• 专利附录
  • 专利说明
  • 权利要求
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    • 专利摘要:
    This is a method for automatically monitoring soil surface roughness as a tillage operation is being performed within a field, which may include receiving pre-operation surface roughness data associated with a given portion. field data and receive post-operative surface roughness data associated with the given portion of the field. In addition, the method may include analyzing pre-operation and post-operation surface roughness data to determine a surface roughness differential associated with the tillage performance and actively adjusting the operation of at least one of one. associated working vehicle and / or implement when the surface roughness differential differs from a target definition for the surface roughness differential.
    公开号:BR102018001565A2
    申请号:R102018001565
    申请日:2018-01-25
    公开日:2018-10-30
    发明作者:T Turpin Bret；H Posselius John；Ferrari Luca；C Bybee Taylor
    申请人:Autonomous Solutions Inc；Cnh Ind America Llc；
    IPC主号:
    专利说明:

    (54) Title: 7SYSTEM AND METHOD FOR AUTOMATICALLY MONITORING SOIL SURFACE RUGOSITY (51) Int. Cl .: A01B 79/00 (30) Unionist Priority: 2/3/2017 US 15 / 423,811 (73) Holder (s): CNH INDUSTRIAL AMERICA LLC, AUTONOMOUS SOLUTIONS, INC.
    (72) Inventor (s): LUCA FERRARI; JOHN H. POSSELIUS; BRET T. TURPIN; TAYLOR C. BYBEE (85) National Phase Start Date:
    25/01/2018 (57) Abstract: This is a method to automatically monitor the surface roughness of the soil as a soil tillage operation is being carried out within a field, which may include receiving roughness data from pre-operation surface associated with a given portion of the field and receive post-operation surface roughness data associated with the given portion of the field. In addition, the method may include analyzing pre-operation and post-operation surface roughness data to determine a surface roughness differential associated with the performance of the tillage operation and actively adjusting the operation of at least one of a associated work vehicle and / or implement when the surface roughness differential differs from a target definition for the surface roughness differential.
    1/39
    SYSTEM AND METHOD FOR AUTOMATICALLY MONITORING SOIL SURFACE RUGOSITY
    Field of the Invention [001] The present article refers, in general, to carrying out soil preparation operations using working vehicles and / or associated implements and, more particularly, to a system and method to automatically monitor the soil surface roughness of a field during the performance of a tillage operation.
    Background of the Invention [002] Soil surface roughness, in general, refers to the flatness or smoothness of soil within a field and is typically impacted by uneven soil profiles, clods, crop residue and foreign objects within the field (e.g., stones). For several reasons, soil surface roughness is an important field characteristic to consider when performing a soil tillage operation, such as a cultivation operation, a planting operation, a fertilization operation and / or the like. For example, the roughness of the soil surface can impact the environmental quality of the soil, which includes resistance to erosion and moisture content. In addition, the roughness of the soil surface can affect the quality of the bed. As such, the ability to monitor and / or adjust the surface roughness of soil within a field can be very important in maintaining a productive, healthy field, particularly when it comes to performing various soil tillage operations.
    [003] Although several systems and methods have been developed to allow the surface roughness of soil within a field to be estimated, such systems and methods have several drawbacks or disadvantages. For example, roughness estimation techniques for
    Petition 870180006520, of 01/25/2018, p. 84/137
    2/39 more conventional surfaces require manual measurements that can be very time consuming and labor intensive. To address the problems associated with manual techniques, efforts have been made to develop systems that can automatically measure the surface roughness of soil. However, to date, such systems have failed to provide a system configuration that allows the change in soil surface roughness that occurs as a result of the performance of a soil tillage operation to be monitored reliably and efficiently.
    [004] Consequently, an improved system and method for automatically monitoring the soil surface roughness of a field, while performing a soil tillage operation that overcomes one or more of the problems in the prior art, would be welcome in technology.
    Description of the Invention [005] The aspects and disadvantages of the invention will be presented, in part, in the following description, or they may be obvious from the description, or they can be learned through the practice of the invention.
    [006] In one aspect, the present matter is directed to a method for automatically monitoring the surface roughness of the soil, as a soil tillage operation is being carried out within a field, using a working vehicle that tows an implement. The method may include receiving, with one or more computing devices, the pre-operation surface roughness data associated with a given portion of the field, where the pre-operation surface roughness data corresponds to the surface roughness data for the given portion of the field captured before the soil preparation operation is carried out on it. The method may also include receiving, with one or more
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    3/39 plus computing devices, post-operation surface roughness data associated with the given portion of the field, where the post-operation surface roughness data corresponds to the surface roughness data for the given portion of the field captured after the soil preparation operation has been carried out on it. Additionally, the method may include analyzing, with one or more computing devices, the pre-operation and post-operation surface roughness data to determine a surface roughness differential associated with the performance of the tillage operation and adjusting actively, with one or more computing devices, the operation of at least one of the working vehicle or the implement, when the surface roughness differential differs from a target definition for the surface roughness differential.
    [007] In another aspect, the present matter is directed to a system to automatically monitor the surface roughness of the soil as a soil preparation operation is being carried out inside a field using a work vehicle that tows a implement. The system can include at least one non-contact soil roughness sensor configured to capture data associated with field soil roughness as the tillage operation is being carried out. The system can also include a controller commutatively coupled to the non-contact soil roughness sensor (s). The controller can include a processor and an associated memory. The memory can store instructions that, when implanted by the processor, configure the controller to receive, from the non-contact soil roughness sensor (or sensors), pre-operation surface roughness data associated with a given portion of the field, where the prePetition surface roughness data 870180006520, of 25/01/2018, p. 86/137
    4/39 operation corresponds to the surface roughness data for the given portion of the field capture, before the soil preparation operation is carried out on the same. The controller can also be configured to receive, from the non-contact soil roughness sensor (or sensors), post-operation surface roughness data associated with the given portion of the field, in which the powder surface roughness data -operation correspond to the surface roughness data for the given portion of the field captured after the soil preparation operation has been carried out on it. In addition, the controller can be configured to analyze the pre-operation and post-operation surface roughness data to determine a surface roughness differential associated with the performance of the tillage operation and actively adjust the operation of at least one between the work vehicle or the implement, when the surface roughness differential differs from a target definition for the surface roughness differential.
    [008] These and other features, aspects and disadvantages of the present invention will become better understood with reference to the following description and appended claims. The attached drawings, which are incorporated and constitute a part of this specification, illustrate the achievements of the invention and, together with the description, serve to explain the principles of the invention.
    Brief Description of the Drawings [009] A complete and enabling description of the present invention, which includes its best mode, directed to a person of common skill in the technique, is presented in the specification, which makes reference to the attached figures, in which :
    Figure 1 illustrates a perspective view of an
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    5/39 a work vehicle that tows an implement in accordance with aspects of the present matter, particularly that illustrates non-contact soil roughness sensors provided in operative association with the work vehicle and the implement;
    Figure 2 illustrates a perspective view of the cultivation implement shown in Figure 1;
    Figure 3 illustrates a schematic top-down view of the working vehicle and the implement shown in Figures 1 and 2, particularly illustrating several examples of alternative locations for installing soil roughness sensors on the working vehicle and / or the implement;
    Figure 4 illustrates a schematic view of an implementation of a system to automatically monitor the soil surface roughness of a field during the performance of a soil tillage operation, according to aspects of the present matter;
    Figure 5 illustrates an implementation of a flowchart that shows several steps of data processing that can be performed when analyzing the data of soil surface roughness, according to the aspects of the present matter;
    Figure 6 illustrates a plot of sample data of soil surface roughness data for a field that has a relatively rough soil profile;
    Figure 7 illustrates an example data plot of soil surface roughness data for a field that has a relatively smooth soil profile; and
    Figure 8 illustrates a flow chart of an implementation of a method to automatically monitor the surface roughness of a
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    6/39 field during the performance of a tillage operation, according to aspects of the present matter.
