Patent Publication Number: US-2023135705-A1

Title: Non-transitory computer-readable media and devices for blade wear monitoring

Description:
FIELD 
     Some example embodiments provide non-transitory computer-readable media and devices for blade wear monitoring. For example, non-transitory computer-readable media and devices may be provided for vision-based wear monitoring of a cutter blade of a harvester. 
     BACKGROUND 
     Harvesters separate crops, such as sugar cane, into billets using cutter blades. When these blades become worn through use, the cutting performance of the blades decreases. For example, a harvester with worn cutter blades fails to separate the crops into billets (e.g., eject billets of excessive length), fails to provide cleanly cut billets, etc. Accordingly, proper maintenance of the blades includes sharpening the blades when they become worn. 
     SUMMARY 
     Some example embodiments provide a non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to obtain a first signal based on an input image using a trained machine learning model, the input image being an image of a plant cut by a blade, and the first signal indicating a wear level of the blade, determine whether a level of the first signal is greater than or equal to a threshold, generate a second signal in response to determining the level of the first signal is greater than or equal to the threshold, and output the second signal. 
     Some example embodiments provide a device for blade wear monitoring, the device comprising processing circuitry configured to obtain a first signal based on an input image using a trained machine learning model, the input image being an image of a plant cut by a blade, and the first signal indicating a wear level of the blade, determine whether a level of the first signal is greater than or equal to a threshold, generate a second signal in response to determining the level of the first signal is greater than or equal to the threshold, and output the second signal. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The various features and advantages of the non-limiting embodiments herein may become more apparent upon review of the detailed description in conjunction with the accompanying drawings. The accompanying drawings are merely provided for illustrative purposes and should not be interpreted to limit the scope of the claims. The accompanying drawings are not to be considered as drawn to scale unless explicitly noted. For the purposes of clarity, various dimensions of the drawings may have been exaggerated. 
         FIG.  1    is a side view of a harvester according to some example embodiments; 
         FIG.  2    is a perspective view of the harvester shown in  FIG.  1    according to some example embodiments; 
         FIG.  3    illustrates a cross section through a chopper and a separator of the harvester shown in  FIGS.  1 - 2    according to some example embodiments; 
         FIG.  4    is a knife sharpening detection system according to some example embodiments; 
         FIG.  5    is a method for blade wear monitoring according to some example embodiments; 
         FIG.  6    is a method for training a machine learning model based on inspection of blade wear according to some example embodiments; 
         FIG.  7    is a method for training a machine learning model based on an ordered set of images according to some example embodiments; 
         FIG.  8    is a diagram of a system for blade wear monitoring according to some example embodiments; 
         FIG.  9    is a table of wear level alerts according to some example embodiments; and 
         FIG.  10    is a diagram of a device and system for training a machine learning model according to some example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     With reference to  FIGS.  1  and  2   , a harvester  10 , such as a sugarcane harvester may include a prime mover (not shown), such as an internal combustion engine, for providing motive power and a throttle  11  for controlling a speed of the prime mover and thus a ground speed of the harvester  10 . Further, the harvester  10  may include a frame  12  supported on wheels  14  having continuous tracks  15 , tires, or other traction devices that engage a field  16 . The tracks  15  may interact directly with the field  16  and be responsible for harvester  10  movement and tractive effort, although in other constructions the harvester  10  is provided only with wheels (rather than tracks as illustrated). An operator&#39;s cab  18  may be mounted on the frame  12  and contain a seat  20  for an operator. A pair of crop lifters  22  having side by side augers or scrolls may be mounted to the front of the frame  12 , and operate on opposite sides of a row of crop to be harvested. The crop lifters  22  may cooperate with upper and lower knock-down rollers and a base cutter including counter-rotating discs which cut off the stalks of crop close to the field  16  after being knocked down by the rollers. The crop lifters  22  may be configured to lift the sugar cane for feeding into a feed section (not shown). Additionally, the harvester  10  may be equipped with a topper  24  extending from the frame  12  on a boom  25 . The topper  24  may have a blade or blades  26  for cutting the top off a crop and allowing for easier processing of the remaining crop by the harvester  10 . 
     Referring to  FIGS.  1  and  3    the harvester  10  may include a chopper  28  and/or a separator  55 . The chopper  28  may cut the crop, and the separator  55  may receive the cut crop from the chopper  28  and generally separate the cut crop by way of a crop cleaner. The crop cleaner may include any suitable mechanism for cleaning the cut crop, such as a fan (as in the illustrated construction that will be described below), a source of compressed air, a rake, a shaker, or any other mechanism that discriminates various types of crop parts by weight, size, shape, etc., in order to separate extraneous plant matter from billets. Referring to  FIGS.  1 - 3   , the separator  55  may include any combination of one or more of a cleaning chamber  32 , a cleaning chamber housing  34 , a crop cleaner such as a fan  40 , a fan enclosure  36 , a motor  50  driving the fan  40 , a hood  38  having an opening  54 , and/or a centrifugal blower wheel  46 . 
     The separator  55  may be coupled to the frame  12  and located downstream of the crop lifters  22  for receiving cut crop from the chopper  28 . The chopper  28  may include counter-rotating drum cutters  30  with overlapping blades for cutting stalks of the crop, such as cane C, into billets B, which are pieces of the stalk. In other constructions, the chopper  28  may include any suitable blade or blades for cutting the stalks of crop. The crop may also include dirt, leaves, roots, and other plant matter, which will be collectively referred to herein as extraneous plant matter, which may also be cut in the chopper  28  along with the cane C. The chopper  28  may direct a stream of the cut crop (billets B and/or cut extraneous plant matter) to the cleaning chamber  32 , which may be generally defined by the cleaning chamber housing  34 , the fan enclosure  36 , and/or the hood  38 , all of which may be coupled to the frame  12  and located just downstream of the chopper  28  for receiving the cut crop from the chopper  28 . The fan enclosure  36  may be coupled to the cleaning chamber housing  34  and may include deflector vanes  31 . 
