WO2023180823A1

WO2023180823A1 - Predicting a difficulty of cutting crop to be harvested and/or separating harvested crop

Info

Publication number: WO2023180823A1
Application number: PCT/IB2023/051166
Authority: WO
Inventors: Jared J Koch; Manish NARYAL
Original assignee: Agco Corporation
Priority date: 2022-03-21
Filing date: 2023-02-09
Publication date: 2023-09-28

Abstract

The present invention relates to a mechanism for determining a difficulty (295) of harvesting crop and/or separating harvested crop using a combine harvester. Values for crop condition parameters (210) are weighted (232) based on contextual information (220) about the crop, the combine harvester and/or environmental conditions. The weighted values are then processed to generate a predictive indicator (240) of the difficulty (295).

Description

PREDICTING A DIFFICULTY OF CUTTING CROP TO BE HARVESTED AND/OR SEPARATING

HARVESTED CROP

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] Not applicable.

FIELD

[0002] Embodiments of the present disclosure generally relate to the field of combine harvesting.

BACKGROUND

[0003] With ever-increasing population numbers and ongoing interest in more environmentally friendly farming practices, there is an increasing desire to reduce waste when harvesting crop whilst increasing speed and efficiency of harvesting.

[0004] There are a number of factors that influence the efficiency of crop harvesting, and/or the amount and/or speed at which crop can be harvested, by a combine harvester. For instance, it is known that the threshing efficiency of a combine harvester that harvests cereal crops will reduce with increased moisture levels of the crop. Generally, the more difficult it proves to harvest a crop, the less efficiently a combine harvester will be able to operate.

[0005] One area in which additional information about harvesting efficiency or difficulty would be useful is in cutting and/or separating crop during harvesting. This would provide useful information for setting parameters of the combine harvester and/or informing an operator about the current state of the harvesting process, the combine harvester and/or the crop itself.

[0006] It would therefore be useful to determine a difficulty of cutting any crop that is to be harvested and/or separating harvested crop.

BRIEF SUMMARY

[0007] The invention is defined by the claims.

[0008] According to examples in accordance with an aspect of the invention, there is provided a computer-implemented method for predicting a difficulty of cutting crop to be harvested and/or separating harvested crop at a harvesting location using a combine harvester.

[0009] The computer-implemented method comprises: obtaining values for a plurality of crop condition parameters, each crop condition parameter being a measurable property of the crop that changes with maturation of the crop and/or environmental conditions; obtaining values for one or more contextual parameters, each contextual parameter being a property of the crop, the combine harvester or the harvesting location; weighting the values for the plurality of crop condition parameters in dependence on (e.g., responsive to) the values of the one or more contextual parameters; and processing at least the weighted values of the properties to generate a predictive indicator, the predictive indicator indicating a likely level of difficulty for cutting the crop and/or separating harvested crop using the combine harvester.

[0010] The proposed concept is to predict a difficulty of crop cutting and/or separation (during harvesting by a combine harvester) by processing values of crop condition parameters. It has been recognized that the difficulty of cutting/separating changes based on the condition of the crop. However, it has also been recognized that the influence of the crop condition on the difficulty of cutting/separating is dependent upon other contextual information, e.g. non-condition dependent information about the crop, as well as information about the combine harvester and the harvesting location.

[0011] Embodiments propose an approach in which crop condition parameters are weighted responsive to contextual parameters, such as the identity of the crop and/or the combine harvester. [0012] The predictive indicator may provide a binary, categorical or continuous value that represents or indicates a predicted/likely level of difficulty for the cutting and/or separating.

[0013] The contextual parameter may be a property of the crop, the combine harvester or the harvesting location that is independent of any parameter of the crop that changes with maturation of the crop and/or environmental conditions.

[0014] This approach recognizes that non-condition dependent parameters influence which crop condition parameters contribute more significantly to crop cutting and/or separation difficulty. In particular, it is recognized that, for the same crop conditions, a set-up of the combine harvester or harvesting location will influence how the crop conditions affect a difficulty in harvesting.

[0015] Optionally, each contextual parameter provides non-continuous data. Non- continuous data is more likely to contain information about a context of the crop, combine harvester and/or environmental conditions.

[0016] In some examples, the step of weighting the values for the plurality of crop condition parameters comprises processing the values of the plurality of crop condition parameters using a set of weights to produce weighted values of the properties.

