device or component having a communication module capable of transmitting and receiving data over the network 105. For example, each networked device 110 and 120 can include a server, desktop computer, laptop computer, tablet computer, smart phone, handheld computer, personal digital assistant (“PDA”), processing chip, memory, or any other wired or wireless, processor-driven device. In the example embodiment depicted in FIG. 6, the network devices 110 and 120 are operated by end-users and backend server operators / administrators (not depicted). A user can use the application 121, such as a web browser application or a stand-alone application to view, upload, download, or otherwise access fdes or web pages via a distributed network 105.

[0074] It will be appreciated that the network connections shown are example and other means of establishing a communication link between the computers and devices can be used. Moreover, those having ordinary skill in the art and having the benefit of the present disclosure will appreciate that the devices 110 and 120 illustrated in FIG. 6 can have any of several other suitable computer system configurations. For example, a user device 120 embodied as a mobile phone or handheld computer many not include all components described above. In some embodiments, the device 110 is a head-mounted forage intake monitoring device of the present disclosure.

[0075] In certain example embodiments, the network computing devices and any other computing machines associated with the embodiments presented herein may be any type of computing machine or applicable components thereof such as, but not limited to, those discussed in more detail with respect to FIG. 6. Furthermore, any components associated with any of these computing machines, such as components described herein or any other components (scripts, web content, software, firmware, or hardware) associated with the technology presented herein may be any of the components discussed in more detail with respect to FIG. 6. The computing machine discussed herein may communicate with one another as well as other computer machines orcommunication systems over one or more networks, such as network 105. The network 105 may include any type of data or communication network.

[0076] FIG. 7 depicts a computing machine 2000 and a module 2050 in accordance with certain example embodiments. The computing machine 2000 may correspond to any of the various computers, servers, mobile devices, embedded systems, and / or computing systems presented herein. The module 2050 may comprise one or more hardware or software elements configured to facilitate the computing machine 2000 in performing the various methods and processing functions presented herein. The computing machine 2000 may include various internal or attached components such as a processor 2010, system bus 2020, system memory 2030, storage media 2040, input / output interface 2060, and a network interface 2070 for communicating with a network 2080.

[0077] The computing machine 2000 may be implemented as a conventional computer system, an embedded controller, a laptop, a server, a mobile device, a smartphone, a set-top box, a kiosk, a router or other network node, a vehicular information system, one more processors associated with a television, a customized machine, any other hardware platform, or any combination or multiplicity thereof. The computing machine 2000 may be a distributed system configured to function using multiple computing machines interconnected via a data network or bus system.

[0078] The processor 2010 may be configured to execute code or instructions to perform the operations and functionality described herein, manage request flow and address mappings, and to perform calculations and generate commands. The processor 2010 may be configured to monitor and control the operation of the components in the computing machine 2000. The processor 2010 may be a general purpose processor, a processor core, a multiprocessor, a reconfigurable processor, a microcontroller, a digital signal processor (“DSP”), an application specific integrated circuit (“ASIC”), a graphics processing unit (“GPU”), a field programmable gate array (“FPGA”), a programmable logic device (“PLD”), a controller, a state machine, gated logic, discrete hardware components, any other processing unit, or any combination or multiplicity thereof. The processor 2010 may be a single processing unit, multiple processing units, a single processing core, multiple processing cores, special purpose processing cores, co-processors, or any combination thereof. According to certain embodiments, the processor 2010 along with other components of thecomputing machine 2000 may be a virtualized computing machine executing within one or more other computing machines.

[0079] The system memory 2030 may include non-volatile memories such as read-only memory (“ROM”), programmable read-only memory (“PROM”), erasable programmable readonly memory (“EPROM”), flash memory, or any other device capable of storing program instructions or data with or without applied power. The system memory 2030 may also include volatile memories such as random access memory (“RAM”), static random access memory (“SRAM”), dynamic random access memory (“DRAM”), and synchronous dynamic random access memory (“SDRAM”). Other types of RAM also may be used to implement the system memory 2030. The system memory 2030 may be implemented using a single memory module or multiple memory modules. While the system memory 2030 is depicted as being part of the computing machine 2000, one skilled in the art will recognize that the system memory 2030 may be separate from the computing machine 2000 without departing from the scope of the subject technology. It should also be appreciated that the system memory 2030 may include, or operate in conjunction with, a non-volatile storage device such as the storage media 2040.

[0080] The storage media 2040 may include a hard disk, a floppy disk, a compact disc read only memory (“CD-ROM”), a digital versatile disc (“DVD”), a Blu-ray disc, a magnetic tape, a flash memory, other non-volatile memory device, a solid state drive (“SSD”), any magnetic storage device, any optical storage device, any electrical storage device, any semiconductor storage device, any physical-based storage device, any other data storage device, or any combination or multiplicity thereof. The storage media 2040 may store one or more operating systems, application programs and program modules such as module 2050, data, or any other information. The storage media 2040 may be part of, or connected to, the computing machine 2000. The storage media 2040 may also be part of one or more other computing machines that are in communication with the computing machine 2000 such as servers, database servers, cloud storage, network attached storage, and so forth.

[0081] The module 2050 may comprise one or more hardware or software elements configured to facilitate the computing machine 2000 with performing the various methods and processing functions presented herein. The module 2050 may include one or more sequences of instructions stored as software or firmware in association with the system memory 2030, the storage media2040, or both. The storage media 2040 may therefore represent examples of machine or computer readable media on which instructions or code may be stored for execution by the processor 2010. Machine or computer readable media may generally refer to any medium or media used to provide instructions to the processor 2010. Such machine or computer readable media associated with the module 2050 may comprise a computer software product. It should be appreciated that a computer software product comprising the module 2050 may also be associated with one or more processes or methods for delivering the module 2050 to the computing machine 2000 via the network 2080, any signal-bearing medium, or any other communication or delivery technology. The module 2050 may also comprise hardware circuits or information for configuring hardware circuits such as microcode or configuration information for an FPGA or other PLD.

