SYSTEM AND METHOD FOR AUTOMATED GRAIN INSPECTION DURING HARVEST
A system and method for automated grain inspection and analysis of results during harvest, using an inspection system mounted on a combine harvester with geolocation tracking, allowing for real time analysis during harvest and tracking of grain quality by location of harvest.
Priority is claimed in the application data sheet to the following patents or patent applications, the entire written description of each of which is expressly incorporated herein by reference in its entirety:
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- U.S. Ser. No. 16/436,592
- 62/605,957
- 62/606,332
- U.S. Ser. No. 16/122,853
- U.S. 16/434,497
The disclosure relates to the field of image analysis, and more particularly to the field of using image analysis to automatically inspect and analyze grains (seeds and pulses) during harvest.
Discussion of the State of the ArtGrains inspections and related applications for quality control, process control, food safety and grading for commercial value are based on subjective measures, use human interpretation of the inspected objects with pictures provided and descriptive specifications provided by the standards bodies.
Inspection of various grains (for example, various plant grains such as wheat or rice, mineral or metallic grains, or granulated or powdered substances) for various purposes such as safety or marketability is generally limited by factors such as subjectivity and speed, due to reliance on manual inspection methods. These methods also do not scale well and thus inspection is restricted to a sample group that is assumed to be an accurate representation of the entire lot, and study has shown visual inspection to have an error rate of 20-30%. Further, the grains must usually be sent to a laboratory for inspection, resulting in delays in inspection, and not allowing for tracking of the location at which the grains were harvested.
What is needed is a system and method for automated grain inspection and analysis of results during harvest, using an inspection system mounted on a combine harvester with geolocation tracking, allowing for real time analysis during harvest and tracking of grain quality by location of harvest.
SUMMARYAccordingly, the inventor has conceived and reduced to practice, a system and method for automated grain inspection and analysis of results during harvest, using an inspection system mounted on a combine harvester with geolocation tracking, allowing for real time analysis during harvest and tracking of grain quality by location of harvest.
According to a preferred embodiment, a system for automated food safety analysis, quality analysis, and grading of grains, comprising: an image processor, comprising at least a plurality of programming instructions stored in the memory of, and operating on at least one processor of, a computing device, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to: receive a digital image of grains; identify and count within the image the areas associated with individual grains; extract dimension information for each individual grain identified; create, for each individual grain identified, a pixel map of the color data for each pixel within the area of the image associated with that individual grain; and transmit or store the data comprising individual grain count, dimension information, and pixel map for each individual grain for analysis; and a food safety analyzer, comprising at least a plurality of programming instructions stored in the memory of, and operating on at least one processor of, a computing device, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to: receive or obtain data from the image processor for a sufficient number of images from a single lot of grain to constitute a statistically representative sample for the lot of grain; compare each pixel map in the data against pixel maps from reference images of infected grains of the type being inspected; perform a food safety analysis, based on the pixel map comparisons, comprising at least the type and extent of infection for each individual grain and the percentage of infected grains in the data for the statistically representative sample; and compare the results of the food safety analysis against at least one pre-defined standard for assessing food safety; and provide a certificate of analysis for the lot of grain detailing the extent to which the lot of grain meets the at least one pre-defined standard for assessing food safety; and a quality analyzer, comprising at least a plurality of programming instructions stored in the memory of, and operating on at least one processor of, a computing device, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to: device to: receive or obtain data from the image processor for a sufficient number of images from a single lot of grain to constitute a statistically representative sample for the lot of grain; compare each pixel map in the data against pixel maps from reference images of damaged grains of the type being inspected; perform a quality analysis, based on the pixel map comparisons, comprising at least the type and extent of damage for each individual grain and the percentage of damaged grains in the data for the statistically representative sample; and compare the results of the quality analysis against at least one industry standard for assessing grain quality; and provide a certificate of analysis for the lot of grain detailing the extent to which the lot of grain meets the at least one industry standard for assessing grain quality, is disclosed.