[0017] Optionally, the set of weights are defined in advance of the values of the crop condition parameters being measured. This embodiment recognizes that the set of weights can be determined in advance, e.g. before the condition of the crop is known. This reduces a processing burden, and can advantageous save processing resource and/or memory when determining the predictive indicator.

[0018] The plurality of crop condition parameters may include at least one of: a quantity of the crop; a quantity of crop per unit area; a moisture content of non-grain material; a moisture content of the crop; a level of decomposition of the crop; a standing state of the crop; a diameter of a stalk of the crop; a height of the crop; a temperature of the crop; and/or a ratio of grain to non-grain material in harvested crop.

[0019] Optionally, the one or more contextual parameters includes at least one property of the crop. In examples, the one or more contextual parameters includes a crop type, a crop variety or both.

[0020] The one or more contextual parameters may include at least one property of the combine harvester. The one or more contextual parameters may include: a type of combine harvester; a type of engine; a width of a header of the combine harvester; a type of the header of the combine harvester; an identifier of whether the combine harvester operates using an axial or transverse mechanism; a rotor configuration of the combine harvester; and/or a drive configuration of the combine harvester.

[0021] In at least one example, the step of weighting the values for the plurality of crop condition parameters comprises defining a subset of the plurality of crop condition parameters responsive to the one or more contextual parameters; and processing at least the weighted values of the properties comprises processing only the subset of the plurality of crop condition parameters to determine the predictive indicators.

[0022] Effectively, this embodiment may apply a weight of 0 to a number of the crop condition parameters, e.g. at least one of the crop condition parameters, responsive to (i.e. dependent upon) the contextual parameters. The subset of the plurality of crop condition parameters does not include all of the crop condition parameters.

[0023] Optionally, the predictive indicator provides a categorical value representing a predicted level of difficulty for crop cutting and/or separation by the combine harvester.

[0024] In some examples, the method further comprises a step of modifying one or more electrical components of the combine harvester responsive to the predictive indicator.

[0025] The method may further comprise a step of providing, at a user interface, a visual representation of the predictive indicator.

[0026] There is also proposed a computer program product comprising computer program code means which, when executed on a computing device having a processing system, cause the processing system to perform all of the steps of any herein described method. [0027] There is also proposed a processing system for predicting a difficulty of cutting crop to be harvested and/or separating harvested crop at a harvesting location using a combine harvester. The processing system is: obtain values for a plurality of crop condition parameters, each crop condition parameter being a measurable property of the crop that changes with maturation of the crop and/or environmental conditions; obtain values for one or more contextual parameters, each contextual parameter being a property of the crop, the combine harvester or the harvesting location; weight the values for the plurality of crop condition parameters in dependence on the values of the one or more contextual parameters; and process at least the weighted values of the properties to generate a predictive indicator, the predictive indicator indicating a likely level of difficulty for cutting the crop and/or separating harvested crop using the combine harvester.

[0028] The skilled person would be readily capable of adapting the processing system to perform any herein described method, and vice versa.

[0029] Within the scope of this application it should be understood that the various aspects, embodiments, examples and alternatives set out herein, and individual features thereof may be taken independently or in any possible and compatible combination. Where features are described with reference to a single aspect or embodiment, it should be understood that such features are applicable to all aspects and embodiments unless otherwise stated or where such features are incompatible.

[0030] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0031] One or more embodiments of the invention / disclosure will now be described, byway of example only, with reference to the accompanying drawings, in which:

[0032] FIG. 1 illustrates a combine harvester;

[0033] FIG. 2 illustrates a method according to an embodiment;

[0034] FIG. 3 illustrates a processing system according to an embodiment;

[0035] FIG. 4 illustrates further detail of the processing system according to an embodiment.

DETAILED DESCRIPTION

[0036] The invention will be described with reference to the figures.

[0037] It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the apparatus, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems and methods of the present invention will become better understood from the following description, appended claims, and accompanying drawings. It should be understood that the figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the figures to indicate the same or similar parts.

[0038] The invention provides a mechanism for determining a difficulty of harvesting crop and/or separating harvested crop using a combine harvester. Values for crop condition parameters are weighted based on contextual information about the crop, the combine harvester and/or environmental conditions. The weighted values are then processed to generate a predictive indicator of the difficulty.