[0082] The input / output (“I / O”) interface 2060 may be configured to couple to one or more external devices, to receive data from the one or more external devices, and to send data to the one or more external devices. In some embodiments, an external device is a head-mounted forage intake monitoring device of the present disclosure described in greater detail elsewhere herein or a component thereof. Such external devices along with the various internal devices may also be known as peripheral devices. The I / O interface 2060 may include both electrical and physical connections for operably coupling the various peripheral devices to the computing machine 2000 or the processor 2010. The I / O interface 2060 may be configured to communicate data, addresses, and control signals between the peripheral devices, the computing machine 2000, or the processor 2010. The I / O interface 2060 may be configured to implement any standard interface, such as small computer system interface (“SCSI”), serial-attached SCSI (“SAS”), fiber channel, peripheral component interconnect (“PCI”), PCI express (PCIe), serial bus, parallel bus, advanced technology attached (“ATA”), serial ATA (“SATA”), universal serial bus (“USB”), Thunderbolt, FireWire, various video buses, and the like. The VO interface 2060 may be configured to implement only one interface or bus technology. Alternatively, the I / O interface 2060 may be configured to implement multiple interfaces or bus technologies. The I / O interface 2060 may be configured as part of, all of, or to operate in conjunction with, the system bus 2020. The I / O interface 2060 may include one or more buffers for buffering transmissions between one or more external devices, internal devices, the computing machine 2000, or the processor 2010.

[0083] The I / O interface 2060 may couple the computing machine 2000 to various input devices including mice, touch-screens, scanners, biometric readers, electronic digitizers, sensors, receivers, touchpads, trackballs, cameras, microphones, keyboards, any other pointing devices, or any combinations thereof. The I / O interface 2060 may couple the computing machine 2000 to various output devices including video displays, speakers, printers, projectors, tactile feedback devices, automation control, robotic components, actuators, motors, fans, solenoids, valves, pumps, transmitters, signal emitters, lights, and so forth.

[0084] The computing machine 2000 may operate in a networked environment using logical connections through the network interface 2070 to one or more other systems or computing machines across the network 2080. The network 2080 may include wide area networks (WAN), local area networks (LAN), intranets, the Internet, wireless access networks, wired networks, mobile networks, telephone networks, optical networks, or combinations thereof. The network 2080 may be packet switched, circuit switched, of any topology, and may use any communication protocol. Communication links within the network 2080 may involve various digital or an analog communication media such as fiber optic cables, free-space optics, waveguides, electrical conductors, wireless links, antennas, radio-frequency communications, and so forth.

[0085] The processor 2010 may be connected to the other elements of the computing machine 2000 or the various peripherals discussed herein through the system bus 2020. It should be appreciated that the system bus 2020 may be within the processor 2010, outside the processor 2010, or both. According to some embodiments, any of the processor 2010, the other elements of the computing machine 2000, or the various peripherals discussed herein may be integrated into a single device such as a system on chip (“SOC”), system on package (“SOP”), or ASIC device.

[0086] In situations in which the systems discussed here collect personal information about users, or may make use of personal information, the users may be provided with a opportunity to control whether programs or features collect user information (e.g., information about a user’s social network, social actions or activities, profession, a user’s preferences, or a user’s current location), or to control whether and / or how to receive content from the content server that may be more relevant to the user. In addition, certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user’s identity may be treated so that no personally identifiable information can be determined for theuser, or a user’s geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over how information is collected about the user and used by a content server.

[0087] Embodiments may comprise a computer program that embodies the functions described and illustrated herein, wherein the computer program is implemented in a computer system that comprises instructions stored in a machine-readable medium and a processor that executes the instructions. However, it should be apparent that there could be many different ways of implementing embodiments in computer programming, and the embodiments should not be construed as limited to any one set of computer program instructions. Further, a skilled programmer would be able to write such a computer program to implement an embodiment of the disclosed embodiments based on the appended flow charts and associated description in the application text. Therefore, disclosure of a particular set of program code instructions is not considered necessary for an adequate understanding of how to make and use embodiments. Further, those skilled in the art will appreciate that one or more aspects of embodiments described herein may be performed by hardware, software, or a combination thereof, as may be embodied in one or more computing systems. Moreover, any reference to an act being performed by a computer should not be construed as being performed by a single computer as more than one computer may perform the act.

[0088] The example embodiments described herein can be used with computer hardware and software that perform the methods and processing functions described herein. The systems, methods, and procedures described herein can be embodied in a programmable computer, computer-executable software, or digital circuitry. The software can be stored on computer- readable media. For example, computer-readable media can include a floppy disk, RAM, ROM, hard disk, removable media, flash memory, memory stick, optical media, magneto-optical media, CD-ROM, etc. Digital circuitry can include integrated circuits, gate arrays, building block logic, field programmable gate arrays (FPGA), etc.

[0089] The example systems, methods, and acts described in the embodiments presented previously are illustrative, and, in alternative embodiments, certain acts can be performed in a different order, in parallel with one another, omitted entirely, and / or combined between differentexample embodiments, and / or certain additional acts can be performed, without departing from the scope and spirit of various embodiments. Accordingly, such alternative embodiments are included in the invention claimed herein.

[0090] Although specific embodiments have been described above in detail, the description is merely for purposes of illustration. It should be appreciated, therefore, that many aspects described above are not intended as required or essential elements unless explicitly stated otherwise. Modifications of, and equivalent components or acts corresponding to, the disclosed aspects of the example embodiments, in addition to those described above, can be made by a person of ordinary skill in the art, having the benefit of the present disclosure, without departing from the spirit and scope of embodiments defined in the following claims, the scope of which is to be accorded the broadest interpretation so as to encompass such modifications and equivalent structures.