According to another preferred embodiment, a method for automated food safety analysis, quality analysis, and grading of grains, comprising the steps of: receiving, at an image processor, comprising at least a plurality of programming instructions stored in the memory of, and operating on at least one processor of, a computing device, a digital image of grains; identifying and counting within the image the areas associated with individual grains; extracting dimension information for each individual grain identified; creating, for each individual grain identified, a pixel map of the color data for each pixel within the area of the image associated with that individual grain; transmitting or store the data comprising individual grain count, dimension information, and pixel map for each individual grain for analysis; receiving or obtaining, at a food safety analyzer, comprising at least a plurality of programming instructions stored in the memory of, and operating on at least one processor of, a computing device, data from the image processor for a sufficient number of images from a single lot of grain to constitute a statistically representative sample for the lot of grain; comparing each pixel map in the data against pixel maps from reference images of infected grains of the type being inspected; performing a food safety analysis, based on the pixel map comparisons, comprising at least the type and extent of infection for each individual grain and the percentage of infected grains in the data for the statistically representative sample; comparing the results of the food safety analysis against at least one pre-defined standard for assessing food safety; providing a certificate of analysis for the lot of grain detailing the extent to which the lot of grain meets the at least one pre-defined standard for assessing food safety; receiving or obtaining, at a quality analyzer, comprising at least a plurality of programming instructions stored in the memory of, and operating on at least one processor of, a computing device, data from the image processor for a sufficient number of images from a single lot of grain to constitute a statistically representative sample for the lot of grain; comparing each pixel map in the data against pixel maps from reference images of damaged grains of the type being inspected; performing a quality analysis, based on the pixel map comparisons, comprising at least the type and extent of damage for each individual grain and the percentage of damaged grains in the data for the statistically representative sample; comparing the results of the quality analysis against at least one industry standard for assessing grain quality; and providing a certificate of analysis for the lot of grain detailing the extent to which the lot of grain meets the at least one industry standard for assessing grain quality, is disclosed.
The accompanying drawings illustrate several aspects and, together with the description, serve to explain the principles of the invention according to the aspects. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary, and are not to be considered as limiting of the scope of the invention or the claims herein in any way.
The inventor has conceived, and reduced to practice, a system and method for automated grain inspection and analysis of results during harvest, using an inspection system mounted on a combine harvester with geolocation tracking, allowing for real time analysis during harvest and tracking .rain quality by location of harvest.
What is needed is a system and method that measures, counts, calculates, classifies, and reports size, shape, color, color distribution, damages, unsafe properties, quality score and other important properties that replaces the manual and subjective interpretation methods based on description and pictures as reference, with accurate, measurable, repeatable, and empirical values. The ability to quantify good grains, damages, health risks, commercial grading arid quality score using an absolute and objective process and the results of such a process are critical to the entire agriculture ecosystem and humanity in general. The results include safer food, higher quality and desired taste and texture for the consumer. In addition, accurate data measured by a robust system can be used for further analysis, process control, yield improvement, event prediction, alerts, and other uses thus enabling collaboration among the industry ecosystem to help address issues in a faster and more reliable way.
The system includes cameras to capture the image of each grain in a sample, and illumination units to combine light sources with visible and invisible wavelengths (for example, infrared or ultraviolet). Both the cameras and light controls are connected to a computer that runs operating software and application programs, In addition, the results of an inspection may be uploaded automatically to cloud applications that store and later use the data and images to run additional applications Where big data, is required from one or more instruments in a. facility or multiple facilities, or in different steps in the processing, or steps in the supply chain. For example, quality trends and comparisons, alerts on food safety events, issue of certificates of analysis, visualization of color distribution in grains, or other uses.
One or more different aspects may be described in the present application. Further, for one or more of the aspects described herein, numerous alternative arrangements may be described; it should be appreciated that these are presented for illustrative purposes only and are not limiting of the aspects contained herein or the claims presented herein in any way. One or more of the arrangements may be widely applicable to numerous aspects, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects. Particular features of one or more of the aspects described herein may be described with reference to one or more particular aspects or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects. It should be appreciated, however, that such features are not limited to usage the one or more particular aspects or figures with reference to which they are described. The present disclosure is neither a literal description of all arrangements of one or more of the aspects nor a listing of features of one or more of the aspects that must be present in all arrangements.
