Patent Publication Number: US-2022229412-A1

Title: Nir sensor calibration method and system

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application claims priority under 35 U.S.C. § 119 to German Patent Application No. DE 102020125422.9 filed Sep. 29, 2020, the entire disclosure of which is hereby incorporated by reference herein. 
     TECHNICAL FIELD 
     The present invention relates to the creation of NIR sensor calibration models and their use in agricultural work machines. 
     BACKGROUND 
     This section is intended to introduce various aspects of the art, which may be associated with exemplary embodiments of the present disclosure. This discussion is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the present disclosure. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art. 
     Near-Infrared (NIR) sensors measure the amount of the light transmitted or reflected by a sample in the near infrared range. Organic substances generally have structure-rich absorption or reflection spectra within this spectral range that arises from the excitation of oscillations of bonds between atoms in these substances. 
     DE 10 2004 048 103 discloses an NIR sensor system in that spectroscopically records properties of streams of substances in an agricultural work machine. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present application is further described in the detailed description which follows, in reference to the noted drawings by way of non-limiting examples of exemplary implementation, in which like reference numerals represent similar parts throughout the several views of the drawings, and wherein: 
         FIG. 1  illustrates a schematic representation of an example agricultural application using the database structure. 
         FIG. 2  illustrates a detailed view of an example of the database structure. 
         FIG. 3  illustrates a schematic representation of the interaction of a user with the database structure. 
         FIGS. 4A-D  illustrates different examples of the database structure. 
         FIG. 5  illustrates another version of the database structure. 
         FIG. 6  illustrates another yet another version of the database structure. 
     
    
    
     DETAILED DESCRIPTION 
     As discussed in the background, NIR sensors measure the amount of the light transmitted or reflected by a sample in the near infrared range. Within this spectral range, there are however no distinctive lines that could be assigned to a specific chemical compound; instead, most every organic compound has C—C bonds and C—H bonds whose lines may be shifted slightly from one substance to the other from interactions with adjacent atoms, but they are also so widespread that the shifts may be difficult to identify. Accordingly, the number of degrees of freedom in which the NIR spectra may distinguish different compounds from each other is generally much less than the number of compounds that may be contained in the sample. To be able to extract useful information on the composition of a sample from an NIR spectrum, far-reaching assumptions may be made beforehand about the nature of the sample, and the correctness of these assumptions may determine the validity of the information obtained from the spectrum. In practice, this means that to be able to draw conclusions on important components of a grain sample by analyzing an NIR sample, the type and variety of grain should be known as well as potentially any other influential variables in order to be able to choose an appropriate calibration model for the sample. The optimization of these calibration models is the subject of intensive development. Results obtained by using various calibration models may not be readily comparable with each other. 
     Also, systems (such as disclosed in DE 10 2004 048 103 B4, referenced in the background) may analyze very precisely determined contents of a flow of substance, and may determine their components in the overall substance flow. In order for this to be possible, such NIR sensors comprise calibration models that must be structured very differently depending on the type and constitution of the flow of substance and the properties to be determined. The quality of these calibration models significantly determines the quality of the analysis of the particular flow of substance. Consequently, there is therefore a need to structure the calibration models so that they may analyze the components of the particular flow of substance with high quality. In this case, the known NIR sensor systems have the particular disadvantage that the calibration models are not flexibly adaptable to changing substance properties and conditions of use. 
     Thus, in one or some embodiments, various aspects, including a database structure (such as a database structural system that may include one or both of electronic hardware and software) and an agricultural work machine, are disclosed that may create calibration methods that are flexibly adaptable to changing substance properties and conditions of use. 
     In particular, in one or some embodiments, a database structure (such as a database structure system) for creating calibration models for an NIR sensor system is disclosed. The database structure (such as a database structure system) comprises raw data of the NIR spectra of plant material and/or flows of material, with the raw data being generated by one or more NIR sensor systems assigned to an agricultural work machine (e.g., at least one memory for storing one or both of the raw data or the one or more calibration models). The one or more NIR sensor systems are configured to transmit the raw data via an interface for the data traffic with at least one data processing unit outside of or external to the agricultural machine. The database structure comprises at least one or more calibration models in addition to the raw data and is configured to: receive the raw data for storage in the at least one memory; generate user-specific calibration models by using the saved raw data and/or the calibration models and to provide them to a user; and transmit the one or more user-specific calibration models to a user (various different users are contemplated, as discussed further below). In this way, a calibration method may be used that is flexibly adaptable to changing substance properties and conditions of use, and that may more precisely measure substance properties using NIR sensors. 