[0091] Further embodiments are illustrated in the following Examples which are given for illustrative purposes only and are not intended to limit the scope of the invention.EXAMPLES

[0092] Now having described the embodiments of the present disclosure, in general, the following Examples describe some additional embodiments of the present disclosure. While embodiments of the present disclosure are described in connection with the following examples and the corresponding text and figures, there is no intent to limit embodiments of the present disclosure to this description. On the contrary, the intent is to cover all alternatives, modifications, and equivalents included within the spirit and scope of embodiments of the present disclosure. The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of howto perform the methods and use the probes disclosed and claimed herein. Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.), but some errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, temperature is in °C, and pressure is at or near atmospheric. Standard temperature and pressure are defined as 20 °C and 1 atmosphere.Example 1 - Spectral and Motion Sensing for Behavioral Discrimination in GrazingAnimalsI. Introduction

[0093] Horses gastrointestinal tracts are optimized to consume the majority of their diet as forage [1], In the equine industry, this is typically accomplished through the provision of hay or pasture. Aside from nutritional considerations, grazing is recommended for horses due to their need for physical activity and social interaction, and can improve animal welfare and encourage positive, appropriate behavior [2], While horse owners can examine feed tags and regulate grain rations, forage intake in pastures is largely ambiguous due to the challenge of limited capacity to monitor animal behavior in real time, and due to substantial variability in forage selection and quality.

[0094] Forage intake has been studied in a variety of different ways. Calculations of forage biomass yield have been utilized to estimate forage intake during a grazing event, as well as indigestible plant markers [3], Despite acceptable accuracy, these methods are impractical for everyday use [4,5], Other precision livestock feeding technologies, such as the Smarttag sensor (Nedap Smarttag Neck, Nedap Livestock Management, Groenlo, the Netherlands [6,7] and the RumiWatch System (RWS) have been developed from cattle studies with the potential for applications in equine management. The EquiWatch System (EWS) was developed from the RWS to track horse eating behaviors and differences in mastication patterns among haylage, hay, and concentrate [8], Although these technologies have promise in monitoring grazing time, they may struggle to characterize feed intake and forage quality. Technologies such as near-infrared spectroscopy (NIRS), a relatively inexpensive, quick, noninvasive and nontoxic way to measure the energy content of fresh herbage, have been studied to explore live analysis of forage quality [9], The use of inertial measurement units (IMU) has also been studied in small ruminants for comparison of grazing behavior and forage intake

[0010] , Although individual technologies and studies shed light on key components of equine grazing, such as forage preference

[0011] , calculating forage biomass yield [4], determination of grazing versus non-grazing behavior [6], forage chemical composition

[0012] , and mastication rate

[0013] , there is no single unit of technology capable of measuring all of these characteristics concurrently to provide a precise measurement of the quantity and quality of forage being consumed in a grazing period. In addition, forage samples canbe submitted for proximate analysis, but pasture forage is not a uniform distribution of nutrition and horses are known to be selective grazers [1,14]. Therefore, being able to chronologically measure animal behavior within a grazing period and simultaneously perform live-analysis of forage nutritional composition would open the door for developing precision feeding methods in horses.

[0095] Toward this goal, the primary objective of this study was to evaluate the use of the ICM-20948 9-degree of freedom (DoF) inertial measurement unit and a SparkFun AS7265x spectral sensor in distinguishing grazing from non-grazing behavior in horses and evaluate this technology’s potential for use in future equine grazing research and precision feeding applications. Applicant hypothesized that the fly mask unit equipped with these sensors would be able to distinguish between grazing and non-grazing behavior with acceptable accuracy.2. Materials and Methods2.1. Construction of the Unit

[0096] A Comfort Fit fly mask (Professional’s Choice, El Cajon, CA) was outfitted with a TTGO-T-Beam microprocessor (LILYGO, Shenzen, China) with on-board GPS and LoRa radio on the right cheek of the fly mask to transmit data through FM radio back to a LoRa gateway. These units were linked to a SparkFun AS7265x spectral sensor (SparkFun Electronics, Niwot, CO) on the right ear of the fly mask, as well as the ICM-20948 9-DoF (Adafruit, New York, NY) inertial measurement unit on the poll. A thorough description of the LoRa network system used for data transfer and storage can be found in dos Reis, etal.

[0015] ,2.1.1. Spectrophotometer

[0097] The SparkFun AS7265x spectral sensor (spectrophotometer) is composed of three AS7265x sensors paired with visible, UV, and IR LEDs to permit light spectroscopy testing

[0016] , Anatomically, the horse’s ear faces the direction it is looking; thus, the spectrophotometer was positioned on the front side of the right ear, so that when the horse was grazing, the spectrophotometer was angled at the ground, collecting data from herbage just a few inches in front of the horses’ muzzle (nose).2.1.2. Inertial Measurement Unit

[0098] The inertial measurement unit (IMU) is composed of an accelerometer, gyroscope, and magnetometer. Accelerometers sense gravity to produce an acceleration value

[0017] , Gyroscopescan determine angular velocity on a moving object

[0018] , The magnetometer measures Earth’s magnetic field, specifically strength and orientation

[0019] , Collectively, these instruments reflect the degrees of freedom in an IMU, showing the position and orientation with yaw, pitch, and roll directions

[0020] , The ICM-20948 9-DoF inertial measurement unit was positioned on the poll to collect data on head movement. If it was placed on the cheek, chewing motions may interfere with the IMU. In addition, by placing the unit on the poll can reduce the risk of damage by rubbing or scratching if the horse was left to graze unsupervised. The IMU used in this protocol features 9 DoF, allowing for measurements of three axes by each instrument (accelerometer, magnetometer, and gyroscope).2.2. Animals, Husbandry, and Experimental Design

[0099] All procedures in this study were approved by the Virginia Tech Institutional Animal Care and Use Committee (protocol 22-137). Five horses were randomly selected from the Virginia Tech collegiate equestrian program herd. Two warmblood mares, two warmblood geldings, and one thoroughbred gelding were used. The five horses had a mean age of 18.4 years (range 15 to 22) and mean body condition score (BCS) of 5.1 (range 4 to 6.5). All horses were housed in Virginia Tech barn facilities, were given water ad libitum, and offered diets of hay, pasture, and grain pellets formulated to meet or exceed requirements.

