Optimizing captured images

Abstract

The invention relates to the monitoring of arthropods on the basis of captured images. The disclosed subject matter is a computer-implemented method, an apparatus and a computer program.

Description

[0001] BCS243041 FC

[0002] Image optimization

[0003] TECHNICAL AREA

[0004] The present revelation deals with the monitoring of arthropods based on image recordings.

[0005] The subject matter of the present disclosure is a computer-implemented method, a device, and a computer program.

[0006] INTRODUCTION

[0007] W02020 / 058175A1 discloses a method, a device, and a computer program for monitoring arthropods. The device comprises a camera that captures an image of a collection area containing one or more arthropods. The device includes a transmitter that sends the image to a computer system via a network. The computer system can analyze the image manually and / or automatically, for example, to detect, identify, and / or count arthropods in the image.

[0008] The device is designed for outdoor use (e.g., in a field of crops). Outdoors, it is exposed to environmental influences such as wind, precipitation, humidity, temperature fluctuations, and / or sunlight. Animals and / or plants may interact with the device. Contamination may occur. All of these factors can affect the quality of the images produced by the camera. Reduced image quality can make it difficult to detect, identify, and / or count arthropods in the images.

[0009] SUMMARY

[0010] This and other aspects are addressed in the present revelation.

[0011] The first subject of the present disclosure is a computer-implemented method comprising:

[0012] Receiving an image, wherein the image shows a collection area for arthropods and a label,

[0013] Reducing the deviation between the license plate depicted in the image and a reference by transforming the image,

[0014] Storing the transformed image and / or transmitting the transformed image to a separate computer system and / or detecting, locating, identifying and / or counting arthropods in the transformed image.

[0015] Another subject of the present disclosure is a device for monitoring arthropods comprising

[0016] - a camera and

[0017] - a control unit, wherein the control unit is configured to cause the camera to produce an image capture, wherein the image capture depicts a collection area for arthropods and a marker, to reduce any deviation between the marker depicted in the image capture and a reference by transforming the image capture, to store the transformed image capture and / or transmit it to a separate computer system and / or to detect, locate, identify and / or count arthropods in the transformed image.

[0018] Another subject of the present disclosure is a non-volatile, computer-readable storage medium on which a computer program is stored which, when executed by a control unit of a device for monitoring arthropods, causes the control unit to perform the following steps:

[0019] Receiving an image showing a collection area for arthropods and a label,

[0020] Reducing the deviation between the license plate depicted in the image and a reference by transforming the image,

[0021] Storing the transformed image and / or transmitting the transformed image to a separate computer system and / or detecting, locating, identifying and / or counting arthropods in the transformed image.

[0022] BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Fig. 1 shows an embodiment of the computer-implemented method of the present disclosure in the form of a flowchart.

[0024] Fig. 2 shows, by way of example and schematic representation, an embodiment of the device of the present disclosure.

[0025] DETAILED REVELATION

[0026] The subject matter of the present disclosure is explained in more detail below, without distinguishing between the subject matter of the present disclosure (method, device, computer program). Rather, the following explanations are intended to apply analogously to all subject matter of the invention, regardless of the context in which they are described (method, device, computer program).

[0027] If the present description or the claims specify steps in a sequence, this does not necessarily mean that the disclosure is limited to the specified sequence. Rather, it is conceivable that the steps could also be carried out in a different sequence or even in parallel with one another, unless, for example, one step builds upon another, which requires that the building step be carried out subsequently (this will become clear in the specific case). The specified sequences are therefore exemplary embodiments of the present disclosure.

[0028] The subject matter of this disclosure is further explained in some places with reference to drawings. These drawings depict specific embodiments with specific features and combinations of features, primarily for illustrative purposes; this disclosure should not be understood as being limited to the features and combinations of features shown in the drawings. Furthermore, statements made in the description of the drawings with regard to features and combinations of features are intended to be generally applicable, that is, transferable to other embodiments and not limited to the embodiments shown. The article "a" means "one or more," unless, for example, "only" or "merely" precedes it. This also applies analogously to the article "a."

[0029] The expressions “based on” and “based on” mean “at least partially based on”, unless explicitly stated otherwise.

[0030] The term “or” is not to be understood as an exclusive “or”, i.e. the expression “A or B” includes “A”, “B” as well as “A and B”.

[0031] The terms used in this disclosure have the meaning they have in the prior art, in particular in the prior art cited in this disclosure.

[0032] The present disclosure provides means to optimize the quality of an image recording of a collection area.

[0033] The foraging area is an area that can be visited by arthropods. This can be a flat surface, such as a board, map, or similar object. It can also be the bottom of a container. It can also be a liquid in a container. It can also be a part of a plant, such as a leaf, fruit, or other plant part.

[0034] In one embodiment, the collection area has a rectangular shape, with the corners potentially being rounded. In another embodiment, the aspect ratio of the collection area corresponds to the image format (aspect ratio) of the image sensor used in the camera.

[0035] In one embodiment of the present disclosure, the collecting area has an extent in the range of 100 mm x 200 mm to 200 mm x 250 mm.

[0036] In one embodiment of the present disclosure, the collecting area has an extent in the range of 100 mm x 160 mm to 130 mm x 190 mm.

[0037] In one embodiment of the present disclosure, the collecting area has an extent in the range of 160 mm x 210 mm to 180 mm x 230 mm.

[0038] In one embodiment, the collection area includes means for immobilizing arthropods. The term "immobilizing" means that pests entering the device cannot leave it autonomously, at least for a certain period of time. Immobilizing means can be, for example, a liquid (e.g., water). Immobilizing means can also be, for example, an adhesive.

[0039] In one embodiment of the present disclosure, the collecting area is part of a trapping device for arthropods.

[0040] In one embodiment of the present disclosure, the catching device comprises a container filled with a liquid, e.g. a catching tray, as described in W02020 / 058175A1, W02020 / 058I70AI, WO2021 / 213824A1 or WO2022 / 243150A1.

[0041] Arthropods that enter the liquid are usually unable to leave it autonomously or are at least held in place by the liquid for a certain period of time.

[0042] The liquid is usually water, or the liquid typically consists of water, usually as its main component. One or more additives may be added to the liquid. Such an additive may be, for example, a surfactant to reduce surface tension. Such an additive may be, for example, a thickener to increase the viscosity of the liquid. Such an additive may also be an attractant to lure (specific) arthropods. Such an additive may also be an agent to prevent algae growth (for example, a herbicide). Such an additive may also be a dye. Other additives are conceivable. The shell may have a specific color and / or pattern to attract pests.For example, many rapeseed pests are attracted by a yellow color; therefore, the husk can be entirely or partially yellow to attract rapeseed pests. Some moths, for instance, are attracted by a striped pattern; therefore, the husk can have a striped pattern, either entirely or partially. Some pests are attracted by electromagnetic radiation in a specific wavelength range; therefore, the husk can incorporate one or more sources of electromagnetic radiation within a specific wavelength range.

[0043] When liquid is introduced into the interior of the dish, the volume of liquid within the dish can define a collection area where pests can gather. The pests can float on the surface of the liquid, remain suspended within the liquid, and / or sink to the bottom of the dish. The volume of the collection area is bounded on one side by the bottom of the dish, on the other sides by the dish walls, and on the open side by the liquid surface. The collection area can also be the bottom of the dish or encompass it.

[0044] A bowl according to the present invention can, for example, have the shape of a cylinder, wherein one of the base faces of the cylinder is missing (a cylinder open on one side). A bowl according to the present invention can, for example, have the shape of a truncated cone, wherein one base face of the truncated cone (preferably the one with the larger area) is missing. A bowl according to the present invention can, for example, have the shape of a cuboid, wherein one base face of the cuboid is missing. A bowl according to the present invention can, for example, have the shape of a truncated pyramid, wherein one base face of the truncated pyramid (preferably the one with the larger area) is missing. Other shapes, in particular combinations of the aforementioned shapes, are conceivable.

[0045] The base of the dish can (in top view) be round, oval, elliptical, angular (triangular, square, pentagonal, hexagonal, or generally n-sided, where n is an integer greater than or equal to three), or any other shape. In one embodiment, the base (in top view) has a round or rectangular (particularly square) shape. A rectangular shape has the advantage that the entire surface of the dish (or the base of the dish and / or the liquid surface of a liquid in the dish) can be imaged on a camera sensor, thus optimizing sensor utilization. In another embodiment, the base has a rectangular shape (where the corners may be rounded) with an aspect ratio that corresponds to the aspect ratio of the camera sensor (e.g., 4:3, 3:2, 16:9, or another common format).An oval, elliptical or round bowl, or a bowl with rounded corners, has the advantage of being easier to clean than a bowl with corners.

[0046] The walls preferably extend in a conical or cylindrical shape at an angle to the base surface in the range of 60° to 120°, for example in the range of 80° to 120° or in the range of 90° to 110° from the base, so that the base and the walls form a space that is open on one side (towards the top) but otherwise delimited from the environment by the base and the walls. This space serves to hold the liquid. This space is also referred to in this disclosure as the interior of the bowl. The base preferably covers an area of ​​10 cm² in plan view. 2 up to 2000 cm 2 , even more so from 50 cm 2 up to 1000 cm 2 .

[0047] In one embodiment, the floor has a plurality of depressions. In another embodiment, the depressions extend over a large portion of the floor. In other words, there are more areas of the floor that have depressions than areas of the floor without depressions. In other words, the floor comprises one or more first areas that do not contain depressions and one or more second areas that contain a plurality of depressions, wherein the one or more first areas occupy a first surface and the one or more second areas occupy a second surface, the first surface being smaller than the second surface. In another embodiment, the depressions are arranged to form a regular pattern, for example, a triangular pattern, a square grid, a hexagonal grid, or another pattern.Such a pattern can be helpful in focusing the camera, correcting lens defects (and / or other imaging errors), and / or determining the size of pests. In one embodiment, the depressions are arranged so that a tessellation is visible in plan view. A tessellation is defined as the complete and non-overlapping covering of the ground by uniform sub-areas. The tessellations can be Platonic or non-Platonic. In one embodiment, the tessellations are Platonic.

[0048] In one embodiment, the depressions have a round cross-section and are arranged in the form of a hexagonal grid. Such a structure is also referred to as a honeycomb structure.

[0049] The depressions have a round, elliptical, angular (for example, triangular, square, pentagonal, hexagonal, heptagonal, octagonal, or generally n-sided, where n is an integer greater than 2) or other cross-section. Depressions with different cross-sections (shapes) are also conceivable.

[0050] In this description, the term cross-section preferably means a section in the plane of the ground (without depressions), unless otherwise specified.

