Implement position management based on images of a light beam interacting with the ground or vegetation in a field

Abstract

Technologies for implement position management based on images of a light beam interacting with the ground or vegetation in a field. In some embodiments, a method includes receiving, by a computing system (200), images (104) of a light beam (330) interacting with the ground or vegetation in a field, wherein the source (332) of the light beam is attached to a mobile machine (110, 310) moving through the field, wherein the location where the beam interacts with the ground or vegetation changes relative to the mobile machine as the distance between the ground or vegetation and the source of the light beam changes, and wherein the change in the location of the beam is captured in the images. The method also includes using, by the computing system, the received images to generate implement position information (112) that is useable for controlling a position of an implement (322) of the machine as the machine moves through the field.

Description

IMPLEMENT POSITION MANAGEMENT BASED ON IMAGES OF A LIGHT BEAM INTERACTING WITH THE GROUND OR VEGETATION IN A FIELDTECHNICAL FIELD

[0001] The present disclosure relates to implement position management based on images interacting with the ground or vegetation in a field.BACKGROUND

[0002] Agricultural implement position management, such as boom height management, assists in the control of implement position to limit damage to crops and the implements under the management. Also, implement position management can assist in improving the efficiency and effectiveness of implements. For example, boom height management can facilitate the control of the distance of a spray nozzle from the crop or the ground and can reduce mechanical damage to the boom or its parts from contact with the ground surface. As an example, boom height management systems have been around for some time. Such systems can prevent booms or another type of implement from hitting the ground unintentionally as well as maintain a certain distance from the crop to provide proper application of the implement.

[0003] Although implement position management has improved over the years, with corresponding systems there are still many problems to be solved. For example, lag is a significant issue with such systems as is cost. With implement position management, most of the sensors are located underneath the implement or are positioned very near the front of the implement. With such positioning, sensing the ground or crop (which needs to be reacted to immediately) and then moving an implement rapidly (such as a large and wide steel boom) can be difficult, to say the least, and usually lag causes problems. Also, many systems use sophisticated non-contact sensors employing radar or ultrasonic technologies. Such systems are costly to add and replace as well as maintain. Thus, it would be advantageous to provide a system (and associated method) that overcomes or at least mitigates one or more problems associated with the prior art systems for agricultural implement position management.SUMMARY

[0004] Described herein are techniques for implement position management based on images of a light beam interacting with the ground or vegetation in a field. In some embodiments, a method includes receiving, by a computing system, images of a light beam interacting with the ground or vegetation in a crop field. The source of the light beam is attached to an implement of a mobile machine or the mobile machine itself, and the source can shine the beam of light on the field as the machine moves through the field. The location where the beam interacts with the ground or vegetation changes relative to the mobile machine as the distance between the ground or vegetation and the source of the light beam changes. Also, the change in the location of the beam is captured in the images. The method also includes using, by the computing system, the received images to generate implement position information that is useable for controlling a position of the implement as the machine moves through the field. In some cases, the implement is or includes a spray boom, and the method includes using the position information to control the height of the boom as the machine moves through the field. The techniques disclosed herein provide specific technical solutions to at least overcome the technical problems mentioned in the background section or other parts of the application as well as other technical problems not described herein but recognized by those skilled in the art.

[0005] In some embodiments, the techniques include technologies that control implement position based on images of a light beam interacting with the ground or vegetation in a field. With respect to some embodiments, disclosed herein are computerized methods for generating implement position information based on images of a light beam interacting with the ground or vegetation in a field, as well as a non-transitory computer-readable storage medium for carrying out technical operations of the computerized methods. The non-transitory computer-readable storage medium has tangibly stored thereon, or tangibly encoded thereon, computer-readable instructions that when executed by one or more devices (e.g., one or more personal computers or servers) cause at least one processor to perform a method for improved systems and methods for generating implement position information based on images of a light beam interacting with the ground or vegetation in a field.

[0006] In some cases, the technologies use a simple model or a more complex model such as a machine learning or deep learning based model to assist in the generation of implement position information. The techniques described herein can also leverage a model that is not trained viamachine learning or deep learning, such as a predetermined and static rules-based model for generating implement position information based on images of a light beam interacting with the ground or vegetation in a field. Furthermore, in some examples, the technologies described herein can use a model that is trained or frequently updated by a computing technique or other type of technique other than machine learning or deep learning, such as a dynamic rules-based model for generating implement position information based on images of a light beam interacting with the ground or vegetation in a field.