     The hood  38  may be coupled to the fan enclosure  36  and have a domed shape, or other suitable shape, and include an opening  54  angled out from the harvester  10  and facing slightly down onto the field  16 . In some constructions, the opening  54  may be generally perpendicular to the drive shaft  52 . The hood  38  may direct the cut crop through the opening  54  to the outside of the harvester  10 , e.g., for discharging a portion of cut crop removed from the stream of cut crop back onto the field  16 . 
     Mounted for rotation in the cleaning chamber  32  may be the fan  40 . For example, the fan  40  may be in the form of an extractor fan having axial flow fan blades  42  radiating out from, and joined to, a hub  44 . In the illustrated construction, the fan  40  (or other crop cleaner) may be configured to draw air and extraneous plant matter from the cleaning chamber  32 . In other constructions, the fan  40  (or other crop cleaner) may be configured to blow rather than extract, e.g., to blow or push the air through the cleaning chamber  32  to clean the crop. The fan  40  may include other types of fans with other types of blades, such as a centrifugal fan, amongst others. The centrifugal blower wheel  46  may be mounted for rotation with the fan  40  radially inwardly of the deflector vanes  31 . For example, a plurality of generally right-angular blower blades  48  may be fixed to the underside of the centrifugal blower wheel  46  radiating out therefrom. 
     The motor  50 , such as a hydraulic motor, may include a drive shaft  52  operatively coupled to drive the fan  40 . For example, the drive shaft  52  may be keyed to the hub  44  or operatively coupled in other suitable ways to drive the fan  40 . The motor  50  may also be operatively coupled to drive the centrifugal blower wheel  46  in a similar manner. In other constructions, the motor  50  may be electric, pneumatic, or may include any other suitable type of motor, an engine, or a prime mover to drive the fan  40  and/or the centrifugal blower wheel  46 . 
     Referring again to  FIGS.  1 - 2   , a conveyor  56  may be coupled to the frame  12  for receiving cleaned crop from the separator  55 . The conveyor  56  may terminate at a discharge opening  58  (or outlet) elevated to a height suitable for discharging the cleaned crop into a collection receptacle of a vehicle (not shown), such as a truck, wagon, or the like following alongside the harvester  10 . A secondary cleaner  60  may be located adjacent the discharge opening  58  for cleaning the crop a second time before being discharged to the vehicle. For example, the secondary cleaner  60  may include a fan, compressed air, a rake, a shaker, or other suitable device for cleaning the crop. 
     Briefly, the billets B may be generally separated from the extraneous plant matter in the cleaning chamber  32  as the fan  40  draws the generally lighter extraneous plant matter into the hood  38  and out the opening  54 . All the cut crop directed through the opening  54 , which is ejected back onto the field  16 , may be referred to herein as residue. Residue typically includes primarily the extraneous plant matter (which has generally been cut) and may include some billets B. 
     The cleaning chamber housing  34  may direct the cleaned crop to the conveyor  56 . The cleaned crop typically includes primarily billets B, although some extraneous plant matter may still be present in the cleaned crop. Thus, some extraneous plant matter may be discharged with the billets B from the discharge opening  58 . Extraneous plant matter discharged from the discharge opening  58  to the vehicle may be referred to herein as trash. 
     Illustrated schematically in  FIG.  2   , a hydraulic circuit  62  for powering the motor  50  may be operatively coupled thereto. In other constructions, the circuit  62  may be electric, pneumatic, may comprise mechanical linkages, etc. For example, the hydraulic circuit  62  may be a closed loop hydraulic circuit, which is powered by a pump  64 . The pump  64  may be driven by the prime mover (not shown) of the harvester  10  or other power source. 
     The harvester  10  may also include an operator interface  66  (e.g., a display, buttons, a touch screen, a graphical user interface, any combination thereof, or the like) with which a user may input settings, preferences, commands, etc. to control the harvester  10 . The operator interface may be operatively coupled with a control unit  68 , such as a microprocessor-based electronic control unit or the like, for receiving signals from the operator interface  66  and from several sensors and for sending signals to control various components of the harvester  10  (examples of which will be described in greater detail below). Signals, as used herein, may include electronic signals (e.g., by circuit or wire), wireless signals (e.g., by satellite, internet, mobile telecommunications technology, a frequency, a wavelength, Bluetooth®), or the like. The control unit  68  may include a memory and programming, such as algorithms. The harvester  10  may also include a global positioning system (“GPS”) receiver  70  operatively connected to send signals to the control unit  68 . The aforementioned sensors may include a yield monitoring sensor  72 , a billet loss sensor  74 , a fan speed sensor  76 , a load sensor  78 , a moisture sensor  80 , an imaging sensor  82 , and/or a ground speed sensor  84 . The control unit  68  may be programmed to include a monitoring system that monitors harvester functions, switch states, ground speed, and/or system pressures. 
     According to some example embodiments, operations described herein as being performed by the harvester  10  and/or the control unit  68  may be performed by processing circuitry. The term ‘processing circuitry,’ as used in the present disclosure, may refer to, for example, hardware including logic circuits; a hardware/software combination such as a processor executing software; or a combination thereof. For example, the hardware more specifically may include, but is not limited to, a central processing unit (CPU), an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a System-on-Chip (SoC), a programmable logic unit, a microprocessor, application-specific integrated circuit (ASIC), etc. 