    Description of Realizations of the Invention [010] Reference will now be made in detail to the realizations of the invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the invention, without limitation of the invention. Indeed, it will be apparent to those skilled in the art that various modifications and variations can be made to the present invention, without departing from the scope or spirit of the invention. For example, the aspects illustrated or described as part of an embodiment can be used with another embodiment to further produce an additional embodiment. Thus, it is intended that the present invention covers such modifications and variations as they are presented in the scope of the attached claims and their equivalents.
    [011] In general, the present matter is directed to a system and a method to automatically monitor the roughness of a field's soil surface during the performance of a soil tillage operation. Specifically, in various embodiments, one or more non-contact soil roughness sensors (for example, a LIDAR scanning device, a stereo camera, an ultrasound device, a radar device and / or the like) can be provided in an operating association with the working vehicle and / or the implement to capture field surface roughness data, as the tillage operation is being carried out. As will be described below, in one embodiment, the soil roughness sensor (or sensors) can (or can) be used (or used) to capture surface roughness data for various portions of the field, both before and after the operation soil preparation has been carried out
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    7/39 in such portions of the field. The pre-operation and post-operation surface roughness data can then be automatically analyzed using an associated controller to estimate or calculate the change in surface roughness that occurs as a result of the tillage operation (also called, in this document, as the surface roughness differential), which can provide an indication of the implement's effectiveness in handling or otherwise adjusting the soil roughness as the operation is being carried out. After that, if it is determined that the implement's effectiveness is deficient (for example, due to the surface roughness differential that differs from a given target value or that is within a given target range), the controller can be configured to automatically adjust the operation of the working vehicle and / or the implement in a manner designed to modify the effectiveness of the implement in decreasing or increasing the surface roughness of the soil, as desired. For example, the controller can be configured to automatically adjust the implement's ground speed and / or adjust a soil tillage parameter (or parameters) associated with one or more implement tillage tools (for example, a depth of penetration and / or downward pressure to one or more of the tillage tools).
    [012] In addition, to provide an indication of the current operation effectiveness of the implement, the surface roughness differential, associated with the change in surface roughness that occurs as a result of the tillage operation, can also provide a indication of one or more parameters related to the roughness associated with the field. For example, if the soil surface differential is less than expected, based on the current operating definitions for the implement, the
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    8/39 reduced differential can provide an indication that there is a large concentration of stones or other foreign objects within the field. Similarly, a greater or lesser than expected surface roughness differential can also provide an indication that the soil texture is either too hard or too soft for the current implement settings of the implement.
    [013] Referring now to the drawings, Figures 1 and 2 illustrate perspective views of an embodiment of a working vehicle 10 and an associated implement 12, according to aspects of the present matter. Specifically, Figure 1 illustrates a perspective view of the working vehicle 10 that tows the implement 12 (for example, along a field). Additionally, Figure 2 illustrates a perspective view of the implement 12 shown in Figure 1. As shown in the illustrated embodiment, the working vehicle 10 is configured as an agricultural tractor, and the implement 12 is configured as an associated cultivation implement. However, in other embodiments, the working vehicle 10 can be configured as any other suitable agricultural vehicle and / or any other suitable type of working vehicle, such as a construction vehicle. Similarly, in other embodiments, implement 12 can be configured like any other suitable agricultural implement and / or any other suitable type of implement configured to be towed by a work vehicle.
    [014] As shown particularly in Figure 1, the work vehicle 10 includes a pair of front rail assemblies 14, a pair or rear rail assemblies 16 and a frame or chassis 18 coupled and supported by the rail assemblies 14, 16. An operator's station 20 can be supported by a portion of the chassis 18 and can accommodate multiple input devices to allow an operator to control the operation of
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    9/39 one or more components of the working vehicle 10 and / or one or more components of the implement 12. Additionally, as understood in general, the working vehicle 10 can include an engine 22 (Figure 4) and a transmission 24 ( Figure 4) mounted on the chassis 18. The transmission 24 can be operably coupled to the motor 22 and can provide variable adjustable gear ratios to transfer the power from the motor to the rail assemblies 14, 16 by means of a drive shaft assembly (not shown) (or via axes, if multiple driving axes are used).
    [015] Additionally, as shown in Figures 1 and 2, the implement 12 can, in general, include a set of transport frames 30 configured to be towed by the working vehicle by means of a drawbar or tow bar 32 in a direction of travel of the vehicle (for example, as indicated by arrow 34). As is generally understood, the transport frame set 30 can be configured to hold a plurality of soil tillage tools, such as a plurality of cables, disc blades, leveling blades, basket sets, teeth, spikes and / or the like. In various embodiments, the various soil tillage tools can be configured to perform a cultivation operation or any other suitable soil tillage operation across the field, along which the implement 12 is being towed. It should be noted that, in addition to being towed by the working vehicle 10, the implement 12 can also be a semi-assembled implement connected to the working vehicle 10 by means of a two-point hitch (not shown), or the implement 12 can be a fully assembled implement (for example, mounted on the 3-point hitch of work vehicle 10 (not shown)).
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    10/39 [016] As shown particularly in Figure 2, the carrier frame set 30 may include rearward extending carrier frame members 36 coupled to the tow bar 32. In addition, reinforcement bracket plates 38 may be used to reinforce the connection between the tow bar 32 and the carrier frame members 36. In various embodiments, the carrier frame assembly 30 can generally function to support a central frame 40, a front frame 42 positioned ahead of the central frame 40 in the direction of travel 34 of the working vehicle 10, and a rear frame 44 positioned behind the central frame 40 in the direction of travel 34 of the working vehicle 10. As shown in Figure 2, in one embodiment, the frame center 40 can correspond to a cable frame configured to support a plurality of soil tillage cables 46. In such an embodiment, cables 46 can be configured to cultivate or otherwise engage and to the ground as the implement 12 is towed across the field. However, in other embodiments, the central frame 40 can be configured to support any other suitable soil tillage tools.
    [017] Additionally, as shown in Figure 2, in one embodiment, the front frame 42 can correspond to a disk frame configured to hold several groups or sets 48 of disk blades 50. In such an embodiment, each disk blade 50 can , for example, includes both a concave side (not shown) and a convex side (not shown). In addition, the various groups 48 of disc blades 50 can be oriented at an angle to the travel direction 34 of the working vehicle 10 to promote more effective cultivation of the soil. However, in other embodiments, the front frame 42 can be configured to hold any other suitable tillage tools.