[0007] Some embodiments include a method for generating implement position information based on images of a light beam interacting with the ground or vegetation in a field. The method can begin with receiving, by a computing system (e.g., see computing system 200), images (e.g., see image data 104) of a light beam (e.g., see light beam 330 shown in FIG. 4) interacting with the ground or vegetation in a field (e.g., see step 502 of method 500 shown in FIG. 5). In some examples, the source of the light beam (e.g., see light source 332 shown in FIGS. 3 and 4) is attached to a mobile machine (e.g., see mobile machine 110 shown in FIG. 1 or agricultural crop sprayer 310 shown in FIGS. 3 and 4) or an implement of the mobile machine as the machine is moving through the field. In some cases, the location where the beam interacts with the ground or vegetation changes relative to the mobile machine or the implement as the distance between the ground or vegetation and the source of the light beam changes. Also, the change in the location of the beam is captured in the images. In some examples, the source of the light beam includes a spotlight.

[0008] The method can also include using, by the computing system, the received images to generate implement position information (e.g., see implement position information 112 shown in FIG. 1) that is useable for controlling a position of an implement (e.g. see boom 322 shown in FIGS.3 and 4) of the mobile machine as the machine moves through the field (e.g., see step 504 of method 500). Also, the method can include generating the implement position information using the received images (e.g., see step 506 of method 500). In some examples, the method includes using the generated implement position information as input to control, by a controller (e.g., see controllers 102d shown in FIGS. 1 and 2), the position of the implement (e.g., see step 508 of method 500). In some examples, the controlling of the position of the implement includes controlling the height of the implement (e.g., see step 704 of method 700 shown in FIG. 7, wherein the position of the implement is controlled according to implement position information). In some cases, theimplement includes a boom (e.g., see boom 322) and the method can include using the generated implement position information as input to control, by the controller, the height of the boom (e.g., see step 704). In some examples, the source of the light beam is attached to the boom (e.g., see light source 332).

[0009] In some examples, the source of the light beam is configured to shape the light beam to have an elliptical or circular cross-section (e.g., see FIG. 4). In some examples, the shape of the interaction between the beam and the ground or vegetation changes as the position of the implement changes. And, in some cases, the change in the shape of the interaction is captured in the images.

[0010] In some embodiments, the method includes capturing the images by a camera (e.g., see camera 334 shown in FIG. 4) attached to the mobile machine (e.g., see step 702 of method 700). Also, in some embodiments, the method includes capturing the images by a camera (e.g., see camera 334) attached to the implement (e.g., see step 702).

[0011] In some examples, the generation of the implement position information corresponds to minimizing contact between the implement and the ground or vegetation. In some examples, the generation of the implement position information corresponds to enhancing operation of the implement. In some examples, the generation of the implement position information corresponds to maintaining a target distance between the implement and the ground or vegetation.

[0012] Throughout the disclosure herein, the majority of examples refer to the use of the received images to be used as input to generate implement position information or more specifically, in some embodiments, boom position information of a sprayer; however, it is to be understood that some embodiments include the use and generation of such images or information for applicators in general, which include applications of seeders, planters, spreaders, and sprayers.

[0013] These and other important aspects of the invention are described more fully in the detailed description below. The invention is not limited to the particular methods and systems described herein. Other embodiments can be used and changes to the described embodiments can be made without departing from the scope of the claims that follow the detailed description. Within the scope of this application, it should be understood that the various aspects, embodiments, examples, and alternatives set out herein, and individual features thereof can be taken independently or in any possible and compatible combination. Where features are described with reference to asingle aspect or embodiment, it should be understood that such features are applicable to all aspects and embodiments unless otherwise stated or where such features are incompatible.BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present disclosure will be understood more fully from the detailed description given below and from the accompanying drawings of various example embodiments of the disclosure.

[0015] FIG. 1 illustrates an example technical solution to the example technical problems described herein, in accordance with some embodiments of the present disclosure.

[0016] FIG. 2 illustrates a block diagram of example aspects of a computing system, in accordance with some embodiments of the present disclosure.

[0017] FIGS. 3 and 4 illustrate different views of an agricultural crop sprayer, in accordance with some embodiments of the present disclosure.

[0018] FIGS. 5 to 7 illustrate methods in accordance with some embodiments of the present disclosure.DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0019] Details of example embodiments of the invention are described in the following detailed description with reference to the drawings. Although the detailed description provides reference to example embodiments, it is to be understood that the invention disclosed herein is not limited to such example embodiments. But to the contrary, the invention disclosed herein includes numerous alternatives, modifications, and equivalents as will become apparent from consideration of the following detailed description and other parts of this disclosure.

