Patent Description:
A mower implement includes a cutter having a blade for cutting standing crop material. In many implementations, the blade rotated about a generally vertical axis to cut the standing crop material. However, in other implementations, the blade may move in a back-and-forth reciprocal motion to cut the standing crop material. The mower implement is moved across a ground surface, whereby the blade engages and cuts the standing crop material, severing an upper portion of the standing crop material from a lower portion of the standing crop material. The lower portion of the crop material disposed nearest the ground surface remains standing and may be referred to as the crop stubble. When a cutting edge of the blade is sharp, the blade cleanly cuts the crop material creating a clean, straight cut end on both the crop stubble and the upper portion of the crop that was severed from the lower portion. When the cutting edge of the blade is dull, i.e., not sharp, the blade tends to tear the crop material creating a jagged, uneven cut end on both the crop stubble and the upper portion of the crop that was severed from the lower portion. Accordingly, the cut end of the crop stubble may be examined for indications of the sharpness of the cutting edge of the blade.

It is desirable to maintain a sharp cutting edge of the blade to achieve a sharp cut. When the cut crop material is to be used as feed for animals, the sharp cut improves digestion in the animals. Additionally, a cut end that is torn, such as occurs with a dull cutting edge on the blade, may increase the susceptibility of the plant to disease, may slow future growth, and may lead to lesser yield and/or loss of nutritional value. Accordingly, it is desirable to maintain a sharp cutting edge on the blade to achieve a high level of digestibility for the feed as well as maintain a high growth rate and nutritional value in future growth of the crop material. <CIT> discloses a work machine comprising a control device including: an image acquiring section that acquires image data of an image of a work target of the work machine; a feature recognizing section that recognizes a feature about an appearance of the work target after work, based on the image data acquired by the image acquiring section; and a control parameter deciding section that decides a parameter for controlling work of the work machine, based on the feature recognized by the feature recognizing section.

A mower implement is provided. The mower implement includes a frame that is moveable across a ground surface in a direction of travel during operation. A cutter is coupled to the frame. The cutter includes a blade operable to cut crop material as the frame moves across the ground surface. An image sensor is coupled to the frame. The image sensor is positioned to capture an image of cut crop stubble rearward of the cutter relative to the direction of travel during operation. The mower implement further includes a blade diagnostic controller including a processor and a memory having a diagnostic algorithm stored therein. The processor of the blade diagnostic controller is operable to execute the diagnostic algorithm to capture an image of the cut crop stubble rearward of the cutter with the image sensor as the frame moves across the ground surface. The blade diagnostic controller identifies a cut end of the cut crop stubble in the image, determine a cut quality of the cut end of the cut crop stubble, and correlate the cut quality of the cut end to a blade sharpness index. The blade diagnostic controller then communicates an index signal to a communicator. The index signal controls the communicator to generate a communication indicating the blade sharpness index.

In one aspect of the disclosure, the processor is operable to execute the diagnostic algorithm to automatically communicate a maintenance request signal to the communicator when the blade sharpness index is below a sharpness threshold. The maintenance request signal controls the communicator to generate a communication requesting maintenance for the blade. Accordingly, when the sharpness index drops below the sharpness threshold, the blade diagnostic controller may automatically request maintenance for the blade, such as but not limited to sharpening a cutting edge of the blade, rotating the blade to expose a different cutting edge or replacing the blade.

In one aspect of the disclosure, the processor is operable to execute the diagnostic algorithm to estimate a remaining life of the blade based on the blade sharpness index. The blade diagnostic controller may then communicate a life expectancy signal to the communicator. The life expectancy signal controls the communicator to generate a communication indicating the remaining life of the blade. As such, an operator of the mower implement and/or a service manager of the mower implement may confidently plan usage and operation of the mower implement, such as scheduling the mower implement for a work interval based on the remaining life of the blade and/or scheduling maintenance for the blade.