     The control unit  68  may also have other inputs, such as an elevator speed sensor (not shown) for detecting a speed of the conveyor  56 , a chopper speed sensor (not shown) for detecting a speed of the counter-rotating drum cutters  30  or other type of chopper  28 , and/or a base cutter speed sensor (not shown) for detecting a speed of the counter-rotating discs, or other cutting device, of the base cutter. The control unit  68  may also have other outputs, such as for controlling the fan pump  64 , the fan motor  50 , a pump, valve, or motor (not shown) of the centrifugal blower wheel  46 , the speed of the chopper  28 , the height, direction, speed, and input control of the base cutter (not shown), the secondary cleaner  60 , and/or the height and input control of the topper. 
     The yield monitoring sensor  72  may be coupled to the conveyor  56  and send a crop yield signal to the control unit  68  corresponding to an amount (e.g., a mass or a volume) of crop being discharged from the discharge opening  58 . 
     The billet loss sensor  74  may include one or more accelerometers and/or any sensor that measures displacement or strain, or the like. The billet loss sensor  74  may be associated with the separator  55 , or more specifically coupled to the separator  55 . For example, the billet loss sensor  74  may be associated with, or coupled to, the cleaning chamber housing  34 , the fan enclosure  36 , the hood  38 , the fan  40 , the fan blades  42 , the hub  44 , the centrifugal blower wheel  46 , the right angular blower blades  48 , the drive shaft  52 , etc., or any of the associated structures. The billet loss sensor  74  may be configured for sending a signal to the control unit  68  corresponding to each billet B passing through the separator  55  and, more specifically, out the opening  54 . For example, the billet loss sensor  74  may include an accelerometer that detects the impact of a billet B hitting the fan  40  and/or a housing part, such as the hood  38 . In other constructions, the billet loss sensor  74  may include a piezoelectric sensor or employ another suitable sensing technology. The billet loss sensor  74  may send a signal to the control unit  68  each time a billet is detected. The control unit  68  may record and count the billets, and may associate the billet signal data with a time, a location (e.g., from the GPS  70 ), etc. 
     The fan speed sensor  76  may be associated with, or coupled to, the fan  40 , and more specifically may be coupled to, for example, the blades  42 , the hub  44 , the drive shaft  52 , etc., or to any suitable location adjacent the fan  40 . For example, the fan sensor  76  may include magnets, proximity sensors, Hall Effect sensors, etc., to count revolutions of the blades  42 , the drive shaft  52 , or other part of the fan  40  and send signals to the control unit  68  corresponding to, and used to determine, the fan speed. The fan sensor  76  may also include other suitable sensing technologies for determining fan speed. 
     The moisture sensor  80  may be positioned to detect moisture of the crop. The moisture sensor  80  may include a near infrared sensor or other suitable moisture-detecting technologies. For example, the moisture sensor  80  is disposed on the harvester  10  and may be positioned in the chopper  28 , in the separator  55 , and/or in the conveyor  56  and, more specifically, in any of the components of the harvester  10  associated therewith as described above. In the illustrated construction, the moisture sensor  80  may be disposed in the separator  55  and, more specifically, in the hood  38 . The moisture sensor  80  may send a signal to the control unit  68  corresponding to a level of moisture in the crop. 
     Referring to  FIGS.  1 - 2   , the imaging sensor  82  may include vision technology disposed proximate the conveyor  56  and/or the discharge opening  58  and sending an imaging signal to the control unit  68 . According to some example embodiments, the imaging sensor  82  may be a three-dimensional or stereo style camera able to output three-dimensional representations. According to some example embodiments, the imaging sensor  82  may include LIDAR, structured light, stereo vision, RADAR, etc. The imaging signal may include images of the billet B on the conveyor  56  and/or being discharged from the discharge opening. The imaging signal may also include images of trash being discharged from the discharge opening  58 . The imaging sensor  82  (in combination with the control unit  68 ) may quantify the amount of trash as an absolute amount or as a percentage of total yield through the discharge opening  58 . The imaging sensor  82  may be disposed in the conveyor  56 . 
     Referring back to  FIG.  2   , the ground speed sensor  84 , which may include a speedometer, a radar sensor, a velocimeter such as a laser surface velocimeter, a wheel sensor, or any other suitable technology for sensing vehicle speed, may be configured to send a ground speed signal to the control unit  68  corresponding to the speed of the harvester  10  with respect to the field  16 . The ground speed signal may also be sent by the GPS  70 . 
     The load sensor  78  may sense a load on the separator  55 . For example, the load sensor  78  may measure a load on the motor  50  and may include any suitable type of sensor for the type of motor employed, e.g., electric, pneumatic, hydraulic, etc. In some constructions, the load sensor  78  may include a strain gage(s) for measuring a torque load or an amp meter for measuring an electrical load. The load on the motor  50  may also be measured indirectly, such as by measuring a load on the fan  40  and/or the centrifugal blower wheel  46 . In some constructions, such as the illustrated construction employing a hydraulic motor  50 , the load sensor  78  may include a pressure transducer, or other pressure sensing technology, in communication with the hydraulic circuit  62  for measuring pressure within the circuit  62 . For example, the load sensor  78  may be coupled to the fan motor  50  or to the pump  64 , or anywhere along the circuit  62  to measure the associated pressure in the circuit  62 . The load sensor  78  may send load signals to the control unit  68 . 
     When the blades of the chopper  28  become worn through use, the cutting performance of the blades decreases. Such worn blades fail to separate the crops into billets of desired/selected length (e.g., eject billets of excessive length), fail to provide cleanly cut billets, etc. For example, in a sugarcane harvester, the main cutter blades are designed to make several clear and straight cuts such that each stalk is cut into billets approximately 6-8 inches in length. When these blades become worn, they will fail to make clean cuts—sometimes even allowing entire stalks to pass through into the unloading elevator. A similar set of blades strips the leafy material away from the stalk so that the primary extractor fan may blow this leafy material away before the material reaches the elevator. Accordingly, proper maintenance of the blades includes sharpening the blades when they become worn. 