[0020] Described herein are techniques for implement position management based on images of a light beam interacting with the ground or vegetation in a field (e.g., see the light beam interacting with the crop field shown in FIG. 4). The ground includes objects on the ground, such as rocks, trees or shrubs that extend above a surface of the ground. In some embodiments, a method includes receiving, by a computing system, images of a light beam interacting with the ground or vegetation in a crop field. The source of the light beam is attached to an implement of a mobile machine or the mobile machine itself, and the source can shine the beam of light on the field as themachine moves through the field (e.g., see the example light source and mobile machine shown in FIG. 4). The location where the beam interacts with the ground or vegetation changes relative to the mobile machine as the distance between the ground or vegetation and the source of the light beam changes. Also, the change in the location of the beam is captured in the images. The method also includes using, by the computing system, the received images to generate implement position information that is useable for controlling a position of the implement as the machine moves through the field. In some cases, the implement is or includes a spray boom (e.g., see the boom of an agricultural sprayer illustrated in FIGS. 3 and 4), and the method includes using the position information to control the height of the boom as the machine move through the field (e.g., see the methods shown in FIGS. 5 to 7). The techniques disclosed herein provide specific technical solutions to at least overcome the technical problems mentioned in the background section or other parts of the application as well as other technical problems not described herein but recognized by those skilled in the art.

[0021] For many years, boom height management has been needed and offered by multiple companies. Many of the example embodiments disclosed herein can provide for boom height management that can help keep an applicator of a mobile machine (e.g., a spray nozzle of an agricultural sprayer) at the proper height from the target crop or ground surface and also limit mechanical damage to the boom from contact with the ground surface. In some embodiments, a powerful light source can be attached to the boom. The source can either be a spotlight or a shaped light. In some examples, a camera can be mounted to the chassis of the mobile machine. For example, as the boom goes up and down, the location where the light interacts or intersects with the ground or crop will change. The camera can then process such information and take the appropriate action for boom height adjustment. In some examples, the light source can include or be a laser source and the light beam can be a laser beam.

[0022] FIG. 1 illustrates an example technical solution to the example technical problems described herein, such as a solution for generating implement position information based on image data. The technical solution, shown in FIG. 1, can include or be a part of the techniques and technologies described herein (such as described with respect to method 500, method 600, or method 700) and can provide specific technical solutions to at least overcome the technical problemsmentioned in the background section or other parts of the application as well as other technical problems not described herein but recognized by those skilled in the art.

[0023] FIG. 1 depicts a network 100, such as a computer network, within which a computing system 102 receives various inputs (e.g., see image data 104 and geographic location information 114). These inputs and others can be received from other computing systems within the network 100 or from sensor systems in the network. The various inputs can include or are related to some of the efficiencies and factors in farming crops (such as operational time efficiency, fuel efficiency, reduced soil compaction, and machine capabilities) that are considered by the computing system in determination of the implement position information 112. As shown, the computing system includes a model 108 that can be used to determine the implement position information 112. As shown, the implement position information 112 is an output of the model 108 and can be an input for controllers 102d.

[0024] Depending on the embodiment, the model 108 can be a simple model or a more complex model such as a machine learning or deep learning based model to assist in the generation of implement position information. In some examples, the model 108 is not trained via machine learning or deep learning and can include a predetermined and static rules-based model for generating implement position information based on images of a light beam interacting with the ground or vegetation in a field. Furthermore, in some examples, the model 108 is trained or frequently updated by a computing technique or other type of technique other than machine learning or deep learning, such as a dynamic rules-based model for generating implement position information based on images of a light beam interacting with the ground or vegetation in a field. In some cases leveraging machine learning or deep learning, the model 108 includes digital signal processing or some other form of preprocessing of the image data 104 prior to the use of the data as inputs for an artificial neural network which may include, for example, a convolutional neural network. Such computing schemes or networks can be used by the model 108 to determine the implement position information or parts of the implement position information.

[0025] Also, as shown, the computing system 102 is a part of a mobile machine 110 or the network 100 depending on the embodiment as are the inputs and outputs of the computing system (including the inputs and the outputs of the model 108). In some embodiments, the computing system 102 and the inputs and outputs of the computing system are part of a remote system in thatthe remote system is physically and geographically separated from the mobile machine 110 but communicates with a system or controller of the machine over a telecommunications or computer network (such as network 100). The mobile machine 110 can be or include a sprayer, seeder, planter, or spreader, for example. The mobile machine 110 or an implement of the machine can also be configured to follow instructions entirely or to some extent via a control system for automated control of the machine or implement (e.g., see implement position information 112 and controllers 102d).