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    11/39 [018] In addition, similar to the central and front frames 40, 42, the rear frame 44 can also be configured to hold a plurality of soil tillage tools. For example, in the illustrated embodiment, the rear frame is configured to hold a plurality of leveling blades 52 and to laminate (or knead) basket sets 54. However, in other embodiments, any other suitable soil tillage tools can be coupled and supported by the rear frame 44, such as a plurality of closing discs.
    [019] In addition, implement 12 may also include any number of suitable actuators (for example, hydraulic cylinders) to adjust relative positioning, depth of penetration and / or downward force associated with the various tillage tools 46, 50, 52, 54. For example, implement 12 may include one or more first actuators 56 coupled to the central frame 40 to raise or lower the central frame 40 in relation to the ground, thereby allowing the depth of penetration and / or the downward pressure of the cables 46 is adjusted. Similarly, the implement 12 can include one or more second actuators 58 coupled to the front frame 42 to adjust the depth of penetration and / or the downward pressure of the disc blades 50. In addition, the implement 12 can include one or more third actuators 60 coupled to the rear frame 44 to allow the rear frame 44 to be moved relative to the central frame 40, thereby allowing the relevant operating parameters of the tillage tools 52, 54 supported by the rear frame 44 (e.g. downward pressure and / or penetration depth) are adjusted.
    [020] It must be verified that the work vehicle 10 configuration described above and shown in Figure 1 is provided only for
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    12/39 place this article in an exemplary field of use. Thus, it must be verified that the present material can be readily adaptable to any mode of work vehicle configuration. For example, in an alternative embodiment, a separate structure or chassis can be provided to which the engine, transmission and drive axle assembly are coupled, a common configuration on small tractors. Still, other configurations may use an articulated chassis to drive the work vehicle 10, or rely on tires / wheels in place of the rail assemblies 14, 16.
    [021] It should also be verified that the implement 12 configuration described above and shown in Figures 1 and 2 is provided for exemplary purposes only. Thus, it must be verified that the present material can be readily adaptable to any mode of implement configuration. For example, as indicated above, each frame section of implement 12 can be configured to hold any suitable type of tillage tools, such as installing any combination of cables, disc blades, leveling blades, basket sets, teeth, peaks and / or the like in one or more sections of the set of frames 30. Alternatively, as opposed to the illustrated cultivation implement, implement 12 can be configured as a planting implement, a fertilizing implement and / or any other type of agricultural implement.
    [022] Additionally, according to aspects of the present matter, the working vehicle 10 and / or the implement 12 can include one or more non-contact soil roughness sensors 104 coupled to it and / or supported in it to monitor the roughness field surface as a tillage operation (for example, a
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    13/39 cultivation, a planting operation, a fertilization operation and / or the like) that is being carried out on the same by means of implement 12. Specifically, in various embodiments, the soil roughness sensor (or sensors) can ( or they can) be supplied (or supplied) in operative association with the working vehicle 10 and / or the implement 12, so that the sensor (or sensors) 104 has (or has) a field of view or detection range sensor directed towards a portion (or portions) of the field adjacent to the working vehicle 10 and / or the implement 12. As such, the soil roughness sensor (or sensors) 104 can be used to detect surface roughness adjacent portions of the field as the tractor 10 and / or implement 12 passes through such portions of the field during the performance of the tillage operation.
    [023] In general, the non-contact soil roughness sensor (or sensors) 104 can match any suitable (or suitable) detection device (or devices) configured (or configured) to detect or capture data associated with surface roughness from soil. For example, in various embodiments, the ground roughness sensor (or sensors) 104 can (or can) correspond to a Light Distance Detection and Measurement device (or devices), such as a scan (or scanning devices) of LIDAR. In such embodiments, the soil roughness sensor (or sensors) 104 can (or can) be configured (or configured) to emit light pulses from a light source (for example, a laser that emits a beam of light) pulse) and detect the reflection of each pulse from the soil surface. Based on the flight time of the light pulses, the specific location (for example, 3D coordinates) of the ground surface, relative to the sensor (or sensors) 104 can be calculated. Sweeping the pulsed light through a given application bandwidth,
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    14/39 the surface roughness of the soil can be detected through a given section of the field. In this way, by continuously sweeping the pulsed light along the soil surface as the working vehicle 10 and the implement 12 are moved across the field, a point cloud can be generated, which includes surface roughness data for the whole field or for a portion of it.
    [024] In another embodiment, the soil roughness sensor (or sensors) 104 can (or can) correspond to a suitable camera (or suitable cameras) configured (or configured) to capture three-dimensional images of the soil surface, thereby allowing Thus, the roughness of the soil surface is calculated or estimated by analyzing the content of each image. For example, in a particular embodiment, the ground roughness sensor (or sensors) may (or can) correspond to a stereographic (or stereographic) camera that has (or has) two or more lenses with a separate image sensor for each lens to allow the camera (or cameras) to capture (or capture) stereographic or three-dimensional images. In an additional embodiment, the soil roughness sensor (or sensors) can (or can) match any other suitable device (or suitable devices) configured (or configured) to detect or capture surface roughness data using a non-contact detection methodology, such as an acoustic sensor or an electromagnetic sensor. For example, the soil roughness sensor (or sensors) may (or may) correspond to an ultrasound device configured to emit ultrasound waves and detect the reflection of such waves from the soil surface in order to allow the roughness surface area is estimated. Alternatively, the soil roughness sensor (or sensors) 104 may (or may) correspond to a
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    15/39 radar configured to emit radar waves and detect the reflection of such waves from the soil surface, in order to allow the surface roughness to be estimated.
    [025] In various embodiments, two or more soil roughness sensors 104 can be provided in operative association with the working vehicle 10 and / or the implement 12. For example, as shown in Figures 1 and 2, in one embodiment, a first soil roughness sensor 104A can be provided at a rear end 70 (Figure 3) of the working vehicle 10 to allow sensor 104A to capture data associated with soil roughness from a first adjacent section 106 of the field in front of the working vehicle 10. For example, for each detection event, the first soil roughness sensor 104A can be configured to capture soil roughness data along a flat or reference line that generally extends perpendicular to the direction of travel 34 of the working vehicle 10 directly in front of the vehicle 10. Similarly, as shown in Figures 1 and 2, a second ground roughness sensor 104B can be provided at the end from rear 76 or adjacent to it (Figure 3) of implement 12, in order to allow sensor 104B to capture data associated with soil roughness from a second adjacent section 108 of the field disposed behind implement 12. For example, for each event detection, the second soil roughness sensor 104B can be configured to capture soil roughness data along a flat or reference line that generally extends perpendicular to the direction of travel 34 of the work vehicle 10 in one location directly behind implement 12.
    [026] Capturing soil surface roughness data at a location in front of the soil tillage tools 46, 50, 52, 54 of
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    16/39 implement 12 (for example, at the location detected by the first soil roughness sensor 104A) and at a location after the soil tillage tools 46, 50, 52, 54 (for example, at the location detected by the second soil roughness sensor) soil roughness 104B), as the working vehicle 10 tows the implement 12 to allow a soil tillage operation to be carried out over a given section of the field, sensors 104A, 104B can be used to collect data both before and after the performance of the tillage operation. As will be described below, by analyzing the pre-operation and post-operation roughness data captured by the soil roughness sensors 104A, 104B, an associated controller 102 (Figure 4) can then be configured to calculate or estimate a differential surface roughness for the field that provides an indication of the current effectiveness of implement 12 in adjusting the surface roughness of the soil. As indicated above, the surface roughness differential can also provide an indication of one or more parameters related to the soil roughness, such as the concentration of stones or foreign objects within the soil and / or the texture of the soil. Based on the estimated surface roughness differential, controller 102 can, for example, control / adjust the operation of the working vehicle 10 and / or the implement 12, as needed, to ensure that the surface roughness differential is maintained at a given target value and / or within a given target range (for example, an operating range defined around a desired roughness target differential for the specific tillage operation that is carried out within the field ).