     Conventionally, a current wear level of the blades of the chopper  28  is determined by visually inspecting the blades. However, the operation of the chopper  28  is halted to permit safe visual inspection of the blades. Accordingly, a tradeoff exists between sufficient monitoring of the blades, to allow for sufficient cutting performance, and avoiding stoppages of the harvester. 
     However, according to some example embodiments, improved devices and methods are provided for blade wear monitoring. For example, the imaging sensor  82  may capture images of the billets B on the conveyor  56 , and the control unit  68  may determine a wear level of the blades of the chopper  28  based on the captured images. Accordingly, the wear level of the blades is determined without halting operations of the harvester  10 . Thus, the improved devices and methods provide monitoring of the blades while avoiding stoppages of the harvester, thereby increasing the processing speed of the harvester  10  while improving the quality of the resulting billets B. 
     Referring to  FIG.  4   , according to some example embodiments, the imaging sensor  82  may output a stereo image  302  including a single camera image  304  and a disparity image  306 . The control unit  68  may determine a volume of trash, billets B, etc. based on the disparity image  306  (operation  308 ). The control unit  68  may output the determined volume to a telematics service (operation  312 ). According to some example embodiments, the control unit  68  may perform post-processing (e.g., filtering, etc.) on data corresponding to the determined volume before outputting the data to the telematics service (operation  310 ). According to some example embodiments, the telematics service may be the JDLink™ Machine Monitoring System. The JDLink™ Machine Monitoring System is an example of an agricultural vehicle telematics service, which is available from John Deere &amp; Company. As another example, OnStar® is a telematics service available from the General Motors Corporation. JDLink™ and OnStar® are examples of subscription telematics services that are provided to customers for a price. 
     The control unit  68  may input the single camera image  304  into a trash detection machine learning model (MLM)  318 . The trash detection MLM  318  may be trained to output an amount of trash (e.g., as an absolute amount, percentage of total yield, etc.) based on an input image. The trash detection MLM  318  may be trained using a set of labelled images of trash  320  (e.g., trash on the conveyor  56 ). The control unit  68  may output the amount of trash to a telematics service (operation  324 ). According to some example embodiments, the control unit  68  may perform post-processing (e.g., filtering, etc.) on data corresponding to the amount of trash before outputting the data to the telematics service (operation  322 ). According to some example embodiments, the telematics service may be the JDLink™ Machine Monitoring System. The control unit  68  may also control the harvester  10  based on the amount of trash (e.g., by controlling the speed of the harvester  10 , the speed of the fan  40 , etc.) (operation  326 ). 
       FIG.  4    also illustrates operations performed by a blade (e.g., a knife) sharpening detection system  300 . For example, the blade sharpening detection system  300  may include the control unit  68  and/or a blade wear detection MLM  350 . The control unit  68  may input the single camera image  304  into the blade wear detection MLM  350 . The blade wear detection MLM  350  may be trained to output blade wear level (e.g., as an absolute amount, percentage of wear, etc.) based on an input image. The blade wear detection MLM  350  may be trained using a set of labelled images for billet damage  352  (e.g., cuts of billets on the conveyor  56 ). According to some example embodiments, the control unit  68  may perform post-processing (e.g., filtering, etc.) on data corresponding to the blade wear level (operation  354 ). For example, a filter (e.g., an integral function) that provides a moving average may be applied to the data corresponding to the blade wear level to smooth the data. Such a filter may adjust for varying amounts of billets included in different images among the set of labeled images  352 . 
     The control unit  68  may determine whether a signal (e.g., a first signal) output by the blade wear detection MLM  350  (e.g., a first signal indicative of the wear level of a blade of the chopper  28 ) is greater than a threshold (operation  356 ). According to some example embodiments, the threshold may be set by an operator of the harvester  10 . According to some example embodiments, the threshold may be a design parameter determined through empirical study. In response to determining the first signal is greater than the threshold, the control unit  68  may output a signal (e.g., a second signal) to a user interface (e.g., a terminal device of the operator, the operator interface  66 , etc.) (operation  360 ) and/or a telematics service (e.g., the JDLink™ Machine Monitoring System) (operation  358 ). The second signal may be an alert indicating that the blade of the chopper  28  has become worn and should be sharpened. According to some example embodiments, the second signal indicates the wear level of the blade. In response to determining the first signal is equal to or less than the threshold, the control unit  68  may not output the second signal (operation  362 ). While some example embodiments are described where an alert is output when a signal (e.g., a signal level) is greater than a threshold, it should be understood that some example embodiments are not limited thereto. For example the alert may be output when the signal is greater than or equal to the threshold. 
     According to some example embodiments each of the trash detection MLM  318  and the blade wear detection MLM  350  may be implemented using a convolutional neural network (CNN). For example, the CNN may be an artificial neural network containing a spatially invariant, and/or recurrent, convolutional layer. In some example embodiments, the processing circuitry may perform some operations (e.g., the operations described herein as being performed by the trash detection MLM  318  and/or the blade wear detection MLM  350 ) by artificial intelligence and/or machine learning. As an example, the processing circuitry may implement an artificial neural network that is trained on a set of training data by, for example, a supervised, unsupervised, and/or reinforcement learning model, and wherein the processing circuitry may process a feature vector to provide output based upon the training. Such artificial neural networks may utilize a variety of artificial neural network organizational and processing models, such as CNNs, recurrent neural networks (RNNs) optionally including long short-term memory (LSTM) units and/or gated recurrent units (GRUs), stacking-based deep neural networks (S-DNNs), state-space dynamic neural networks (S-SDNNs), deconvolution networks, deep belief networks (DBNs), and/or restricted Boltzmann machines (RBMs). Alternatively or additionally, the processing circuitry may include other forms of artificial intelligence and/or machine learning, such as, for example, linear and/or logistic regression, statistical clustering, Bayesian classification, decision trees, dimensionality reduction such as principal component analysis, and expert systems; and/or combinations thereof, including ensembles such as random forests. 