[0026] The computing system 102 includes electronics such as one or more controllers, sensors, busses, and computers. The computing system 102 includes at least a processor, memory, and a communication interface and can include one or more sensors, which can make the mobile machine 110 an individual computing device. In the case of the network 100 including the Internet, the mobile machine 110 can be considered an Internet of Things (loT) device. Also, in some embodiments, the computing system 102 is a part of a cloud computing system. The computing system 102 and the mobile machine 110 can include both electronic hardware and software that can integrate between the systems of the computing system and the mobile machine 110. And, such hardware and software (such as controllers and sensors and other types of electrical or mechanical devices) can be configured to communicate with a remote computing system via the communications network 100.

[0027] As mentioned, the mobile machine 110 and the other mobile machines shown in FIG. 1 are agricultural machines such as applicators (e.g., sprayers, seeders, planters, spreaders, etc.). In some embodiments, the mobile machine 110 can be or include a vehicle in that it is self-propelling. Also, in some embodiments, the mobile machine 110 can be a part of a group of similar machines or a group of different types of mobile machines (e.g., see mobile machines 110a and 110b).

[0028] The network 100 can include one or more local area networks (LAN(s)) or one or more wide area networks (WAN(s)). In some embodiments, the network 100 includes the Internet or any other type of interconnected communications network. The network 100 can also include a single computer network or a telecommunications network. More specifically, in some embodiments, the network 100 includes a local area network (LAN) such as a private computer network that connects computers in small physical areas, a wide area network (WAN) to connect computers located in different geographical locations, or a middle area network (MAN) to connect computers in ageographic area larger than that covered by a large LAN but smaller than the area covered by a WAN.

[0029] At least each shown component of the network 100 (including computing system 102) can be or include a computing system that includes memory that includes media. The media includes or is volatile memory components, non-volatile memory components, or a combination thereof. In general, in some embodiments, each of the computing systems includes a host system that uses memory. For example, the host system writes data to the memory and reads data from the memory. The host system is a computing device that includes a memory and a data processing device. The host system includes or is coupled to the memory so that the host system reads data from or writes data to the memory. The host system is coupled to the memory via a physical host interface. The physical host interface provides an interface for passing control, address, data, and other signals between the memory and the host system.

[0030] In some examples, the geographic location information 114 includes a series of time-stamped locations of the mobile machine 110 as it moves through an area of land during a time period. As shown, the geographic location information 114 is received from some of the sensors 102c. In some embodiments, the linking of the geographic location (e.g., GPS coordinates) of the mobile machine 110 to a date and time (such as via a timestamp) includes geotagging the date and time or the time stamp. Such tagging can include adding geographical identification metadata to an item including the image or a file of data that has date and time information associated with it. For example, the image data 104 can include such date and time information and parts of the data can be linked to corresponding parts of the geographic location information 114. In some embodiments, the metadata can be embedded in the image or the item or sensed data (such as embedded in parts of the image data 104). And, in some embodiments, the metadata is stored separately and linked to the image or the item, or the sensed data (such as linked to the image data 104). The item can be a data log, a control system or sensor output signal, an image file, an image stream, an image object, etc. Also, in some embodiments, the item is a data log, a control system or sensor output signal, an image file or a video file, a media feed, a message file, or another type of item that is configurable to include a time and date information such as a timestamp and that can be geotagged. And, in some embodiments, the metadata related to the geotag includes latitude and longitude coordinates, altitude, bearing, distance, accuracy data, a place name, or a time stamp.

[0031] In some embodiments, the computing system 102 can link the image data 104 or the geographic location information 114 to the other types of information of the system via identifiers of parts of the information, which can become a part of the metadata before or after being linked to the location information 114. This makes the geotagging advanced geotagging. In some embodiments, a location tracking system configured to retrieve at least part of the location information 114 includes a GPS or is part of a GPS (e.g., which can be one or more of the sensors of the mobile machine 110).

[0032] As mentioned, the techniques disclosed herein can resolve many problems in implement position information determinations or automated control of implements, such as implements for sprayers, planters, seeders, and spreaders stemming from the complex nature of such determinations, including the model-based determination of the implement position information (e.g., see information 112). In some embodiments, the technologies disclosed herein include a system that can control height of a sprayer boom or another type of implement. Also, the information 112 can be used to generate farming management information system (FMIS) maps— in some examples (e.g., see FIG. 6).

