WO2023094111A1 - Procédé d'identification de mauvaises herbes dans une rangée de plantes d'un champ agricole - Google Patents

Procédé d'identification de mauvaises herbes dans une rangée de plantes d'un champ agricole Download PDF

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Publication number
WO2023094111A1
WO2023094111A1 PCT/EP2022/080200 EP2022080200W WO2023094111A1 WO 2023094111 A1 WO2023094111 A1 WO 2023094111A1 EP 2022080200 W EP2022080200 W EP 2022080200W WO 2023094111 A1 WO2023094111 A1 WO 2023094111A1
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WIPO (PCT)
Prior art keywords
plants
identified
plant
area
row
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PCT/EP2022/080200
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German (de)
English (en)
Inventor
Farid Khani
Andreas Weimer
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Robert Bosch Gmbh
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Publication of WO2023094111A1 publication Critical patent/WO2023094111A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/188—Vegetation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects

Definitions

  • the invention is based on a method for identifying weeds in a row of plants on an agricultural area and a corresponding computing unit, a plant identification unit and an agricultural working machine according to the species of the independent claims.
  • the subject matter of the present invention is also a computer program and a machine-readable storage medium
  • DE 10 2017 210 804 A1 discloses a method for applying a spray to a field, the spray being applied depending on the degree of coverage of an evaluation area.
  • plants are segmented using a threshold in the NDVI. From this segmentation, the culture series are recognized. A culture row hose with a certain width is placed around the culture rows. Any segmented object (plant) between the tubes is classified as a weed by definition. All objects that lie within this hose width of the rows or are connected to objects in this hose width are classified as crops by definition.
  • the subject matter of the present invention is a method for identifying weeds in a row of plants on an agricultural area, with the steps:
  • the subject matter of the present invention is also a computing unit which is set up to carry out and/or control the steps of a previously described method.
  • the subject matter of the present invention is also a plant identification unit with an optical detection unit for detecting a field section of an agricultural area with plants in order to obtain image information from the detected field section, and a previously described computing unit.
  • the subject matter of the present invention is also an agricultural working machine, in particular a field sprayer, with an agricultural working tool, in particular a spraying device, and a previously described plant identification unit, the working tool, in particular the spraying device, depending on the identified weeds in the identified row of plants, using the computing unit is controlled.
  • the present invention also relates to a computer program that is set up to carry out and/or control the steps of a method described above when the computer program is executed on a computer, and a machine-readable storage medium on which the computer program is stored.
  • the method according to the invention now makes it possible to identify weeds in a row of plants or rows of cultivated plants in a very simple and resource-saving manner, without requiring an image database or the like.
  • the method can therefore be used very flexibly, since it makes use of the length of the plants and weeds actually on the agricultural area and extracts precisely these in order to identify weeds in the plant rows.
  • this is done in that length values of identified plants of a defined crop plant area are determined in the image information in the “transverse areas” unit and are compared with a frequency distribution of length values of previously identified plants. That means, in other words, that by means of statistical evaluation of length values of plants in identified rows of plants or within defined crop plant areas, which are based on a history of previous plants is based in the plant row, a distinction is made in the current crop row between crop plants and weeds.
  • a classifier is a method that ends up calculating a probability of how well an object fits into a class.
  • the information about the identified weeds in the crop area can be used for subsequent steps in order to identify rows of plants better or more precisely, since the identified or recognized weeds are not taken into account for this or can be "ignored”.
  • An agricultural area can be understood to mean an area used for agriculture, an area under cultivation for plants or also a parcel of such an area or area under cultivation.
  • the agricultural area can thus be arable land, grassland or pasture.
  • the plants include cultivated plants or useful plants, the fruit of which is agricultural is used, for example as food, animal feed or as an energy crop, as well as weeds or weeds.
  • the field section can be a detection section or a detected image section of an optical detection unit.
  • the image information can, for example, be an image of the detected field section.
  • An optical detection unit can be understood to mean, for example, a camera or a 3D camera or an infrared detection unit.
  • the optical detection unit can be calibrated to e.g. B. to calculate the height assignment from captured images.
  • the method can include a step of capturing a field section of an agricultural area with plants by means of the optical capturing unit.
  • the detecting step can be performed during a transit or a flight of the plant identification unit.
  • At least one further step of the method, in particular all steps of the method, can be carried out during a crossing or a flight of the plant identification unit.
  • the plant identification unit can comprise a mobile unit or be arranged on a mobile unit, wherein the mobile unit can be designed in particular as a land vehicle and/or aircraft and/or trailer.
  • the mobile unit can also be a self-propelled or autonomous robot.
  • the plant identification unit is preferably part of an agricultural working machine.
  • the agricultural working machine is preferably a weed regulating machine, in particular a field sprayer.