[0051] The depressions can be in the shape of a spherical segment (also called a spherical section), a truncated cone, a truncated cylinder, a truncated pyramid, a cube, a cuboid, or any other shape. Preferably, they are in the shape of a spherical segment, the volume of which is preferably less than half the volume of the sphere from which the segment originates. Depressions with such a trough or dome shape are easier to clean than depressions with corners and / or edges.

[0052] In one embodiment, each recess of the plurality of recesses provides a volume larger than the volume occupied by a specific (defined) arthropod but smaller than the volume occupied by two specific (defined) arthropods. In other words, the recess is preferably dimensioned such that one specific (defined) arthropod fits into it, but not two. Depending on the specific arthropod intended to be caught in the trap, a person skilled in the art can dimension the recesses accordingly.

[0053] It is conceivable that the specific arthropod fits completely into the depression, or that the specific arthropod fits into the depression with part (e.g. a large part) of its body, while part of the body protrudes above the (otherwise preferably flat) bottom surface.

[0054] The specific (defined) arthropod can, for example, be a pest that occurs at the location where the device is in use or is intended to be used. It is conceivable, for instance, that the device according to the invention is intended to be used, or is used, in a field for specific crops to check for and / or monitor the presence of a specific pest of that specific crop. An example of a specific crop is rapeseed; an example of a specific pest is the rapeseed stem weevil.Other examples of specific pests include: codling moth, aphid, thrips, fruit peel moth, Colorado potato beetle, cherry fruit fly, cockchafer, European corn borer, plum moth, rhododendron leafhopper, seed moth, scale insect, gypsy moth, spider mite, grape berry moth, walnut fruit fly, whitefly, cabbage stem weevil, pollen beetle, cabbage seed weevil, cabbage seed midge or flea beetle, or forest pests such as aphids, blue pine jewel beetle, bark beetle, oak jewel beetle, oak processionary moth, oak tortrix moth.

[0055] Spruce sawfly, Common woodworm, Large brown bark beetle, Pine bush sawfly, Pine moth, Pine looper, Small spruce sawfly, Nun moth, Horse chestnut leaf miner, Gypsy moth, Powderpost beetle.

[0056] The recesses can have a cross-sectional area of ​​1 mm to 2 cm. In one embodiment, the recesses have a cross-sectional area of ​​2 mm to 8 mm, more preferably 3 mm to 6 mm. In another embodiment, the minimum cross-sectional area is at least 2 mm and the maximum cross-sectional area is a maximum of 8 mm.

[0057] For example, a depression with a square shape when viewed from above can have a side length of the square of 2 to 8 mm.

[0058] A depression with a round shape when viewed from above can, for example, have a circular diameter of 2 to 8 mm.

[0059] A depression with a rectangular shape when viewed from above can, for example, have side lengths of 2 to 8 mm each.

[0060] In one embodiment, the recesses have a depth of 1 mm to 5 mm, for example, 1 mm to 3 mm. In another embodiment, the recesses have a minimum depth of at least 1 mm and a maximum depth of 1 cm.

[0061] In a preferred embodiment, the recesses are arranged and dimensioned such that a camera mounted above the recesses can detect pests located in the recesses without significant shadowing or obscuration by the recesses (especially in the edge area near the walls).

[0062] In one embodiment of the present disclosure, the catching device comprises a surface provided with an adhesive, as described, for example, in WO2023 / 043871A1, WO2018 / 131853A1 or W02004 / 095919A2.

[0063] The adhesive is used to immobilize arthropods. The adhesive can be, for example, a glue. The surface can be provided by a sheet, for example. In other words, the surface can be the surface of a sheet. The surface of the sheet can be coated with the adhesive.

[0064] The arc can be flexible and adapt to the shape of a receiving surface on which it is placed. The arc can also be rigid. If the arc is rigid, it is usually planar.

[0065] The sheet can be, for example, a board or card (or another body) coated with an adhesive, for example, glue. The sheet can be round (e.g., circular), elliptical, triangular, square, pentagonal, hexagonal, heptagonal, octagonal, or generally n-sided, where n can be an integer greater than 2. The sheet can be symmetrical or asymmetrical. In one embodiment of the present disclosure, the sheet has the shape of a rectangle, the corners of which may be rounded. In another embodiment of the present disclosure, the aspect ratio of the rectangular sheet corresponds to the aspect ratio of an image sensor of the camera.

[0066] In one embodiment of the present disclosure, the arc has dimensions ranging from 100 mm x 200 mm to 200 mm x 250 mm.

[0067] In a further embodiment of the present disclosure, the arc has dimensions ranging from 100 mm x 160 mm to 130 mm x 190 mm.

[0068] In a further embodiment of the present disclosure, the arc has dimensions ranging from 160 mm x 210 mm to 180 mm x 230 mm.

[0069] The area of ​​the sheet coated with adhesive can define the collection area or part of it.

[0070] In one embodiment, the device of the present disclosure is designed to receive a sheet whose surface is at least partially coated with an adhesive. In another embodiment, the device of the present disclosure is designed to allow the sheet to be replaced, i.e., exchanged for another sheet (e.g., if the sheet is soiled). The device may have a receiving surface for receiving such a sheet.

[0071] The sheet can be placed on such a receiving surface. The receiving surface is, for example, a flat surface with a round, oval, elliptical, or angular (triangular, square, pentagonal, hexagonal, or generally n-sided, where n is an integer greater than or equal to three) shape. In one embodiment, the shape is round or rectangular (for example, square). In another embodiment, the receiving surface is larger than the sheet.

[0072] In one embodiment, the sheet and the receiving surface have the same shape. The sheet can be made of a flat and rigid material such as plastic, paper, and / or cardboard.

[0073] In one embodiment, fastening means are provided to fix the bow to the mounting surface.

[0074] The fasteners can be or include clamps that exert a force (e.g., mediated by one or more springs) on the bow and press it against the mounting surface. The fasteners can be or include slots that can hold the bow. Slots that hold the bow on different sides (e.g., opposite sides) can prevent the bow from slipping. The fasteners can be clamps. The fasteners can be and / or include latches and / or brackets. The fasteners can be and / or include adhesive strips. The fasteners can be straps and / or include screws.

[0075] The fastening means can, for example, include one or more magnetic elements. These magnetic elements can be used to attach the bow to the receiving surface. One or more primary permanent magnets can be located in, under, or at the edge of the receiving surface. If a bow is placed on such primary permanent magnets, one or more secondary permanent magnets can be attached to the bow in such a way that an attractive force exists between the primary and secondary permanent magnets, fixing the bow between them. Instead of permanent magnets on both sides of the bow, metal elements containing a ferromagnetic material (e.g., iron, nickel, and / or cobalt) can also be used on one side.Instead of permanent magnets, current-carrying coils can also be used, which may contain a core made of a ferromagnetic material.

[0076] The permanent magnets and / or metal elements and / or coils may be at least partially coated and / or encased in plastic.

[0077] Such fasteners have the advantage of allowing the bow to be reversibly attached and thus replaced. They also leave no or no significant traces on the bow and / or the device. Such fasteners are described, for example, in European patent application EP24180656.1, the contents of which are hereby incorporated in their entirety into this disclosure by reference.

[0078] In one embodiment of the present disclosure, the trapping device comprises a tent-like frame that defines an interior space into which arthropods can enter. Such trapping devices are also known as delta traps (see, e.g., WO2018 / 078638A1); however, they can have shapes other than prisms (see, e.g., EP24206951.6). An adhesive-coated sheet can be placed and / or attached within this interior space as described; the sheet is at least partially protected from environmental influences (e.g., rain, wind, contamination) within the interior space. The trapping area can be colored (e.g., yellow or red) to attract specific arthropods. In addition to or instead of color, other attractants may be present. For example, a pheromone or a scent that mimics a food source could be used.The use of a source of electromagnetic radiation in the infrared, visible, and / or ultraviolet range to attract (specific) arthropods is also conceivable. The use of sounds that imitate, for example, mating males and / or females is also conceivable. The use of specific patterns that, for example, mimic a plant is also conceivable.

[0079] If a container filled with a liquid is used, it can be filled with water and optionally with one or more additives. Such an additive could be, for example, a surfactant to reduce surface tension. It could also be an attractant to lure (specific) arthropods. Or it could be an agent to prevent algae growth (for example, a herbicide).

[0080] In the case of a map or board, it may be coated with an adhesive to immobilize arthropods.

[0081] "Arthropods" are a diverse group of invertebrate animals belonging to the phylum Arthropoda.

[0082] Arthropods play an important role in ecosystems as pollinators, decomposers, and / or as part of the food web. They can also be of economic importance, both beneficial (e.g., pollination, silk production) and detrimental (e.g., as agricultural pests, disease vectors). A "pest" is defined as an arthropod that can appear during plant cultivation and damage plants or negatively impact crop yields.

[0083] Arthropods are divided into several groups (subphyla and classes), including insects and arachnids.

[0084] In one embodiment of the present disclosure, the term "arthropods" refers exclusively to insects and arachnids.

[0085] In another embodiment of the present disclosure, the term "arthropods" refers exclusively to insects.

[0086] In another embodiment of the present disclosure, the term "arthropods" refers exclusively to adult insects.

[0087] In another embodiment of the present disclosure, the term "arthropods" refers exclusively to insects in the form of caterpillars.

[0088] In another embodiment of the present disclosure, the term refers to

[0089] "Arthropods" refers exclusively to arachnids.

[0090] In another embodiment of the present disclosure, the term refers to

[0091] "Arthropods" exclusively on mites.

[0092] In one embodiment of the present disclosure, the term "arthropods" refers exclusively to arthropods that are plant pests.

[0093] In a first step, an image is received, in which a collection area is depicted. In other words, an image is received that shows or represents a collection area.

[0094] The term "receive" can mean that an image is transmitted from a camera or a separate computer system. The term "receive" can mean that an image is retrieved from a camera or a separate computer system. The term "receive" can mean that an image is read from a data storage device. The term "receive" can mean that an image is entered by a user into the computer system of this disclosure.

[0095] An "image capture" is a typically visual representation of a scene and / or one or more objects, captured or generated by the interaction of light with light-sensitive substances or sensors. The term "image capture" encompasses a wide range of formats, including but not limited to digital photographs, videos, and thermal images.

[0096] Typically, the image capture is digital. The term "digital" means that the image can be processed by a machine, usually a computer system. "Processing" refers to the familiar methods of electronic data processing (EDP).

[0097] Digital images can be processed, edited, and reproduced using computer systems and programs, as well as converted into standardized data formats such as JPEG (Joint Photographic Experts Group graphics format), PNG (Portable Network Graphics), or SVG (Scalable Vector Graphics). Digital images can be visualized using suitable display devices, such as computer monitors, projectors, and / or printers.