[0033] FIG. 2 illustrates a block diagram of example aspects of a computing system 200 that can implement the technical solution shown in FIG. 1 or each computing part of the solution (e.g., see computing systems 102 and 102a). Also, FIG. 2 illustrates parts of the computing system 200 within which a set of instructions are executed for causing a machine (such as a computer processor or processing device 202) to perform any one or more of the methodologies discussed herein performed by a computing system (e.g., see the method steps of the methods 500, 600, and 700 shown in FIGS. 5, 6, and 7 respectively). In some embodiments, the computing system 200 operates with additional computing systems to provide increased computing capacity in which multiple computing systems operate together to perform any one or more of the methodologies discussed herein that are performed by a computing system (e.g., also see the computing system 102 that is connected and interoperable with the remote computing system 102a to provide increased computing capacity).

[0034] In some embodiments, the computing system 200 corresponds to a host system that includes, is coupled to, or utilizes memory or is used to perform the operations performed by any one of the computing systems described herein. In some embodiments, the machine is connected(e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. In some embodiments, the machine operates in the capacity of a server in a client-server network environment, as a peer machine in a peer-to-peer (or distributed) network environment, or as a server in a cloud computing infrastructure or environment. In some embodiments, the machine is a personal computer (PC), a tablet PC, a cellular telephone, a web appliance, a server, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein performed by computing systems.

[0035] The computing system 200 includes a processing device 202, a main memory 204 (e.g., read-only memory (ROM), flash memory, dynamic random-access memory (DRAM), etc.), a static memory 206 (e.g., flash memory, static random-access memory (SRAM), etc.), and a data storage system 210, which communicate with each other via a bus 220. The processing device 202 represents one or more general-purpose processing devices such as a microprocessor, a central processing unit, or the like. More particularly, the processing device can include a microprocessor or a processor implementing other instruction sets, or processors implementing a combination of instruction sets. Or, the processing device 202 is one or more special-purpose processing devices such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, or the like. The processing device 202 is configured to execute instructions 214 for performing the operations discussed herein performed by a computing system. In some embodiments, the computing system 200 includes a network interface device 208 (e.g., see network interface 102b) to communicate over a communications network (e.g., see communications network 101). Such a communications network can include one or more local area networks (LAN(s)) or one or more wide area networks (WAN(s)). In some embodiments, the communications network includes the Internet or any other type of interconnected communications network. The communications network can also include a single computer network or a telecommunications network.

[0036] The data storage system 210 includes a machine-readable storage medium 212 (also known as a computer-readable medium) on which is stored one or more sets of instructions 214 orsoftware embodying any one or more of the methodologies or functions described herein performed by a computing system. The instructions 214 also reside, completely or at least partially, within the main memory 204 or within the processing device 202 during execution thereof by the computing system 200, the main memory 204 and the processing device 202 also constituting machine-readable storage media. While the machine-readable storage medium 212 is shown in an example embodiment to be a single medium, the term “machine-readable storage medium” should be taken to include a single medium or multiple media that store the one or more sets of instructions. The term “machine-readable storage medium” shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that causes the machine to perform any one or more of the methodologies of the present disclosure performed by a computing system. The term “machine-readable storage medium” shall accordingly be taken to include solid-state memories, optical media, or magnetic media.

[0037] Also, as shown, the computing system 200 includes user interface or UI 216 that includes a display, in some embodiments, and, for example, implements functionality corresponding to any one of the UI devices disclosed herein. A UI, such as UI 216, or a UI device described herein includes any space or equipment where interactions between humans and machines occur. A UI described herein allows operation and control of the machine from a human user, while the machine simultaneously provides feedback information to the user. Examples of a user interface, or UI device include the interactive aspects of computer operating systems (such as GUIs), machinery operator controls, and process controls.

[0038] Also, as shown, the computing system 200 includes hardware interfaces 218 that include sensor interfaces to interface sensors to the computing system (e.g., see sensors 102c) and controller interfaces to interface controllers to the computing system (e.g., see controllers 102d). The interfaces 218 can implement at least some of the functionality corresponding to the respective hardware devices that they interface with. The interfaces 218 can provide the connections for the communications between the computing system 200 and any one of the electronics described herein such as any one of the controllers described herein or sensors described herein.

[0039] FIGS. 3 and 4 illustrate different views of an agricultural crop sprayer 310, in accordance with some embodiments of the present disclosure. FIG. 3 shows the agricultural crop sprayer 310 used to deliver chemicals to agricultural crops in a field. Agriculturalsprayer 310 includes a chassis 312 and a cab 314 mounted on the chassis 312. Cab 314 can house an operator and a number of controls for the agricultural sprayer 310. An engine 316 can be mounted on a forward portion of chassis 312 in front of cab 314 or can be mounted on a rearward portion of the chassis 312 behind the cab 314. The engine 316 can include, for example, a diesel engine or a gasoline powered internal combustion engine. The engine 316 provides energy to propel the agricultural sprayer 310 and also can be used to provide energy used to spray fluids from the sprayer 310. Although a self-propelled application machine is shown and described hereinafter, it should be understood that the embodied invention is applicable to other agricultural sprayers including pull-type or towed sprayers and mounted sprayers, e.g., mounted on a 3 -point linkage of an agricultural tractor.