[0039] Embodiments are based on the realization that the contribution or effect that a crop condition has on the difficulty of harvesting the crop changes depending upon its context. By weighting values based on contextual information, a more accurate an indicator of difficulty (harvesting or later separation) for the crop will be. This indicator provides useful information for assessing the condition of the crop to be harvested and/or for providing recommendations or control parameters for modifying the combine harvester to improve the harvesting.

[0040] Herein disclosed approaches can be employed in any suitable environment, particularly agricultural environments, in which harvesting of crop using a combine harvester is to be performed.

[0041] FIG. 1 conceptually illustrates a combine harvester 10, for improved contextual understanding.

[0042] FIG. 1 shows a known combine harvester 10 in which embodiments may be integrated. The combine harvester includes a threshing unit 20 for detaching grains of cereal from the ears of cereal, and a separating apparatus 30 which is connected downstream of the threshing unit 20. The grains after separation by the separating device 30 pass to a grain cleaning apparatus 40.

[0043] The combine harvester has a front elevator housing 12 at the front of the machine for attachment of a crop cutting head (known as the header, not shown). The header when attached serves to cut and collect the crop material as it progresses across the field, the collected crop stream being conveyed up through the elevator housing 12 into the threshing unit 20. In the example shown, the threshing unit 20 is a transverse threshing unit, i.e. formed by rotating elements with an axis of rotation in the side-to-side direction of the combine harvester and for generating a tangential flow.

[0044] The operation of the combine harvester may be controlled by a control system (not shown). The control system may receive input from a user interface and/or sensing apparatus and control the operation of the various units and apparatus responsive to the received input.

[0045] The combine harvester 10 may also comprise a user support 50, e.g. a cab, for housing an operator/individual. The user support will often contain a user interface to allow the operator/individual to influence or control the operation of the elements of the combine harvester (e.g. via the control system). The user interface may also provide information about the combine harvester and/or the status of the combine harvester.

[0046] FIG. 2 illustrates a method 200 according to an embodiment. The method 200 is preferably computer-implemented, and could be executed by a processing system. This processing system may form part of the control system of the combine harvester previously illustrated.

[0047] The method 200 comprises a step 210 of obtaining values for a plurality of crop condition parameters, each crop condition parameter being a measurable property of the crop that changes with maturation of the crop and/or environmental conditions.

[0048] The values obtained in step 210 may have been originally sampled by one or more sensors, e.g. coupled to or forming part of the combine harvester. For instance, a sensor may be positioned and configured to analyze crop after it has been cut and collected by the header of the combine harvester (e.g. as it is being moved in the front elevator housing). As yet another example, crop residue, such as material-other-than-grain (MOG) - also known as non-grain material, could be analyzed to generate information about crop conditions. As another example, the sensor may be positioned and configured to analyze crop before it has been cut or collected by the combine harvester, e.g. whilst it is still standing in the field.

[0049] In other examples, the values obtained in step 210 may be obtained by a sensor unit or arrangement that is separate to the combine harvester. For instance, the sensor arrangement may comprise a stand-alone sensing unit that analyzes one or more crop condition parameters.

[0050] Although possible, step 210 does not need to itself comprise the step of generating, measuring or sampling the values for the plurality of crop condition parameters. For instance, step 210 may instead comprise receiving the values (e.g. at an input to a processing system) or retrieving previously generated and stored values from a memory or storage system.

[0051] Examples of suitable crop condition parameters include: a quantity of the crop; a quantity of crop per unit area; a moisture content of non-grain material (MOG); a moisture content of the crop; a level of decomposition of the crop; a standing state of the crop; a diameter of a stalk of the crop; a height of the crop; a temperature of the crop; and/or a ratio of grain to non-grain material in harvested crop.

[0052] Various approaches for obtaining or deriving values for crop condition parameters are well known in the art. A few demonstrative examples are hereafter provided.

[0053] US Patent No. 6,185,990 Bl, dated 13 February 2001 by Inventor B.M.A. Missotten et al., discloses a method and device for assessing the humidity or moisture content of a crop, which makes use on the electrical conductivity of incoming crop. This same document also discloses an approach for determining a crop density (i.e. a quantity of crop per unit area). [0054] US Patent Application having publication number US 2021/015039 Al, published 21 January 2021 by Inventor Nathan R. Vandike et al, discloses an approach for analyzing crop residence (i.e. MOG) to derive one or more crop residue parameters. A crop residue parameter (such as crop residue moisture or crop residue dispersion) may represent a crop condition parameter for use in embodiments of this disclosure.