[0098] In digital image capture, image content is typically represented and stored using integers. In most cases, these are two-dimensional images, which are binary encoded and may be compressed. Digital images are usually raster graphics, in which the image information is stored in a uniform grid. Raster graphics consist of a grid-like arrangement of so-called image elements, e.g., pixels in the case of two-dimensional representations or voxels in the case of three-dimensional representations, each assigned a color or a grayscale value. The main characteristics of a 2D raster graphic are therefore the image size (width and height measured in pixels, also commonly referred to as image resolution) and the color depth. Each image element of a digital image is typically assigned a color or a grayscale value.The color encoding used for an image element is defined, among other things, by the color space and color depth. The simplest case is a binary image, where each image element stores a black and white value. In an image whose color is defined by the so-called RGB color space (RGB stands for the primary colors red, green, and blue), each image element comprises three color values: one for red, one for green, and one for blue. The color of an image element results, for example, from the superposition (additive mixing) of these three color values. The individual color value is discretized into, for example, 256 distinguishable levels called tonal values, which typically range from 0 to 255. The color nuance "0" of each color channel is the darkest. If all three channels have a tonal value of 0, the corresponding image element appears black; if all three channels have a tonal value of 255, the corresponding image element appears white.For the sake of simplicity, this description assumes that the images in question are RGB raster graphics with a specific number of image elements. However, this assumption should in no way be considered limiting. Those skilled in image processing will understand how to apply the principles outlined in this description to images in other formats and / or where color values ​​are encoded differently.

[0099] The at least one image capture can also be one or more excerpts from a video sequence.

[0100] The at least one image capture is usually created using one or more cameras.

[0101] A "camera" is a device or system designed to capture and record images of external objects and phenomena. A camera uses, for example, electromagnetic radiation, sound waves, or other physical processes that can be visually represented. The camera converts received signals (e.g., optical or acoustic) into other signals (e.g., electrical) and / or data that can be stored, processed, displayed, and / or transmitted. The term "camera" encompasses devices that operate with all media or technologies, including analog and digital, optical, electronic, chemical, or other methods of image capture.The term "camera" encompasses a wide range of devices including, but not limited to, still cameras, video cameras, thermal imaging cameras, radar systems, ultrasound imaging devices, electron microscopes and all future technologies that can perform the function of image acquisition.

[0102] In one embodiment of the present disclosure, the camera is an optical camera. In another embodiment, the camera is a digital camera that electrically generates two-dimensional images from light using one or more image sensors. These are typically semiconductor-based image sensors such as CCD (charge-coupled device) or CMOS (complementary metal-oxide semiconductor) sensors. Optical elements such as lenses, apertures, and the like serve to achieve the sharpest possible image of arthropods in the collecting area on the image sensor. A digital camera is configured to produce digital images.

[0103] The camera is pointed at the collection area. In other words, the camera is aligned and configured to take pictures of the collection area or a part of it.

[0104] The camera is used to generate digital images of the collection area or a part thereof. The generated images can be used (i) to detect whether one or more arthropods are present in the imaged collection area (arthropod detection), (ii) to determine the position of an arthropod in the image (arthropod localization), (iii) to count arthropods in the imaged collection area, and / or (iv) to identify arthropods, i.e., to determine which arthropod (e.g., subclass, superorder, order, suborder, family, genus, species, stage, beneficial organism, pest) it is.

[0105] The collection area can encompass one license plate. It is possible for the collection area to encompass multiple license plates. The license plate can be the collection area itself or a part thereof. The license plate can be attached to, affixed to, or embedded within the collection area. The license plate can be adjacent to the collection area. The device's camera is positioned so that an image of the collection area captures the license plate. In other words, an image of the collection area shows both the collection area and the license plate.

[0106] A distinguishing mark is a characteristic feature. This can be a shape, a color, a structure, a texture, a pattern, hatching, a symbol, a code (e.g., an alphanumeric code and / or a barcode and / or a matrix code) and / or another visible feature.

[0107] A license plate can be a combination of several characteristics.

[0108] It is possible that the collection area has such a characteristic due to its manufacturing process. For example, the collection tray disclosed in WO2022 / 243150A1 has indentations that are visible in a photograph. Such indentations are a characteristic feature of the collection area. The collection area may have a characteristic shape (e.g., round or angular) that can be depicted in a photograph. Such a characteristic shape is a feature of the collection area.

[0109] Identification marks can also be incorporated into the collection area. Identification marks can be engraved, etched, burned, embossed, and / or otherwise incorporated into a surface of the collection area. Identification marks can be printed and / or affixed to a surface of the collection area. Identification marks can be punched into a surface of the collection area. Identification marks can be injection-molded into a surface of the collection area. Identification marks can be laser-etched into a surface of the collection area. If the collection area is provided by a tray filled with a liquid, the identification mark can be provided by the tray itself (e.g., the shape of the tray, the color of the tray, and / or one or more markings incorporated into the tray).

[0110] If the collection area is provided by a sheet coated with an adhesive, the marking can be provided by the sheet (e.g., the shape of the sheet, the color of the sheet and / or one or more markings incorporated into the sheet and / or markings applied to the sheet).

[0111] One or more identifiers may be incorporated into the sheet, attached to the sheet and / or affixed to the sheet.

[0112] One or more fasteners for attaching an adhesive-coated sheet to a receiving surface can also constitute the identifier. The shape of one or more fasteners can constitute the identifier. The arrangement of one or more fasteners can constitute the identifier. The color of one or more fasteners can constitute the identifier. One or more identifiers can be incorporated into and / or applied to one or more fasteners.

[0113] One or more markings can be incorporated into a fastening device, attached to a fastening device and / or applied to a fastening device.

[0114] One or more markings can be incorporated into the receiving surface, attached to the receiving surface and / or applied to the receiving surface.

[0115] The license plate can consist of one or more straight lines. Multiple lines can be parallel or perpendicular to each other.

[0116] The emblem may include a characteristic geometric shape such as an equilateral triangle, a square and / or a circle.

[0117] The license plate can include an area that has a defined color.

[0118] The license plate can include several areas that have different colors, for example the colors red, green, blue, cyan, magenta, yellow, black, white and / or one or more shades of gray.

[0119] At least one license plate is visible in the image of the collection area captured by the camera. In other words, the image captured by the camera shows the collection area or a part of it, as well as at least one license plate.

[0120] In a further step, any deviation between the identifier depicted in the image and a reference is reduced. This reduction is achieved by transforming the image. The process of transforming an image is also called transformation. The result of such a transformation is a transformed image.

[0121] A "transformation" is a function or operator (or a combination of different functions or operators) that takes one or more images as input and produces a transformed image as output. Examples of transformations are described below.

[0122] "Reducing" usually means that by transforming the image, a transformed image is created in which the deviation between the license plate depicted in the transformed image and the reference is smaller than the deviation between the license plate depicted in the (camera-generated) image and the reference. It is possible that reducing / transforming will produce a transformed image in which no deviation between the license plate depicted in the transformed image and the reference is detectable. However, it is also possible that the deviation cannot be completely removed (eliminated) by transformation.

[0123] The reference can be a reference image in which the license plate is depicted true to life, that is, depicted as it appears in the collection area, e.g., under defined lighting conditions.

[0124] The deviation may result from defective lighting, reflections, a defective camera, and / or other / further functional impairments. Examples of functional impairments are described in disclosures WO2024165430A1 and W02024180056A1, the content of which is hereby fully incorporated into this disclosure by reference.

[0125] The discrepancy between the license plate shown in the image and the reference may involve color. It is possible that at least one license plate shown in the image has a different color or color distribution than a reference.

[0126] Reducing color deviation can include the following:

[0127] Determining a color value of an image element, where the image element represents the identifier or a part thereof,

[0128] Determining a deviation of the color value from a reference color value,

[0129] Reducing the deviation by performing color correction during image capture.

[0130] In this embodiment, the transformation includes color correction.

[0131] It is possible to determine multiple color values ​​from multiple image elements.

[0132] It is possible to compare several determined color values ​​with one or more reference color values ​​in order to identify one or more color deviations.

[0133] It is possible to determine a color value distribution.

[0134] It is possible to compare a determined color value distribution with a reference color value distribution in order to identify one or more color deviations.

[0135] The color value distribution can be a histogram of an area of ​​the image capture that includes the license plate. The color value distribution can be a part of a histogram of an area of ​​the image capture that includes the license plate.

[0136] A histogram is typically the result of a statistical analysis of the frequencies of color values ​​in an image. A histogram represents the frequency distribution of color values ​​in an image. A histogram can be presented, for example, as a diagram or graphical representation, indicating for each color value or range of color values ​​how many image elements (e.g., in absolute numbers or relative to the total number of image elements) exhibit that color value or a color value within that range.

[0137] The area can be defined by those image elements that lie within a bounding frame that includes the identifier.

[0138] A bounding box is a rectangular frame defined by the coordinates of its corners that encloses an object of interest (e.g., a license plate) in a photograph. The bounding box is characterized by its position, typically specified by the coordinates of its upper left corner (xi, yi) and lower right corner (x2, y2), or alternatively, by the coordinates of its center point (cx, cy) along with its width (w) and height (h). The bounding box serves as a spatial representation that delineates the object's (e.g., the license plate's) perimeter, enabling its identification and / or analysis. Bounding boxes are frequently used to mark objects in images. It should be noted that a bounding box does not necessarily have to be rectangular; other geometric shapes are also suitable for marking objects, such as circles, ellipses, hexagons, or other forms.In this respect, the term "boundary frame" should be interpreted broadly and is not limited to rectangular frames.

[0139] Typically, a color deviation is identified for a specific area of ​​the image (where the area includes the license plate), a transformation (in this case, a color correction) is determined that reduces the deviation, and the transformation is then applied to the entire image (not just the area containing the license plate). This also applies analogously to other transformations such as adjusting brightness, sharpness, contrast, and / or reducing distortion.

[0140] The deviation between the license plate shown in the image and the reference may relate to brightness.

[0141] It is possible that the license plate in the image is displayed with a different brightness (e.g., a lower brightness or a higher brightness) than in the reference.

[0142] Reducing brightness deviation can include the following:

[0143] Determining the luminance of an image element, where the image element represents the identifier,

[0144] Determining a deviation of the luminance from a reference luminance,

[0145] Reducing the deviation by performing a brightness adjustment during image capture.

[0146] The "luminance" represents the brightness of a color and can be, for example, a (optionally weighted) sum of the RGB components (red, green, blue) of each image element.