       FIG.  5    depicts a method for blade wear monitoring according to some example embodiments. According to some example embodiments, the control unit  68  may perform the method illustrated in  FIG.  5   . In operation  402 , the method may include obtaining a signal from the blade wear detection MLM  350  based on an image input to the blade wear detection MLM  350 . For example, the image may be a recently captured image of the conveyor  56 , and depict the billets B obtained by cutting the crop C using the blade of the chopper  28 . In operation  404 , the method may include determining whether the signal is greater than the threshold. In response to determining the signal is greater than the threshold (‘Yes” in operation  404 ), the method may include generating the alert signal in operation  406  and outputting the alert signal (e.g., to the user interface and/or telematics service) in operation  408 . In response to determining the signal is less than or equal to the threshold (‘No” in operation  404 ), the method may include obtaining a next image in operation  410  and returning to operation  402  in which a next signal is obtained from the blade wear detection MLM  350  based on the next image input to the blade wear detection MLM  350 . 
     According to the method illustrated in  FIG.  5   , the wear level of the blade of the chopper  28  may be monitored during operation of the harvester  10 , and an alert output in response to determining the wear level of the blade has exceeded a threshold wear level. According to some example embodiments, the images of the conveyor  56  may be captured, and the illustrated method performed, in real-time to permit detection of worn blades. In some example embodiments, the images of the conveyor  56  may be captured, and the illustrated method performed, periodically to conserve resources (e.g., power, processor, memory, etc.). 
     Referring to  FIG.  6   , depicted is a method for training a machine learning model based on expert inspection of blade wear according to some example embodiments. For example, the method illustrated in  FIG.  6    may be used to obtain the set of labelled images for billet damage  352  (also referred to herein as the labeled image set  352 ) discussed in association with  FIG.  4   . As used herein, wear level of a blade may also be discussed with reference to a lack of wear, e.g., a sharpness of the blade. 
     In operation  502 , a blade of the chopper  28  may be sharpened to a highest degree of sharpness (may also be referred to herein as 100% sharp or 0% worn). In operation  504 , first images of billets map be captured (e.g., images of the billets B captured using the imaging sensor  82 ). According to some example embodiments, the first images of the billets may be captured for a time period T. In operation  506 , the first images may be labeled as 100% sharp (e.g., 100% knife life). In operation  508 , an operator may remove a cover of the chopper  28  and inspect the wear of the blades. In operation  510 , the operator may to determine a wear level (e.g., a percentage of knife life or wear) based on the inspection performed in operation  508 , and may register (e.g., record, store, etc.) the determined wear level. According to some example embodiments, the operator may be an expert in determining blade sharpness, and/or may use a sensor, tool, etc., to aid in determining blade sharpness. For example, the operator may determine the blade sharpness based on billet characteristics such as: (a) uniformity or variation in billet length, e.g., variance or standard deviation of billet cut length versus harvested quantity (e.g., by weight or volume) for a sampling interval of harvested material on the elevator (e.g., the conveyor  56 ) in real-time; (b) shape of billet cut end, such as crisp right angle to the stalk axis or deviation from a right angle during a sampling interval; (c) rectilinear or straightness of one or both billet cut end(s) of each billet (or presence of jagged or irregular cut, or uncut hang-nail fibers of stalk at or near the cut ends); and/or (d) figure, merit or uniformity of the cut ends of the billet to estimate billet cut quality. 
     In operation  512 , second images of the billets may be captured for the time period T. According to some example embodiments, the first images and second images may be captured over the same time period T, similar time periods T or different time periods T. In operation  514 , the second images may be labeled according to the wear level determined by the operator (e.g. as determined in operation  510 ). In operation  516 , a determination is made whether the wear level has reached a lowest degree of sharpness (may also be referred to herein as 0% sharp or 100% worn). If it is determined that the wear level is not 0% sharp (“No” in operation  516 ), operations  508 - 516  may be repeated. Otherwise, if it is determined that the wear level is 0% sharp (“Yes” in operation  516 ), the image collection process may be stopped (operation  518 ), and the labeled image set  352  may be provided for use in training the blade wear detection MLM  350 . According to some example embodiments, the labeled image set may include the first images of the billets (e.g., the images collected in operation  504 ) and the second images of the billets (e.g., the images collected in operation  512 ). For example, the second images of the billets may include a plurality of sets of images, each set of images corresponding to an iteration in which operations  508 - 516  are performed (or repeated). 
     According to some example embodiments, operations  504  and  512  may be performed using the imaging sensor  82 , and operations  502 ,  506 ,  508 ,  510 ,  514  and/or  516  may be performed by the operator (e.g., with the aid of a tool, a sensor, processing circuitry, etc.). According to some example embodiments, instead of removing the cover of the chopper  28  and directly inspecting the wear of the blades in operation  508 , the operator may inspect the billets in the images without removing the cover or directly inspecting the wear of the blades. For example, the operator may be an expert in detecting a wear level of the blades based on visual inspection of the cuts of the billets (e.g., billet length, sharpness of the cuts, etc.). According to some example embodiments, in operation  508 , the operator may determine a wear level (e.g., a percentage of knife life or wear) of the blades based on this visual inspection of the cuts of the billets (e.g., by reviewing collected billet images or by directly monitoring the billets on the conveyor  56 ). According to some example embodiments, in addition to visually inspecting the cutes of the billets, the operator may determine the wear level based on visual inspection of the trash. 