  • the agricultural working tool is preferably a spray device, but can also be a mechanical tool for weed control.
  • the method includes a step of identifying at least one row of plants or a row of cultivated plants in the image information of the recorded field section by means of the computing unit.
  • the at least one row of plants is preferably identified using at least one of the following items of information: color component, in particular red color component of plants in the recorded field section, infrared component of plants in the recorded field section field section, plant spacing, plant row spacing, growth stage of the plants, geo-coordinates of a sowing of the plants.
  • the rows of plants can be identified in a simple manner, since, for example, crop plants are usually planted equidistantly or the crop plants are more advanced in growth stage than the weeds or weeds.
  • all rows of plants are preferably identified in the image information or in the recorded field section.
  • the step of identifying at least one row of plants understandably includes the detection of plants or plant parts in the image information or in the detected field section.
  • a detection of plants can be understood, for example, as determining the presence of plants or plant mass/biomass in the field section, in particular without the individual plants being classified in the process.
  • the step of detecting plants can include detecting a color component, in particular a red color component and/or an infrared component in the field section or image section.
  • the optical detection unit e.g.
  • NDVI value Normalized Differenced Vegetation Index, it is formed from reflection values in the near infrared and visible red wavelength range of the light spectrum
  • the method also includes a step of defining a crop plant area comprising the at least one identified plant row using the at least one identified plant row by means of the computing unit.
  • the crop area is preferably defined using neighborhood pixels of detected plants of the crop area.
  • the cultivated plant area is preferably defined around a generated plant row center line, in particular with the generated plant row center line extending essentially in a straight line.
  • the crop range may fully encompass the plants of the plant row.
  • the crop area can also include the row of plants without that the individual plants of the plant series are completely covered. Accordingly, the cultivated plant area can also only partially include the individual plants of the plant row. In other words, all identified plants which are at least partially arranged in the respective defined crop area are assigned to the crop area or are evaluated or viewed as plants of the crop area.
  • the cultivated plant area can be defined around the respective plant row center line with a constant or defined width.
  • the crop area can also have a variable width, wherein the width can depend on a growth stage of a plant arranged in a corresponding area of the crop area.
  • the cultivated plant area is thus designed in the form of a tube.
  • the crop area has a smaller width than the recorded field section or the corresponding image information.
  • the method also includes a step of determining a length value for each of the identified plants of the defined crop plant area in the image information using the computing unit, the image information being divided into transverse regions and the length value being the number of transverse regions over which an identified plant of the defined area extends Cultivated plant area extends represented.
  • the defined longitudinal direction preferably runs along the plant row center line.
  • the transverse areas run transversely, preferably at an angle of greater than or equal to 45° to less than or equal to 90°, more preferably at an angle of greater than or equal to 85° to less than or equal to 90°, to the defined longitudinal direction or the plant row center line.
  • the transverse areas can also run horizontally in the image information or the image independently of the defined longitudinal direction or the plant row center line.
  • the transverse areas preferably have a substantially equal length in the defined longitudinal direction. In this case, the length of the transverse regions is greater than 1, preferably in a range from greater than or equal to ... pixels to less than or equal to ... X pixels.
  • the plants or individual plants are defined or formed in the longitudinal direction in that an adjacent transverse area in the respective cultivated plant area is empty.
  • plants and/or plant parts of the defined crop area between two empty transverse areas are regarded as a single plant.
  • the procedure is preferably such that as soon as a plant or a plant part or plant mass is identified in a transverse area, counting begins and only ends when—seen in the longitudinal direction—a subsequent transverse area is empty.
  • empty means that no plant or part of a plant was identified in the transverse area in the respective crop area. This determines the length of the plants in the unit “transverse areas”.
  • only plants are preferably taken into account, i.e. filtered out and evaluated, which are at least partially arranged in the respective defined crop plant area, i.e. are assigned to the crop plant area.
  • the method also includes a step of comparing the determined length value of the respective identified plant of the defined crop area with a frequency distribution, i.e. a history of length values of previously identified plants of the defined crop area and/or defined crop areas of previously recorded image information of the agricultural area by means of the computing unit.
  • a frequency distribution i.e. a history of length values of previously identified plants of the defined crop area and/or defined crop areas of previously recorded image information of the agricultural area by means of the computing unit.
  • the defined crop plant areas of the previously recorded image information preferably include the same at least one identified row of plants or previously recorded sections of the same row of plants.
  • the previously captured image information is preferably image information captured immediately beforehand by the image capturing unit, in particular during the same movement or crossing of the image capturing unit over the agricultural area.