    [027] It must be verified that, in alternative embodiments, the soil roughness sensor (or sensors) 104A, 104B can (or can) be installed (or installed) in any other location (or any other location)
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    17/39 suitable (or suitable) that allows (or allows) the sensor (or sensors) 104A, 104B to capture (or capture) surface roughness data before and after the performance of the associated tillage operation. For example, Figure 3 illustrates a schematic top-down view of the working vehicle 10 and the implement 12 shown in Figures 1 and 2, which particularly illustrate alternative sensor locations for the first and second sensors (or sensors ) of soil roughness 104A, 104B. As shown, as an alternative to positioning the first soil roughness sensor (or sensors) 104A at the rear end 70 of the working vehicle 10, the first soil roughness sensor (or sensors) 104A can (or can) be positioned (or positioned) in any other suitable location (or any other suitable location) in front of one or more of the tillage tools 46, 50 52, 54 of the implement 10 in the direction of travel 34 of the working vehicle 10, such as at the end rear 72, or adjacent to it, of the working vehicle 10, at the rear end 74, or adjacent to it, of the implement 12, on one side, or adjacent to one of them, of the working vehicle 10 and / or on one of the sides, or adjacent to one of them, of implement 12. Similarly, as an alternative to positioning the second soil roughness sensor (or sensors) 104B at the rear end 76 of implement 12, the second roughness sensor (or sensors) 104B can be (or can) be positioned (or positioned) in any other suitable location after one or more of the soil tillage tools 45, 50, 52, 54 of the implement 12, in the direction of travel 34 of the work vehicle 10. For example, as shown in Figure 3, the second soil roughness sensor (or sensors) 104B can (or can) be positioned (or positioned) in a location immediately behind a given drilling tool.
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    18/39 soil preparation 46, 50, 52, 54 of implement 12. In such an embodiment, a first soil roughness sensor (or sensors) 104A can (or can), for example, be similarly positioned (or positioned) in a location immediately in front of the tillage tool 46, 50, 52, 54 to allow data associated with soil surface roughness to be captured immediately in front of and behind the tillage tool 46, 50, 52, 54 , thereby providing a means to assess or analyze the individual performance or effectiveness of the tool.
    [028] It should also be noted that, as opposed to the inclusion of a first 104A simple soil roughness sensor and a second simple 104B soil roughness sensor, an array of the first and second 104A soil roughness sensors can be provided on the working vehicle 10 and / or on the implement 12. For example, as shown in Figure 3, a matrix of the first soil roughness sensors 104A can be provided at the rear end 74, or adjacent to it, of the implement 12 for allow surface roughness data to be captured for portions 106 of the field that pass between the working vehicle 10 and the implement 12 in several different locations, along the width of the implement 12. Similarly, as shown in Figure 3, an array of second soil roughness sensors 104B may be provided at the rear end 76, or adjacent to it, of implement 12 to allow surface roughness data cies are captured for the portions 108 of the field that pass behind the implement 12 in several different locations, along the width of the implement 12.
    [029] Additionally, it should be noted that, although the achievements shown in Figures 1 to 3 illustrate two or more
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    19/39 soil roughness 104A, 104B installed on work vehicle 10 and / or implement 12, a simple roughness sensor can be installed, in relation to work vehicle 10 and / or implement 12, to allow data surface roughness to the field are captured. For example, in one embodiment, it may be desirable to just have a simple soil roughness sensor that captures surface roughness data before or after the tillage operation is carried out. Alternatively, a simple roughness sensor can be used to capture soil roughness data both before and after the tillage operation is carried out. For example, by making a second pass through the same portion of the field or by mounting the surface roughness sensor along the side of the working vehicle 10 or the implement 12, the surface roughness data before and after, for the same section of the field, can be captured using a simple surface roughness sensor.
    [030] Referring now to Figure 4, a schematic view of an implementation of a system 100 to automatically monitor the surface roughness of the soil, as a tillage operation is being carried out within a field, it is illustrated according to aspects of this matter. In general, system 100 will be described in this document with reference to the working vehicle 10 and implement 12 described above, with reference to Figures 1 to 3. However, it must be verified that the system revealed 100 can, in general, be used with work vehicles that have any suitable configuration and / or vehicle implements that have any suitable implement configuration.
    [031] In several embodiments, system 100 may include a
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    20/39 controller 102 and several other components configured to be communicatively coupled and / or controlled by controller 102, such as one or more soil roughness sensors 104 and / or various components of the working vehicle 10 and / or the implement 12 As will be described in more detail below, controller 102 can be configured to receive data from the soil roughness sensor (or sensors) 104 that are associated with soil surface roughness within sections of the field that pass through the work vehicle 10 and implement 12, as a soil tillage operation is being carried out. Based on an analysis of the data received from sensor (or sensors) 104, controller 102 can be configured to estimate the surface roughness of the soil through the various sections of the field for which the surface roughness data has been captured. As indicated above, in one embodiment, the surface roughness data can be captured by the sensor (or sensors) 104 for the same section of the field, both before and after the tillage operation has been carried out. In such an embodiment, controller 102 can be configured to analyze the pre-operation and post-operation data to determine a surface roughness differential for the analyzed section of the field. In addition, based on the analysis of the surface roughness data, controller 102 can also be configured to adjust the operation of the working vehicle 10 and / or the implement 12, as needed, to ensure that the soil surface roughness, in general, and / or the surface roughness differential is maintained at a given target value and / or within a given target range.
    [032] In general, controller 102 can match any suitable processor-based device (or any suitable processor-based devices), such as a computing device
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    21/39 or any combination of computing devices. Thus, as shown in Figure 4, controller 102 can, in general, include one or more processor (or processors) 110 and associated memory devices 112 configured to perform a variety of computer-implemented functions (for example, performing methods , steps, algorithms, calculations and the like disclosed in this document). As used in this document, the term “processor” refers not only to integrated circuits called, in the art, as being included in a computer, but also to a controller, a microcontroller, a microcomputer, a programmable logic controller ( PLC), an application-specific integrated circuit and other programmable circuits. In addition, memory 112 may, in general, comprise a memory element (or elements) that includes (or includes), but is not limited to, computer-readable medium (e.g., random access memory (RAM)), readable non-volatile medium computer (for example, a fast memory), a floppy disk, a compact disc (CD-ROM) read-only memory, an optical-magnetic disk (MOD), a digital versatile disk (DVD) and / or other data elements adequate memory. Such memory 112 can, in general, be configured to store information accessible to the processor (or processors) 110, which includes data 114 that can be retrieved, manipulated, created and / or stored by the processor (or processors) 110 and instructions 116 that can be performed by the processor (or processors) 110.
    [033] In various embodiments, data 114 can be stored in one or more databases. For example, memory 112 may include a roughness database 118 for storing surface roughness data received from the surface roughness sensor (or sensors).