[0100] The experiment was conducted on one day between the hours of 1000 h and 1200 h. During testing periods, horses were outfitted with the fly mask and affiliated sensors. Each horse participated in a five-minute grazing period outside the barn, followed by a five-minute standing period in the bam driveway. An observer recorded visual observations every minute within each grazing or standing period for each horse for a total of 10 observations per horse, or five observations per horse per period (grazing or standing) as shown in Error! Reference source not found.. Fifty observations were recorded in total. Behavior during the grazing period was classified as grazing or non-grazing (stomping flies, itching, staring off, etc.). Behavior during the standing period was classified as standing or non-standing (walking around, staring off, etc.), data collected by the sensor were received and downloaded into a .txt file for analysis.2.3. Statistical Analysis

[0101] All data were analyzed in R version 2022.07.1

[0021] , Data were analyzed as a linear mixed-effects regression model using the lme4 package of R with a fixed effect for horse behavior(standing versus grazing) and a random effect for animal

[0022] . Analysis of variance was performed and estimated marginal means calculated. Significance was considered when P < 0.05 and a tendency when 0.05 < P < 0.10. Using the recursive feature selection for the spectrophotometer data, the highest accuracy of 94.7%, was achieved when three variables were used. The top three variables (wavelengths) were D (485 nm), R (610 nm), and I (645 nm).3. Results

[0102] Differences were identified (P < 0.05) in all measured wavelengths, as shown in Table 1. For motion sensing, pitch (QI) was nonsignificant (P = 0.26) and yaw (Q2) demonstrated a tendency (P = 0.056). Roll (Q3) was highly significant (P < 0.001). Significant differences (P < 0.05; Table 1) were identified in all measured wavelengths. Contrasts in motion and wavelengths between grazing and standing periods are presented in FIG. 1.Table 1. Estimated marginal means and standard errors of wavelengths and motion variables for grazing and standing behaviors.Wavelength1Grazing SE Standing SE P valueA (410nm) 1345 210 2348 263 <0.001B (435 nm) 1437 254 4233 339 <0.001C (460 nm) 2058 266 5446 429 <0.001D (485 nm) 1334 272 5218 430 <0.001E (510 nm) 1547 296 5237 466 <0.001F (535 nm) 1882 440 5996 610 <0.001G (560 nm) 1491 352 5171 504 <0.001H (585 nm) 1476 273 4187 349 <0.001R (610 nm) 2022 270 6316 290 <0.001I (645 nm) 685 118 2556 174 <0.001S (680 nm) 2126 278 6153 312 <0.001J (705 nm) 1597 271 3489 279 <0.001T (730 nm) 4791 835 6773 843 <0.001U (760 nm) 6553 1157 8419 1168 <0.001V (810 nm) 6684 1184 8791 1196 <0.001W (860 nm) 8312 1521 11761 1536 <0.001K (900nm) 2161 386 2789 390 <0.001MotionQI20.0888 0.0535 0.1759 0.0591 0.26Q23-0.010 0.0588 0.154 0.0649 0.056Q340.294 0.0744 -0.195 0.0822 <0.001’Light frequencies measured.2Pitch.3Yaw.4Roll.

[0103] The confusion matrices in Error! Reference source not found, reflect respective accuracies of IMU and spectrophotometer diagnostic ability to classify standing versus grazing behaviors. Number of trees and variables tried at each split were calculated by default in R. Generating a random forest from IMU data with 500 trees and two variables tried at each split shows an estimated error rate of 26.5%. Furthermore, grazing data had an error rate of 18.2%, while standing data had an error rate of 41.7%. When evaluating discrepancies in parsimonious spectrophotometer wavelengths with 500 trees and two variables tried at each split, the estimated error rate was 5.88%, with grazing data having 0.00% error and standing data having 16.7% error. Using a full model, spectrophotometer data had an estimated error rate of 8.82% with 500 trees and five variables tried at each split. Additionally, grazing data had a 4.55% error while standing had a 16.7% error.Table 1. Confusion matrix for classification of behavioral data given as numbers of observations obtained from a sensor classified into categorical responses reflecting grazing or standing behaviors.True observationMotion sensing prediction Grazing Standing Error, %'Grazing 18 4 18.2Standing 5 7 41.7Estimation of error rate 26.5Parsimonious spectral sensing predictionGrazing 22 0 0.0Standing 2 10 16.7Estimation of error rate 5.88Full model spectral sensing predictionGrazing 21 1 4.55Standing 2 10 16.7Estimation of error rate 8.82’Error = percent error of sensor predictions versus true observations.4. Discussion

[0104] This investigation evaluated the ability of a spectrophotometer coupled with an IMU to distinguish between grazing and non-grazing behavior in horses when worn attached to a flymask. Low error rates from spectrophotometer grazing predictions show promise for this technology as a useful behavior-tracking tool in equines. Although the IMU data for horses in a standing period had a high error rate, IMU data for horses in the grazing period showed lower error rates. The repetitive movement of biting and chewing in grazing horses appears to produce cleaner IMU data, whereas standing behavior may have outlying movements (e g., head shaking, scratching leg, biting at flies) that overlap with grazing movements, leading to incorrect categorizations of movement. Further study of these devices on horses let out on pasture, free from handlers would allow for better evaluation of durability of the unit, as well as horse-tolerance of the unit. Secondly, an extended observation and data collection period of a horse exhibiting natural behavior in the field would offer more data points of grazing and non-grazing behavior, further proving the efficacy (or lack thereof) of the devices. Thirdly, grazing the same horse on several pastures of varying forage height may also be evaluated to look for motion relationships between grazing behavior and forage height.

[0105] Automated behavior-monitoring systems that are low-cost, durable, reliable, and remotely controlled offer opportunities to improve livestock nutrition management. While continuous automated monitoring systems for cattle have been extensively developed and tested, equine eating behaviors, largely grazing, have not been thoroughly investigated [23,24], The Smarttag sensor (Nedap Smarttag Neck, Nedap Livestock Management, Groenlo, the Netherlands), developed for tracking eating behaviors in dairy cows, is composed of a G-sensor, comparable to the inertial measurement unit used in the presented study [6,7], The G-sensor was mounted to a neck collar, while the IMU used in the presented study was mounted to the pole region of the fly mask. Mounting to the pole of the fly mask rather than the neck collar can allow for more accurate recording of head motions, as the collar may be able to easily slide up and down the neck when the animal raises and lowers its head, thus generating unwanted additional movement. Overall, the Smarttag demonstrated a strong correlation (r = 0.97) between observed events and sensor data when differentiating grazing from non-grazing behavior, while the presented study was highly successful in determining grazing behavior from non-grazing behavior with the implementation of the spectrophotometer (0.0% error in parsimonious model). Further comparison of our technology to other investigations was difficult due to the deficiency of reports of similar technologies in the literature. Other technologies to track equine grazing behaviorinclude mastication pattern trackers and sound recognition devices to differentiate and track chewing versus biting [8,24,25], These technologies, while utilizing different detection techniques from our device, were also demonstrated to predict behavior with adequate levels of accuracy.