According to the invention, the processor is operable to execute the diagnostic algorithm to identify the cut end of the cut crop stubble in the image via pattern matching and recognition using a convolutional neural network. The convolutional neural network is operable to classify the cut end of the cut crop stubble as one of a sharp cut end and a dull cut end.

The blade diagnostic controller then calculates a frequency of dull cut ends of the cut crop stubble. The frequency of dull cut ends may include, for example, a percentage of the identified cut ends that are classified as dull cut ends. The frequency of the dull cut ends may be calculated over a period of time from a plurality of different images, thereby enabling the blade diagnostic controller to track the condition of the blade over time and the change of the sharpness of the blade over time.

The processor is operable to execute the diagnostic algorithm to determine the cut quality based on the frequency of dull cut ends of the cut crop stubble. For example, the blade diagnostic controller may include a model, e.g., a blade sharpness index model, saved on the memory of the blade diagnostic controller. The processor may be configured to execute the diagnostic algorithm to use the blade sharpness index model to determine the cut quality of the cut end of the cut crop stubble by using the frequency of dull cut ends as an input of the model, which in turn outputs the cut quality. In addition to the frequency of dull cut ends, the blade sharpness index model may further use one or more inputs, such as but not limited to a moisture content of the crop material and/or a speed of the blade to better refine the model and more accurately predict and/or determine the cut quality.

In one implementation of the disclosure, the processor may be operable to execute the diagnostic algorithm to determine the cut quality of the cut end of the cut crop stubble by measuring light diffraction from the cut end of the cut crop stubble. When light diffraction from the cut end is above a diffraction threshold, the cut end may be classified as a dull cut end. When light diffraction from the cut end is below the diffraction threshold, the cut end may be classified as a sharp cut end.

A method of monitoring a blade of a mower implement is also provided. The method includes capturing an image of cut crop stubble rearward of the cutter with an image sensor mounted to the mower implement as the mower implement moves across a ground surface. A cut end of the cut crop stubble is identified in the image with a blade diagnostic controller using a convolutional neural network. A cut quality of the cut end of the cut crop stubble is determined with the blade diagnostic controller using the convolutional neural network. The blade diagnostic controller then correlates the cut quality of the cut end to a blade sharpness index, and communicate an index signal to a communicator. The index signal controls the communicator to generate a communication indicating the blade sharpness index.

In one aspect of the disclosure, the method of monitoring the blade includes the blade diagnostic controller automatically communicating a maintenance request signal to the communicator when the blade sharpness index is below a sharpness threshold. The maintenance request signal controls the communicator to generate a communication requesting maintenance for the blade.

In one aspect of the disclosure, the method of monitoring the blade includes the blade diagnostic controller automatically estimating a remaining life of the blade based on the blade sharpness index and communicating a life expectancy signal to the communicator. The life expectancy signal controls the communicator to generate a communication indicating the remaining life of the blade.

Referring to the Figures, wherein like numerals indicate corresponding parts throughout the several views, a mower implement <NUM> is generally shown embodied as a drawn mower-conditioner implement in <FIG>. Referring to <FIG> the example implementation of the mower implement <NUM> is shown being drawn by a traction unit <NUM>, e.g., a tractor. The mower implement <NUM> may be pulled by the traction unit <NUM> to mow and/or condition crops or grasses. In other implementations, the mower implement <NUM> may be pushed by the traction unit <NUM> to mow and/or condition crops or grasses. In yet other implementations, the mower implement <NUM> and the traction unit <NUM> may be combined as a self-propelled mower implement <NUM>, e.g., a self-propelled windrower.

Referring to <FIG>, the implementation of the mower implement <NUM> includes a frame <NUM>. One or more ground engaging elements <NUM> may be coupled to the frame <NUM> and may be moveable relative to the frame <NUM> to move and/or guide the frame <NUM> across a ground surface in a direction of travel <NUM> during operation. The ground engaging elements <NUM> may include, but are not limited to tires, tracks, skies, etc..