  • the frequency distribution is preferably a distribution of the absolute frequency of the length values. However, it is possible that the frequency distribution is a relative frequency, e.g. based on the sum of 300 plant objects. Understandably, 2 or more length values in the frequency distribution can also be combined into length classes depending on a specific parameter, without departing from the scope of the present application. For example, plants with a length value of 1 or 2 can be grouped in length class 1, plants with a length value of 3 or 4 in length class 2, etc. in the frequency distribution.
  • the method also comprises a step of identifying the plants of the defined crop area with a length value whose frequency is less than or equal to a defined frequency threshold value of the frequency distribution in order to identify them as weeds in an identified plant row.
  • the defined frequency threshold of the frequency distribution can be specified.
  • the defined frequency threshold value can depend, for example, on the species and/or the genus and/or the growth stage and/or the distribution of the area/size of the useful plants. However, the defined frequency threshold can also be adjusted depending on a specific parameter.
  • the frequency distribution is dynamically adapted with an increasing number of previously identified plants or a defined number of plants identified immediately previously during the method, in particular during a movement of the optical detection unit over the agricultural area.
  • the frequency distribution changes dynamically while crossing the field, since (new) length values of newly identified plants are always included.
  • (old) length values of old identified plants are preferred, e.g. if only a defined constant number of currently identified plants is taken into account, so that the frequency distribution reflects the latest conditions on the agricultural Adjust surface.
  • only plants are preferably taken into account, i.e. filtered out and evaluated, which are at least partially arranged in the respective defined crop area, i.e. are assigned to the crop area.
  • the defined length threshold value can advantageously be specified or determined as a function of the frequency distribution of length values. It is advantageous here if, when determining the length threshold value, the greatest length value is determined with a frequency above the frequency threshold value, and the length threshold value is defined as the next greater length value or the length value greater or lesser by a defined value.
  • the defined value by which the length threshold should be greater may depend on the species and/or genus and/or growth stage and/or area/size spread of the crops. This measure makes it possible to effectively identify large weeds in particular, i.e. weeds that are longer than the plants or crops in the row of plants.
  • a minimum value for the length threshold value can also be defined here, above which a plant may be identified/classified as a weed or reclassified from the crop plant class into a weed class.
  • the minimum value can depend, for example, on the species and/or the genus and/or the growth stage and/or the distribution of the area/size of the useful plants. This is a kind of security so that the dynamic and self-learning algorithm only reclassifies larger weeds. Smaller weeds with a smaller length threshold should remain unchanged according to this strategy, e.g. if the crop plants are smaller and only larger weeds should be identified.
  • the method includes a step of classifying the identified weeds in the identified row of plants into a plant class, in particular a weed class, by means of the computing unit. It is particularly advantageous here if a step of classifying the, i.e. all identified plants of the defined crop plant area into a first plant class, in particular a crop plant class, is initially provided, and then a step of reclassifying the identified weeds in the identified plant row of the defined crop plant area into a second plant class different from the first plant class, in particular a weed class, is provided by means of the computing unit.
  • the method includes a step of controlling an agricultural working machine, in particular an agricultural working tool of an agricultural working machine, in particular a spray device of a field sprayer, depending on the identified weeds in the identified row of plants.
  • an agricultural working machine in particular an agricultural working tool of an agricultural working machine, in particular a spray device of a field sprayer, depending on the identified weeds in the identified row of plants.
  • the arithmetic unit is or the arithmetic units are designed or set up for image processing, so that they carry out calculation steps or image processing steps for carrying out the method according to the invention can execute. Accordingly, each computing unit has corresponding image processing software.
  • the arithmetic unit can be, for example, a signal processor, a microcontroller or the like, with the memory unit being able to be a flash memory, an EPROM or a magnetic memory unit.
  • the communication interface can be designed to read in or output data wirelessly and/or by wire, with a communication interface that can read in or output wire-bound data reading this data, for example electrically or optically, from a corresponding data transmission line or outputting it into a corresponding data transmission line.
  • the method according to the invention can be implemented, for example, in software or hardware or in a mixed form of software and hardware in the processing unit or a control unit.
  • the processing unit can be arranged completely or partially on the agricultural working machine or integrated into it.
  • the computing unit can also be completely or partially external, for example integrated in a cloud.
  • the arithmetic unit can thus also be divided among different units, for example mobile and stationary units.
  • a computer program product or computer program with program code which can be stored on a machine-readable carrier or storage medium such as a semiconductor memory, a hard disk memory or an optical memory and for carrying out, implementing and/or controlling the steps of the method according to one of the embodiments described above, is also advantageous used, especially when the program product or program is run on a computer or device.
  • FIG. 4 shows a flowchart of a method according to an embodiment.
  • Fig. 1 shows image information or an image 10 of a field section 12 of an agricultural area with plants 14 captured by means of an optical detection unit or camera (not shown).