[0147] It is possible to determine multiple luminance values ​​from multiple image elements.

[0148] It is possible to compare several measured luminance values ​​with one or more reference luminance values ​​to identify one or more brightness deviations.

[0149] In this embodiment, the transformation includes a brightness adjustment.

[0150] Such a brightness adjustment can include, for example, exposure correction, gamma correction, and / or other / further brightness adjustments.

[0151] Exposure correction adjusts the overall exposure. A higher exposure makes the image brighter, a lower exposure makes the image darker.

[0152] Gamma correction is a nonlinear transformation. It typically affects the midtones of an image while keeping the black and white points constant. Gamma correction is presented here as one example of a nonlinear brightness adjustment. Many other nonlinear transformations exist for brightness adjustment. This disclosure is not limited to gamma correction as a nonlinear transformation.

[0153] The discrepancy between the license plate shown in the image and the reference can affect image sharpness.

[0154] It is possible that the license plate in the image is displayed with a different image sharpness (e.g. less sharp or sharper) than in the reference.

[0155] Reducing image sharpness deviation can include the following:

[0156] Determining the image sharpness of an area of ​​the image capture that includes the license plate,

[0157] Detecting a deviation of the image sharpness from a reference image sharpness, reducing the deviation by performing an image sharpness adjustment during image capture.

[0158] “Image sharpness” refers to the clarity of details and the distinctness of edges in a photograph.

[0159] Image sharpness can be quantified in various ways.

[0160] One approach is to determine the contrast at edges within an image. Higher contrast at the edges generally indicates a sharper image. This can be done by applying edge detection algorithms (such as the Sobel or Canny operators) and subsequently measuring the gradient strength at these edges.

[0161] Image sharpness can also be determined by analyzing the image (or a portion thereof encompassing the depicted characteristic) in the frequency domain after a Fourier transform of the image. Sharper images generally exhibit higher frequencies, indicating more significant detail. The presence and strength of these high-frequency components can be quantified to determine image sharpness.

[0162] In this embodiment, the transformation includes an image sharpness adjustment.

[0163] A transformation that results in a change in image sharpness is generally referred to as "sharpening" if it increases sharpness, or as "blurring" if it decreases sharpness.

[0164] Sharpening increases the contrast at the edges of an image, making it appear clearer and more detailed. This is typically achieved by emphasizing the color and / or brightness differences between adjacent image elements. Common sharpening techniques include unsharp masking, high-pass filtering, and edge enhancement algorithms.

[0165] Blurring reduces contrast at edges, making the image appear softer and less detailed. Common blurring techniques include Gaussian blur, box blur (averaging), and median blur.

[0166] The discrepancy between the license plate shown in the image and the reference may involve a contrast.

[0167] It is possible that the license plate in the image is displayed with a different contrast (e.g., with less contrast or with more contrast) than in the reference.

[0168] Reducing contrast deviation can include the following:

[0169] Determining a contrast within an area of ​​the image capture that includes the license plate,

[0170] Determining a deviation of the contrast from a reference contrast,

[0171] Reducing the deviation by performing a contrast adjustment during image capture.

[0172] The contrast in a photograph refers to the difference in luminance and / or color that makes an object (e.g., the license plate shown in the photograph) distinguishable.

[0173] Quantifying contrast typically involves measuring the color distribution in an image, from the darkest shadows to the brightest highlights. Various methods and measurements can be used to quantify contrast.

[0174] Global contrast can be determined by subtracting the minimum color value occurring in the area of ​​the image capture that includes the license plate from the maximum color value. This provides a basic measure of the contrast range. Another measure is the calculation of the standard deviation of the luminance values ​​of all image elements in the area of ​​the image capture that includes the license plate. A higher standard deviation indicates a greater spread of the luminance values ​​and thus a higher contrast.

[0175] The Michelson contrast is defined as the difference between the maximum and minimum luminances divided by the sum of the maximum and minimum luminances.

[0176] The Weber contrast is defined as the ratio of the difference between the luminance of the image elements representing the mark and the luminance of the background to the luminance of the background.

[0177] In this embodiment, the transformation includes contrast adjustment. Typically, contrast adjustment involves increasing contrast. However, it can also involve decreasing contrast.

[0178] To change the contrast of an image, the distribution of its luminance values ​​and / or em values ​​must be adjusted to make the differences between them more or less apparent.

[0179] Linear contrast stretching rescales the range of luminance and / or color values ​​in an image to cover, for example, the entire possible range (e.g., 0 to 255 for an 8-bit image). This makes shadows darker and highlights brighter, effectively increasing contrast.

[0180] Histogram adjustment redistributes the luminance and / or color values ​​of an image.

[0181] Adaptive Histogram Equalization (AHE) is a variant of histogram equalization in which multiple histograms corresponding to specific image areas are calculated to improve local contrast and increase edge sharpness in each image region. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a modified version of AHE that prevents noise over-amplification by limiting contrast enhancement.

[0182] The discrepancy between the license plate shown in the image and the reference may involve distortion and / or distortion.

[0183] It is possible that the license plate in the image appears distorted compared to the reference. Distortions can be caused by imperfections in the camera lens. The most common types of distortion include barrel distortion, pincushion distortion, chromatic aberration, and vignetting.

[0184] Reducing distortion can include the following:

[0185] Determining distortion based on the license plate depicted in the image,

[0186] Reducing distortion in image capture.

[0187] Barrel and pincushion distortions can be detected by analyzing straight lines that should appear straight but are curved in the image. Quantification can be achieved by fitting these curves to a mathematical model (e.g., a polynomial) and measuring the deviation from linearity.

[0188] Chromatic aberration can be detected by examining edges within the image for color fringing. Quantifying chromatic aberration includes, for example, measuring the shift between the color channels.

[0189] To detect vignetting, the brightness levels in the image can be analyzed. Vignetting can be quantified, for example, by comparing the brightness at different points in the image. A radial brightness profile can be created and fitted to a model to determine the degree of brightness falloff.

[0190] Depending on the type of distortion, various transformations are available for reducing it. In one embodiment of the present disclosure, the reduction of a deviation between the mark depicted in the image and the reference is achieved by transforming the image using a trained machine learning model.

[0191] A "machine learning model" can be understood as a computer-implemented data processing architecture. The model can receive input data and provide output data based on this input data and model parameters. Through training, the model can learn a relationship between the input data and the output data. During training, the model parameters can be adjusted to produce a desired output for a given input.

[0192] When training such a model, it is presented with training data from which it can learn. The trained machine learning model is the result of the training process. In addition to input data, the training data includes the correct output data (target data) that the model is to generate based on the input data. During training, patterns are recognized that map the input data to the target data.

[0193] During the training process, the input data for the training data is fed into the model, and the model generates output data. This output data is then compared to the target data (so-called ground truth data). Model parameters are adjusted to reduce the deviations between the output data and the target data to a (defined) minimum.

[0194] During training, a loss function can be used to evaluate the predictive quality of the model. The loss function can be chosen to reward a desired relationship between output data and target data and / or penalize an undesired relationship. Such a relationship could be, for example, similarity, dissimilarity, or another type of relationship.

[0195] The error function can be used to calculate the error (loss) for a given pair of output and target data. The goal of the training process can be to modify (adjust) the parameters of the machine learning model so that the error is reduced to a (defined) minimum for all pairs in the training dataset. This can be achieved using an optimization method such as gradient descent.

[0196] The error function can, for example, quantify the deviation between the model's output data and the target data for specific input data. If both the output and target data are numbers, the error function can be the absolute difference between these numbers. In this case, a high absolute value for the error function may indicate that one or more model parameters need to be significantly modified.

[0197] For output data in the form of vectors, difference metrics between vectors such as the mean squared error, a cosine distance, a norm of the difference vector such as a Euclidean distance, a Chebyshev distance, an Lp norm of a difference vector, a weighted norm, or any other type of difference metric of two vectors can be chosen as the error function.

[0198] For higher-dimensional outputs, such as two-dimensional, three-dimensional, or even higher-dimensional outputs, an element-wise difference metric can be used. Alternatively or additionally, the output data can be transformed before calculating a loss value, for example, into a one-dimensional vector.

[0199] The machine learning model can be trained to receive the image captured by the camera as input data and, based on the generated image, model parameters and optionally further input data, to produce a transformed image as output data.

[0200] The training data can comprise a large number of training images, each depicting a license plate within a defined area. At least one training image can serve as the target data during training. In this at least one training image, the license plate can be depicted as desired, i.e., without distortion, color cast, loss of contrast, sharpness, or brightness.

[0201] At least one training image of the target data can be a reference image in which the license plate is depicted true to life, that is, depicted as it appears in the collection area, e.g., under defined lighting conditions.

[0202] The machine learning model can be trained to transform the remaining training images (the input data) so that they closely approximate the target data. It is also possible to have pairs of training images, where one training image serves as input data and is transformed by the machine learning model, and the other training image serves as target data (reference image).

[0203] In addition to a training image, the input data can include further input data, such as information about the image capture (e.g., histogram or derived values, resolution), information about camera parameters when the image was captured (e.g., focal length, aperture size, exposure time, ISO sensitivity, sensor size, white balance, exposure compensation), information about the time the image was captured (e.g., time of day and / or season, date, time), information about the location where the image was captured (e.g., position information (e.g., geodesists) about the position of the device), information about the collection area (e.g., type of collection area (e.g., collection tray, type of liquid in the collection tray, sticky trap, color of the collection area) and / or other / further information).

[0204] Training the machine learning model can include:

[0205] Inputting the input data into the machine learning model,

[0206] Receiving output data from the machine learning model,

[0207] Reducing discrepancies between output data and target data by modifying the model parameters.

[0208] The output data includes a transformed training image capture.

[0209] The training of the machine learning model can be terminated when a stop criterion is met. Such a stop criterion could be, for example: a predefined maximum number of training steps / cycles / epochs has been performed, deviations between output data and target data can no longer be reduced by changing the model parameters, and / or a predefined minimum error rate has been reached.

[0210] The trained machine learning model can be stored, transferred to a separate computer system and / or used to generate a transformed image capture.

[0211] The trained machine learning model can be used to reduce the deviation of a feature depicted in an image from a reference.

[0212] The machine learning model can, for example, be or include an artificial neural network. The machine learning model can, for example, be or include a convolutional neural network (CNN). The machine learning model can, for example, have the architecture of an autoencoder or include an autoencoder. The machine learning model can be or include a generative adversarial network (GAN).