Referring to <FIG>, the frame <NUM> forms a work area. The work area includes a forwardly located inlet zone <NUM>, and a rearwardly located discharge zone <NUM>. The frame includes a right-side transverse rear wall and a left-side transverse rear wall that extend inwardly from a right-side outer wall and a left-side outer wall, respectively. The right-side transverse rear wall and the left-side transverse rear wall terminate approximately at the ends of a crop conditioning system <NUM>. The example implementation of the crop conditioning system <NUM> is attached to the frame <NUM>, and includes an upper conditioner roll <NUM> and a lower conditioner roll <NUM>. The crop conditioning system <NUM> is positioned to receive crop material therebetween from a cutter <NUM>, described in greater detail below.

The upper conditioner roll <NUM> and the lower conditioner roll <NUM>, which generally define the width of the material discharge zone <NUM>, are located centrally in the mower implement <NUM>. It is to be understood that the locations of the material inlet zone <NUM> and the material discharge zone <NUM> are not critical to the teachings of this disclosure, and that implements having material inlet zones <NUM> and material discharge zones <NUM> which are not centered relative to the implement would benefit from the teachings of the present disclosure. Moreover, various other types of crop conditioning systems <NUM> may be used instead of or in addition to the crop conditioning system <NUM> shown in the Figures and described herein. Such other crop conditioning systems <NUM> may include, but are not limited to, flail/impeller conditioners, and the like.

Referring to <FIG>, a rotatably mounted auger <NUM> extends between the right-side outer wall and the left-side outer wall, and passes in front of the crop conditioning system <NUM>. In particular, the auger <NUM> is positioned in front of the lower conditioner roll <NUM> with a central axis of the auger <NUM> laterally spaced apart from, and lower than a central axis of the lower conditioner roll <NUM>. The auger <NUM> includes a central cylindrical drum with a central portion and outer ends. The outer ends of the auger <NUM> include flighting, and a plurality of fins is attached to the central portion. In operation, the design of the auger <NUM> enables the delivery of cut crop material into a nip or gap area of the crop conditioning system <NUM>, with the auger <NUM> and lower conditioner roll <NUM> co-rotated in the same rotational direction, and with the auger <NUM> and the upper conditioner roll <NUM> counter-rotated in opposite rotational directions.

Referring to <FIG>, the cutter <NUM> is supported by the frame <NUM>, between the inlet zone <NUM> and the discharge zone <NUM>. The cutter <NUM> includes a cutter bar <NUM> that extends along an axis that is disposed generally transverse to the direction of travel <NUM> of the implement. The cutter bar <NUM> extends between the right-side outer wall and the left-side outer wall, and is located just forward of the crop conditioning system <NUM>. While the present disclosure could be advantageously applied to rotary cutter bars <NUM> of various constructions, the cutter bar <NUM> of the exemplary embodiment is a known type containing a plurality of intermeshed spur gears including a plurality of idler gears meshed with each other and arranged in transverse alignment over the length of the cutter bar <NUM>, with selected ones of the idler gears being meshed with drive gears respectively associated, one each, with a plurality of cutting discs <NUM> spaced along the cutter bar <NUM>.

As best shown in <FIG>, the cutter bar <NUM> includes a plurality of spaced apart blade-carrying rotary cutting discs <NUM> disposed in the work area for rotation about respective vertical axes. The cutting discs <NUM> are located forward of the crop conditioning system <NUM>. Each of the cutting discs <NUM> is coupled to an upright drive shaft to which power is coupled for causing them to rotate in appropriate directions, for delivering cut crop material to the crop conditioning system <NUM>. Each respective rotary cutting disc <NUM> includes at least one blade <NUM> coupled to the disc proximate an outer peripheral edge of the rotary cutting disc <NUM>. The blade <NUM> is rotatable with the rotary cutting disc <NUM> and is operable to cut crop material while rotating with the frame <NUM> moving across the ground surface. In the example implementation shown in the Figures and described herein, each respective one of the rotary cutting discs <NUM> includes two diametrically opposed blades <NUM>. However, the number of blades <NUM> per rotary cutting disc <NUM> may vary from the example implementation.