  • the plants 14 include crop plants 16 and weeds 18.
  • rows of plants 20 were identified by means of a computing unit (not shown).
  • the plant rows 20 were identified by fitting defined straight plant row center lines 22 in image trajectories with the highest NDVI value (difference red to NIR) of the plants 14 .
  • the cultivated plant area 24 is tubular and has a defined, constant width around the respective defined plant row center line 22 .
  • the cultivated plant area 24 does not include the entire plant body of the plants 14 of the plant rows 20.
  • FIG. 2 now illustrates how a length value for each of the identified plants 14, 16, 18 is determined in the image information 10.
  • the image information 10 or the image 10 is subdivided into transverse areas 26 .
  • the transverse areas 26 are in this case rectangles of a defined length 28, which over the entire length of the image 10 in the longitudinal direction 30, which along the Plant row center line 22 runs, are stretched.
  • the transverse areas 26 run perpendicularly to the longitudinal direction 30 or plant row center line 22.
  • plants 14, 16, 18 are filtered out, which are at least partially arranged in the respective defined crop area 24. Consequently, only these filtered out plants 14, 16, 18 are subsequently taken into account and evaluated.
  • a length value for each of these plants 14, 16, 18 is then determined by the computing unit, with the length value representing the number of transverse regions 26 over which a plant 14, 16, 18 of the defined crop plant region 24 extends.
  • a plant 14, 16, 18 of the corresponding row of plants 20 begins by definition as soon as plant mass is identified in a transverse area 26 and ends when a transverse area 26 is empty, i.e. no more plant mass is identified.
  • plant 14 and plant 16 have the length value 2 and plant 18 has the length value 3.
  • These length values or data are continuously stored during a crossing over the agricultural area.
  • a history is built up in which the last, for example 100, plants 14, 16, 18 from the previously recorded image information 10 are stored.
  • 3 shows the frequency distribution or history of the determined length values using a bar chart, which is always dynamically adapted based on new image information 10 .
  • the length value or the number of transverse regions 26 is plotted on the abscissa and the number of plants 14 with the respective length value is plotted on the ordinate.
  • a frequency threshold value 32 was defined in the frequency distribution, which is at 3 plants in the example shown.
  • all plants 14 of the defined crop plant area 24 whose frequency is less than or equal to the defined frequency threshold value 32, i.e. occur 3 times or less, are identified or classified as weeds 18.
  • plants 14 with a length value of 4, 7 and 8 are identified as weeds 18 in this exemplary embodiment.
  • All remaining plants 14 with a frequency of more than 3 are identified or classified as crop plants 16 .
  • a length threshold value 34 is also defined, with only plants 14 with a length value greater than or equal to this length threshold value 34 being identified as weeds 18 .
  • the length threshold was determined by first determining the largest length value with a frequency above the frequency threshold, which is 6 in the present example. The next largest length value, i.e. 7, was then chosen as the length threshold value.
  • plants 14 with a length value of 7 and 8 are identified or classified as weeds 18 in this exemplary embodiment, while plants 14 with a length value of 4 and all others are identified or classified as crop plants 16 .
  • large weeds 18 can be effectively identified and, if necessary, reclassified.
  • a length threshold value 36 can be defined, which is greater by a defined value, for example by one length unit, than the determined greatest length value.
  • a length threshold value 36 can be defined, which is greater by a defined value, for example by one length unit, than the determined greatest length value.
  • FIG. 4 shows a flow chart of an embodiment of the approach presented here as a method 100 for identifying weeds 18 in a row of plants 20 of an agricultural area.
  • Method 100 includes a step of receiving 102 image information 10 from a field section 12 of an agricultural area with plants 14, 16, 18 that is detected by means of an optical detection unit.
  • Method 100 also includes a step of identifying 104 at least one row of plants 20 in the image information 10 using identified plants 14, 16, 18 by means of a computing unit.
  • the method 100 also includes a step of defining 106 a crop plant region 24 comprising the at least one identified row of plants 20 using the at least one identified row of plants 20 in the image information 10 by means of the computing unit.
  • the method 100 further comprises a step of determining 110 a length value for each of the identified plants 14, 16, 18 of the defined crop plant area 24 in the image information 10 by means of the computing unit, wherein the image information 10 is divided into transverse areas 26 and the length value is the number of Transverse areas 26, over each of which an identified plant 14, 16, 18 of the defined crop area 24 extends.
  • the method also includes a step of comparing 112 the determined length value of the respective identified plant 14, 16, 18 of the defined crop area 24 with a frequency distribution of length values of previously identified plants 14 of the defined crop area 24 and/or defined crop areas 24 of previously recorded image information 10 of the agricultural Area using the unit of account.
  • the method also includes a step of identifying 114 the plants 14 of the defined crop area 24 with a length value whose frequency is less than or equal to a defined frequency threshold value 32 of the frequency distribution in order to identify them as weeds 18 in an identified row of plants 20 .
  • the method 100 includes an optional step of classifying 108 the identified plants 14, 16, 18 of the defined crop plant area 24 into a first plant class, in particular a crop plant class, and a further optional step of reclassification 116 of the identified weeds 18 in the identified plant row 20 of the defined cultivated plant area 24 into a second plant class different from the first plant class, in particular a weed class, by means of the computing unit.