[0055] US Patent No. US 9,301,446 B2, dated 05 April 2016 by Inventor Ole Peters et al, discloses various approaches for assessing crop to be harvested by a combine harvester. In particular, this document proposes approaches for determining a quantity of crop, a standing state of the crop and a moisture level of the crop. Any of these parameters may act as a crop condition parameter for use in embodiments.

[0056] German Patent Application No. DE 10346541 A, published 14 July 2005 by Inventor Ehlert Detlef et al, proposes an approach for monitoring plant (crop) density using a vehicle-mounted sensor.

[0057] US Patent Application No. US 5106339 A, published 21 April 1994 by Inventor Braun Keith et al, proposes moisture and temperatures sensors for grain that are integrated with a combine harvester.

[0058] Any of these sensing approaches could be adopted to generate the values that are obtained in step 210 of the method 200. In yet other examples, the values may be generated at a user interface responsive to a user input (e.g. if an individual wishes to input values obtained from a separate sensing system).

[0059] The method 200 also comprises a step 220 of obtaining values for one or more contextual parameters, each contextual parameter being a property of the crop, the combine harvester or the harvesting location. A contextual parameter provides background or supplementary information about the crop, harvester or harvesting location that contextualizes the crop condition parameters.

[0060] The contextual parameter may be a parameter that is independent of any parameter of the crop that changes with maturation of the crop and/or environmental conditions. In other words, the contextual parameter may be a non-condition dependent parameter. In this way, the contextual parameter may be a "long-term" parameter that is unlikely to quickly change (e.g. during the course of harvesting).

[0061] Suitable examples of properties of the crop include a crop type, a crop variety or both. Information on these properties may be defined, for instance, by a user or individual providing this information at a user interface.

[0062] Suitable examples of properties of the combine harvester: a type of combine harvester; a type of engine; a width of a header of the combine harvester; a type of the header of the combine harvester; an identifier of whether the combine harvester operates using an axial or transverse mechanism; a rotor configuration of the combine harvester; and/or a drive configuration of the combine harvester.

[0063] One or more properties of the combine harvester may therefore be predefined. For instance, if the method is performed by a processing system for a particular combine harvested then some information about the combine harvester will be defined in advance (e.g. the type of combine harvester, the type of engine, an identifier of whether the combine harvester operates using an axial or transverse mechanism, a rotor configuration of the combine harvester; and/or a drive configuration of the combine harvester).

[0064] Similarly, one or more properties of the combine harvester may need to be defined or identified, e.g. depending upon the state or mode of operation of the combine harvester. This may be determined automatically (e.g. by identifying the mode of operation of the combine harvester) or in response to a user input. As an example, a type of the header of the combine harvester could be defined by a user inputting (at a user interface) an identifier of the type of header, or through automatic determination of the type of header (e.g. based on an exchange of information between the header and the rest of the combine harvester).

[0065] Suitable examples of properties of the harvesting location include: a temperature of the harvesting location, a global position of the harvesting location and so on. One or more of these properties may be monitored automatically (e.g. using a temperature sensor or satellite navigation sensor), or may be provided by a user/individual, e.g. via a user interface.

[0066] The method 200 then performs step 230 of weighting the values for the plurality of crop condition parameters in dependence on the values of the one or more contextual parameters. In the context of the present disclosure, weighting refers to a process of multiplying a value of a crop condition parameters by a particular value (or "weight") to control a relative extent to which that value contributes during later processing.

[0067] Thus, the weighting of the values for the plurality of crop condition parameters is responsive to or based on the values of the one or more contextual parameters. Thus, as the values of the one or more contextual parameters changes, so the weighting of the values for the plurality of crop condition parameters changes.

[0068] In the illustrated example, step 230 comprises a step 231 of determining or defining a set of weights to weight the values of the one or more contextual parameters. Step 231 may be performed independently of the values for the plurality of crop condition parameters, e.g. before the values have been measured/sampled or before they are otherwise available for processing by the method. [0069] In this way, a weight may be generated for each contextual parameter. The weight may define an extent to which a value of that contextual parameter contributes to later processing of the values.