Referring to <FIG>, when sharp, the blades <NUM> cut the crop material, leaving a clean, generally straight cut end. It should be appreciated that both the crop stubble and the severed upper end of the cut crop material may exhibit a respective sharp cut end when cut with a sharp blade <NUM>. As used herein, the term "crop stubble" is defined as the basal part of herbaceous plants and especially cereal grasses remaining attached to the soil after harvest. Referring to <FIG>, when dull, the blades <NUM> tend to tear the crop material instead of cut the crop material, leaving a jagged, torn and uneven end. It should be appreciated that both the crop stubble and the severed upper end of the cut crop material may exhibit a respective jagged end when cut with a dull blade <NUM>.

Referring to <FIG>, the mower implement <NUM> may further include an image sensor <NUM>. The image sensor <NUM> may be coupled to the frame <NUM> and positioned to capture an image of cut crop rearward of the cutter <NUM> relative to the direction of travel <NUM> during operation. The image sensor <NUM> may include, but is not limited to, a camera, a high speed video camera, an infra-red camera, or some other device capable of capturing an image of the cut crop material and saving and/or communicating the captured image as an electronic data file or electronic sensor signal. The image sensor <NUM> may be positioned to capture the image of the cut crop stubble, and/or the upper ends of the cut crop material, i.e., the harvested crop. The image sensor <NUM> is positioned to capture the image of the cut crop rearward of the mower implement <NUM>.

As shown in <FIG>, the mower implement <NUM> further includes a blade diagnostic controller <NUM>. The blade diagnostic controller <NUM> is disposed in communication with at least the image sensor <NUM> and a communicator <NUM>, e.g., an audio and/or visual communication device. The blade diagnostic controller <NUM> is operable to receive image signals from the image sensor <NUM>, and communicate a signal to the communicator <NUM>. While the blade diagnostic controller <NUM> is generally described herein as a singular device, it should be appreciated that the blade diagnostic controller <NUM> may include multiple devices linked together to share and/or communicate information therebetween. Furthermore, it should be appreciated that the blade diagnostic controller <NUM> may be located on the mower implement or located remotely from the mower implement, such as but not limited to being located on the associated traction unit <NUM>.

The blade diagnostic controller <NUM> may alternatively be referred to as a computing device, a computer, a controller, a control unit, a control module, a module, etc. The blade diagnostic controller <NUM> includes a processor <NUM>, a memory <NUM>, and all software, hardware, algorithms, connections, sensors, etc., necessary to manage and control the operation of the image sensor <NUM> and the communicator <NUM>. As such, a method may be embodied as a program or algorithm operable on the blade diagnostic controller <NUM>. It should be appreciated that the blade diagnostic controller <NUM> may include any device capable of analyzing data from various sensors, comparing data, making decisions, and executing the required tasks.

As used herein, "blade diagnostic controller <NUM>" is intended to be used consistent with how the term is used by a person of skill in the art, and refers to a computing component with processing, memory, and communication capabilities, which is utilized to execute instructions (i.e., stored on the memory <NUM> or received via the communication capabilities) to control or communicate with one or more other components. In certain embodiments, the blade diagnostic controller <NUM> may be configured to receive input signals in various formats (e.g., hydraulic signals, voltage signals, current signals, CAN messages, optical signals, radio signals), and to output command or communication signals in various formats (e.g., hydraulic signals, voltage signals, current signals, CAN messages, optical signals, radio signals).

The blade diagnostic controller <NUM> may be in communication with other components on the mower implement <NUM> and/or traction unit <NUM>, such as hydraulic components, electrical components, and operator inputs within an operator station of the associated traction unit <NUM>. The blade diagnostic controller <NUM> may be electrically connected to these other components by a wiring harness such that messages, commands, and electrical power may be transmitted between the blade diagnostic controller <NUM> and the other components. Although the blade diagnostic controller <NUM> is referenced in the singular, in alternative embodiments the configuration and functionality described herein can be split across multiple devices using techniques known to a person of ordinary skill in the art.