     In one or some embodiments, the user provides a calibration model, and the database structure creates an optimized calibration model considering the saved raw data and/or calibration models. This has the effect that the user may optimize any calibration models that he or she has used via the database structure. In particular, the processor may be configured to: receive a calibration model resident in the agricultural work machine; generate, based on the calibration model received, an optimized calibration model using one or both of the raw data or the calibration models; and transmit the optimized calibration model to the agricultural work machine for use by the agricultural work machine. 
     Moreover, in one or some embodiments, the database structure may be such that the user defines an application, and the database structure generates a user-specific new calibration model taking into account at least the saved calibration models and raw data. This has the effect that the user may have a suitable calibration model created for any applications without personally having expert knowledge in this field. In this way, the processor may be configured to: receive an indication of an application for the one or more NIR sensor systems; generate, based on the calibration model received, a newly-created calibration model using both of the raw data and the calibration models; and transmit the newly-created calibration model for use in a specific agricultural work machine. 
     In one or some embodiments, a created calibration model may be efficiently used when the database structure transmits the created calibration model to the particular user, such as to the NIR sensor system assigned to the particular agricultural work machine. In particular, the processor may be configured to: generate the one or more user-specific calibration models by using the raw data generated by an NIR sensor system from a particular agricultural work machine; and transmit the generated one or more user-specific calibration models to the particular agricultural work machine for use by the NIR sensor system resident on the particular agricultural work machine. 
     In one or some embodiments, the user may retrieve the calibration model created by the database structure and transmit it to the particular NIR sensor system assigned to an agricultural work machine. In particular, this has the effect that the user may exchange calibration models saved in his or her agricultural work machine in a controlled manner. In particular, the processor may be configured to receive a request from the user for the one or more user-specific calibration models. And, responsive to the request, the processor may: generate the one or more user-specific calibration models (or access previously generated user-specific calibration model(s); and transmit the one or more user-specific calibration models to an NIR sensor system assigned to a particular agricultural work machine. 
     In one or some embodiments, the database structure is configured to modify a calibration model so that an adapted calibration model is derived/created for a special crop type. In this way, the NIR sensor system may be able to detect and analyze any type of crop in a highly flexibly manner. Specifically, the processor may: receive from the user an indication of a special crop type and a calibration model; and generate the one or more user-specific calibration models by modifying the calibration model based on the special crop type so the modified calibration model is derived for the special crop type. 
     In one or some embodiments, the database structure is configured to create an adapted calibration model taking into account one or more modified environmental conditions and/or one or more changing crop type properties. This may improve the NIR analysis of a detected flow of substance. In particular, the processor may: receive from the user an indication of one or more modified environmental conditions and a calibration model; and generate the one or more user-specific calibration models by modifying the calibration model based on the one or more modified environmental conditions so the modified calibration model accounts for the one or more modified environmental conditions. 
     In one or some embodiments, the database structure may be such that the raw data from different operating times may be compared with each other so that the same calibration model is taken into account for all operating times. This may allow for a standardized comparison of the data generated by the NIR system at different times. In this context, the operating time may comprise a campaign, and the comparison of the raw data from different operating times within the campaign may be used to correct derived field maps. 
     In one or some embodiments, the database structure may comprise a so-called “automated process data interpretation” (APDI) module that is configured to recognize or identify incorrect entries that are entered by a user and to correct the incorrect entries taking into account the selected calibration model and the associated raw data. In this context, various types of incorrect entries entered by the user are contemplated. For example, the recognition of misuse may be focused on the entry of the incorrect or wrong crop type since this is a parameter that may be frequently entered incorrectly by the operator in practical use. 
     In one or some embodiments, the database structure may be such that the user may convert a basic calibration model using the database structure into an expanded calibration model. In turn, the expanded calibration model may then comprise model components that enable the particular NIR sensor system to determine an expanded spectrum of contents. In this way, the user of a calibration model may expand the spectrum of use of his or her NIR sensor system. This is particularly advantageous when the particular NIR sensor system is used in so-called contracting companies that fulfill customer orders and whose customers seek a wide variety of NIR data, such as the composition of a flow of liquid manure, or the composition of a flow of harvested material processed by a forage harvester, or the composition of a flow of harvested material processed by a combine, to cite only a few example applications that may be performed through using a NIR sensor system. Other applications that may analyze different aspects of flows using the NIR sensor system are contemplated. 