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Abstract

L'invention concerne un procédé d'identification de mauvaises herbes (18) dans une rangée (20) de plantes d'un champ agricole. Des valeurs de longueur pour des plantes identifiées (14, 16, 18) d'une zone de culture définie sont déterminées, les informations d'image (10) étant divisées en régions transversales (26), et la valeur de longueur représentant le nombre de régions transversales (26) sur lesquelles s'étend chaque plante identifiée (14, 16, 18) de la région de culture définie, les valeurs de longueur déterminées étant comparées à une distribution de fréquence de valeurs de longueur de plantes précédemment identifiées (14), et les plantes (14, 16, 18) de la région de culture définie ayant une valeur de longueur ayant une fréquence qui est inférieure ou égale à un seuil de fréquence défini de la distribution de fréquence étant identifiés comme des mauvaises herbes (18) dans une rangée identifiée de plantes (20).
PCT/EP2022/080200 2021-11-25 2022-10-28 Procédé d'identification de mauvaises herbes dans une rangée de plantes d'un champ agricole WO2023094111A1 (fr)

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DE102021213280.4 2021-11-25
DE102021213280.4A DE102021213280A1 (de) 2021-11-25 2021-11-25 Verfahren zum Identifizieren von Beikräutern in einer Pflanzenreihe einer landwirtschaftlichen Fläche

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DE102022212169A1 (de) 2022-11-16 2024-05-16 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren zum Klassifizieren von Pflanzen in und/oder zwischen Pflanzenreihen einer landwirtschaftlichen Fläche
DE102022212162A1 (de) 2022-11-16 2024-05-16 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren zum Ermitteln einer Pflanzenkennzahl von Pflanzen einer Pflanzenreihe einer landwirtschaftlichen Fläche
DE102022212171A1 (de) 2022-11-16 2024-05-16 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren zum Klassifizieren von Pflanzen in und/oder zwischen Pflanzenreihen einer landwirtschaftlichen Fläche
DE102022212161A1 (de) 2022-11-16 2024-05-16 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren zum Ermitteln einer Breitenkennzahl von Pflanzen einer Pflanzenreihe einer landwirtschaftlichen Fläche

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