The blade diagnostic controller <NUM> may be embodied as one or multiple digital computers or host machines each having one or more processors, read only memory (ROM), random access memory (RAM), electrically-programmable read only memory (EPROM), optical drives, magnetic drives, etc., a high-speed clock, analog-to-digital (A/D) circuitry, digital-to-analog (D/A) circuitry, and any required input/output (I/O) circuitry, I/O devices, and communication interfaces, as well as signal conditioning and buffer electronics.

The computer-readable memory <NUM> may include any non-transitory/tangible medium which participates in providing data or computer-readable instructions. The memory <NUM> may be non-volatile or volatile. Non-volatile media may include, for example, optical or magnetic disks and other persistent memory. Example volatile media may include dynamic random access memory (DRAM), which may constitute a main memory. Other examples of embodiments for memory <NUM> include a floppy, flexible disk, or hard disk, magnetic tape or other magnetic medium, a CD-ROM, DVD, and/or any other optical medium, as well as other possible memory devices such as flash memory.

The blade diagnostic controller <NUM> includes the tangible, non-transitory memory <NUM> on which are recorded computer-executable instructions, including a diagnostic algorithm <NUM>. The processor <NUM> of the blade diagnostic controller <NUM> is configured for executing the diagnostic algorithm <NUM>. The diagnostic algorithm <NUM> implements a method of monitoring the status and/or sharpness of the blade <NUM> of the mower implement <NUM>, described in detail below.

Referring to <FIG>, the process described herein includes capturing an image of the cut crop rearward of the cutter <NUM> with the image sensor <NUM> as the frame <NUM> moves across the ground surface. The step of capturing the image of the cut crop is generally indicated by box <NUM> shown in <FIG>. The image may include the cut crop stubble <NUM> and/or the upper ends of the cut crop material. In the example implementation described herein, the image sensor <NUM> is positioned to capture the image of the cut crop stubble <NUM> rearward of the cutter <NUM> as the mower implement <NUM> moves across the ground surface. However, in other implementations, it should be appreciated that the image sensor <NUM> may be positioned to capture the image of a swath or windrow formed by and behind the mower implement <NUM> including the upper or harvested cut ends of the cut crop material.

The blade diagnostic controller <NUM> controls the image sensor <NUM> to capture the image of the crop stubble <NUM> immediately rearward of the mower implement <NUM>. The image sensor <NUM> communicates a sensor signal including the captured image to the blade diagnostic controller <NUM>. The blade diagnostic controller <NUM> then analyzes the captured image of the crop stubble <NUM> to identify a cut end of the cut crop stubble <NUM> in the image. The step of identifying the cut ends of the crop in the image are generally indicated by box <NUM> shown in <FIG>. It should be appreciated that the captured image may include several different cut ends <NUM>, <NUM> of the crop stubble <NUM>. As such, the blade diagnostic controller <NUM> may identify multiple cut ends <NUM>, <NUM> of the cut crop stubble <NUM> in the captured image.

The blade diagnostic controller <NUM> identifies the cut end of the cut crop stubble <NUM> via pattern matching and recognition using a convolutional neural network <NUM>. As is understood by those skilled in the art, pattern recognition is the process of recognizing patterns by using a machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. Pattern recognition involves the identification of an unknown object, the comparison of that unknown object to many different known learned object images, whereby the known object image most closely resembling the unknown object may be used to identify the unknown object. The unknown object may then be classified based on this correlation and determination. As understood by those skilled in the art, convolutional neural network <NUM> are a specialized type of artificial neural networks that use a mathematical operation called convolution in place of general matrix multiplication in at least one of their layers. They are specifically designed to process pixel data and are used in image recognition and processing. The specific features, functions, and operations of the Convolutional neural network <NUM> and image pattern recognition and classification operations performed thereby are understood by those skilled in the art, and are therefore not described in greater detail herein.