     In one or some embodiments, the analytical quality of the calibration models to be generated with the database structure may be further improved if the database structure is configured to consider stationarily determined sample analysis values in generating the particular calibration model. 
     In one or some embodiments, the database structure may be configured to create the user-specific calibration models for compensation, wherein the user may pay a one time or user-dependent fee depending on the scope of creating the particular calibration model. In this regard, the processor may determine whether a fee has been paid depending on a scope of creating a particular user-specific calibration model, and responsive to determining that the fee has been paid, generate the particular user-specific calibration model. For the user of the generated calibration models, this may have the advantage that the user only has to pay for creating actually required calibration models. 
     In one or some embodiments, the database structure may be used inside or along with an agricultural work machine as part of an agricultural work machine system (which may further include the data processing unit along with the database structure system). For example, the agricultural work machine may comprise: an NIR sensor system that is configured to detect NIR spectra of plant material and/or other substances and output them as raw data; an evaluation unit for deriving at least one parameter of the plant material or the other substance in real time from the raw data; and interface communicating with an external at least one data processing unit in order to exchanging data. In one or some embodiments, the data processing unit may comprise a database structure (such as a database structure system) configured to create calibration models for an NIR sensor system, wherein raw data of the NIR spectra of plant material or other substances may be saved in the database structure, wherein the raw data are generated by one or more of the NIR sensors assigned to the agricultural work machines. Moreover, the database structure (such as a database structure system) may comprise one or more calibration models in addition to the raw data and is configured to generate user-specific calibration models by using the saved raw data and/or calibration models and make them available to a user. 
     In one or some embodiments, the NIR sensor system generates the raw data and transmits them to the database structure. Further, the NIR sensor system may comprise one or more sensor heads and a common evaluation unit that is assigned to each sensor head separately, or that is assigned to all sensor heads. Further, the evaluation unit may comprise sensor software. The spectral data generated by the sensor head may be further processed using the sensor software and the calibration model(s) assigned to this sensor software into sensor data for internal use in the agricultural work machine and into the raw data. In this way, intensive interactive communication between an NIR sensor system and the database structure is possible, which may ensure that the NIR sensor system creates a high-quality NIR analysis of a flow of material. 
     Moreover, highly flexible communication between the agricultural work machine and the database structure is possible when the raw data generated in the particular evaluation unit is transmitted via an interface (resident on the agricultural work machine) to an external electronic device (e.g., outside of the agricultural work machine). The data transmission paths between the agricultural work machine and database structure may comprise at least one satellite and/or one radio system, and the database structure may be saved in a data processing apparatus, such as in a data cloud or stationary server. 
     Referring to the figures,  FIG. 1  shows an agricultural work machine  1  designed as a forage harvester  2  that harvests a plant crop  4  grown on a field  3 , such as corn, and transfers it to a transport vehicle  5 . An example forage harvester is disclosed in U.S. Pat. No. 11,109,537, incorporated by reference herein in its entirety. The agricultural work machine  1  may be assigned an interface  6 , described in greater detail below, through which its data  7  may be transmitted from the agricultural work machine  1  to the outside  8 . The data may be transmitted to the outside  8  for example by satellite system  9  or stationary radio system  10 , wherein the data  7  are either transmitted to stationary servers  11  or to a so-called cloud  12 . The transmitted data  7  may comprise at least the raw data  13 , described in detail below, generated by at least one NIR sensor system  14  assigned to the agricultural work machine  1 . 
     As shown in  FIG. 1 , the NIR sensor system  14  assigned to the agricultural work machine  1  designed as a forage harvester  2  comprises two NIR sensor heads  15  which are known per se and therefore will not described further. It lies within the scope of the invention that the agricultural work machine  1  may be designed as any type of agricultural work machine; harvesters such as combines and special crop harvesting machines or liquid manure tankers are mentioned here merely as examples. Other applications are contemplated. Moreover, it lies within the scope of the invention that the agricultural work machine  1  may be assigned only one NIR sensor head  15  or a plurality of NIR sensor heads  15  (e.g., at least 2 NIR sensor heads  15 ; at least 3 NIR sensor heads  15 ; etc.). The harvested plant crop  4  travels through the forage harvester  2  as a flow of material comprising (or consisting of) plant material  16 . The plant material  16  passes the particular sensor region  17  of the NIR sensor head(s)  15  on its way through the forage harvester  2 . Via spectral analysis, spectral data  20  are generated by the NIR sensor system  14  in a manner known per se of the contents  18  to be identified. In addition to the directly detectable contents such as protein, fat and sugar, any contents  18  may be determined by using suitable calibration models  19 . 