     In operation  552 , the blade wear detection MLM  350  may be selected and/or initialized (e.g., using processing circuitry). In operation  554 , a training program may be developed, loaded from a memory and/or downloaded via a communication link (e.g., using the processing circuitry). In operation  556 , the blade wear detection MLM  350  may be trained using the labeled image set  352 . For example, the individual images from the labeled image set  352  may be sequentially input to the blade wear detection MLM  350  (e.g., using the processing circuitry). The blade wear detection MLM  350  may output a blade wear signal based on each input image. The blade wear signal may provide an indication of a blade wear level. The blade wear signal output based on each respective input image may be compared to the wear level indicated by the label of the respective input image (e.g., using the processing circuitry). Feedback may be provided to the blade wear detection MLM  350  based on an amount difference between the output blade wear signal and the label (e.g., using the processing circuitry). The blade wear detection MLM  350  may be adjusted based on the feedback. Accordingly, the image labels may be used as a ground truth for iteratively adjusting the blade wear detection MLM  350  to more accurately output a blade wear signal consistent with the labels. 
     In operation  558 , the trained blade wear detection MLM  350  may be frozen. For example, once the blade wear detection MLM  350  has been successfully trained to output a blade wear signal consistent with the image labels (e.g., within a particular degree of error among a particular number of images), training is ended and the trained blade wear detection MLM  350  is frozen. In operation  560 , the trained blade wear detection MLM  350  may be pruned. According to some example embodiments, the trained blade wear detection MLM  350  may be pruned to remove elements corresponding to the smallest weights (e.g., close to zero). According to some example embodiments, the trained blade wear detection MLM  350  may be pruned by training a larger, more computationally-intensive, network and using this larger network to train a smaller network (e.g., the trained blade wear detection MLM  350 ) in a teacher-student type of framework. In operation  562 , the trained blade wear detection MLM  350  may be deployed for use in detecting a blade wear level based on input images of billets (e.g., as discussed in connection with the methods depicted in  FIGS.  4 - 5   ). 
     According to some example embodiments, the processing circuitry used to perform operations  552 ,  554 ,  556 ,  558 ,  560  and/or  562  may be included in the harvester  10  (e.g., the control unit  68 ). According to some example embodiments, the processing circuitry used to perform operations  552 ,  554 ,  556 ,  558 ,  560  and/or  562  may be included in a device external to the harvester  10  as discussed in association with  FIG.  10   . 
       FIG.  7    depicts a method for training a machine learning model based on an ordered set of images according to some example embodiments. According to some example embodiments, the method illustrated in  FIG.  7    may be performed using processing circuitry included in the harvester  10  (e.g., the control unit  68 ) and/or in a device external to the harvester  10  as discussed in association with  FIG.  10   . 
     In operation  602 , the method may include obtaining an ordered set of images (e.g., reference images 1−n, where n is a natural number equal to or greater than 3) of billets (e.g., the billets B) on a conveyor (e.g., the conveyor  56 ). For example, before the images are captured by an imaging sensor (e.g., the imaging sensor  82 ), a blade of a chopper (e.g., the chopper  28 ) may be sharpened to a highest degree of sharpness. The sharpened blade may then be used to separate a crop (e.g., sugar cane) into billets. While the blade is in use, the imaging sensor may periodically capture images of the resulting billets and label each respective image with a sequence number corresponding to an order in which the respective image was captured. The use of the blade and the capturing of the images may continue for a period of time or until the blade is worn to a lowest degree of sharpness. The captured images may constitute the ordered set of images and may be provided for use in training the blade wear detection MLM  350 . 
     In operation  604 , each individual image in the ordered set of images may be may be sequentially input to the blade wear detection MLM  350  (e.g., the “ML Model”). In operation  606 , the blade wear detection MLM  350  may output a blade wear signal based on each input image. The blade wear signal may provide an indication of a blade wear level. In operation  608 , the blade wear signal output based on each respective input image may be compared to the sequence number of the respective input image. For example, the blade wear signal may be compared to a position of the sequence number of the respective input image among the ordered set of images (e.g., a percentage of the ordered set of images sequentially input into the blade wear detection MLM  350  through the respective input image, a ratio of the sequence number of the respective input image to the quantity of the ordered set of images, etc.). In operation  610 , feedback may be provided to the blade wear detection MLM  350  based on an amount difference between the output blade wear signal and the position of the sequence number. For example, the sequence number may be used as an indication of wear level. The blade wear detection MLM  350  may be adjusted based on the feedback. Accordingly, the sequence numbers of the ordered set of images may be used as a ground truth for iteratively adjusting the blade wear detection MLM  350  to more accurately output a blade wear signal consistent with the sequence numbers. 
     Operations  604 ,  606 ,  608  and  610  may be iteratively repeated until the blade wear detection MLM  350  has been successfully trained to output a blade wear signal consistent with the image labels (e.g., within a particular degree of error among a particular number of images). The trained blade wear detection MLM  350  may be deployed for use in detecting a blade wear level based on input images of billets (e.g., as discussed in connection with the methods depicted in  FIGS.  4 - 5   . 
       FIG.  8    is a diagram of a system for blade wear monitoring according to some example embodiments. The system may include a harvester  710  (e.g., the harvester  10 ), a server  720  and/or a terminal  730 . The harvester  10  may include processing circuitry  712  (e.g., the control unit  68 ), a transceiver  714 , a memory  716  (e.g., the memory of the control unit  68 ) and/or an imaging sensor  718  (e.g., the imaging sensor  82 ). The harvester  10  is connected to the server  720  and/or the terminal  730  via a first communication link. According to some example embodiments, the harvester  710  may transmit an alert signal (see, e.g., operation  408  depicted in  FIG.  5   ) to the server  720  in response to determining a wear level signal exceeds a threshold. According to some example embodiments, operations described herein as being performed by the harvester  710 , the server  720  and/or the terminal  730  may be performed by processing circuitry. According to some example embodiments, the memory  716 , and/or a memory of the server  720 , may be a tangible, non-transitory computer-readable medium, such as a Random Access Memory (RAM), a flash memory, a Read Only Memory (ROM), an Electrically Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), registers, a hard disk, a removable disk, a Compact Disk (CD) ROM, any combination thereof, or any other form of storage medium known in the art. 