The blade diagnostic controller <NUM> uses the convolutional neural network <NUM> and pattern recognition and classification operations to classify the cut ends <NUM>, <NUM> of the cut crop stubble <NUM> identified in the image as one of a sharp cut end <NUM> and a dull cut end <NUM>. The step of classifying the cut ends as sharp cut ends <NUM> or dull cut ends <NUM> is generally indicated by box <NUM> shown in <FIG>. The sharp cut end <NUM> is characterized by a generally clean even cut through the stalk of the crop material, whereas the dull cut end <NUM> is characterized by a generally ragged or torn cut through the stalk of the crop material. It should be appreciated that the blade diagnostic controller <NUM> classifies each respective one of a plurality of different cut ends <NUM>, <NUM> identified in the image as either a dull cut end <NUM> or a sharp cut end <NUM>.

The blade diagnostic controller <NUM> then determines a cut quality of the cut ends <NUM>, <NUM> of the cut crop stubble <NUM>. The step of determining the cut quality of the cut ends <NUM>, <NUM> is generally indicated by box <NUM> shown in <FIG>. The blade diagnostic controller determines the cut quality based on a frequency of dull cut ends <NUM> of the cut crop stubble <NUM>. The frequency of the dull cut ends <NUM> may be represented as a percentage or ratio of the total number of cut ends <NUM>, <NUM> classified as a dull cut end <NUM> relative to a total number of classified cut ends <NUM>, <NUM>, i.e., sum of the total number of cut ends <NUM>, <NUM> classified as a dull cut end <NUM> and the total number of cut ends <NUM>, <NUM> classified as a sharp cut end <NUM>.

The blade diagnostic controller <NUM> then calculates a frequency of dull cut ends <NUM> of the cut crop stubble <NUM>. For example, referring to <FIG>, an image of the cut crop stubble is generally shown at <NUM>. If the blade diagnostic controller <NUM> identifies a total number of twenty two (<NUM>) cut ends <NUM>, <NUM> in the respective image <NUM>, and classifies eighteen (<NUM>) of the total identified cut ends <NUM>, <NUM> as sharp cut ends <NUM>, i.e., a total of eighteen (<NUM>) sharp cut ends <NUM>, and classifies four (<NUM>) of the total identified cut ends <NUM>, <NUM> as dull cut ends <NUM>, i.e., a total of four (<NUM>) dull cut ends <NUM>, then the blade diagnostic controller <NUM> may determine the frequency of dull cut ends <NUM> of the crop stubble <NUM> in that image by dividing the total number of identified dull cut ends <NUM>, i.e., four (<NUM>) dull cut ends <NUM> identified in the image, by the total number of identified cut ends <NUM>, <NUM> in the image, i.e., twenty two (<NUM>) total cut ends <NUM>, <NUM> identified in the image, to calculate a frequency of dull cut ends <NUM> of twenty three point five percent (<NUM> %). It should be appreciated that the frequency of dull cut ends <NUM> identified in the image may be calculated and expressed in some other manner than described herein.

The blade diagnostic controller <NUM> may calculate the frequency of dull cut ends <NUM> of the cut crop stubble <NUM> in each respective image. Additionally, it should be appreciated that the blade diagnostic controller <NUM> may capture a plurality of images over a period of time. The blade diagnostic controller <NUM> may calculate the frequency of dull cut ends <NUM> of the crop stubble <NUM> over the period of time from the plurality of images. By so doing, the frequency of dull cut ends <NUM> may be tracked over a period of time, and as such, the change in the frequency of dull cut ends <NUM> may be tracked over that period of time. It should be appreciated that the frequency of the dull cut ends <NUM> may be calculated from a plurality of images over a period of time by aggregating the total number of identified cut ends <NUM>, <NUM> in all of the images, and aggregating the total number of the identified cut ends <NUM>, <NUM> classified as dull cut ends <NUM> in all of the images.