[0106] In addition to the measurements of intake mentioned above, technologies to explore live analysis of forage quality have been utilized. Near-infrared spectroscopy (NIRS) has been leveraged to measure the energy content of fresh herbage [9], NIRS uses infrared light to measure absorption rates of low energy infrared light radiation within a substance to determine its chemical properties

[0016] , While previously and predominantly used to measure dried forage samples, recalibration methods of NIRS data show promise for the use of NIRS on fresh samples [12.26], IMU data has been compared against sward surface height for correlations between small ruminant grazing behavior and forage intake

[0010] , as well as visual observations of grazing behavior and rumination of cattle [27,28], Overall, these technologies have promise for use in determining grazing behavior in animals on pasture

[0010] , Pairing the IMU technology utilized in the presented study with an NIRS unit and mastication tracker would allow for simultaneous collection of grazing behavior, measurement of forage intake, and nutrient composition of potentially consumed forage. An all-in-one unit proven with these measuring capacities would allow for automated determination of forage intake quality and quantity within a grazing period. With the potential for re-calibration across species to account for variations in grazing behavior, farm managers could more precisely supplement concentrates and reduce instances of over-feeding, providing both economic and environmental benefits.5. Discussion

[0107] This study demonstrated that head-mounted spectrophotometer and IMU have abilities to accurately record grazing versus non-grazing behavior, showing low error percent between observations and data, as well as distinct contrast in wavelengths between grazing and standing periods. Further development and calibration of the spectrophotometer unit is needed to determine the efficacy of live forage analysis, which has potential for providing a panel of nutrients obtained in a grazing period. Incorporating a mastication tracker technology, such as the EquiWatch halter

[0024] or equivalent, to account for number of bites, would allow for a holistic view of forage quality and quantity consumed in a grazing period.References for Example 1

[0108] 1. Lawrence, L.A. Nutrient requirements and balancing rations for horses. 2000.

[0109] 2. Molle, G.; Cannas, A.; Gregorini, P. A review on the effects of part-time grazing herbaceous pastures on feeding behaviour and intake of cattle, sheep and horses. Livestock Science 2022, 104982.

[0110] 3. Decruyenaere, V.; Buldgen, A.; Stilmant, D. Factors affecting intake by grazing ruminants and related quantification methods: a review. Base 2009.

[0111] 4. Martinson, K.L.; Siciliano, P.D.; Sheaffer, C.C.; McIntosh, B.J.; Swinker, A.M.;Williams, C.A. A review of equine grazing research methodologies. Journal of Equine Veterinary Science 2017, 51, 92-104.

[0112] 5. Harmoney, K.R.; Moore, K.J.; George, J.R.; Brummer, E C.; Russell, J.R.Determination of pasture biomass using four indirect methods. Agronomy Journal 1997, 89, 665- 672.

[0113] 6. Rue, B.D.; Lee, J.; Eastwood, C.; Macdonald, K.; Gregorini, P. Evaluation of an eating time sensor for use in pasture-based dairy systems. Journal of dairy science 2020, 103, 9488-9492.

[0114] 7 Hut, P.; Mulder, A.; Van den Broek, J.; Hulsen, J.; Hooijer, G.; Stassen, E.; vanEerdenburg, F.; Nielen, M. Sensor based eating time variables of dairy cows in the transition period related to the time to first service. Preventive veterinary medicine 2019, 169, 104694.

[0115] 8. Werner, J.; Umstatter, C.; Zehner, N.; Niederhauser, J.; Schick, M. Validation of a sensor-based automatic measurement system for monitoring chewing activity in horses. Livestock Science 2016, 186, 53-58.

[0116] 9. De Boever, J.; Cottyn, B.; Vanacker, J.; Boucque, C.V. The use of NIRS to predict the chemical composition and the energy value of compound feeds for cattle. Animal Feed Science and Technology 1995, 51, 243-253.

[0117] 10. Guo, L.; Welch, M.; Dobos, R.; Kwan, P.; Wang, W. Comparison of grazing behaviour of sheep on pasture with different sward surface heights using an inertial measurement unit sensor. Computers and electronics in agriculture 2018, 150, 394-401.

[0118] 11. Allen, E.; Sheaffer, C.; Martinson, K. Yield and persistence of cool-season grasses under horse grazing. Agronomy journal 2012, 104, 1741-1746.

[0119] 12. Murphy, D.J.; O'Brien, B.; O'Donovan, M.; Condon, T.; Murphy, M.D. A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures. Information Processing in Agriculture 2022, 9, 243-253.

[0120] 13. Werner, J.; Leso, L.; Umstatter, C.; Niederhauser, J.; Kennedy, E.; Geoghegan, A.;Shalloo, L.; Schick, M.; O’Brien, B. Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows. Journal of Neuroscience Methods 2018, 300, 138-146.

[0121] 14. Archer, M. The species preferences of grazing horses. Grass and Forage Science1973, 28, 123-128.

[0122] 15. dos Reis, B.R.; Easton, Z.; White, R.R.; Fuka, D. A LoRa sensor network for monitoring pastured livestock location and activity 1. Translational Animal Science 2021, 5, doi: 10.1093 / tas / txab010.

[0123] 16. Hedau, S.D. Soil Nutrients Testing using IR Photo Spectrometer. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 2021, 12, 2255-2263.

[0124] 17. Kim, M.-S.; Yu, S.-B.; Lee, K.-S. Development of a high-precision calibration method for inertial measurement unit. International journal of precision engineering and manufacturing 2014, 15, 567-575.

[0125] 18. Passaro, V.M.N.; Cuccovillo, A.; Vaiani, L.; De Carlo, M.; Campanella, C.E.Gyroscope Technology and Applications: A Review in the Industrial Perspective. Sensors 2017, 17, 2284.