[0070] In one or more examples, the weighting performed in step 230 may define which of the crop condition parameters are used in later processing. Thus, step 230 may comprise a step 232 of selecting only a subset (i.e. not all) of the values of the crop condition parameters for further processing by the method. Effectively, this applies a weighting of 0 to at least some of the values of the crop condition parameters.

[0071] This approach recognizes that some crop condition parameters will not affect a difficulty of harvesting depending upon contextual information.

[0072] The approach in which only a subset of the values are used for further processing effectively represents different processing configurations for different contexts of the harvesting. In this way, a different processing configuration of the crop condition parameters is performed dependent upon the context in which the harvesting will take place.

[0073] The method 200 then moves to a step or process 240 of processing at least the weighted values of the properties to generate a predictive indicator 295. The predictive indicator indicates a likely level of difficulty for cutting the crop and/or separating harvested crop using the combine harvester. Generally, the greater the difficulty of harvesting, the lower the efficiency of the combine harvester.

[0074] The predictive indicator 295 may provide a categorical value representing a predicted level of difficulty for crop cutting and/or separation by the combine harvester. For instance, the predictive indicator may provide a value of "Excellent": indicating that there is no significant factor that would result in the loss of capacity. As another example, the predictive indicator may have a value of "Good": indicating that there is a slight difficulty in harvesting or separating harvested crop during the course of harvesting. As yet another example, the predictive indicator may have a value of "Average": indicating there is mild to moderate difficulty in harvesting or separating harvested crop during the course of harvesting. As yet another example, the predictive indicator may have a value of "Poor": indicating there is severe difficulty in harvesting or separating harvested crop during the course of harvesting.

[0075] However, other suitable examples for a predictive indicator would be apparent to the skilled person. For instance, the predictive indicator may comprise a numeric indicator (e.g. on a predetermined scale, such as 0 to 1, 0 to 10, 1 to 10, 0 to 100 or 1 to 100) of the predicted difficulty of harvesting.

[0076] In some examples, step 240 processes further (unweighted) values (in addition to the weighted values) to generate the predictive indicator. In particular, some crop condition parameters for determining difficulty may be important regardless of the context, such that they undergo no weighting before being processed in step 240. One suitable example includes a MOG moisture level.

[0077] As one example, step 240 may comprise comparing each weighted value (and optionally further values) to one or more predetermined ranges. The one or more predetermined ranges may differ for each weighted value.

[0078] Consider a scenario in which the predictive indicator may provide a value of "Excellent", "Good", "Average" or "Poor". In this scenario, each weighted value (or further value) that is used may be associated with a predetermined range for each possible value of the predictive indicator, i.e. four predetermined ranges - an "Excellent" range, a "Good" range, an "Average" range and a "Poor" range. If each weighted value falls, i.e. all weighted values fall, within an "Excellent" range for that value, then the predictive indicator may provide a value of "Excellent". If each weight value falls within either a "Good" or "Excellent" range for that value, and at least one value falls within the "Good" range for that value, then the predictive indicator may provide a value of "Good". If each weight value falls within an "Average", "Good" or "Excellent" range for that value, and at least one value falls within the "Average" range for that value, then the predictive indicator may provide a value of "Average". If any weight value falls within a "Poor" range for that value, then the predictive indicator may provide a value of "Poor".

[0079] The values of the ranges may be chosen or determined empirically, e.g. based on historic and/or expert understanding of the effect of crop conditions.

[0080] If a weighted value is 0, then the weighted value may be ignored for the purposes of determining whether the predictive indicator should be "Excellent", "Good", "Average" or "Poor". This prevents weighted values that should not contribute to determining the difficulty of cutting crop to be harvested and/or separating harvested crop at a harvesting location using a combine harvester from affecting the outcome of step 240.

[0081] As one example, step 240 may comprise processing the weighted values (and optionally further values) using a machine-learning model to generate the predictive indicator.

[0082] Machine-learning models provide well established and increasingly accurate approaches for predicting output data by processing input data. In the context of this disclosure, the input data comprises the weighted values (and optionally further values) and the output data comprises the predictive indicator.

[0083] Any suitable machine-learning model may be used in different embodiments for the present disclosure. Suitable machine-learning models include (artificial) neural networks, support vector machines (SVMs), Naive Bayesian models and decision tree algorithms, although other appropriate examples will be apparent to the skilled person. [0084] There are a number of well-established approaches for training a machine-learning model. Typically, such training approaches make use of a large database of known input and output data. The machine-learning model is modified until an error between predicted output data, obtained by processing the input data with the machine-learning model, and the actual (known) output data is close to zero, i.e. until the predicted output data and the known output data converge. The value of this error is often defined by a cost function. The precise mechanism for modifying the machinelearning model depends upon the type of model. Example approaches for use with a neural network include gradient descent, backpropagation algorithms and so on.