     The spectral data  20  generated by the NIR sensor heads  15  are transmitted to an evaluation unit  21 , wherein the evaluation unit  21  is either arranged outside of the particular sensor head  15  as depicted in  FIG. 1  in the agricultural work machine  1 , or is integrated directly in the particular sensor head  15  as depicted in  FIG. 2 . In a manner to be described further, in one or some embodiments, the raw data  13  are generated in the particular evaluation unit  21  and are transmitted by the interface  6  to the outside  8 , a satellite  9  and/or radio system  10 , and from there to a data cloud  12  and/or to a server  11 . 
     Evaluation unit  21  may include computing functionality, such as at least one processor and at least one memory (not shown), which may be the same or similar to processor  53  and memory  54  discussed below. The computing functionality may be manifested in one of several ways, such as illustrated in the figures, such as within the evaluation unit  21 , including sensor software  23 . 
     In one or some embodiments, external data processing unit (discussed below), such as server  11  and/or data cloud  12 , may include computing functionality. As depicted in  FIG. 1 , server  11  may include may comprise any type of computing functionality, such as at least one processor  53  (which may comprise a microprocessor, controller, PLA, or the like) and at least one memory  54  in order to perform the disclosed analysis and/or any other processing disclosed herein. The memory  54  may comprise any type of storage device (e.g., any type of memory). Though the processor  53  and memory  54  are depicted as separate elements, they may be part of a single machine, which includes a microprocessor (or other type of controller) and a memory. Examples of computer-readable media include computer-readable non-transitory storage media, such as a random-access memory (RAM), which may be SRAM, DRAM, SDRAM, or the like, read-only memory (ROM)  708 , which may be PROM, EPROM, EEPROM, or the like. RAM and ROM hold user and system data and programs, as is known in the art. Thus, the processor  53  and/or the memory  54  may include a computer-readable medium for determining a portion of one or both of broken grain or non-grain components in a stream of harvested material, comprising instructions stored thereon, that when executed on a processor  53 , performs any one, any combination, or all of the steps described herein. 
     The processor  53  and memory  54  are merely one example of a computational configuration. Other types of computational configurations are contemplated. For example, all or parts of the implementations may be circuitry that includes a type of controller, including an instruction processor, such as a Central Processing Unit (CPU), microcontroller, or a microprocessor; or as an Application Specific Integrated Circuit (ASIC), Programmable Logic Device (PLD), or Field Programmable Gate Array (FPGA); or as circuitry that includes discrete logic or other circuit components, including analog circuit components, digital circuit components or both; or any combination thereof. The circuitry may include discrete interconnected hardware components or may be combined on a single integrated circuit die, distributed among multiple integrated circuit dies, or implemented in a Multiple Chip Module (MCM) of multiple integrated circuit dies in a common package, as examples. Alternatively, or in addition, data cloud  12  may include the same or similar computing functionality, such as including both processor  53  and memory  54 . 
       FIG. 2  schematically shows the generation of the raw data  13  by the NIR sensor system  14  and its transmission to the database structure  22  via sensor interface (illustrated in  FIG. 1 ) according to one or some embodiments. Depending on the embodiment, the particular sensor head  15  may comprise an evaluation unit  21  arranged in the sensor head  15  or externally. The particular evaluation unit  21  comprises so-called sensor software  23 . The spectral data  20  generated by the sensor head  15  may be processed further by the sensor software  23  and the calibration model(s)  24  assigned to this sensor software  23  (e.g., a calibration model of dry material  24   a  is illustrated in  FIG. 2  merely for exemplary purposes) into sensor data  25  for internal use in the agricultural work machine  1  and into the raw data  13  according to one aspect of the invention. In a manner known per se, the sensor data  25  generated for internal use may be used to optimize certain operating parameters  26  of the agricultural work machine  1 . For example, reference is made here to the known determination of the percentage of dry matter that is ultimately considered in the forage harvester  2  in the creation of the so-called chaff length, wherein the chaff length may be adjusted responsive to a determination of the percentage of dry matter (e.g., the chaff length is adjusted to be increasingly shorter the greater the percentage of dry matter in the plant material  16 ). 