[0040] The sprayer 310 further includes a liquid storage tank 318 used to store a spray liquid to be sprayed on the field. The spray liquid can include chemicals, such as herbicides, pesticides, or fertilizers. Liquid storage tank 318 is mounted on chassis 312, either in front of or behind cab 314. The crop sprayer 310 includes a storage tank 318 to store different chemicals to be sprayed on the field. The stored chemicals can be dispersed by the sprayer 310 one at a time or different chemicals can be mixed and dispersed together in a variety of mixtures. The sprayer 310 further includes a rinse water tank 320 used to store clean water, which can be used for storing a volume of clean water for use to rinse the plumbing and tank 318 after a spraying operation.

[0041] The boom 322 on the sprayer 310 is used to distribute the fluid from the tank 318 over a wide swath as the sprayer 310 is driven through the field. The boom 322 is provided as part of a spray applicator system, which further includes an array of spray nozzles (not depicted) arranged along the length of the boom 322 and suitable sprayer plumbing (also not depicted) used to connect the liquid storage tank 318 with the spray nozzles 324. The sprayer plumbing will be understood to include any suitable tubing or piping arranged for fluid communication on the sprayer 310.

[0042] FIG. 4 also shows the agricultural crop sprayer 310 used to deliver chemicals to agricultural crops in a field. However, from the view of sprayer 310 shown in FIG. 4, a light beam 330 is depicted emitting from a light source 332 onto a crop field. The light source 332 is attached to the boom 322 and as the boom moves the light source will move. Thus, the position of the light beam interacting or intersecting with the ground or crop in the field will change. Also, the shape of the interaction between the light beam and the ground or crop in the field can change. The interactionor intersection (or the light spot) on the ground or the crop can be captured by camera 334 attached to the chassis 312 of the sprayer 310. In some embodiments, the camera 334 can be attached to a different part of the sprayer 310. For example, the camera 334 could be attached to cab 314, the storage tank 318 or the boom 322. In some embodiments, the light source is attached to a part of the sprayer 310 other than the boom, such as the cab 314 or the storage tank 318.

[0043] The light source 332 may generate (and the camera 334 may detect) visible light or non-visible light, such as infrared. For example, the light source 332 may generate light within the range of 900nm to 1600nm. In some embodiments, the light source 332 generates light characterized by wavelengths between 1200nm and 1400nm. An advantage of using light between 1200nm and 1400nm is that sunlight is mostly filtered out by the atmosphere at those wavelengths and therefore does not interfere with the operation of the sensor such that the system is equally effective during the day and at night.

[0044] For many years, boom height management has been needed and offered by multiple companies. The example embodiment, shown in FIGS. 3 and 4, provides for boom height management that can help keep a spray nozzle of the sprayer 310 at the proper height from the target crop and also limit mechanical damage to the boom 322 from contact with the ground surface. In some embodiments, a powerful light source can be attached to the boom for the light source 322. This can either be a spotlight or a shaped light. In such cases, the camera 334 can be mounted to the chassis of the sprayer 310. For example, as the boom goes up and down, the location where the light interacts or intersects with the ground or crop will change. The camera can then process such information and take the appropriate action for boom height adjustment. In some examples, the light source 332 can include or be a laser source and the light beam 330 can be a laser beam.

[0045] Some embodiments described herein include a method for generating implement position information based on images of a light beam interacting with the ground or vegetation in a field. The implement can be a part of or be attached to a mobile machine such as an agricultural sprayer, seeder, planter, or spreader. For example, the implement can be or include or be a part of a spray boom. Also, the method can include generating the implement position information according to a model using the images as input for the model (e.g., see model 108 shown in FIG. 1). Furthermore, the method can include generating the implement position information according to a geographic location information corresponding to the images, and, in some embodiments, both theimages and geographic location information can be used as input for the model to generate the implement position information that in turn can be used to generate an FMIS map. Also, the implement position information or a derivative thereof can be used as input for a controller to control the implement or a mobile machine having the implement. For example, FIGS. 5 to 7 illustrate methods in accordance with some of such embodiments. Also, although much of the description of the methods 500, 600, and 700 refers to the use and generation of implement information for sprayers, it is to be understood that some embodiments include the use and generation of implement information for other types of agricultural applicators in general, which include sprayers, seeders, planters, and spreaders.