[0126] 19. Frezza, L.; Santoni, F.; Piergentili, F. Sun direction determination improvement by albedo input estimation combining photodiodes and magnetometer. Acta Astronautica 2022, 190, 134-148.

[0127] 20. Kopfinger, A.; Ahlsen, D. Identification of absolute orientation using inertial measurement unit. 2019.

[0128] 21. R Core Team R: A language and environment for statistical computing., RFoundation for Statistical Computing: Vienna, Austria, 2022.

[0129] 22. Bates, D.; Maehler, M.; Bolker, B.; Walker, S. Fitting Linear Mixed-Effects ModelsUsing lme4. Journal of Statistical Software 2015, 67, 1-48, doi: 10.18637 / jss.v067.i01.

[0130] 23. Andriamandroso, A.; Bindelle, J.; Mercatoris, B.; Lebeau, F. A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnologie, Agronomie, Societe et Environnement 2016, 20.

[0131] 24. Weinert, J.R.; Werner, J.; Williams, C.A. Validation and implementation of an automated chew sensor-based remote monitoring device as tool for equine grazing research. Journal of equine veterinary science 2020, 88, 102971.

[0132] 25. Nunes, L.; Ampatzidis, Y.; Costa, L.; Wallau, M. Horse foraging behavior detection using sound recognition techniques and artificial intelligence. Computers and Electronics in Agriculture 2021, 183, 106080.

[0133] 26. Park, R.; Agnew, R.; Gordon, F.; Steen, R. The use of near infrared reflectance spectroscopy (NIRS) on undried samples of grass silage to predict chemical composition and digestibility parameters. Animal Feed Science and Technology 1998, 72, 155-167.

[0134] 27. Andriamandroso, A.L.H.; Lebeau, F.; Beckers, Y.; Froidmont, E.; Dufrasne, I.;Heinesch, B.; Dumortier, P.; Blanchy, G.; Blaise, Y.; Bindelle, J. Development of an open-source algorithm based on inertial measurement units (IMU) of a smartphone to detect cattle grass intake and ruminating behaviors. Computers and electronics in agriculture 2017, 139, 126-137.

[0135] 28. Andriamandroso, A.; Lebeau, F.; Bindelle, J. Changes in biting characteristics recorded using the inertial measurement unit of a smartphone reflect differences in sward attributes. In Proceedings of the 7th Conference on Precision Livestock Farming, 2015.Example 2Summary

[0136] Forage intake of pastured animals is challenging to monitor. Equations to estimate forage intake and quality exist; however, they do not always represent individual behaviors well. The objective of this work was to explore multimodal sensing as a strategy for monitoring grazing behavior. Data were collected using a fly mask equipped with a TTGO-T-Beam microprocessor with GPS (right cheek) and LoRa radio linked to a SparkFun AS7265x spectral sensor (right ear), and ICM-20948, 9 degree of freedom inertial measurement unit (poll). The fly mask was used to collect data on 5 horses, each for a five-minute grazing and five-minute standing period as a proof- of-concept. Linear, mixed effect regression models with a fixed effect for horse behavior (standingvs grazing) and random effect for animal identified significant differences (P<0.05) in all measured wavelengths and in mean roll angle (P<0.01) and variability in pitch angle (P=0.049). These data suggest opportunity to leverage spectral and motion sensing to discriminate among grazing and standing behaviors.Introduction

[0137] While horse owners can examine feed tags and regulate grain rations, forage intake in pastures are largely ambiguous due to the challenge of limited capacity to monitor animal behavior in real time and substantial variability in forage selection and quality. Since domestication of horses, part time grazing is recommended for horses due to their need for physical activity and social nature, and can also improve animal welfare and encourage positive, appropriate behavior (Molle et al., 2022). While individual technologies and studies shed light on key components of equine grazing, such as forage preference (Allen et al., 2012), calculating forage biomass yield (Martinson et al., 2017), determination of grazing vs. non-grazing behavior (Rue et al., 2020), forage chemical composition (Murphy et al., 2022), and mastication rate (Werner et al., 2018), there is no single unit of technology capable of measuring all of these characteristics concurrently to provide a precise measurement of the quantity and quality of forage being consumed in a grazing period.Objectives

[0138] This Example at least validates the use of the ICM-20948,9 degree of freedom inertial measurement unit and SparkFun AS7265x spectral sensor in determining grazing from nongrazing behavior in horses and potential for use of this technology in future equine grazing research and equine precision feeding techniques.Materials and Methods

[0139] Five horses from the Virginia Tech Campbell Barn were used. During testing periods, horses were outfitted with the fly mask and affiliated sensors. Sensors included the ICM-20948,9 degree of freedom inertial measurement unit and SparkFun AS7265x spectral sensor. Each horse participated in a five-minute grazing period outside the barn, followed by a five-minute standing period in the barn driveway. An observer recorded visual observations every 1 -minute within each grazing or standing period for each horse for a total of 10 observations per horse, or five observations per horse per period (grazing or standing). Use of animals for the trial was approvedby Virginia Tech’s Institute of Animal Care and Use Committee under protocol number 22-137 (APSC).Grazing and Standing Behavior Data

[0140] FIG. 2 shows an RStudio ggplot. This plot depicts the contrast in spectrophotometer data readings between standing and grazing periods.IML data Accuracy

[0141] Table 3 shows results for twenty -two of twenty -two grazing observations, the data was correctly classified as grazing. For ten of twelve standing observations, the data was correctly classified as standing; however, two of twelve standing observations were incorrectly classified as grazing instead of standing.Results

[0142] Linear, mixed effect regression models with a fixed effect for horse behavior (standing vs. grazing) and random effect for animal identified significant differences (P<0.05) in all measured wavelengths. In addition, visual contrast in wavelengths between grazing and standing periods can be seen in Figure 1. Overall, this demonstrates the potential use for head-mounted spectral sensors for the collection of grazing behavior in horses. Ten of twelve standing observations were correctly correlated with standing IMU data; however, two of twelve standing observations were classified as grazing rather than standing, producing a 16.67% error.Discussion

[0143] Automated behavior-monitoring systems that can be controlled remotely, are low-cost, durable, and reliable offer opportunities to improve grain ration assignment and ultimately, grain costs. This study demonstrated that a spectrophotometer and an IMU have abilities to accurately record grazing vs. non-grazing behavior, showing low error precents between observations and data. An extended observation and data collection period of a horse exhibiting natural behavior inthe field would offer more data points of grazing and non-grazing behavior, further proving the efficacy (or lack thereof) of the devices.References for Example 2

[0144] Molle, G., A. Cannas, and P. Gregorini. 2022. A review on the effects of part-time grazing herbaceous pastures on feeding behaviour and intake of cattle, sheep and horses. Livestock Science: 104982. Allen, E., C. Sheaffer, and K. Martinson. 2012. Yield and persistence of coolseason grasses under horse grazing. Agronomy j oumal 104(6): 1741-1746.