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    22/39 soil 104. For example, the soil roughness sensor (or sensors) 104 can (or can) be configured (or configured) to continuously or periodically capture surface roughness data from the adjacent portion (or adjacent portions) of the field, as the soil tillage operation is being carried out by means of implement 12. In such a realization, the surface roughness data transmitted to controller 102, from the soil roughness sensor (or sensors) 104, can be stored within the roughness database 118 for further processing and / or analysis.
    [034] In addition to the raw or initial sensor data received from the soil roughness sensor (or sensors) 104, post-processing or final processing roughness data (as well as any intermediate roughness data created during the data processing) can also be stored within the roughness database 118. For example, as described below, controller 102 can be configured to analyze data received from the soil roughness sensor (or sensors) 104 using one or more data processing techniques, or algorithms, to determine the surface roughness values for the analyzed portions of the field. In such an accomplishment, the processed roughness data and / or the roughness-related data generated during the implementation of data processing techniques or algorithms can be stored within the database 118.
    [035] Additionally, in various embodiments, memory 12 may also include a location database 120 that stores location information about the working vehicle 10 and / or the implement 12. Specifically, as shown in Figure 4, the controller 102 may
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    23/39 be communicatively coupled to a positioning device (or devices) 124 installed (or installed) on the work vehicle 10, or within it, and / or that of the implement 12, or within it. For example, in one embodiment, the positioning device (or devices) 124 can (or can) be configured (or configured) to determine the exact location of the working vehicle 10 and / or the implement 12 using a positioning system satellite navigation system (eg, a GPS system, a Galileo Global positioning system, the Global Navigation Satellite System (GLONASS), the BeiDou Navigation and Positioning System and / or similar). In such an embodiment, the location determined by the positioning device (or devices) 124 can be transmitted to the controller 102 (for example, in the shape location coordinates) and subsequently stored within the location database 120 for subsequent processing and / or analyze. It should be noted that, in one embodiment, a first positioning device (or devices) 124 can (or can) be provided (or supplied) in the work vehicle 10 or and / or within it, as a second separate device (or separate positioning devices 124 can (or can) be provided (or supplied) on the implement 12 and / or within it.
    [036] In various embodiments, the location data stored within the location database 120 can also be correlated to the surface roughness data stored within the roughness database 118. For example, in one embodiment, the coordinates of location derived from the positioning device (or devices) 124 and the surface roughness data captured by the sensor (or sensors) 104 can both have timestamp. On such
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    24/39 realization, the timestamp data may allow each individual set of roughness data captured by the soil roughness sensor (or sensors) 104 to be compatible with a corresponding set of location coordinates received from the device (or positioning devices 124, or correlated thereto, thereby allowing the precise location of the portion of the field associated with a given set of surface roughness data to be known (or at least capable of calculation) by the controller 102.
    [037] Additionally, as shown in Figure 4, memory 12 can include a database field 122 to store information related to the field, such as field map data. In such an achievement, by matching each set of surface roughness data captured by the soil roughness sensor (or sensors) with a corresponding set of location coordinates, controller 102 can be configured to generate or update a field map corresponding to the field, which can then be stored within the database field 122 for subsequent processing and / or analysis. For example, in situations where controller 102 already includes a field map stored within database field 122 that includes location coordinates associated with various points across the field, the surface roughness data captured by the sensor (or sensors) of soil roughness 104 (for example, the point cloud) can be mapped or otherwise correlated to the corresponding locations within the field map. Alternatively, based on location data and associated sensor data, controller 102 can be configured to generate a field map that includes the geolocalized surface roughness data associated with it.
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    25/39 [038] Still, in reference to Figure 4, in various embodiments, the instructions 116 stored within the memory 112 of the controller 102 can be executed by the processor (or by the processors) 110 to implant a data analysis module 126. In general, data analysis module 126 can be configured to analyze raw or initial sensor data captured by soil roughness sensor (or sensors) 104 to allow controller 102 to estimate the surface roughness of one or more sections from Camp. For example, data analysis module 126 can be configured to perform one or more suitable data processing techniques or algorithms that allow controller 102 to accurately and efficiently analyze sensor data, such as applying corrections or adjustments to the data based on the type of sensor, sensor resolution and / or other parameters associated with the soil roughness sensor (or sensors) 104, filtering the data to remove outliers, implementing required subroutines or intermediate calculations to estimate the surface roughness of the soil and / or performing any other techniques or algorithms related to the desired data processing.
    [039] For example, Figure 5 illustrates a simplified flowchart that shows various steps or data processing elements that can be implemented by controller 102 through data analysis module 126 when analyzing raw or initial surface roughness data received from the soil roughness sensor (or sensors) 104. It should be noted that, although Figure 5 shows several exemplary data processing steps that can be used to process the initial surface roughness data received from the sensor (or 104) (for example, in box 200) and subsequently output final or processed values of surface roughness (for example, in box 212), the
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    26/39 data analysis module 126 does not need to be configured to perform all the illustrated data processing steps. For example, in one embodiment, the data analysis module 126 can perform only one of the data processing steps or only a subset of the data processing steps. In addition, although Figure 5 represents data processing steps performed in a particular order for purposes of illustration and discussion, the data flow described in this document is not limited to any particular order or arrangement. A person skilled in the art, using the disclosure provided in this document, will find that the various data processing steps disclosed in this document can be omitted, rearranged, combined and / or adapted in various ways, without departing from the scope of this document. revelation.
    [040] As shown in Figure 5, upon receiving the initial surface roughness data from the sensor (or sensors) 104, the data analysis module 126 can, for example, be configured to apply a sensor calibration to data (for example, in box 202) to adjust or correct the data based on one or more parameters associated with the soil roughness sensor (or sensors) 104. In general, the specific sensor calibration applied to the data may vary, depending on numerous factors, which include, but are not limited to, the type of sensor (sensors) 104 that is used, the location and / or the orientation of the sensor (or sensors) 104, in relation to the soil surface, and / or any other suitable sensor-related variables. For example, when the soil roughness sensor (or sensors) 104 (or corresponds) to a LIDAR scanning device (or devices), a sensor calibration may be required to take into account the specific range and / or the intensity
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    27/39 associated with the LIDAR scanning device (s). Similarly, when the ground roughness sensor (or sensors) 104 (or corresponds) to a stereographic camera, a sensor calibration may be necessary to take into account distortions within the images.
    [041] Additionally, as shown in Figure 5, the data analysis module 126 can also be configured to filter or remove outliers from the data (for example, in box 204). The outliers of data can, for example, correspond to points not related to the soil captured by the sensor (or sensors) 104, such as dust, unwanted crop residues and / or the like. In one embodiment, the data analysis module 126 can be configured to implement a machine learning classification algorithm to remove any outliers from the data, such as by deploying decision trees, support vector machines, clustering and / or similar. In this regard, the actual geometry of the surface roughness data alone can produce features that can be identified as outliers using any suitable data processing technique. It should be noted that, similar to sensor calibration, the specific algorithm or technique used to remove outliers from the data may be dependent on the type of sensor (or sensors) 104 that is used. For example, a LIDAR scanning device may produce measurements of intensity or reflectivity in connection with the point cloud that may need to be removed as outliers.