The blade diagnostic controller <NUM> determines the cut quality based only on the frequency of dull cut ends <NUM> of the cut crop stubble <NUM>. In other implementations, the blade diagnostic controller <NUM> may determine the cut quality based on the frequency of dull cut ends <NUM> of the cut crop stubble <NUM> along with other factors affecting cut quality. For example, the blade diagnostic controller <NUM> may include as inputs into a blade sharpness index model saved on the memory <NUM> of the blade diagnostic controller <NUM>, the frequency of the dull cut ends <NUM> of the cut crop stubble <NUM>, a moisture content of the crop material and a speed of the blade <NUM>. Using these factors as inputs into the blade sharpness index model, the blade diagnostic controller <NUM> may determine the cut quality of the cut ends <NUM>, <NUM>. It should be appreciated that the blade sharpness index model may include other factors as inputs, and may output the cut quality. The cut quality may be expressed as a number, a grade, a ration, or some other form of expressing the quality of the cut performed on the crop material.

In another implementation, the blade diagnostic controller <NUM> may determine the cut quality of the cut ends <NUM>, <NUM> of the cut crop stubble <NUM> by measuring light diffraction from the cut end of the cut crop stubble <NUM>. Light will diffract from the cut ends <NUM>, <NUM> of the crop stubble <NUM> at different angles depending upon the number and shape of the edges formed in the crop stubble <NUM>. The higher the number of edges on the cut end of the cut crop stubble <NUM>, the higher the amount of light diffraction. It should be appreciated that a dull cut end <NUM>, being more ragged and torn than a clean smooth cut end, will exhibit a greater number of edges and have a higher degree of light diffraction than will a sharp cut end <NUM>. As such, light diffraction from the cut end above a diffraction threshold may be classified as a dull cut end <NUM>, whereas light diffraction from the cut end below the diffraction threshold may be classified as a sharp cut end <NUM>. It should be appreciated that the image captured of the cut crop stubble <NUM> may capture the light diffraction from the cut crop stubble <NUM>, thereby allowing the blade diagnostic controller <NUM> to analyze the image to determine the degree of light diffraction in the image, and thereby determine the cut quality of the cut ends <NUM>, <NUM> of the cut crop stubble <NUM>. It should be appreciated that the cut quality of the cut ends <NUM>, <NUM> may be determined in some other manner not described herein.

Once the cut quality of the cut ends <NUM>, <NUM> of the cut crop stubble <NUM> has been determined, the blade diagnostic controller <NUM> then correlates the cut quality of the cut ends <NUM>, <NUM> to a blade sharpness index. The step of correlating the cut quality of the cut ends <NUM>, <NUM> to a blade sharpness index is generally indicated by box <NUM> shown in <FIG>. The blade sharpness index may be expressed in a suitable manner, such as but not limited to, a number within a numeric range, a binary grade, e.g., good or bad, a color associated with a color spectrum between good and bad etc. In one implementation, the blade sharpness index may include a word descriptor selected from a few options, such as new, good, medium, poor, and dull. The blade diagnostic controller <NUM> may use the quality of the cut ends <NUM>, <NUM> as an input into a reference table <NUM> which generates and/or correlates the quality of the cut ends <NUM>, <NUM> into the blade sharpness index.

Once the blade diagnostic controller <NUM> has determined the blade sharpness index, the blade diagnostic controller <NUM> then communicates an index signal to the communicator <NUM>. The step of communicating the index signal to the communicator <NUM> is generally indicated by box <NUM> shown in <FIG>. The communicator <NUM> may include any device capable of communicating a message to a machine or an operator. For example, the communicator <NUM> may include, but is not limited to, a touch screen display, an audio speaker, a light emitter, a radio transmitter, a cellular device, etc. The index signal controls the communicator <NUM> to generate a communication indicating the blade sharpness index. In one implementation, the communicator <NUM> may be configured as a touch screen display disposed in a work station of the associated traction unit. The index signal may command the touch screen display to generate a signal indicating the blade sharpness index to the operator of the traction unit <NUM>. In other implementations, the communicator <NUM> may include a wireless network transmitter operable to generate a wireless signal through the Cloud network to generate the blade sharpness index on a screen of a computing device located at a remote location, e.g., a fleet management service location.