[0046] Steps performed by a computing system in methods 500, 600, and 700 are performed by any one of the computing systems described herein (e.g., see computing system 102, 102a, or 200 depicted in FIGS. 1 and 2 respectively). In some systems of the technologies disclosed herein, any steps of embodiments of the methods described herein are implementable by executing instructions corresponding to the steps, which are stored in memory (such as the instructions 214).

[0047] As shown in FIG. 5, method 500 begins with step 502, which includes receiving, by a computing system (e.g., see computing system 200), images (e.g., see image data 104) of a light beam (e.g., see light beam 330 shown in FIG. 4) interacting with the ground or vegetation in a field. In some examples, the source of the light beam (e.g., see light source 332 shown in FIGS. 3 and 4) is attached to a mobile machine (e.g., see mobile machine 110 shown in FIG. 1 or agricultural crop sprayer 310 shown in FIGS. 3 and 4) or an implement of the mobile machine as the machine is moving through the field. In some cases, the location where the beam interacts with the ground or vegetation changes relative to the mobile machine or the implement as the distance between the ground or vegetation and the source of the light beam changes. Also, the change in the location of the beam is captured in the images. In some examples, the source of the light beam includes a spotlight.

[0048] At step 504, the method 500 also includes using, by the computing system, the received images to generate implement position information (e.g., see implement position information 112 shown in FIG. 1) that is useable for controlling a position of an implement (e.g., see boom 322 shown in FIGS. 3 and 4) of the mobile machine as the machine moves through the field. Also, asshown, the method 500 includes generating the implement position information using the received images, at step 506.

[0049] Also, the method 500 includes using the generated implement position information as input to control, by a controller (e.g., see controllers 102d shown in FIGS. 1 and 2), the position of the implement. In some examples, the controlling of the position of the implement includes controlling the height of the implement (e.g., see step 704 wherein the position of the implement is controlled according to implement position information). In some cases, the implement includes a boom (e.g., see boom 322) and a method of some embodiments can include using the generated implement position information as input to control, by the controller, the height of the boom (e.g., see step 704). In some examples, the source of the light beam is attached to the boom (e.g., see light source 332).

[0050] In some examples, the source of the light beam is configured to shape the light beam to have an elliptical or circular cross-section (e.g., see FIG. 4). In some examples, the shape of the interaction between the beam and the ground or vegetation changes as the position of the implement changes. And, in some cases, the change in the shape of the interaction is captured in the images.

[0051] In some embodiments, the method includes capturing the images by a camera (e.g., see camera 334 shown in FIG. 4) attached to the mobile machine (e.g., see step 702 shown in FIG. 7). Also, in some embodiments, the method includes capturing the images by a camera (e.g., see camera 334) attached to the implement (e.g., see step 702).

[0052] In some examples, the generation of the implement position information corresponds to minimizing contact between the implement and the ground or vegetation. In some examples, the generation of the implement position information corresponds to enhancing operation of the implement. In some examples, the generation of the implement position information corresponds to maintaining a target distance between the implement and the ground or vegetation.

[0053] As mentioned, in some embodiments, the images or image data and geographic location information can be used as input to generate the implement position information that in turn can be used to generate an FMIS map according to the two inputs. For example, method 600 includes all the steps of method 500 and, at step 602, the method 600 further includes receiving geographic location information corresponding to the images and the mobile machine moving through the field(e.g., see geographic location information 114). And, at step 604, the method 600 includes using the generated implement position information and the received geographic location information as input to generate an FMIS map. In some embodiments, the FMIS maps are generated according to geographically-linked images or advance geographically-linked images from the contact sensor. And, in some embodiments, the geographically-linked images or advance geographically-linked images include or are geographically-tagged images or advance geographically-tagged images, respectively. In some embodiments, the technologies generate and provide geographically-linked images or advance geographically-linked images. And, then based on the linked images or image data or the information linked to the data, the technologies can provide FMIS mapping and generate agricultural maps.

[0054] Also, as shown in FIG. 7, embodiments of the method can include the steps of capturing the images with a camera and the controlling of the position of the implement based on the implement position information by the controller. Method 700 includes all the steps of methods 500 and 600 and at step 702 the method 700 further includes capturing the images by a camera. This data is then received in method 500 at step 502. And, subsequent to step 508 of method 500, the method 700 can continue with, at step 704, controlling, by the controller, the position of the implement based on the implement position information.

[0055] Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a predetermined result. The operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. It should be borne in mind, however, that these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. The present disclosure can refer to the action and processes of a computing system, or similar electronic computing device, which manipulates andtransforms data represented as physical (electronic) quantities within the computing system's registers and memories into other data similarly represented as physical quantities within the computing system memories or registers or other such information storage systems.