    [042] In addition, as shown in Figure 5, data analysis module 126 can also be configured to estimate a landline reference surface based on surface roughness data (for example, in box 206). The land surface of
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    28/39 reference can in general be configured to serve as the reference point for calculating or determining the surface roughness measurements or values for a given section of the field. In one embodiment, the land surface of the reference line may correspond to a line of best correlation defined for the surface roughness data collected by the soil roughness sensor (or sensors) 104. For example, a roughness data set of surface for a given section of the field can be analyzed to calculate a line of better correlation, in relation to the data (for example, using the method of least squares). The best calculated correlation line can then be established as the land surface of the reference line for the surface roughness data set that is analyzed.
    [043] It must be verified that, in one embodiment, the land surface of the reference line can be determined based only on the calibrated sensor data (less outlier values). This may be true, for example, when data from the soil roughness sensor (or sensors) 104 contains little or no sensor noise. However, if there is significant sensor noise, it may be desirable to further process the data to take into account any variations due to sensor noise, before estimating the land line surface of the reference line. For example, a principal component analysis (for example, to remove linear trends in the data), a Gaussian process regression analysis (for example, to assist with point interpolation), and / or any other suitable algorithm, can be used to process sensor data that contains a significant amount of sensor noise.
    [044] Still, with reference to Figure 5, the data analysis module 126 can also be configured to estimate or calculate a value of
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    29/39 surface roughness (or surfaces) for the field based on surface roughness data (for example, in box 208). Specifically, in various embodiments, the surface roughness value (or surfaces) for a given set of surface roughness data can be calculated as a function of the estimated reference land surface for such data. For example, in one embodiment, the surface roughness value (or surfaces) for a given set of roughness data may correspond to the standard deviation of the vertical distances defined between the associated data points and the best correlation line that defines the land line of reference line. In such an achievement, a low standard deviation from the land surface of the reference line (for example, data points tend to be closer to the line of better correlation) may indicate that the soil has a low surface roughness ( that is, it is more flat or smooth). Similarly, a high standard deviation from the terrestrial surface of the reference line (for example, data points tend to be more spread out, relative to the line of better correlation) may indicate that the soil has a high surface roughness. (that is, it is less flat or smooth).
    [045] Additionally, as shown in Figure 5, the data analysis module 126 can also be configured to filter the calculated surface roughness values (for example, in box 210). Specifically, in various embodiments, the surface roughness values can be filtered over time using a low-pass filter to produce stable roughness values or measurements that are robust to occasional outliers. For example, in one embodiment, a autoregressive model of moving averages (ARMA) can be used to filter the calculated roughness values.
    [046] Again, with reference to Figure 4, processing the
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    30/39 raw sensor data received from the soil roughness sensor (or sensors) 104 (for example, through the data processing steps described above, with reference to Figure 5), the data analysis module 126 can be configured to determine surface roughness values for each section of the field for which the data was captured. In one embodiment, such roughness values can then be used by controller 102 as an input to perform one or more control actions, such as automatically controlling the operation of the working vehicle 10 and / or the implement 12 (for example, as described below), automatically transmitting a notification to the operator, regarding the surface roughness of the soil that is processed, and / or using the surface roughness values to automatically generate or update a field map.
    [047] In addition, as indicated above, surface roughness values can also be used to calculate a surface roughness differential that provides an indication of the current effectiveness of implement 12 in adjusting soil roughness. For example, when surface roughness data is captured for the same section of the field, both before and after the tillage operation has been carried out, the data analysis module 126 can be configured to analyze the pre-operation data and post-operation to determine both a pre-operation surface roughness value and a post-operation surface roughness value for the field. The pre-operation and post-operation surface roughness values can then be compared to calculate the surface roughness differential that follows the performance of the tillage operation. In one embodiment, the surface roughness differential can be calculated or expressed as
    Petition 870180006520, of 01/25/2018, p. 113/137
    31/39 a percentage of differential, such as using the following equation (Equation 1):, srvo-srvi % Differential = [ SRV (] ) * 100 where, SRV ti corresponds to the roughness value (or values) of pre-operation surface or before operation and 5 / ¾ corresponds to the value (or values) of post-operation surface roughness or after operation.
    [048] As shown in Figure 4, instructions 116 stored within memory 112 of controller 102 can also be executed by processor (or processors) 110 to deploy an active control module 128. In general, active control module 128 can be configured to adjust the operation of the working vehicle 10 and / or the implement 12 by controlling one or more components of the vehicle 10 and / or the implement 12. Specifically, in one embodiment, when the estimated surface roughness value for a given section of the field differs from a target value of the defined roughness target range for the field, the active control module 128 can be configured to fine tune the operation of the working vehicle 10 and / or implement 12 in a manner designed to adjust the resulting surface roughness. Similarly, in one embodiment, when the estimated surface roughness differential for a given section of the field differs from a target value or differential target range defined for the field, the active control module 128 can be configured to fine tune the operation of the working vehicle 10 and / or cultivation implement 12 in a manner designed to adjust the resulting surface roughness differential. For example, when it is desired to have a surface roughness differential that corresponds to at least a 25% reduction in soil surface roughness after the performance of the tillage operation,
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    32/39 the active control module 128 can be configured to adjust the operation of the working vehicle 10 and / or the implement 12 so that it increases the differential (that is, a control action configured to increase the efficiency of the implement 12 in the reduction of field surface roughness) when the estimated surface roughness differential is determined to be less than the target percentage.
    [049] It should be verified that the controller 102 can be configured to implement several different control actions to adjust the operation of the working vehicle 10 and / or the implement 12, so that it increases or decreases the field surface roughness after the performance of the tillage operation. In one embodiment, controller 102 can be configured to increase or decrease ground speed or operational of implement 12 to affect an increase or decrease in the resulting surface roughness of the soil. For example, as shown in Figure 4, controller 102 can be communicatively coupled to both motor 22 and transmission 24 of the working vehicle 10. In such an embodiment, controller 102 can be configured to adjust the operation of motor 22 and / or the transmission 24, so that it increases or decreases the ground speed of the working vehicle 10 and, thus, the ground speed of the implement 12, as well as transmitting appropriate control signals to control an engine or speed governor (not shown) associated with motor 22 and / or transmitting appropriate control signals to control the engagement / disengagement of one or more groups (not shown) provided in operative association with the transmission 24.
    [050] In addition, for adjusting the speed of the implement 12 on the ground (or as an alternative to it), controller 102 can also be configured to adjust a soil preparation parameter
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    33/39 associated with implement 12 tillage tools. For example, as shown in Figure 4, controller 102 can be communicatively coupled to one or more valves 130 configured to regulate the fluid supply (for example, air or fluid hydraulic) for one or more corresponding actuators 56, 58, 60 of the implement 12. In such an embodiment, by regulating the fluid supply to the actuator (or actuators) 56, 58, 60, controller 104 can automatically adjust the depth of penetration, downward force and / or any other parameter (or parameters) of suitable soil tillage (or suitable) associated (or associated) with the implement's soil tillage tools 12.