[0085] The known input data may here comprises input data entries, each entry containing a set of example weighted values (and optionally further values) of the crop condition parameters. The known output data may here comprises output data entries, each output data entry corresponding to an input data entry and containing a predictive indicator. The predictive indicator for the output data entries may be defined, e.g. by an expert or based on a measured efficiency of performing crop cutting and/or separation.

[0086] The combination of steps 230 and 240 thereby results in the predictive indicator 295 being dependent upon weighted values for a plurality of crop condition parameters.

[0087] In one working example, some crop condition parameters may only be relevant for establishing the difficulty of cutting and/or separating certain types or variety of crops.

[0088] For instance, a ratio of amount of MOG to ear size may only affect cutting or separating efficiency (i.e. difficulty) when the crop is corn or maize - but can still represent an important parameter for assessing an ease of separating such crop.

[0089] As another example, the sensitivity of a difficulty of harvesting a particular crop may change responsive to a type of the crop. For example, wheat should be harvested at moisture levels between 14% and 20%, whereas corn should be harvested at moisture levels between 22% and 25%. Embodiments recognize that this difference means that the contribution of moisture level to a crop cutting or separating difficulty is less for some crop types than others, which can be taken into account when generating the predictive indicator.

[0090] For another working example, the type of the combine harvester may result in certain crop condition parameters having no influence on the difficulty in cutting and/or separating. For instance, certain brands, versions or lines of combine harvesters may perform equally efficiently for different values of a particular crop condition (e.g. crop temperature or diameter of a stalk). In this way, the contribution of such crop condition parameters to determining the difficulty may be zero.

[0091] These examples demonstrate the advantage of weighting crop condition parameters based on contextual information or contextual parameters before generating the predictive indicator. In particular, this weighting facilitates improved efficiency in determining a difficulty of cutting crop to be harvested and/or separating harvested crop at a harvesting location using a combine harvester. [0092] The method may comprise a step 250 of modifying one or more electrical components of the combine harvester responsive to the predictive indicator.

[0093] In this way, the predictive indicator may act as feedback parameter for a control system of the combine harvester.

[0094] As a simple example, a maximum speed of the combine harvester may be increased responsive to the predictive indicator indicating that a cutting difficulty is low (or decreased if the cutting difficulty is high). As another example, a threshing speed may be increased responsive to the predictive indicator indicating that a cutting difficulty is low (or decreased if the cutting difficulty is high). As yet another example, a maximum throughput of the combine harvester may be reduced responsive to the predictive indicator indicating that a cutting difficulty is high (or increase if the cutting difficulty is low).

[0095] Other suitable examples will be readily apparent to the skilled person.

[0096] The method 200 may comprise a step 260 of providing, at a user interface, a visual representation of the predictive indicator. Thus, step 260 may comprise controlling a display to provide or display the predictive indicator. This can be used to provide an individual with useful information about the condition of the crop and the current state of the harvesting process. In particular, this aids an operator in performing a harvesting decision process, to decide how to harvest (or what parameters to use in harvesting).

[0097] FIG. 3 illustrates a processing system 300 according to an embodiment. The processing system is configured to perform the method 200.

[0098] The processing system 300 may thereby receive or obtain values for a plurality of crop condition parameters. Each crop condition parameter being a measurable property of the crop that changes with maturation of the crop and/or environmental conditions. The processing system may receive these values from a memory or storage unit 310 and/or from one or more sensors 320 and/or from a user interface 330.

[0099] The processing system 300 is further configured to obtain values for one or more contextual parameters, each contextual parameter being a property of the crop, the combine harvester or the harvesting location. The processing system may receive these values from a memory or storage unit and/or from one or more sensors and/or from a user interface.

[0100] The processing system 300 may comprise an input interface 301 configured to receive all of the above-identified values.

[0101] The processing system 300 is also configured to weight the values for the plurality of crop condition parameters in dependence on the values of the one or more contextual parameters. [0102] The processing system 300 is also configured to process at least the weighted values of the properties to generate a predictive indicator, the predictive indicator indicating a likely level of difficulty for cutting the crop and/or separating harvested crop using the combine harvester.