[0213] In a further step, the transformed image is stored and / or transmitted to a separate computer system and / or subjected to analysis in order to detect, locate, identify, and / or count arthropods in the transformed image. The detection, location, identification, and / or counting of arthropods in the transformed image can be performed using the device described in this disclosure. The detection, location, identification, and / or counting of arthropods in the transformed image can be performed using the separate computer system.

[0214] The transformed image exhibits less distortion, higher image sharpness, higher contrast, more uniform brightness distribution, no or reduced color cast, and / or other advantages compared to the original image from which it was created. These advantages facilitate the detection, localization, identification, and / or counting of arthropods. The transformed image depicts the collection area more accurately or at least more closely than the image produced by the camera.

[0215] Fig. 1 shows, by way of example and schematically, an embodiment of the computer-implemented method of the present disclosure in the form of a flowchart.

[0216] The procedure (100) comprises the following steps:

[0217] (110) Receiving a photograph showing a collection area for arthropods and a label,

[0218] (120) Reducing a deviation between the mark shown in the image and a reference by transforming the image,

[0219] (130) Storing the transformed image image and / or transmitting the transformed image image to a separate computer system and / or detecting, locating, identifying and / or counting arthropods in the transformed image image.

[0220] Another subject of the present disclosure is a device for monitoring arthropods.

[0221] The device includes one or more cameras.

[0222] The at least one camera is positioned so that it can produce images of a collection area for arthropods. In other words, the at least one camera is positioned so that an image of the collection area, or a part of it, falls onto an image sensor of the at least one camera.

[0223] The collection area can be part of the device. The collection area can be part of a trapping device for arthropods. The trapping device can be part of the device or a separate unit.

[0224] The device includes a control unit. The control unit is configured to cause the camera to produce an image capturing a collection area for arthropods and a marker, to reduce any deviation between the marker depicted in the image and a reference by transforming the image capturing, to store the transformed image capturing and / or transmit it to a separate computer system, and / or to detect, locate, identify, and / or count arthropods in the transformed image.

[0225] The control unit also serves to control the electrical / electronic components of the device and / or to process signals and / or data. The control unit typically includes a processor, program memory, and main memory. The control unit may also include non-volatile data storage, for example, implemented as semiconductor memory, which can be used, for example, to store images, measurements, models, and / or analysis results. The control unit can be configured to cause the camera to take an image of the collection area at defined times and / or intervals and / or upon the occurrence of defined events. The control unit can be configured to transmit images, measurements, analysis results, geocoordinates, and / or other information to a separate computer system using a transmitter.The control unit can be configured to receive images from the camera and / or retrieve images from the camera and / or read images from a data storage device, which may be part of the device.

[0226] The control unit can be configured to detect, locate, count, and / or identify arthropods depicted in (e.g., transformed) image images. This can be achieved, for example, using a trained machine learning model. Such a machine learning model can be configured and trained to detect, locate, count, and / or identify arthropods depicted in (e.g., transformed) image images. Details on the automated detection, localization, counting, and / or identification of arthropods in image captures are described in publications on this topic (see, for example: DCK Amarathunga et al.: Methods of Insect Image Capture and Classification: A Systematic Literature Review, Smart Agricultural Technology, Volume 1, 2021, 100023; C. Zhu et al.: Insect Identification and Counting in Stored Grain: Image Processing Approach and Application Embedded in Smartphones, Mob. Inf. Syst.2018, 5491706: 1-5, W02020058175A1, W02020058170A1).

[0227] The device may include a transmitting unit to send information over a network to a separate computer system. This information may include, for example, transformed image images of the collection area. This information may also include the results of an analysis of a transformed image, such as the number of arthropods depicted in a transformed image, identified species, and / or messages regarding the device's status.

[0228] The transmitting unit can be designed to transmit information via a mobile network (e.g., GSM: Global System for Mobile Communications, GPRS: General Packet Radio Service, UMTS: Universal Mobile Telecommunications System, LTE: Long Term Evolution), via a WLAN (Wireless Local Area Network), via Bluetooth, via DECT (Digital Enhanced Cordless Telecommunications), via a Low Power Wide Area Network (LPWAN or LPN) such as a NarrowBand IoT network, and / or via a combination of different transmission methods.

[0229] The transmitting unit can be designed to transmit information via a short-range radio connection (e.g., Bluetooth) to a base station, from which the information is then forwarded via cable and / or a long-range radio connection (e.g., a mobile network).

[0230] In one embodiment of the present disclosure, the transmitting unit comprises a modem and an antenna for transmitting information via a GSM, GPRS, 2G, 3G, LTE, 4G, 5G, 6G mobile network or via another mobile network.

[0231] The device may include means for a power supply. In one embodiment of the present disclosure, the device is designed for autonomous operation outdoors for a period of several days, weeks, months, or even years. The means for a power supply include, for example, one or more electrochemical cells, accumulators, solar cells, fuel cells, and / or generators (e.g., in combination with a wind turbine).

[0232] The device can be designed to harvest electrical energy from its environment. This environmental energy can be provided in the form of light, electric fields, magnetic fields, electromagnetic fields, motion, pressure, heat, and / or other forms of energy, and can be used or "harvested" by the device. This type of electrical energy generation is known as energy harvesting. Energy harvesting refers to methods that capture and store minute amounts of freely available energy from the environment. This technique makes it possible to power a device throughout its entire lifespan. Energy harvesting systems typically include an energy converter, an energy management unit, and an energy storage device, usually a capacitor.The energy converter, also called a microgenerator, converts energy from the environment into electrical energy. The conversion can utilize, for example, the piezoelectric effect, the thermoelectric effect, or the photoelectric effect. Further details are described in the prior art (see, for example, http: / / www.harvesting-energy.de / and the publications listed there).

[0233] In one embodiment, the device comprises one or more solar cells and one or more accumulators for power supply. The at least one solar cell and the at least one accumulator are connected in such a way that the solar cell charges the accumulator when electromagnetic radiation (e.g., sunlight) strikes the at least one solar cell.

[0234] To image the collection area, comprising at least one identifier, on one or more image sensors, a light source is required to illuminate the collection area so that light (electromagnetic radiation in the infrared, visible, and / or ultraviolet range of the spectrum) is scattered / reflected from the illuminated collection area towards the camera. Daylight can be used for this purpose. However, it is also conceivable to use a lighting unit that provides defined illumination independent of daylight. This unit is preferably mounted to the side of the camera so that no shadow is cast by the camera onto the collection area.

[0235] It is also conceivable to position a light source below and / or next to the collection area, illuminating the collection area "from below" and / or "from the side", while a camera produces one or more images "from above".

[0236] It is conceivable that several light sources illuminate the collection area from different directions.

[0237] The terms "light" and "illumination" should not be interpreted as meaning that the spectral range is limited to visible light (approximately 380 nm to approximately 780 nm). It is equally conceivable that electromagnetic radiation with a wavelength below 380 nm (ultraviolet light: 100 nm to 380 nm) or above 780 nm (infrared light: 780 nm to 1000 pm) is used for illumination. The image sensor and optical elements are typically adapted to the electromagnetic radiation used.

[0238] Fig. 2 shows, by way of example and schematic representation, an embodiment of the device of the present disclosure.

[0239] The device (1) comprises a processing unit (20) and a memory (50). The processing unit (20) and the memory (50) can together form a control unit within the meaning of this disclosure.

[0240] The processing unit (20) may comprise one or more processors alone or in combination with one or more memories. The processing unit (20) may be ordinary computer hardware capable of processing information such as digital images, computer programs, and / or other digital information. The processing unit (20) typically consists of an arrangement of electronic circuits, some of which may be implemented as an integrated circuit or as several interconnected integrated circuits (an integrated circuit is sometimes referred to as a "chip"). The processing unit (20) may be configured to execute computer programs, which may be stored in memory (50).

[0241] The memory (50) can be ordinary computer hardware capable of storing information such as digital image acquisitions (e.g., representations of the study area), data, computer programs, and / or other digital information, either temporarily and / or permanently. The memory (50) can include volatile and / or non-volatile memory and can be permanently installed or removable. Examples of suitable memory include RAM (Random Access Memory), ROM (Read-Only Memory), a hard disk, flash memory, or a combination thereof.

[0242] In addition to the memory (50), the processing unit (20) can also be connected to one or more interfaces (11, 12, 30, 41, 42) to display, transmit, and / or receive information. The interfaces can include one or more communication interfaces (41, 42) and / or one or more user interfaces (11, 12, 30). The one or more communication interfaces (41, 42) can be configured to send and / or receive information, e.g., to and / or from a camera, other computer systems, networks, data storage devices, or the like. The one or more communication interfaces (41, 42) can be configured to transmit and / or receive information via physical (wired) and / or wireless communication links. The one or more communication interfaces (41, 42) can include one or more interfaces for connecting to a network, e.g.,using technologies such as mobile phone, Wi-Fi, satellite, cable, DSL, fiber optic and / or the like. In some examples, the one or more communication interfaces (41, 42) may include one or more near-field communication interfaces configured to connect devices using near-field communication technologies such as NFC, RFID, Bluetooth, Bluetooth LE, ZigBee, infrared (e.g., IrDA) or the like.

[0243] The user interfaces (11, 12, 30) may include a display (30). A display (30) may be configured to show information to a user. Suitable examples include a liquid crystal display (LCD), a light-emitting diode (LED) display, a plasma display (PDP), or similar. The user input interface(s) (11, 12) may be wired or wireless and may be configured to receive information from a user into the computer system (1), for example, for processing, storage, and / or display. Suitable examples of user input interfaces (11, 12) include a microphone, an image or video recording device (e.g., a camera), a keyboard or keypad, a joystick, a touch-sensitive surface (separate from or integrated into a touchscreen), or similar.In some examples, the user interfaces may include automatic identification and data capture (AIDC) technology for machine-readable information. This could include barcodes, radio frequency identification (RFID), magnetic stripes, optical character recognition (OCR), integrated circuit cards (ICC), and similar technologies. The user interfaces may also include one or more interfaces for communication with peripheral devices such as printers and / or cameras, and the like.

[0244] One or more computer programs (60) can be stored in memory (50) and executed by the processing unit (20), which is programmed to perform the functions described in this description. The retrieval, loading, and execution of instructions from the computer program (60) can be sequential, with one instruction being retrieved, loaded, and executed at a time. However, the retrieval, loading, and / or execution can also be performed in parallel.

[0245] The device may be or comprise a computer system in the form of a laptop, notebook, netbook, tablet PC, or smartphone; the device may also be a component of a camera. Likewise, one or more cameras may be part of the device.

[0246] In one embodiment of the present disclosure, the device comprises a capture device for arthropods or specific arthropods. In one embodiment of the present disclosure, the capture device comprises the collecting area. In one embodiment of the present disclosure, the camera comprises at least one camera sensor onto which the collecting area is imaged (e.g., by a camera optic, which may be a component of the device).