    [051] Still, with reference to Figure 4, controller 102 may also include a communications interface 132 to provide a means for controller 102 to communicate with any of the various other system components described in this document. For example, one or more links, or interfaces, communicative 134 (for example, one or more data buses or CAN buses, which include ISOBUS connections) can be provided between the communication interface 132 and the sensor (or sensors) of soil roughness104 to allow surface roughness data transmitted from sensor (or sensors) 104 to be received by controller 102. Similarly, one or more links, or interfaces, communicative 136 (for example, one or more data buses or CAN buses, which include ISOBUS connections) can be provided between the communications interface 132 and the positioning device (or devices) 124 to allow the location information generated by the positioning device (or devices) to be received by the controller 102. In addition, as shown in Figure 4, one or more links, or interfaces, communicative 138 (for example,
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    34/39 one or more data buses or CAN buses, which include ISOBUS connections) can be provided between the communications interface 132 and the motor 22, the transmission 24, the control valves 130 and / or the like to allow the controller 102 controls the operation of such system components.
    [052] Referring now to Figures 6 and 7, exemplary data plots of surface roughness data for a given section of a field are shown, which illustrate data indicative of both relatively rough soil (Figure 6) and relatively smooth soil (Figure 7). In Figures 6 and 7, the geometric axis x, in general, represents a horizontal direction across the field (for example, a direction along a plane that extends parallel to the soil surface, assuming a perfectly smooth surface) and the axis geometric y, in general, represents a vertical direction that extends perpendicular to the horizontal direction.
    [053] In each data plot shown in Figures 6 and 7, the surface roughness data was pre-processed (for example, by applying an appropriate sensor calibration and removing outliers) and subsequently plotted for a section or range of the field represented, in general, by a 2D plane. As shown particularly in Figure 6, a first set of roughness data was plotted, which provides a first estimated soil surface for the field (for example, as indicated by solid line 300). In addition, a line of best correlation was correlated to the data to establish a land surface of the reference line for the data (for example, as indicated by the dashed line 302). Similarly, as particularly shown in Figure 7, a second set of roughness data was plotted, which provides a second line of soil surface
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    35/39 estimated (for example, as indicated by solid line 304), with a line of best correlation that was correlated to the data to establish a land line reference surface for the data (for example, as indicated by the dashed line 306) . Based on the land surface of reference line 302, 306 determined for each data set, a surface roughness value (or surfaces) for the field can be estimated by calculating the standard deviation of the heights or vertical distances 308, 310 defined between each data point along each soil surface line 300, 304 and the corresponding reference line land surface 302, 306. As shown in Figure 6, given the large variance in the plotted data in relation to the land surface reference line 302, it can be determined that the section of the field associated with the first data set was relatively rough when the data was captured. Similarly, as shown in Figure 7, given the significantly smaller variance in the plotted data, in relation to the land surface of reference line 306, it can be determined that the section of the field associated with the second data set was relatively smooth, when the data was captured.
    [054] It should be verified that when the first and second data sets correspond to the pre-operation and post-operation data, respectively, for the same section of the field (for example, as determined by the location data associated with the data roughness), the roughness value (or values) calculated (or calculated) for each data set can (or can) be compared (or compared) to determine the surface roughness differential that results from the performance of the preparation operation. associated soil. Specifically, comparing the pre-operation and post-operation roughness values,
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    36/39 it can be determined that implement 12 reduced soil roughness within the field by a given percentage, thereby providing an indication of the effectiveness of implement 12 and / or an indication of a parameter related to soil roughness (for example, example, stone content and / or soil texture). Depending on the target percentage (or target percentages) of differential defined (or defined) for the field, controller 102 can then adjust the aggressiveness of the current operating definitions for implement 12, as needed, to ensure that the target be maintained or achieved. For example, if the surface roughness differential is excessively low, controller 102 can be configured to adjust the aggressiveness of the current operating settings of implement 12, in a way designed to increase the surface roughness differential (for example, by adjusting it whether the depth of penetration and / or the downward force for the implement tillage tools 12). Similarly, if the surface roughness differential is excessively high, controller 102 can be configured to adjust the aggressiveness of the current operating settings of implement 12, in a manner designed to reduce the surface roughness differential.
    [055] Referring now to Figure 8, a flowchart of an implementation of a 400 method to automatically monitor the surface roughness of the soil, as a soil tillage operation is being carried out within a field, it is illustrated according to aspects of this matter. In general, method 400 will be described in this document with reference to the working vehicle 10 and the implement 12 shown in Figures 1 to 3, as well as the various system components shown in Figure 4. However, it must be verified that the method disclosed 400 can be deployed with work vehicles and / or implements that have any other
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    37/39 appropriate configurations and / or within systems that have any other suitable system configuration. In addition, although Figure 8 describes steps taken in a particular order for purposes of illustration and discussion, the methods discussed in this document are not limited to any particular order or arrangement. A person skilled in the art, using the disclosures provided in this document, will observe that various stages of the methods disclosed in this document can be omitted, rearranged, combined and / or adapted in various ways, without departing from the scope of the present disclosure .
    [056] As shown in Figure 8, at (402), method 400 may include receiving pre-operation surface roughness data associated with a given portion of the field. For example, as indicated above, controller 102 can be coupled to one or more non-contact soil roughness sensor 104 configured to capture surface roughness data from various portions of the field prior to the soil tillage operation that is performed on such portions of the field.
    [057] Additionally, in (404), method 400 may include receiving post-operation surface roughness data associated with a given portion of the field. Specifically, in addition to capturing pre-operation surface roughness data for a given portion of the field, the soil roughness sensor (or sensors) can also (or can) be used (or used) to capture roughness data from surface for the same portion of the field, after the soil tillage operation was carried out on the same. As indicated above, the pre-operation and post-operation surface roughness data can be compatible or correlated to each other, for example, using the location data provided by the positioning device (or devices) 124.
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    38/39 [058] In addition, at (406), method 400 may include analyzing pre-operation and post-operation surface roughness data to determine a surface roughness differential associated with the performance of the preparation operation. ground. For example, as indicated above, controller 102 can be configured to analyze surface roughness data to allow pre-operation and post-operation surface roughness values to be calculated. After that, the calculated surface roughness values can be used to determine the surface roughness differential that results from the performance of the tillage operation.
    [059] Additionally, in (408), method 400 may actively include adjusting the operation of at least one of the work vehicle or implement, when the surface roughness differential differs from a target definition for the roughness differential of surface. Specifically, as indicated above, when the surface roughness differential differs from a target value set for that parameter (or is outside a target range defined for that parameter), controller 102 can be configured to actively adjust the operation of the working vehicle 10 and / or the implement 12, so that it increases or decreases the surface roughness differential that results from the soil tillage operation. For example, controller 102 can be configured to adjust the speed on the ground at which implement 12 is being towed and / or adjust one or more tillage parameters associated with implement 12.
    [060] This written description uses examples to reveal the invention, which includes the best way, and also to enable the person skilled in the art to practice the invention, which includes making and using any devices or systems and carrying out any built-in methods. The scope
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    The patentable 39/39 of the invention is defined by the claims and may include other examples that occur to those skilled in the art. Such other examples are intended to be covered by the scope of the claims, if they include structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with non-substantial differences from the literal languages of the claims.