[0145] Martinson, K. L., P. D. Siciliano, C. C. Sheaffer, B. J. McIntosh, A. M. Swinker, and C. A. Williams. 2017. A review of equine grazing research methodologies. Journal of Equine Veterinary Science 51:92-104.

[0146] Murphy, D. J., B. O'Brien, M. O'Donovan, T. Condon, and M. D. Murphy. 2022. A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures. Information Processing in Agriculture 9(2):243-253.

[0147] Rue, B. D., J. Lee, C. Eastwood, K. Macdonald, and P. Gregorini. 2020. Evaluation of an eating time sensor for use in pasture-based dairy systems. Journal of dairy science 103(10):9488-9492.

[0148] Werner, J., L. Leso, C. Umstatter, J. Niederhauser, E. Kennedy, A. Geoghegan, L. Shalloo, M. Schick, and B. O’Brien. 2018. Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows. Journal of Neuroscience Methods 300:138-146.Example 3 - Predicting Forage Quality Using Spectral Sensing

[0149] Traditional methods of measuring forage quality have not taken into account grazing animals on extensive systems. As a solution, Applicant at least demonstrates in this Example use of a spectral sensor to determine forage quality in the field.Materials and Methods

[0150] SparkFun ESP32 Thing Plus microprocessor was connected to a SparkFun Triad Spectroscopy Sensor and a Garmin LIDAR-Lite v4 LED - Distance Measurement Sensor (SparkFun Electronics, Niwot, CO) read via a serial monitor. Samples were collected weekly using the “hula hoop” method. 12 fields, with 2 samples per field were collected each week. Forage sample height was measured and recorded minus the residual grazing height (2 inches). Sampleswere scanned in field then collected. Samples were rescanned in the lab with a spectral sensor and Dairy One Near-Infrared Spectroscopy (NIR) (Dairy One Co-Op Incorporated, Ithaca, NY). Samples were analyzed for dry matter (DM), acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP).Results and Discussion

[0151] Results are shown in FIG. 8-16 and Table 4.***

[0152] Various modifications and variations of the described methods, pharmaceutical compositions, and kits of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific embodiments, it will be understood that it is capable of further modifications and that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in the art are intended to be within the scope of the invention. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure come within known customary practice within the art to which the invention pertains and may be applied to the essential features herein before set forth.

[0153] Further attributes, features, and embodiments of the present invention can be understood by reference to the following numbered aspects of the disclosed invention. Reference to disclosure in any of the preceding aspects is applicable to any preceding numbered aspect and to any combination of any number of preceding aspects, as recognized by appropriate antecedent disclosure in any combination of preceding aspects that can be made. The following numbered aspects are provided:

[0154] 1. A head-mounted forage intake monitoring device comprising: a spectrophotometer configured to detect spectra of an environment located at an optimized distance in front of the mouth of an animal during use thereby detecting one or more spectra of forage to be consumed by the animal.

[0155] 2. A head-mounted forage intake monitoring device comprising: a spectrophotometer; and a headpiece; wherein the spectrophotometer is coupled to or otherwise integrated with the headpiece such that, when worn by an animal during use, the spectrophotometer detects one or more spectra of an environment located at an optimized distance in front of the mouth of the animal during use thereby detecting spectra of forage to be consumed by the animal.

[0156] 3 The head-mounted forage intake monitoring device of aspect 2, further comprising a proximity sensor, wherein the proximity sensor is coupled to or otherwise integrated with the headpiece.

[0157] 4. The head-mounted forage intake monitoring device of any one of aspects 1-3, wherein the spectrophotometer detects visible light, ultraviolet light, infrared light, or any combination thereof.

[0158] 5. The head-mounted forage intake monitoring device of any one of aspects 2-4, wherein the headpiece comprises or is a harness, a halter, a mask, a headcollar, a headstall, a collar, a bonnet, a wrap, an ear cover, helmet, or any combination thereof.

[0159] 6 The head-mounted forage intake monitoring device of any one of aspects 2-5, wherein the spectral sensor is coupled or otherwise integrated with the headpiece such that the spectral sensor is positioned on or within immediate proximity to an ear of the animal.

[0160] 7. The head-mounted forage intake monitoring device of any one of aspects 2-6, further comprising one or more additional sensors or measuring devices.

[0161] 8. The head-mounted forage intake monitoring device of aspect 7, wherein the one or more additional sensors or detection devices selected from a gyroscope, an accelerometer, a temperature sensor, a photosensor, a chemical sensor, a humidity sensor, a proximity sensor, a global positioning system, pedometer, speedometer, a degree of freedom inertial measurement unit, a mastication tracking device, or any combination thereof.

[0162] 9 The head-mounted forage intake monitoring device of any one of aspects 2-8, further comprising a transmitter configured to transmit data to a receiver, wherein the transmitter is (a) coupled the spectral sensor, (b) the one or more additional sensors or detection devices, or (c) both (a) and (b).

[0163] 10. The head-mounted forage intake monitoring device of any one of aspects 1-9, wherein the animal is a domesticated animal or a wild animal.

[0164] 11. The head-mounted forage intake monitoring device of any one of aspects 1-10, wherein the animal is a horse, cattle, sheep, rhinoceros, elephant, zebra, yak, bison, donkey, mule, goat, deer, elk, moose, antelope, alpaca, llama, wildebeest, pronghorn, or reindeer.