     According to some example embodiments, the server  720  may be a base station. The base station may generally refer to a fixed station that communicates with user equipment and/or other base stations, and may exchange data and control information by communicating with user equipment and/or other base stations. For example, the base station may also be referred to as a Node B, an evolved-Node B (eNB), a next generation Node B (gNB), a sector, a site, a base transceiver system (BTS), an access point (AP), a relay node, a remote radio head (RRH), a radio unit (RU), a small cell, or the like. In the present specification, a base station or a cell may be interpreted in a comprehensive sense to indicate some area or function covered by a base station controller (BSC) in CDMA, a Node-B in WCDMA, an eNB in LTE, a gNB or sector (site) in 5G, and the like, and may cover all the various coverage areas such as megacell, macrocell, microcell, picocell, femtocell and relay node, RRH, RU, and small cell communication range. 
     According to some example embodiments, the terminal  730  may be fixed or mobile and may refer to any device that may communicate with a base station, such as the server  720 , to transmit and receive data and/or control information. For example, the terminal  730  may be referred to as a terminal, a terminal equipment, a mobile station (MS), a mobile terminal (MT), a user terminal (UT), a subscriber station (SS), a wireless device, a handheld device, or the like. 
     According to some example embodiments in which the blade wear detection MLM  350  is trained using processing circuitry included in a device external to the harvester  10  (with reference to  FIGS.  6  and  7   ), the device (as discussed in association with  FIG.  10   ) may be implemented using the same components as, or similar components to, those of harvester  710  illustrated in  FIG.  8   . 
     According to some example embodiments, the server  720  is a communication server. According to some example embodiments, the server  720  may be a telematics communication server (e.g., may be used in the JDLink™ Machine Monitoring System). The server  720  may provide the alert signal to an operator via the terminal  730 , the operator interface  66 , etc. For example, the server  720  may transmit the alert signal to the operator via a second communication link. 
     According to some example embodiments, each of the first communication link and second communication link may be a wired link and/or a wireless link. For example, each of the first communication link and second communication link may be an Ethernet link, an 802.11 (WiFi) link, a Radio Frequency (RF) (e.g., cellular) link, a Transmission Control Protocol/Internet Protocol (TCP/IP) link, a Universal Serial Bus (USB) link, a Bluetooth™ link, or any combination thereof. 
     According to some example embodiments, the operator may input one or more thresholds of blade wear for use in generating corresponding alerts. For example, the operator may input the one or more thresholds into the terminal  730 , the operator interface  66 , etc. In the event the operator inputs the one or more thresholds into the terminal  730 , the terminal  730  may transmit the one or more thresholds to the server  720  via the second communication link, and the server  720  may transmit the one or more thresholds to the harvester  710  (e.g., received via the transceiver  714 ) via the first communication link. 
     According to some example embodiments, the operator may input a respective alert type in correspondence with each of the one or more thresholds of blade wear. For example, the alert types may include text message, email, indication on the operator interface  66 , indication on the terminal  730  and/or a particular message to be conveyed by the alert signal. The corresponding alert types may be provided to the server  720  and/or the harvester  710  along with the one or more thresholds. 
     Referring to  FIG.  9   , according to some example embodiments, the processing circuitry  712  may generate a table of wear level alerts  800  in which each respective threshold received from the operator (e.g., threshold 1, threshold 2 . . . threshold m) is stored in association with a respective alert type (e.g., alert 1, alert 2 . . . alert m). ‘m’ may be a natural number equal to or greater than 3. Each of the respective alert types may be the same as, similar to or different from the others. The table of wear level alerts  800  may be stored in the memory  716 . According to some example embodiments, in operation  404  of  FIG.  5   , the processing circuitry  712  may refer to the table of wear level alerts  800  and determine whether the signal is greater than each of the thresholds. In operation  406  of  FIG.  5   , the processing circuitry  712  may generate the alert signal of the alert signal type corresponding to the threshold exceeded with reference to the table of wear level alerts  800 . According to some example embodiments, the generating the alert signal may include generating an email, text message, etc., based on the alert signal type. According to some example embodiments, the thresholds and alert types included in the table of wear level alerts  800  may be set by a manufacturer and/or may not be modifiable by the operator. 
     According to some example embodiments, the server  720  may generate and/or store the table of wear level alerts  800 . In operation  404  of  FIG.  5   , the processing circuitry  712  may determine whether the signal is greater than each of the thresholds with reference to the table of wear level alerts  800  stored on the server  720 . 
     According to some example embodiments, in operation  408  of  FIG.  5   , the processing circuitry  712  may output the alert signal to the operator directly (e.g., via the operator interface  66 , a Bluetooth™ signal, etc.) or via the server  720 . 
     According to some example embodiments, the alert signal type may include a control operation of the harvester  710 . For example, the generated alert signal may cause the processing circuitry  712  (e.g., the control unit  68 ) to decrease the ground speed of the harvester  710 , increase the rotational speed of the blades of the chopper  28  (e.g., a rotational speed of the counter-rotating drum cutters  30 ), etc., in response to determining the corresponding threshold is exceeded. 
     Referring to  FIG.  10   , an MLM Training Device  1010  is illustrated. The MLM Training Device  1010  is a device external to the harvester  710  that may perform the methods discussed in association with  FIGS.  6 - 7    for training the blade wear detection MLM  350 . For example, the MLM Training Device  1010  may perform operations  552 ,  554 ,  556 ,  558 ,  560  and/or  562  discussed in association with  FIG.  6   , and/or the operations discussed in association with  FIG.  7   . The MLM Training Device  1010  may include processing circuitry  1012 , a transceiver  1014 , a memory  1016  and/or an image sensor  1018 . 