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    1/7
    权利要求:
    Claims (15)
    [1]
    Claims
    1. METHOD (400) TO AUTOMATICALLY MONITOR THE SOIL SURFACE RUGOSITY as a soil tillage operation is being carried out inside a field using a working vehicle (10) that tows an implement (12), being that method (400) comprises the steps of:
    receive, with one or more computing devices (102), pre-operation surface roughness data associated with a given portion of the field, with the pre-operation surface roughness data corresponding to the surface roughness data for the given portion of the field captured before the soil tillage operation that is carried out on it, and the method additionally comprises:
    receive, with one or more computing devices (102), post-operation surface roughness data associated with the given portion of the field, and the post-operation surface roughness data correspond to the surface roughness data for the given portion of the field captured after the soil tillage operation was carried out on it, and the method (400) is characterized by the fact that:
    analyzes, with one or more computing devices (102), the pre-operation and post-operation surface roughness data to determine a surface roughness differential associated with the performance of the soil preparation operation; and actively adjusts, with one or more computing devices (102), the operation of at least one of the working vehicle (10) or the implement (12) when the surface roughness differential differs from a target definition for the surface roughness differential.
    [2]
    2. METHOD (400) according to claim 1,
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    2/7 characterized by the fact that pre-operation surface roughness data and post-operation surface roughness data are received by one or more computing devices (102) from at least one soil roughness sensor without contact (104).
    [3]
    3. METHOD (400), according to claim 2, characterized by the fact that receiving the pre-operation surface roughness data comprises receiving the pre-operation surface roughness data from a first soil roughness sensor contactless (104A) provided in operative association with one of the working vehicle (10) or the implement (12), and in which receiving the post-operation surface roughness data comprises receiving the powder surface roughness data -operation of a second non-contact soil roughness sensor (104B) supplied in operative association with the working vehicle (10) or the implement (12).
    [4]
    4. METHOD (400), according to any one of the previous claims, characterized by the fact that it additionally comprises:
    receiving location data associated with at least one of the pre-operation surface roughness data or the post-operation surface roughness data; and correlating the location data to at least one of the pre-operation surface roughness data or the post-operation surface roughness data to generate or update a field map associated with the field.
    [5]
    5. METHOD (400), according to any of the preceding claims, characterized by the fact that analyzing the pre-operation and post-operation surface roughness data comprises:
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    3/7 analyze the pre-operation surface roughness data to determine a pre-operation surface roughness value for the given portion of the field; and analyzing the post-operation surface roughness data to determine a post-operation surface roughness value for the given portion of the field;
    where the surface roughness differential is calculated based on the pre-operation and post-operation surface roughness values.
    [6]
    6. METHOD (400), according to claim 5, characterized by the fact that it additionally comprises:
    estimate a land surface of reference line (302) as a function of at least one of the pre-operation surface roughness data or the post-operation surface roughness data; and determining at least one of the pre-operation surface roughness value or the post-operation surface roughness value as a function of the reference line land surface (302).
    [7]
    7. METHOD (400), according to claim 6, characterized by the fact that determining at least one of the pre-operation surface roughness value or the post-operation surface roughness value as a function of land line reference surface (302) comprises calculating at least one of the pre-operation surface roughness value or the post-operation surface roughness value as a function of a standard deviation of vertical distances (308 ) defined between the land surface of the reference line (302) and the data points (300) associated with at least one of the pre-operation surface roughness data or
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    4/7 post-operation surface roughness.
    [8]
    8. METHOD (400), according to claim 6, characterized by the fact that the land surface of the reference line (302) corresponds to a line of better correlation that is correlated to at least one among the surface roughness data pre-operation or post-operation surface roughness data.
    [9]
    9. METHOD (400), according to any one of the claims, characterized by the fact that it additionally comprises analyzing at least one of the pre-operation surface roughness data or the post-operation surface roughness data to apply a calibration of sensor or to remove outliers from data.
    [10]
    10. SYSTEM (100) TO AUTOMATICALLY MONITOR THE SOIL SURFACE RUGOSITY as a soil tillage operation is being carried out inside a field using a working vehicle (10) that tows an implement (12), being that the system (100) comprises at least the non-contact soil roughness sensor (104) configured to capture data associated with a field soil roughness as the soil tillage operation is being carried out, with the system ( 100) also comprises a controller (102) commutatively coupled to at least one non-contact ground roughness sensor (104), the controller (102) including a processor (110) and an associated memory (112), in which the memory (112) stores instructions that, when deployed by the processor (110), configure the controller (102) to receive, from at least one non-contact ground roughness sensor (104), associated pre-operation surface roughness data a given portion the field, and the roughness data of
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    5/7 pre-operation surface corresponds to the surface roughness data for the given portion of the field capture before the soil tillage operation is carried out on it, in which the controller (102) is additionally configured to receive, from the fur minus a non-contact soil roughness sensor (104), post-operation surface roughness data associated with the given portion of the field, in which the post-operation surface roughness data corresponds to the surface roughness data for the given portion of the field captured after the soil tillage operation was carried out on it, and the system (100) is characterized by the fact that:
    the controller is configured to:
    analyze the pre-operation and post-operation surface roughness data to determine a surface roughness differential associated with the performance of the tillage operation; and actively adjusting the operation of at least one of the working vehicle (10) or the implement (12) when the surface roughness differential differs from a target definition for the surface roughness differential.
    [11]
    11. SYSTEM (100), according to claim 10, characterized by the fact that at least one non-contact soil roughness sensor (104) comprises a first non-contact soil roughness sensor (104A) configured to capture the pre-operation surface roughness data and a second non-contact soil roughness sensor (104B) configured to capture the post-operation surface roughness data.
    [12]
    12. SYSTEM (100) according to either of claims 10 or 11, characterized by the fact that it comprises
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    6/7 additionally a positioning device (124) communicatively coupled to the controller (102), the controller (124) being configured to receive location data from the positioning device (102) associated with at least one of the roughness data of pre-operation surface or post-operation surface roughness data and correlate location data to at least one of the pre-operation surface roughness data or post-operation surface roughness data to generate or update a field map associated with the field.
    [13]
    13. SYSTEM (100) according to any one of claims 10 to 12, characterized in that the controller (102) is configured to analyze the pre-operation surface roughness data to determine a pre-operation surface roughness value for the given portion of the field and analyze the post-operation surface roughness data to determine a post-operation surface roughness value for the given portion of the field, and the surface roughness differential is calculated based on the pre-operation and post-operation surface roughness values.
    [14]
    14. SYSTEM (100), according to claim 13, characterized by the fact that the controller (102) is additionally configured to estimate a land surface of reference line (302) as a function of at least one of the roughness data pre-operation surface roughness or post-operation surface roughness data and determine at least one of the pre-operation surface roughness value or the post-operation surface roughness value, based on the land line surface reference (302).
    [15]
    15. SYSTEM (100) according to claim 14,
    Petition 870180006520, of 01/25/2018, p. 128/137
    7/7 characterized by the fact that the controller (102) is configured to calculate at least one of the pre-operation surface roughness value or the post-operation surface roughness value as a function of a deviation- vertical distance pattern (308) defined between the land surface of the reference line (302) and the data points (300) associated with at least one of the pre-operation surface roughness data or the surface roughness data of post-operation.
    Petition 870180006520, of 01/25/2018, p. 129/137
    1/7
    Petition 870180006520, of 01/25/2018, p. 130/137
    2/7
    Lfl
    Petition 870180006520, of 01/25/2018, p. 131/137
    3/7
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    法律状态:
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    优先权:
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