[0165] 12. A computer-implemented method to determine forage intake, forage quality, and / or foraging behavior, the method comprising: receiving forage spectral data from a head-mounted forage intake monitoring device of any one of aspects 1-11 in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior from the received forage spectral data; and executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non-transitory computer readable memory, or any combination thereof.

[0166] 13. The computer-implemented method of aspect 12, further comprising receiving data from the one or more additional sensors or detection devices in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior of the animal from the received data from the one or more additional sensors or detection devices; and executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non-transitory computer readable memory, or any combination thereof.

[0167] 14. A non-transitory computer readable medium comprising computer-executable instructions recorded thereon for causing a computer to perform the method of any one of aspects 12-13.

[0168] 15. A forage intake monitoring system comprising: the head-mounted forage intake monitoring device of any one of aspects 1-11; non-transitory computer-readable medium; and aprocessor configured to execute instructions stored on the non-transitory computer readable medium which, when executed, cause the processor to perform the method of any one of claims 12-13.

[0169] 16. A method of monitoring forage intake comprising: securing the head mounted forage intake monitoring device of any one of aspects 1-11 or 15 to the animal; and allowing the animal to graze for a period of time thereby and obtaining spectral data of consumable forage by the spectrophotometer.

[0170] 17. The method of aspect 16, further comprising obtaining and / or transmitting, by or to a computing system or device, data from the one or more other sensors or detection devices.

[0171] 18. The method of any one of aspects 16-17, further comprising determining one or more characteristics of forage intake and / or foraging behavior of the animal from the obtained spectral data and / or data from the one or more other sensors or detection devices.

Claims

CLAIMSWhat is claimed is:

1. A head-mounted forage intake monitoring device comprising: a spectrophotometer configured to detect spectra of an environment located at an optimized distance in front of the mouth of an animal during use thereby detecting one or more spectra of forage to be consumed by the animal.

2. A head-mounted forage intake monitoring device comprising: a spectrophotometer; and a headpiece; wherein the spectrophotometer is coupled to or otherwise integrated with the headpiece such that, when worn by an animal during use, the spectrophotometer detects one or more spectra of an environment located at an optimized distance in front of the mouth of the animal during use thereby detecting spectra of forage to be consumed by the animal.

3. The head-mounted forage intake monitoring device of claim 2, further comprising a proximity sensor, wherein the proximity sensor is coupled to or otherwise integrated with the headpiece.

4. The head-mounted forage intake monitoring device of any one of claims 1-3, wherein the spectrophotometer detects visible light, ultraviolet light, infrared light, or any combination thereof.

5. The head-mounted forage intake monitoring device of any one of claims 2-3, wherein the headpiece comprises or is a harness, a halter, a mask, a headcollar, a headstall, a collar, a bonnet, a wrap, an ear cover, helmet, or any combination thereof.

6. The head-mounted forage intake monitoring device of any one of claims 2-3, wherein the spectral sensor is coupled or otherwise integrated with the headpiece such that the spectral sensor is positioned on or within immediate proximity to an ear of the animal.

7. The head-mounted forage intake monitoring device of any one of claims 2-3, further comprising one or more additional sensors or measuring devices.

8. The head-mounted forage intake monitoring device of claim 7, wherein the one or more additional sensors or detection devices selected from a gyroscope, an accelerometer, a temperature sensor, a photosensor, a chemical sensor, a humidity sensor, a proximity sensor, a global positioning system, pedometer, speedometer, a degree of freedom inertial measurement unit, a mastication tracking device, or any combination thereof.

9. The head-mounted forage intake monitoring device of any one of claims 2-3 or 8, further comprising a transmitter configured to transmit data to a receiver, wherein the transmitter is (a) coupled the spectral sensor, (b) the one or more additional sensors or detection devices, or (c) both (a) and (b).

10. The head-mounted forage intake monitoring device of any one of claims 1-3 or 8, wherein the animal is a domesticated animal or a wild animal.

11. The head-mounted forage intake monitoring device of any one of claims 1-3 or 8, wherein the animal is a horse, cattle, sheep, rhinoceros, elephant, zebra, yak, bison, donkey, mule, goat, deer, elk, moose, antelope, alpaca, llama, wildebeest, pronghorn, or reindeer.

12. A computer-implemented method to determine forage intake, forage quality, and / or foraging behavior, the method comprising: receiving forage spectral data from a head-mounted forage intake monitoring device of any one of claims 1-3 or 8 in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior from the received forage spectral data; executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non-transitory computer readable memory, or any combination thereof.

13. The computer-implemented method of claim 12, further comprising receiving data from the one or more additional sensors or detection devices in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior of the animal from the received data from the one or more additional sensors or detection devices; and executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non-transitory computer readable memory, or any combination thereof.

14. A non-transitory computer readable medium comprising computer-executable instructions recorded thereon for causing a computer to perform the method of claim 12.

15. A forage intake monitoring system comprising: the head-mounted forage intake monitoring device of any one of claims 1-3 or 8; non-transitory computer-readable medium; and a processor configured to execute instructions stored on the non-transitory computer readable medium which, when executed, cause the processor to perform a method comprising: receiving forage spectral data from the head-mounted forage intake monitoring device in a format usable by a computing device; executing processing logic configured to determine forage intake, forage quality, and / or foraging behavior from the received forage spectral data; executing processing logic configured to cause information regarding forage intake, forage quality, and / or foraging behavior of the animal to be displayed via an electronic display, transmitted to a user interface program, to be saved to a non-transitory computer readable memory, or any combination thereof.

16. A method of monitoring forage intake comprising: securing the head mounted forage intake monitoring device of any one of claims 1-11 or 15 to the animal; and allowing the animal to graze for a period of time thereby and obtaining spectral data of consumable forage by the spectrophotometer.

17. The method of claim 16, further comprising obtaining and / or transmitting, by or to a computing system or device, data from the one or more other sensors or detection devices.

18. The method of any one of claims 16-17, further comprising determining one or more characteristics of forage intake and / or foraging behavior of the animal from the obtained spectral data and / or data from the one or more other sensors or detection devices.

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