[0247] The present invention also relates to a computer program. Such a computer program can be stored on a non-volatile data carrier such as a CD, a DVD, a USB stick, or another medium for storing data. The computer program can, for example, be offered for download in an app store and / or on a website.

[0248] The computer program can be loaded into the memory of the device of the present disclosure and / or may already be stored there and cause the device to perform the following steps:

[0249] Receiving an image, wherein the image shows a collection area for arthropods and a label,

[0250] Reducing the deviation between the license plate depicted in the image and a reference by transforming the image,

[0251] Storing the transformed image and / or transmitting the transformed image to a separate computer system and / or detecting, locating, identifying and / or counting arthropods in the transformed image.

[0252] Further embodiments are disclosed below. These embodiments are not necessarily subject matter that falls under patent protection. As is known to those skilled in the art in patent law, the scope of protection of a patent is defined by the patent claims. The description and the drawings are to be used to interpret the patent claims. The embodiments described below are part of the description and not of the patent claims. The following embodiments are intended to give the reader guidance on how various features described in this disclosure can be combined. They are therefore part of the present technical teaching and should not be confused with the subject matter of the patent claims.

[0253] Embodiment 1: A computer-implemented method comprising:

[0254] Receiving an image showing a collection area for arthropods and a label,

[0255] Reducing the deviation between the license plate depicted in the image and a reference by transforming the image,

[0256] Storing the transformed image and / or transmitting the transformed image to a separate computer system and / or detecting, locating, identifying and / or counting arthropods in the transformed image.

[0257] Embodiment 2: A computer-implemented method comprising:

[0258] Receiving an image, wherein the image shows a collection area for arthropods, wherein the collection area includes a marker,

[0259] Reducing the deviation between the license plate depicted in the image and a reference by transforming the image,

[0260] Storing the transformed image and / or transmitting the transformed image to a separate computer system and / or detecting, locating, identifying and / or counting arthropods in the transformed image.

[0261] Embodiment 3: A device for monitoring arthropods comprising

[0262] - a camera and

[0263] - a control unit, wherein the control unit is configured to cause the camera to produce an image capture, wherein the image capture shows a collection area for arthropods, wherein the collection area includes a tag, to reduce any deviation between the tag shown in the image capture and a reference by transforming the image capture, to store the transformed image capture and / or transmit it to a separate computer system and / or to detect, locate, identify and / or count arthropods in the transformed image.

[0264] Embodiment 4: A device for monitoring arthropods comprising

[0265] - a camera and

[0266] - a control unit, wherein the control unit is configured to cause the camera to produce an image capture, wherein the image capture depicts a collection area for arthropods and a marker, to reduce any deviation between the marker depicted in the image capture and a reference by transforming the image capture, to store the transformed image capture and / or transmit it to a separate computer system and / or to detect, locate, identify and / or count arthropods in the transformed image.

[0267] Embodiment 5: A non-volatile, computer-readable storage medium on which a computer program is stored which, when executed by a control unit of an arthropod monitoring device, causes the control unit to perform the following steps:

[0268] Receiving an image, wherein the image shows a collection area for arthropods, wherein the collection area includes a marker,

[0269] Reducing the deviation between the license plate depicted in the image and a reference by transforming the image,

[0270] Storing the transformed image and / or transmitting the transformed image to a separate computer system and / or detecting, locating, identifying and / or counting arthropods in the transformed image.

[0271] Embodiment 6: A non-volatile, computer-readable storage medium on which a computer program is stored which, when executed by a control unit of an arthropod monitoring device, causes the control unit to perform the following steps:

[0272] Receiving an image showing a collection area for arthropods and a label,

[0273] Reducing the deviation between the license plate depicted in the image and a reference by transforming the image,

[0274] Storing the transformed image and / or transmitting the transformed image to a separate computer system and / or detecting, locating, identifying and / or counting arthropods in the transformed image.

[0275] Embodiment 7: One of embodiments 1 to 6, wherein the distinguishing mark is or comprises a visible feature.

[0276] Embodiment 8: One of embodiments 1 to 7, wherein the identifier is or comprises a form of the collection area.

[0277] Embodiment 9: One of embodiments 1 to 8, wherein the identifying mark is or comprises a color of the collecting area. Embodiment 10: One of embodiments 1 to 9, wherein the identifying mark is or comprises a structure in the collecting area.

[0278] Embodiment 11: One of embodiments 1 to 10, wherein the distinguishing feature is or comprises a structure of the collecting area.

[0279] Embodiment 12: One of embodiments 1 to 11, wherein the distinguishing feature is or includes recesses in the collecting area.

[0280] Embodiment 13: One of embodiments 1 to 12, wherein the distinguishing feature is or comprises depressions in a surface of the collecting area.

[0281] Embodiment 14: One of embodiments 12 or 13, wherein the indentations form a regular pattern.

[0282] Embodiment 15: One of embodiments 12 to 14, wherein the depressions form a honeycomb pattern

[0283] Embodiment 16: One of embodiments 12 to 15, wherein the recesses serve to separate arthropods.

[0284] Embodiment 17: One of embodiments 12 to 16, wherein the depressions in cross-section have an extent of 1 mm to 2 cm.

[0285] Embodiment 18: One of embodiments 12 to 17, wherein the recesses in cross-section have an extent of 2 mm to 8 mm.

[0286] Embodiment 19: One of embodiments 12 to 18, wherein the recesses have a minimum depth of at least 1 mm and a maximum depth of at most 1 cm.

[0287] Embodiment 20: One of embodiments 1 to 19, wherein the distinguishing mark is or comprises a combination of several features.

[0288] Embodiment 21: One of embodiments 7 to 20, wherein a feature is or comprises a color, a shape, a pattern, a structure, a texture or hatching.

[0289] Embodiment 22: One of embodiments 1 to 21, wherein the mark is or comprises a shape, a color, a structure, a texture, a pattern, a hatching, a character, a code and / or another visible feature.

[0290] Embodiment 23: One of embodiments 1 to 22, wherein the collection area comprises means for immobilizing arthropods.

[0291] Embodiment 24: One of embodiments 1 to 23, wherein the collecting area comprises a tray filled with a liquid.

[0292] Embodiment 25: Embodiment 24, wherein the distinguishing mark is or comprises a shape of the shell.

[0293] Embodiment 26: One of embodiments 24 or 25, wherein the distinguishing mark is or comprises a color of the shell.

[0294] Embodiment 27: One of embodiments 24 to 26, wherein the mark is or comprises a pattern in or on the bottom of the tray.

[0295] Embodiment 28: One of embodiments 1 to 27, wherein the distinguishing feature is or comprises a structure of the collecting area.

[0296] Embodiment 29: One of embodiments 24 to 28, wherein depressions in the bottom of the bowl form the distinguishing feature.

[0297] Embodiment 30: Embodiment 29, wherein the indentations form a regular pattern. Embodiment 31: One of embodiments 29 or 30, wherein the indentations form a honeycomb pattern.

[0298] Embodiment 32: One of embodiments 29 to 31, wherein the recesses serve to separate arthropods.

[0299] Embodiment 33: One of embodiments 29 to 32, wherein the depressions in cross-section have an extent of 1 mm to 2 cm.

[0300] Embodiment 34: One of embodiments 29 to 33, wherein the recesses in cross-section have an extent of 2 mm to 8 mm.

[0301] Embodiment 35: One of embodiments 29 to 34, wherein the recesses have a minimum depth of at least 1 mm and a maximum depth of at most 1 cm.

[0302] Embodiment 36: One of embodiments 1 to 35, wherein the collecting area comprises an adhesive.

[0303] Embodiment 37: One of embodiments 1 to 36, wherein the collecting area is or comprises a surface provided with an adhesive.

[0304] Embodiment 38: Embodiments 37, wherein the distinguishing mark is or comprises a color of the surface coated with adhesive.

[0305] Embodiment 39: One of embodiments 1 to 37, wherein the collecting area comprises a sheet which is at least partially provided with an adhesive.

[0306] Embodiment 40: Embodiments 39, wherein the identifier is or comprises a color of the sheet.

[0307] Embodiment 41: One of embodiments 39 or 40, wherein the distinguishing mark is or comprises a shape of the arc.

[0308] Embodiment 42: One of embodiments 39 to 41, wherein the distinguishing feature is or comprises a structure in the arc.

[0309] Embodiment 43: One of embodiments 39 to 42, wherein the mark is or comprises a pattern, texture and / or hatching in or on the sheet.

[0310] Embodiment 44: One of embodiments 39 to 43, wherein the arc has an extent in the range of 100 mm x 200 mm to 200 mm x 250 mm.

[0311] Embodiment 45: One of embodiments 39 to 44, wherein the arc has an extent in the range of 100 mm x 160 mm to 130 mm x 190 mm.

[0312] Embodiment 46: One of embodiments 39 to 45, wherein the arc has an extent in the range of 160 mm x 210 mm to 180 mm x 230 mm.

[0313] Embodiment 47: One of embodiments 39 to 46, wherein the arc is attached to a receiving surface of the device.

[0314] Embodiment 48: Embodiments 47, wherein the receiving surface has the same shape as the arc.

[0315] Embodiment 49: One of embodiments 47 or 48, wherein the receiving area is larger than the arc.

[0316] Embodiment 50: One of embodiments 1 to 49, wherein the distinguishing mark comprises one or more straight lines.

[0317] Embodiment 51: One of embodiments 1 to 50, wherein the mark comprises several lines that run parallel or perpendicular to each other. Embodiment 52: One of embodiments 1 to 51, wherein the mark is or comprises a geometric shape.

[0318] Embodiment 53: One of embodiments 1 to 52, wherein the geometric shape is an equilateral triangle, a square and / or a circle or comprises an equilateral triangle, a square and / or a circle.

[0319] Embodiment 54: One of embodiments 1 to 53, wherein the mark comprises several areas which have different colors.

[0320] Embodiment 55: One of embodiments 1 to 54, wherein the collection area is provided by a sheet at least partially provided with an adhesive, wherein the sheet is attached to a mounting surface by at least one fastening means, wherein the at least one fastening means serves as a marker.

[0321] Embodiment 56: Embodiments 55, wherein the shape of the at least one fastening means serves as a distinguishing feature.

[0322] Embodiment 57: One of embodiments 55 or 56, wherein the color of the at least one fastening means serves as an identifier.

[0323] Embodiment 58: One of embodiments 55 to 57, wherein the arrangement of the at least one fastening means serves as a distinguishing feature.