     According to some example embodiments, the MLM Training Device  1010  may receive a set of reference images (e.g., the labeled image set  352  discussed in association with  FIG.  6   , and/or the reference images 1−n discussed in association with  FIG.  7   ) via the transceiver  1014 . The processing circuitry  1012  may train the blade wear detection MLM  350  according to the methods discussed in association with  FIG.  6    and/or  FIG.  7   . The set of reference images, the blade wear detection MLM  350  and/or blade wear signals output by the blade wear detection MLM  350  may be stored in the memory  1016 . 
     According to some example embodiments, the set of reference images may be obtained by the MLM Training Device  1010  using the image sensor  1018 . For example, the image sensor  1018  may capture images of sample billets provided for use in training the blade wear detection MLM  350 , and use these captured images as the set of reference images. According to some example embodiments, the MLM Training Device  1010  may include a subset of the components of the harvester  10 , such as a chopper, a separator and/or a conveyor (the same as, or similar to, the chopper  28 , the separator  55  and/or the conveyor  56 ), or all of the components of the harvester  10 . Sample cane provided for use in training the blade wear detection MLM  350  may be fed into the chopper, the resulting billets may pass through the separator and be deposited onto the conveyor for imaging by the image sensor  1018 . 
     According to some example embodiments, the MLM Training Device  1010  may be connected to a server  1020  via a first communication link, and the server  1020  may be connected to the harvester  710  via a second communication link. Each of the first communication link and second communication link may be a wired link and/or a wireless link. For example, each of the first communication link and second communication link may be an Ethernet link, an 802.11 (WiFi) link, a Radio Frequency (RF) (e.g., cellular) link, a Transmission Control Protocol/Internet Protocol (TCP/IP) link, a Universal Serial Bus (USB) link, a Bluetooth™ link, or any combination thereof. The MLM Training Device  1010  may receive the set of reference images via the first communication link. According to some example embodiments, the set of references is obtained from a database stored in a memory of the server  1020 . According to some example embodiments, the set of reference images may be generated by the harvester  710 , and the server  1020  may receive the set of reference images from the harvester  710  via the second communication link. According to some example embodiments, the server  1020  may be the same as, or similar to, the server  720  discussed in association with  FIG.  8   . 
     According to some example embodiments, the trained MLM  350  may be recorded on a non-transitory computer-readable medium (e.g., a flash memory, a removable disk, a CD ROM, etc.). The trained MLM  350  may be transferred to, and installed on, a memory of the harvester  10  (e.g., the memory of the control unit  68 ) using the non-transitory computer-readable medium. According to some example embodiments, the trained MLM  350  may be transmitted to the server  1020  via the first communication link. The trained MLM  350  may be stored in a memory of the server  1020 . The server  1020  may transmit the trained MLM  350  to the harvester  710  via the second communication link. The control unit  68  of the harvester  710  may install the trained MLM  350  received via the second communication link to the memory of the harvester (e.g., the memory of the control unit  68 ). The trained MLM  350  may be used (e.g., by the control unit  68 ) to detect a blade wear level of the blades (of the chopper  28 ). 
     According to some example embodiments, the server  1020  may install the trained MLM  350  to the memory of the server  1020  in addition to, or without, transmitting the trained MLM  350  to the harvester  710 . The harvester  710  may transmit images of billets captured by the image sensor  718  to the server  1020  via the second communication link. The server  1020  may input the images of the billets received from the harvester  710  into the trained MLM  350 , and transmit a resulting blade wear signal to the harvester  710  via the second communication link. Accordingly, the server  1020  may be deployed as a Software as a Service (SaaS) system. 
     According to some example embodiments, the server  1020  may install the trained MLM  350  to the memory of the server  1020  in addition to transmitting the trained MLM  350  to the harvester  710 . The harvester  710  may occasionally (e.g., periodically) transmit images of billets captured by the image sensor  718  to the server  1020 . The server  1020  may re-train (e.g., update) the trained MLM  350  using the images of billets received from the harvester  710  according to the methods used by the MLM Training Device  1010 . The server  1020  may occasionally (e.g., periodically) transmit the re-trained MLM  350  of the harvester  710  may install the re-trained MLM  350  received via the second communication link to the memory of the harvester (e.g., the memory of the control unit  68 . The re-trained MLM  350  may be used (in combination with the control unit  68 ) to detect a blade wear level of the blades (of the chopper  28 ). 
     According to some example embodiments, the harvester  710  may re-train (e.g., update) the trained MLM  350  using the images of billets obtained using the image sensor  718  according to the methods used by the MLM Training Device  1010 . The re-trained MLM  350  may be used (in combination with the control unit  68 ) to detect a blade wear level of the blades (of the chopper  28 ). 
     According to some example embodiments, operations described herein as being performed by the MLM Training Device  1010  and/or the server  1020  may be performed by processing circuitry (e.g., the processing circuitry  1012 ). According to some example embodiments, the memory  1016  and/or a memory of the server  1020 , may be a tangible, non-transitory computer-readable medium, such as a Random Access Memory (RAM), a flash memory, a Read Only Memory (ROM), an Electrically Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), registers, a hard disk, a removable disk, a Compact Disk (CD) ROM, any combination thereof, or any other form of storage medium known in the art. 
     Some example embodiments may be described with reference to acts and symbolic representations of operations (e.g., in the form of flow charts, flow diagrams, data flow diagrams, structure diagrams, block diagrams, etc.) that may be implemented in conjunction with units and/or devices discussed in more detail below. Although discussed in a particular manner, a function or operation specified in a specific block may be performed differently from the flow specified in a flowchart, flow diagram, etc. For example, functions or operations illustrated as being performed serially in two consecutive blocks may actually be performed concurrently, simultaneously, or in some cases be performed in reverse order. 
     It will be understood that when an element is referred to as being “connected” or “coupled” to another element, it may be directly connected or coupled to the other element or intervening elements may be present. As used herein the term “and/or” includes any and all combinations of one or more of the associated listed items.