     The raw data  13  generated in the particular evaluation unit  21  may be transmitted by the interface  6  to the outside  8 . As described previously, these raw data  13 , in the simplest case the unprocessed spectral data  20 , may be transmitted by suitable data transmission paths such as by satellite  9  or radio systems  10  to the database structure  22 . Thereafter, the database structure  22  may for example be saved in a data cloud  12  or a stationary server  11 , with the server  11  and/or the data cloud  12  forming the external data processing unit  27  in one aspect of the invention. At least one or more calibration models  28  may be retrievably saved in addition to the raw data  13  in the database structure  22  according to one aspect of the invention (e.g., the one or more calibration models  28  may be correlated with the saved raw data). The database structure  22  may be configured in a manner to be described in greater detail to generate user-specific calibration models  29  by using the saved raw data  13  and/or calibration models  28  and to provide these to a user  30 . Users  30  may be understood broadly to include any one, any combination, or all of: the immediate operator of the agricultural work machine  1 ; an operator managing a machine fleet; or the particular control and regulation device of an agricultural work machine  1  itself. 
     In one or some embodiments depicted according to  FIG. 3 , the database structure  22  may be such that a user  30  provides a calibration model  19 , and the database structure  22  creates a user-specific calibration model  29  in the form of an optimized calibration model  31  and transfers it to the user  30  taking into account the saved raw data  13  and/or calibration models  28 . The database structure  22  may however also be such that the user  30  defines an application  32  (e.g., a use of the NIR sensor system, such as for analysis of the composition of a flow of liquid manure, the composition of a flow of harvested material processed by a forage harvester, or the composition of a flow of harvested material processed by a combine), and the database structure  22 , using an indication of the application  32 , generates a user-specific calibration model  29  in the form of a newly created calibration model  33  and transfers it to the user  30  taking into account at least the saved calibration models  28  and/or raw data  13 . In this regard, the user may provide one or both of the calibration model  19  or the application  32  to the database structure  22  in order to create the user-specific calibration model  29 . The database structure  22  may be such that it transfers the generated user-specific calibration models  29  either directly to the particular NIR sensor system  14 , or to another user  30 , such as the operator of the agricultural work machine  1  or an operator managing a machine fleet. In one embodiment, the database structure  22  may also be such that the user  30  retrieves the user-specific calibration model  29  created by the database structure  22  and transfers it to the particular NIR sensor system  14  assigned to an agricultural work machine  1 . After transferring the generated user-specific calibration model  29  to the particular NIR sensor system  14 , the calibration model  19  previously saved therein is replaced by this newly created or modified user-specific calibration model  29  (e.g., a version of the calibration model  19  previously saved in the particular NIR sensor system is at least partly modified to generate the modified user-specific calibration model  29 ) so that the NIR sensor system  14  then operates with this newly created or modified user-specific calibration model  29 . 
     According to one or some embodiments as depicted in  FIG. 4 a   , a calibration model  19  may be modified using the database structure  22  into a user-specific calibration model  29  by using the saved raw data  13  and/or the saved calibration model  28  so that an adapted calibration model  29 ,  31 ,  33  for a special crop type  34   a ,  34   b ,  34   c , . . .  34   i  (though only  34   a ,  34   b ,  34   c  are depicted in  FIG. 4A ) is derived. 
     Bearing in mind that the further or new development of crop types  35  and changing environmental conditions  36  may influence the sensing precision of the NIR sensor systems  14 , the database structure  22  according to one or some embodiments depicted in  FIG. 4 b    may be configured to create an adapted user-specific calibration model  29 ,  31 ,  33  taking into account the modified environmental conditions  36  and/or the newly developed crop types  35 . 
     Moreover, the database structure  22  according to one or some embodiments depicted in  FIG. 4 c    may also be such that raw data  13   a ,  13   b ,  13   c  from different operating times  37  are compared with each other in that the same calibration model  38  is considered for all operating times  37   a ,  37   b ,  37   c  (e.g., three different operating times as depicted by each of  37   a ,  37   b ,  37   c ), wherein the same calibration model  38  may be one of the above-described user-specific calibration models  29 . In this manner, the database structure  22  generates measured values  39   a ,  39   b ,  39   c  that standardize the contents  18  of the particular plant material  16  of a certain time period  40   a ,  40   b ,  40   c  (with measured value  39   a  for time period  40   a , measured value  39   b  for time period  40   b , and measured value  39   c  for time period  40   c ) to the underlying common calibration model  38 . In one user-relevant embodiment, the time period  40  comprises a campaign  41 , wherein in one or some embodiments, a campaign may comprise a harvesting campaign, a fertilization campaign, etc. The comparison  42  of the raw data  13   a ,  13   b ,  13   c  standardized in the described manner may ultimately be used to correct so-called field maps  43  that are known per se. In one or some embodiments, the field maps  43  are yield maps or fertilization maps. 