[0103] These processes may be carried out by a processing unit 302 of the processing system 300.

[0104] The processing system 300 may be configured to provide the predictive indicator to a control system 340, e.g. for modifying one or more electrical components of the combine harvester responsive to the predictive indicator. The processing system 300 may be configured to control a user interface 360 to provide at a user interface, a visual representation of the predictive indicator.

[0105] Any output of the processing system may be controlled via an output interface 303. In particular, the output of the processing system may be defined by the processing unit 302 of the processing system via the processing unit.

[0106] Figure 4 illustrates an embodiment of the processing system 300 described with reference to Figure 3. The processing system is able to carry out or perform one or more embodiments of an invention, e.g. for predicting a difficulty of cutting crop to be harvested and/or separating harvested crop at a harvesting location using a combine harvester.

[0107] The processing system 300 comprises an input interface 301 that receives communications from one or more inputting devices. Examples of suitable inputting devices include external memories, user interfaces (such as mice, keyboards, microphones, sensors and so on).

[0108] The processing system 300 also comprises a processing unit 302.

[0109] In one example, the processing unit 302 may comprise an appropriately programmed or configured single-purpose processing device. Examples may include appropriately programmed field-programmable gate arrays or complex programmable logic devices.

[0110] As another example, the processing unit may comprise a general purpose processing system (e.g. a general purpose processor or microprocessor) that executes a computer program 415 comprising code (e.g. instructions and/or software) carried by a memory 410 of the processing system 300.

[0111] The memory 410 may be formed from any suitable volatile or non-volatile computer storage element, e.g. FLASH memory, RAM, DRAM, SRAM, EPROM, PROM, CD-ROM and so on. Suitable memory architectures and types are well known to the person skilled in the art.

[0112] The computer program 415, e.g. the software, carried by the memory 410 may include comprise a sequence of instructions that are executable by the processing unit for implementing logical functions to carry out the desired method or procedure. Each instruction may represent a different logical function, step or sub-step used in performing a method or process according to an embodiment. The computer-program may be formed from a set of sub-programs, as would be known to the skilled person. The computer program 415 may be written in any suitable programming language that can be interpreted by the processing unit 302 for executing the instructions. Suitable programming languages are well known to the skilled person.

[0113] The processing system 300 also comprises an output interface 303. The processing system may be configured to provide information, such as the predictive indicator, via the output interface. In some examples, the processing system may be configured to control one or more other devices connected to the output interface 303 by providing appropriate control signals to the one or more other devices. Suitable control examples include controlling a visual representation (e.g. of the predictive indicator) at a user interface or controlling the operation of one or more other components (e.g. the drive system, threshing unit or separating apparatus) of the combine harvester.

[0114] Different components of the processing system 300 may interact or communicate with one another via one or more intra-system communication systems (not shown), which may include communication buses, wired interconnects, analogue electronics, wireless communication channels (e.g. the internet) and so on. Such intra-system communication systems would be well known to the skilled person.

[0115] It is not essential for the processing system 300 to be formed on a single device, e.g. a single computer. Rather, any of the system blocks (or parts of system blocks) of the illustrated processing system may be distributed across one or more computers.

[0116] The skilled person would be readily capable of developing a processing system for carrying out any herein described method. Thus, each step of the flow chart may represent a different action performed by a processing system, and may be performed by a respective module of the processing system.

[0117] It will be understood that disclosed methods are preferably computer-implemented methods. As such, there is also proposed the concept of a computer program comprising code means for implementing any described method when said program is run on a processing system, such as a computer. Thus, different portions, lines or blocks of code of a computer program according to an embodiment may be executed by a processing system or computer to perform any herein described method.

[0118] A computer program may be stored on a computer-readable medium, itself an embodiment of the invention. A "computer-readable medium" is any suitable mechanism or format that can store a program for later processing by a processing unit. The computer readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device. The computer-readable medium is preferably non- transitory. [0119] In some alternative implementations, the functions noted in the block diagram(s) or flow chart(s) may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

[0120] Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. If a computer program is discussed above, it may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. If the term "adapted to" is used in the claims or description, it is noted the term "adapted to" is intended to be equivalent to the term "configured to". If the term "arrangement" is used in the claims or description, it is noted the term "arrangement" is intended to be equivalent to the term "system", and vice versa. Any reference signs in the claims should not be construed as limiting the scope.