In one aspect of the disclosure, the blade diagnostic controller <NUM> may be configured to automatically communicate a maintenance request signal to the communicator <NUM> when the blade sharpness index is below a sharpness threshold. The step of communicating the maintenance request to the communicator <NUM> is generally indicated by box <NUM> shown in <FIG>. The sharpness threshold may be defined as a level of the blade sharpness index indicating that the blade <NUM> is dull and requires replacement, and/or that the cut quality from the blade <NUM> is no longer acceptable and/or will soon become unacceptable. The maintenance request signal controls the communicator <NUM> to generate a communication requesting maintenance for the blade <NUM>. Maintenance may include, but is not limited to, sharpening and/or replacement.

In one aspect of the disclosure, the blade diagnostic controller <NUM> may be configured to estimate a remaining life of the blade <NUM> based on the blade sharpness index. Through testing, the remaining life of the blade <NUM> may be correlated to the blade sharpness index. The remaining life of the blade <NUM> may be based on and/or adjusted bases on certain parameters, such as but not limited to type of crop material being cut, soil type, moisture content, etc. The blade diagnostic controller <NUM> may consider these other factors when determining the remaining life of the blade <NUM>. The remaining life of the blade <NUM> may be expressed, for example but not limited to, hours of operation for a given set of conditions. The blade diagnostic controller <NUM> may then communicate a life expectancy signal to the communicator <NUM>. The step of communicating the life expectancy signal to the communicator <NUM> is generally indicated by box <NUM> shown in <FIG>. The life expectancy signal controls the communicator <NUM> to generate a communication indicating the remaining life of the blade <NUM>. Such an estimation enables the operator to schedule maintenance for the blade <NUM> in the future, thereby preventing unscheduled machine downtime.

Claim 1:
A mower implement (<NUM>) comprising:
a frame (<NUM>) moveable across a ground surface in a direction of travel (<NUM>) during operation;
a cutter (<NUM>) coupled to the frame (<NUM>) and including a blade (<NUM>) operable to cut crop material as the frame (<NUM>) moves across the ground surface;
an image sensor (<NUM>) coupled to the frame (<NUM>) and positioned to capture an image of cut crop rearward of the cutter (<NUM>) relative to the direction of travel (<NUM>) during operation;
a blade diagnostic controller (<NUM>) including a processor (<NUM>) and a memory (<NUM>) having a diagnostic algorithm (<NUM>) stored therein, wherein the processor (<NUM>) is configured to execute the diagnostic algorithm (<NUM>) to:
capture an image of the cut crop rearward of the cutter (<NUM>) with the image sensor (<NUM>) as the frame (<NUM>) moves across the ground surface;
identify the cut end (<NUM>, <NUM>) of the cut crop in the image via pattern matching and recognition using a convolutional neural network (<NUM>), wherein the convolutional neural network (<NUM>) is configured to classify the cut end (<NUM>, <NUM>) of the cut crop as one of a sharp cut end (<NUM>) and a dull cut end (<NUM>);
determine a cut quality of the cut end (<NUM>, <NUM>) of the cut crop;
calculate a frequency of dull cut ends (<NUM>) of the cut crop;
determine the cut quality based on the frequency of dull cut ends (<NUM>) of the cut crop;correlate the cut quality of the cut end (<NUM>, <NUM>) to a blade sharpness index; and
communicate an index signal to a communicator (<NUM>), wherein the index signal controls the communicator (<NUM>) to generate a communication indicating the blade sharpness index.