[0056] While the invention has been described in conjunction with the specific embodiments described herein, it is evident that many alternatives, combinations, modifications and variations are apparent to those skilled in the art. Accordingly, the example embodiments of the invention, as set forth herein are intended to be illustrative only, and not in a limiting sense. Various changes can be made without departing from the spirit and scope of the invention.

Claims

CLAIMSWhat is claimed is:

1. A method, comprising:receiving, by a computing system (200), images (104) of a light beam (330) interacting with the ground or vegetation in a field (step 502),wherein the source (332) of the light beam is attached to a mobile machine (110, 310) moving through the field,wherein the location where the beam (330) interacts with the ground or vegetation changes relative to the mobile machine (110, 310) as the distance between the ground or vegetation and the source (332) of the light beam changes, and wherein the change in the location of the beam (330) is captured in the images (104);andusing, by the computing system (200), the received images (104) to generate implement position information (112) that is useable for controlling a position of an implement (322) of the machine (110, 310) as the machine moves through the field (step 504).

2. The method according to claim 1, further comprising generating the implement position information (112) using the received images (step 506).

3. The method according to claim 2, further comprising using the generated implement position information (112) as input to control, by a controller (102d), the position of the implement (322).

4. The method according to claim 3, wherein the controlling of the position of the implement (322) comprises controlling the height of the implement (step 704).

5. The method according to claim 4, wherein the implement comprises a boom (322) and the method comprises using the generated implement position information (112) as input to control, by the controller (102d), the height of the boom.

6. The method according to claim 5, wherein the source (332) of the light beam (330) is attached to the boom (322).

7. The method according to claim 1 ,wherein the source (332) of the light beam (330) is configured to shape the light beam to have an elliptical or circular cross-section,wherein the shape of the interaction between the beam and the ground or vegetation changes as the position of the implement (322) changes, andwherein the change in the shape of the interaction is captured in the images (104).

8. The method according to claim 1, wherein the light beam (330) is includes light only with wavelengths between 1200nm and 1400nm.

9. The method according to claim 1 , further comprising capturing the images ( 104) by a camera (334) attached to the mobile machine (step 702).

10. The method according to claim 1 , further comprising capturing the images ( 104) by a camera (334) attached to the implement (step 702).

11. The method according to claim 1, wherein the generation of the implement position information (112) corresponds to minimizing contact between the implement (322) and the ground or vegetation.

12. The method according to claim 1, wherein the generation of the implement position information (112) corresponds to enhancing operation of the implement (322).

13. The method according to claim 1, wherein the generation of the implement position information (112) corresponds to maintaining a target distance between the implement (322) and the ground or vegetation.

14. A system comprising: at least one processor (202); and memory (204) in communication with the at least one processor and storing instructions (214) that are executable by the at least one processor to cause the at least one processor to:receive images (104) of a light beam (330) interacting with the ground or vegetation in a field,wherein the source (332) of the light beam is attached to a mobile machine (110, 310) moving through the field,wherein the location where the beam (330) interacts with the ground or vegetation changes relative to the mobile machine (110, 310) as the distance between the ground or vegetation and the source (332) of the light beam changes, and wherein the change in the location of the beam (330) is captured in the images (104);anduse the received images (104) to generate implement position information (112) that is useable for controlling a position of an implement (322) of the machine as the machine (110, 310) moves through the field.

15. The system according to claim 14, further comprising the source (332) of the light beam.

16. The system according to claim 14, further comprising a camera (334) configured to capture the images (104).

17. The system according to claim 14, wherein the instructions (214) are further executable to generate the implement position information (112) using the received images (104).

18. The system according to claim 14, further comprising a controller configured to control the position of the implement based on the implement position information.

19. The system according to claim 14, wherein the instructions (214) are further executable to generate the implement position information (112) to minimize contact between theimplement (322) and the ground or vegetation, to enhance operation of the implement, or to maintain a target distance between the implement and the ground or vegetation.

20. A non-transitory computer-readable medium (204) storing instructions (214) that when executed cause a computing device to:receive images (104) of a light beam (330) interacting with the ground or vegetation in a field,wherein the source (332) of the light beam is attached to a mobile machine (110, 310) moving through the field,wherein the location where the beam (330) interacts with the ground or vegetation changes relative to the mobile machine (110, 310) as the distance between the ground or vegetation and the source (332) of the light beam changes, and wherein the change in the location of the beam (330) is captured in the images (104);anduse the received images (104) to generate implement position information (112) that is useable for controlling a position of an implement (322) of the machine as the machine (110, 310) moves through the field.

Images