[0121] All references cited herein are incorporated herein in their entireties. If there is a conflict between definitions herein and in an incorporated reference, the definition herein shall control.

Claims

CLAIMS What is claimed is:

1. A computer-implemented method for predicting a difficulty of cutting crop to be harvested and/or separating harvested crop at a harvesting location using a combine harvester, the computer-implemented method comprising: obtaining values for a plurality of crop condition parameters, each crop condition parameter being a measurable property of the crop that changes with maturation of the crop and/or environmental conditions; obtaining values for one or more contextual parameters, each contextual parameter being a property of the crop, the combine harvester or the harvesting location; weighting the values for the plurality of crop condition parameters in dependence on the values of the one or more contextual parameters; and processing at least the weighted values of the properties to generate a predictive indicator, the predictive indicator indicating a likely level of difficulty for cutting the crop and/or separating harvested crop using the combine harvester.

2. The computer-implemented method of claim 1, wherein the contextual parameter is a property of the crop, the combine harvester or the harvesting location that is independent of any parameter of the crop that changes with maturation of the crop and/or environmental conditions.

3. The computer-implemented method of claim 1 or 2, wherein each contextual parameter provides non-continuous data.

4. The computer-implemented method of any of claims 1 to 3, wherein the step of weighting the values for the plurality of crop condition parameters comprises processing the values of the plurality of crop condition parameters using a set of weights to produce weighted values of the properties.

5. The computer-implemented method of claim 4, wherein the set of weights are defined in advance of the values of the crop condition parameters being measured.

6. The computer-implemented method of any of claims 1 to 5, wherein the plurality of crop condition parameters includes at least one of: a quantity of the crop; a quantity of crop per unit area; a moisture content of non-grain material; a moisture content of the crop; a level of decomposition of the crop; a standing state of the crop; a diameter of a stalk of the crop; a height of the crop; a temperature of the crop; and/or a ratio of grain to non-grain material in harvested crop.

7. The computer-implemented method of any of claims 1 to 6, wherein the one or more contextual parameters includes at least one property of the crop.

8. The computer-implemented method of any of claims 1 to 7, wherein the one or more contextual parameters includes a crop type, a crop variety or both.

9. The computer-implemented method of any of claims 1 to 8, wherein the one or more contextual parameters includes at least one property of the combine harvester.

10. The computer-implemented method of any of claims 1 to 9, wherein the one or more contextual parameters includes: a type of combine harvester; a type of engine; a width of a header of the combine harvester; a type of the header of the combine harvester; an identifier of whether the combine harvester operates using an axial or transverse mechanism; a rotor configuration of the combine harvester; and/or a drive configuration of the combine harvester.

11. The computer-implemented method of any of claims 1 to 10, wherein: the step of weighting the values for the plurality of crop condition parameters comprises defining a subset of the plurality of crop condition parameters responsive to the one or more contextual parameters; and processing at least the weighted values of the properties comprises processing only the subset of the plurality of crop condition parameters to determine the predictive indicators.

12. The computer-implemented method of any of claims 1 to 11, wherein the predictive indicator provides a categorical value representing a predicted level of difficulty for crop cutting and/or separation by the combine harvester.

13. The computer-implemented method of any of claim 1 to 12, further comprising a step of modifying one or more electrical components of the combine harvester responsive to the predictive indicator.

14. The computer-implemented method of any of claims 1 to 13, further comprising providing, at a user interface, a visual representation of the predictive indicator.

15. A computer program product comprising computer program code means which, when executed on a computing device having a processing system, cause the processing system to perform all of the steps of the method according to any of claims 1 to 14.

16. A processing system for predicting a difficulty of cutting crop to be harvested and/or separating harvested crop at a harvesting location using a combine harvester, the processing system being configured to: obtain values for a plurality of crop condition parameters, each crop condition parameter being a measurable property of the crop that changes with maturation of the crop and/or environmental conditions; obtain values for one or more contextual parameters, each contextual parameter being a property of the crop, the combine harvester or the harvesting location; weight the values for the plurality of crop condition parameters in dependence on the values of the one or more contextual parameters; and process at least the weighted values of the properties to generate a predictive indicator, the predictive indicator indicating a likely level of difficulty for cutting the crop and/or separating harvested crop using the combine harvester.