[0324] Embodiment 59: One of embodiments 55 to 58, wherein one or more markings are incorporated into and / or applied to and / or attached to the fastening means at least one fastening means.

[0325] Embodiment 60: One of embodiments 55 to 59, wherein one or more markings are incorporated into the receiving surface and / or applied to the receiving surface and / or attached to the receiving surface.

[0326] Embodiment 61: One of embodiments 55 to 60, wherein the distinguishing mark is or comprises a colour or a shape of the receiving surface.

[0327] Embodiment 62: One of embodiments 55 to 61, wherein the mark is or comprises a pattern, hatching, texture, or structure of the receiving surface.

[0328] Embodiment 63: One of embodiments 1 to 62, wherein the collection area includes several identifiers.

[0329] Embodiment 64: One of embodiments 1 to 63, wherein the identifier is depicted in the received image.

[0330] Embodiment 65: One of embodiments 1 to 64, wherein the received image shows the collection area or part thereof and the identifier or part thereof.

[0331] Embodiment 66: One of embodiments 1 to 65, wherein the collecting area is round or has an elliptical shape.

[0332] Embodiment 67: One of embodiments 1 to 65, wherein the collecting area is rectangular, and the corners may be rounded.

[0333] Embodiment 68: One of embodiments 1 to 67, wherein the collection area has an extent in the range of 100 mm x 200 mm to 200 mm x 250 mm.

[0334] Embodiment 69: One of embodiments 1 to 68, wherein the collection area has an extent in the range of 100 mm x 160 mm to 130 mm x 190 mm.

[0335] Embodiment 70: One of embodiments 1 to 69, wherein the collecting area has an extent in the range of 160 mm x 210 mm to 180 mm x 230 mm. Embodiment 71: One of embodiments 1 to 70, wherein the mark is introduced into the collecting area and / or into the sheet and / or into a fastening element and / or into the receiving surface and / or into the tray by engraving, etching, burning, embossing, printing, affixing and / or laser engraving and / or applying it to the collecting area and / or onto the sheet and / or onto a fastening element and / or onto the receiving surface and / or onto the tray and / or attached to the collecting area and / or onto the sheet and / or onto a fastening element and / or onto the receiving surface and / or onto the tray.

[0336] Embodiment 72: One of embodiments 1 to 71, wherein the deviation relates to a color or the deviation relates to a color distribution.

[0337] Embodiment 73: One of embodiments 1 to 72, wherein the deviation relates to brightness.

[0338] Embodiment 74: One of embodiments 1 to 73, wherein the deviation relates to image sharpness.

[0339] Embodiment 75: One of embodiments 1 to 74, wherein the deviation concerns a contrast.

[0340] Embodiment 76: One of embodiments 1 to 75, wherein the deviation concerns a distortion.

[0341] Embodiment 77: One of embodiments 1 to 76, wherein the deviation concerns a tonnage distortion.

[0342] Embodiment 78: One of embodiments 1 to 77, wherein the deviation relates to a cushion distortion.

[0343] Embodiment 79: One of embodiments 1 to 78, wherein the deviation concerns a chromatic aberration.

[0344] Embodiment 80: One of embodiments 1 to 79, wherein the transformation is or includes color correction.

[0345] Embodiment 81: One of embodiments 1 to 80, wherein the transformation is or includes a brightness adjustment.

[0346] Embodiment 82: One of embodiments 1 to 81, wherein the transformation is or includes exposure correction.

[0347] Embodiment 83: One of embodiments 1 to 82, wherein the transformation is or includes a gamma correction.

[0348] Embodiment 84: One of embodiments 1 to 83, wherein the transformation is or includes an image sharpness adjustment.

[0349] Embodiment 85: One of embodiments 1 to 84, wherein the transformation is or includes a contrast adjustment.

[0350] Embodiment 86: One of embodiments 1 to 85, wherein the transformation is a histogram adjustment or includes a histogram adjustment.

[0351] Embodiment 87: One of embodiments 1 to 86, wherein the transformation is or includes sharpening the received image.

[0352] Embodiment 88: One of embodiments 1 to 87, wherein the transformation is or includes blurring the received image.

[0353] Embodiment 89: One of embodiments 1 to 88, wherein the transformation is or includes distortion reduction. Embodiment 90: One of embodiments 1 to 80, wherein reducing a deviation includes:

[0354] Determining one or more color values ​​of one or more image elements of the received image recording, wherein the one or more image elements of the received image recording represent the identifier or a part thereof.

[0355] Embodiment 91: One of embodiments 1 to 90, wherein reducing a deviation comprises:

[0356] Determining the color value distribution of the license plate or part thereof depicted in the received image.

[0357] Embodiment 92: One of embodiments 1 to 91, wherein reducing a deviation comprises:

[0358] Determining a deviation of a color value distribution of the license plate depicted in the received image, or a part thereof, from a reference color value distribution.

[0359] Embodiment 93: One of embodiments 1 to 92, wherein reducing a deviation comprises:

[0360] Determining a deviation of one or more color values ​​of one or more image elements of the received image from one or more reference color values

[0361] Embodiment 94: One of embodiments 1 to 93, wherein reducing a deviation comprises:

[0362] Reducing the deviation by performing color correction on the received image capture.

[0363] Embodiment 95: One of embodiments 1 to 94, wherein reducing a deviation comprises:

[0364] Determining a bounding frame, wherein the bounding frame includes the identifier depicted in the received image.

[0365] Embodiment 96: One of embodiments 1 to 95, wherein reducing a deviation comprises:

[0366] Determining a deviation based on image elements within the bounding frame. Embodiment 97: One of embodiments 1 to 96, wherein reducing a deviation comprises:

[0367] Determining a deviation of one or more features of image elements within the bounding frame from the reference.

[0368] Embodiment 98: One of embodiments 1 to 97, wherein reducing a deviation comprises:

[0369] Determining the luminance of one or more image elements of the received image. Embodiment 99: One of embodiments 1 to 98, wherein reducing a deviation comprises:

[0370] Determining a deviation between the luminance of one or more image elements of the received image and a reference luminance.

[0371] Embodiment 100: One of embodiments 1 to 99, wherein reducing a deviation comprises: reducing a deviation between a luminance of one or more image elements of the received image capture and a reference luminance by performing a brightness adjustment during image capture.

[0372] Embodiment 101: One of embodiments 90 to 100, wherein the one or more image elements represent the trademark or a part thereof.

[0373] Embodiment 102: One of embodiments 1 to 101, wherein reducing a deviation comprises:

[0374] Determining the image sharpness of an area of ​​the received image recording, wherein the area includes the license plate or a part thereof.

[0375] Embodiment 103: One of embodiments 1 to 103, wherein reducing a deviation comprises:

[0376] Determining a deviation in the image sharpness of an area of ​​the received image recording from a reference image sharpness, wherein the area includes the identifier or a part thereof.

[0377] Embodiment 104: One of embodiments 1 to 103, wherein reducing a deviation comprises:

[0378] Reducing a deviation in the image sharpness of an area of ​​the received image recording from a reference image sharpness by performing an image sharpness adjustment on the received image recording, wherein the area includes the mark or a part thereof.

[0379] Embodiment 105: One of embodiments 1 to 104, wherein reducing a deviation comprises:

[0380] Determining the contrast of an area of ​​the received image recording, wherein the area includes the identifier or part thereof.

[0381] Embodiment 106: One of embodiments 1 to 105, wherein reducing a deviation comprises:

[0382] Determining a deviation of a contrast of an area of ​​the received image recording from a reference contrast, wherein the area includes the identifier or a part thereof.

[0383] Embodiment 107: One of embodiments 1 to 106, wherein reducing a deviation comprises:

[0384] Reducing a deviation of a contrast of an area of ​​the received image from a reference contrast by performing a contrast adjustment on the received image, wherein the area includes the mark or a part thereof.

[0385] Embodiment 108: One of embodiments 1 to 107, wherein reducing a deviation comprises:

[0386] Determining a distortion of an area of ​​the received image recording, wherein the area includes the identifier or a part thereof.

[0387] Embodiment 109: One of embodiments 1 to 108, wherein reducing a deviation comprises:

[0388] Determining a distortion of an area of ​​the received image recording relative to a reference, wherein the area includes the identifier or a part thereof.

[0389] Embodiment 110: One of embodiments 1 to 109, wherein reducing a deviation comprises: reducing a distortion of an area of ​​the received image recording relative to a reference by rectification, wherein the area comprises the mark or a part thereof.

[0390] Embodiment 111: One of embodiments 1 to 110, wherein the reference is or comprises a reference image.

[0391] Embodiment 112: One of embodiments 1 to 111, wherein the identifier is shown in the reference image.

[0392] Embodiment 113: One of embodiments 1 to 112, wherein the reference image capture shows the mark in a defined form and / or manner.

[0393] Embodiment 114: One of embodiments 1 to 113, wherein the reference image capture shows the mark in a desired shape and / or manner.

[0394] Embodiment 115: One of embodiments 1 to 114, wherein the reference image capture shows the mark in a targeted form and / or manner.

[0395] Embodiment 116: One of embodiments 1 to 115, wherein the reference image capture shows the mark in a target shape and / or target type.

[0396] Embodiment 117: One of embodiments 1 to 116, wherein the reference image capture shows the mark under defined conditions.

[0397] Embodiment 118: One of embodiments 1 to 117, wherein reducing the deviation comprises:

[0398] Inputting the image capture into a trained machine learning model,

[0399] Receiving a transformed image from the trained machine learning model.

[0400] Embodiment 119: Embodiment 118, wherein the machine learning model is configured and trained to generate a transformed image based on an image acquisition and on the basis of model parameters.

[0401] Embodiment 120: One of embodiments 118 or 119, wherein the machine learning model was trained on the basis of training data, the training data comprising input data and target data, the input data comprising training images, the target data comprising at least one training image, each training image showing a collection area and the identifier, the training of the machine learning model comprising: o inputting the input data into the machine learning model, o receiving output data from the machine learning model, o reducing any deviation between the output data and the target data by modifying the model parameters,

[0402] Embodiment 121: One of embodiments 118 to 120, wherein the at least one training image acquisition of the target data is or comprises at least one reference image acquisition.

[0403] Embodiment 122: Embodiment 121, wherein the at least one reference image shows the mark in a defined form and / or manner.

[0404] Embodiment 123: One of embodiments 121 or 122, wherein the at least one reference image shows the mark in a desired form and / or manner.

[0405] Embodiment 124: One of embodiments 121 to 123, wherein the at least one reference image shows the mark in a targeted form and / or manner.