     In one or some embodiments as depicted in  FIG. 4 d   , the database structure  22  may comprise a so-called APDI module  44  in addition to the raw data  13  and the saved calibration models  28 , wherein APDI stands for “automated process data interpretation” and technically means that the determined raw data  13  are checked for plausibility  45 . In a user-relevant embodiment, the APDI module  44  may be used to check whether the user  30 , such as the operator (e.g., the driver of the agricultural work machine  1 ) has made incorrect entries  46  taking into account the calibration model  19  selected in the particular harvester  1 , such as, for example, the calibration model “grass”  19   a , or “whole plant silage”  19   b , or “corn”  19   c , and the associated raw data  13 . In the simplest case, the incorrect entry  46  may relate to the entry of the wrong type of material in the selected calibration model  19   a ,  19   b ,  19   c.    
     Moreover, in one or some embodiments as depicted in  FIG. 5 , the database structure  22  may be such that the user  30  transmits a basic calibration model  47 , that in the simplest case is the calibration model  19  used in the agricultural works machine  1 , to the database structure  22 . Using the database structure  22  and/or the transferred calibration model  19 , the basic calibration model  47  may be converted or modified into an expanded calibration model  48 , wherein the expanded calibration model  48  then comprises model components  49   a ,  49   b  that enable the particular NIR sensor system  14  to determine an expanded or extended spectrum of properties of the flow of substance. For example, the agricultural work machine  1  may be equipped with an NIR sensor system  14  that only comprises the model detection of dry material  24   a  (see  FIG. 2 ). Then, the user  30  may integrate another function, for example the determination of additional contents  50 , into this calibration model  19  in the described manner via the database structure  22 . 
     To further increase the quality of the user-specific calibration models  29  to be generated by the database structure  22 , the database structure  22  may moreover be configured to consider the stationarily determined sample analysis values  51  according to  FIG. 6  when generating the particular user-specific calibration model  29 . Moreover, the database structure  22  may be configured such that the user-specific calibration models  29  are created for compensation, wherein the user  30  pays a one time or user-dependent fee  52  depending on the scope of creating the particular calibration model  29 . 
     Further, it is intended that the foregoing detailed description be understood as an illustration of selected forms that the invention may take and not as a definition of the invention. It is only the following claims, including all equivalents, that are intended to define the scope of the claimed invention. Further, it should be noted that any aspect of any of the preferred embodiments described herein may be used alone or in combination with one another. Finally, persons skilled in the art will readily recognize that in preferred implementation, some, or all of the steps in the disclosed method are performed using a computer so that the methodology is computer implemented. In such cases, the resulting physical properties model may be downloaded or saved to computer storage. 
     REFERENCE NUMBER LIST 
     
         
           1  Agricultural work machine 
           2  Forage harvester 
           3  Field 
           4  Plant crop 
           5  Transport vehicle 
           6  Interface 
           7  Data 
           8  External 
           9  Satellite 
           10  Stationary radio system 
           11  Server 
           12  Cloud 
           13  Raw data 
           14  NIR sensor system 
           15  NIR sensor head 
           16  Plant material 
           17  Sensing range 
           18  Contents 
           19  Calibration model 
           20  Spectral data 
           21  Evaluation unit 
           22  Database structure 
           23  Sensor software 
           24  Calibration model 
           25  Sensor data 
           26  Operating parameters 
           27  Data processing unit 
           28  Calibration model 
           29  User-specific calibration model 
           30  User 
           31  Optimized calibration model 
           32  Application 
           33  Newly created calibration model 
           34  Crop type 
           35  Newly developed crop type 
           36  Changed environmental conditions 
           37  Operating time 
           38  Calibration model 
           39  Measured value 
           40  Time period 
           41  Campaign 
           42  Comparison 
           43  Field map 
           44  APDI module 
           45  Checking plausibility 
           46  Checking incorrect entry 
           47  Basic calibration model 
           48  Expanded calibration model 
           49  Model components 
           50  Additional contents 
           51  Sample analysis value 
           52  Payment 
           53  Processor 
           54  Memory 
           55  Sensor interface