[0406] Embodiment 125: One of embodiments 121 to 124, wherein the at least one reference image shows the mark in a target shape and / or target type. Embodiment 126: One of embodiments 121 to 125, wherein the at least one reference image shows the mark under defined conditions.

[0407] Embodiment 127: One of embodiments 1 to 126, wherein reducing the deviation includes:

[0408] Determining the value of a parameter based on the identifier depicted in the image capture,

[0409] Determining a deviation between the value and a reference value of the parameter,

[0410] Reducing the deviation by transforming the image capture.

[0411] Embodiment 128: One of embodiments 1 to 127, wherein reducing the deviation comprises:

[0412] Determining a color value of an image element, where the image element represents the identifier,

[0413] Determining a deviation of the color value from a reference color value,

[0414] Reducing the deviation by performing color correction during image capture.

[0415] Embodiment 129: One of embodiments 1 to 128, wherein reducing the deviation comprises:

[0416] Determining the luminance of an image element, where the image element represents the identifier,

[0417] Determining a deviation of the luminance from a reference luminance,

[0418] Reduce the deviation by performing a brightness adjustment during image capture.

[0419] Embodiment 130: One of embodiments 1 to 129, wherein reducing the deviation includes:

[0420] Determining the image sharpness of an area of ​​the image capture that includes the license plate,

[0421] Determining a deviation of image sharpness from a reference image sharpness,

[0422] Reducing the deviation by performing an image sharpness adjustment during image capture.

[0423] Embodiment 131: One of embodiments 1 to 130, wherein reducing the deviation includes:

[0424] Determining a contrast within an area of ​​the image capture that includes the license plate,

[0425] Determining a deviation of the contrast from a reference contrast,

[0426] Reducing the deviation by performing a contrast adjustment during image capture.

[0427] Embodiment 132: One of embodiments 1 to 131, wherein reducing the deviation comprises:

[0428] Determining distortion based on the license plate depicted in the image,

[0429] Reducing distortion in image capture.

[0430] Embodiment 133: One of embodiments 1 to 132, wherein the collecting area is a component of a trapping device for arthropods. Embodiment 134: One of embodiments 1 to 133, wherein the collecting area is a component of the device.

[0431] Embodiment 135: One of embodiments 1 to 134, wherein the device is or comprises a trapping device for arthropods.

[0432] Embodiment 136: One of embodiments 3 to 135, wherein the camera is positioned to produce images of the arthropod collection area and the identification mark.

[0433] Embodiment 137: One of embodiments 3 to 136, wherein the camera is aligned such that an image of the collection area or part thereof and the license plate falls onto an image sensor of the camera.

[0434] Embodiment 138: One of embodiments 3 to 137, wherein the control unit is configured to cause the camera to produce an image of the collection area and the license plate at defined times and / or at defined time intervals and / or upon the occurrence of defined events.

[0435] Embodiment 139: One of embodiments 3 to 138, wherein the control unit is configured to receive image captures from the camera and / or retrieve image captures from the camera and / or read image captures from a data storage device which may be a component of the device.

[0436] Embodiment 140: One of embodiments 3 to 139, wherein the control unit is configured to transmit image captures, measured values, analysis results, geocoordinates and / or other information to a separate computer system by means of a transmitting unit.

[0437] Embodiment 141: One of embodiments 3 to 140, wherein the device comprises a transmitting unit.

[0438] Embodiment 142: One of embodiments 3 to 141, wherein the control unit is configured to detect, locate, count and / or identify arthropods depicted in the transformed image acquisition.

[0439] Embodiment 143: One of embodiments 3 to 142, wherein the detection, localization, counting and / or identification is performed using a trained machine learning model.

[0440] Embodiment 144: One of embodiments 3 to 143, wherein the device includes means for supplying energy.

[0441] Embodiment 145: One of embodiments 3 to 144, wherein the device comprises an accumulator.

[0442] Embodiment 146: One of embodiments 3 to 145, wherein the device comprises at least one accumulator and at least one solar cell, wherein the at least one solar cell and the at least one accumulator are connected to each other in such a way that the at least one solar cell charges the at least one accumulator when electromagnetic radiation hits the at least one solar cell.

[0443] Embodiment 147: One of embodiments 3 to 146, wherein the device comprises at least one light source for illuminating the collection area and the sign.

Claims

Patent claims 1. Computer-implemented method including: Receiving an image showing a collection area for arthropods and a label, Reducing the deviation between the license plate depicted in the image and a reference by transforming the image, Storing the transformed image and / or transmitting the transformed image to a separate computer system and / or detecting, locating, identifying and / or counting arthropods in the transformed image.

2. Method according to claim 1, wherein the distinguishing feature is or comprises a shape of the collecting area and / or a color of the collecting area and / or a structure in the collecting area and / or a structure of the collecting area and / or recesses in the collecting area.

3. Method according to one of claims 1 or 2, wherein the mark is or comprises a shape, a color, a structure, a texture, a pattern, a hatching, a character, and / or a code.

4. Method according to any one of claims 1 to 3, wherein the collection area comprises means for immobilizing arthropods.

5. Method according to any one of claims 1 to 4, wherein the collecting area comprises a tray filled with a liquid, wherein the distinguishing feature is or comprises a shape of the tray and / or the distinguishing feature is or comprises a colour of the tray and / or the distinguishing feature is or comprises a pattern in or on the bottom of the tray and / or is or comprises depressions in the bottom of the tray.

6. Method according to any one of claims 1 to 4, wherein the collecting area comprises a sheet which is at least partially provided with an adhesive, wherein the identifier is or comprises a color of the sheet and / or the identifier is or comprises a shape of the sheet and / or the identifier is or comprises a structure in the sheet and / or the identifier is or comprises a pattern, texture and / or hatching in or on the sheet.

7. Method according to any one of claims 1 to 4, wherein the collecting area comprises a sheet which is at least partially provided with an adhesive, wherein the sheet is attached to a receiving surface by a fastening means, wherein the marking is introduced into the sheet and / or into the fastening means and / or into the receiving surface and / or applied to the sheet and / or to the fastening means and / or to the receiving surface and / or attached to the sheet and / or to the fastening means and / or to the receiving surface.

8. A method according to any one of claims 1 to 4, wherein the collecting area comprises a sheet which is at least partially provided with an adhesive, wherein the sheet is attached to a receiving surface with at least one fastening means, wherein the distinguishing feature is a shape and / or color and / or structure of the sheet and / or the fastening means and / or the receiving surface or includes and / or the marking is the arrangement of at least one fastening device.

9. Method according to any one of claims 1 to 8, wherein the mark was produced by engraving, etching, burning, embossing, printing, gluing and / or lasering.

10. Method according to any one of claims 1 to 9, wherein the deviation is or comprises a color and / or a color distribution and / or a brightness and / or a sharpness and / or a contrast and / or a distortion.

11. A method according to any one of claims 1 to 10, wherein reducing a deviation comprises: Determining one or more color values ​​of one or more image elements of the received image recording, wherein the one or more image elements represent the identifier or a part thereof, and / or Determining a color value distribution of the license plate depicted in the received image or a part thereof, and / or Determining a deviation of a color value distribution of the license plate depicted in the received image, or a part thereof, from a reference color value distribution, and / or Determining a deviation of one or more color values ​​of one or more image elements of the received image from one or more reference color values, wherein one or more image elements represent the identifier or a part thereof, and / or Reducing the deviation by performing color correction on the received image capture, and / or Determining a bounding frame, wherein the bounding frame includes the license plate depicted in the received image, and determining a deviation based on image elements within the bounding frame, and / or Determining the luminance of one or more image elements of the received image recording, wherein the one or more image elements represent the identifier or a part thereof, and / or Determining a deviation between the luminance of one or more image elements of the received image and a reference luminance, wherein the one or more image elements represent the identifier or a part thereof, and / or Reducing a deviation between the luminance of one or more image elements of the received image capture and a reference luminance by performing a brightness adjustment during image capture, wherein the one or more image elements represent the identifier or a part thereof, and / or Determining the image sharpness of an area of ​​the received image recording, wherein the area includes the license plate or a part thereof, and / or Determining a deviation in the image sharpness of an area of ​​the received image recording from a reference image sharpness, wherein the area includes the identifier or a part thereof, and / or Reducing a deviation in the image sharpness of an area of ​​the received image recording from a reference image sharpness by performing an image sharpness adjustment in the received image recording, wherein the area includes the license plate or a part thereof, and / or Determining the contrast of an area of ​​the received image, wherein the area includes the license plate or a part thereof, and / or Determining a deviation of the contrast of an area of ​​the received image from a reference contrast, wherein the area includes the mark or a part thereof, and / or Reducing a deviation of a contrast of an area of ​​the received image from a reference contrast by performing a contrast adjustment on the received image, wherein the area includes the license plate or a part thereof, and / or Determining a distortion of an area of ​​the received image recording, wherein the area includes the license plate or a part thereof, and / or Determining a distortion of an area of ​​the received image recording compared to a reference, wherein the area includes the license plate or a part thereof, and / or Reducing distortion of an area of ​​the received image relative to a reference by rectification, wherein the area includes the identifier or part thereof.

12. Method according to any one of claims 1 to 11, wherein the reference is or comprises a reference image, wherein the identifier is depicted in the reference image, and wherein the reference image shows the identifier under defined conditions.

13. A method according to any one of claims 1 to 12, wherein reducing the deviation comprises: Feeding the image capture into a trained machine learning model, Receiving a transformed image from the trained machine learning model, wherein the machine learning model is configured and trained to generate a transformed image based on an image and model parameters, wherein the machine learning model is trained on training data, the training data comprising input data and target data, wherein the input data comprises training image captures, wherein the target data comprises at least one training image capture, each training image capture showing a collection area and the label, wherein training the machine learning model comprises: o Inputting the input data into the machine learning model, o Receiving output data from the machine learning model, o Reducing any deviation between the output data and the target data by modifying the model parameters.

14. Device for monitoring arthropods comprising - a camera and - a control unit, wherein the control unit is configured, to cause the camera to produce an image capturing a collection area for arthropods and a marker, to reduce any deviation between the marker depicted in the image and a reference by transforming the image capturing, to store the transformed image capturing and / or transmit it to a separate computer system and / or to detect, locate, identify and / or count arthropods in the transformed image.

15. Non-volatile, computer-readable storage medium containing a computer program which, when executed by a control unit of an arthropod monitoring device, causes the control unit to perform the following steps: Receiving an image showing a collection area for arthropods and a label, Reducing the deviation between the license plate depicted in the image and a reference by transforming the image, Storing the transformed image and / or transmitting the transformed image to a separate computer system and / or detecting, locating, identifying and / or counting arthropods in the transformed image.

Images