Headings of sections provided in this patent application and the title of this patent application are for convenience only, and are not to be taken as limiting the disclosure in any way.
Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical.
A description of an aspect with several components in communication with each other does not imply that all such components are required. To the contrary, a variety of optional components may be described to illustrate a wide variety of possible aspects and in order to more fully illustrate one or more aspects. Similarly, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred. Also, steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or sonic occurrences, or some steps may be executed more than once in a given aspect or occurrence.
When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article.
The functionality or the features of a device may be alternatively embodied by one or more other devices that are not explicitly described as haying such functionality or features. Thus, other aspects need not include the device itself.
Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code Which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art.
DefinitionsThe term “damage” as used herein means imperfections in grain caused by physical factors such as heat, cold, flood, mechanical damage from harvesting, transportation, and processing, and insect damage from chewing, boring, or tunneling.
The term “disease” or “infection” as used herein means any of a number of diseases and infections that affect grains, the most common of which are various types of fungal infections.
The term “grain” or “grains” as used herein includes the grains, seeds, and pulses of plants.
The terms “lot” or “batch” as used herein mean a quantity of grain being transported, sold, processed, analyzed, certified, or otherwise handled or disposed of as a single unit.
The term “soundness” as used herein means the overall visual grain quality. The soundness of a particular grain is diminished by damage and disease. Industry standards such as the Official United States Standard for Grain set forth the factors for determining soundness of grain.
Conceptual ArchitectureIn some, cases, one of the color properties assessed in particular may be a degree of chalkiness of some or all of the grains dispersed on the surface of receptacle 102, and more in particular afterglow elects of such chalkiness (for example, multispectral illumination of the grains may reveal certain spectral behaviors associated with chalkiness that may otherwise be difficult to observe, revealing details otherwise obscured in any one particular spectral band). Light emitters 120, 220, 221 may use one or more, or a combination, of LEDs of different color, or by specialized uni-or multi-spectral halide or xenon or similar discharge lamps, or other light-emitting sources, and may be configured as specialized uni.- or multi-spectral lamps, and may optionally be used with any or a combination of filters to further alter the emission spectra. During sampling of grain, lights may be sequenced as needed to achieve optimal image quality or to tune for specific Image or grain features, such as to highlight blemishes or examine for disease (either in general, or to examine for specific diseases or pathogens) or grain damage, or to classify grain type or variety, or to correlate with information regarding the location, methods, or other conditions of the grain's growth, harvest, storage, transport, or processing.
In addition to cameras, other sensor types may include humidity sensors, temperature sensors, light sensors, scanners, scales, or other sensor types, and the data from the sensors and cameras may be used to measure all the details of blemishes, diseases or any other damage to each grain, so the system can identify the grain type, variety, and its diseases and damages. For each grain, a pixel count may be calculated and then organized in a histogram for color and size. These histograms may be hierarchical and may be used to identify and help quickly categorize grains, diseases, qualities, or any measurable metric.
All this information nay be sent over a network to a server or a cloud, and compared to a reference database. Changes over time may be tracked by region, enabling companies, governments and NGOs to assess the safety and sufficiency of the food supply and to recognize supply problems stemming from new diseases quickly and early on.
System 100, 200 may be implemented in a combine harvester or other harvesting or farming equipment, for example diverting a sample from a harvest stream according to a configured time, location, or other schedule or pattern. This may be used to enable real-time (or near real-time) analysis of a harvest, for example to produce a harvest quality map that may be used to optimize field preparation (such as to direct the use of fertilizers or pesticides, for example) or for storage or transmission. Grain may also be classified and tracked based on harvest time, location, methods, or other such metrics, which may optionally be presented alongside analysis results in reporting.
The chromaticity space is used in conjunction with color visual references provided by various groups, industries, and governmental agencies for identifying disease and damage in crops. For example, the USDA visual reference library contains color photographs of a variety of grain defects, disease, damage, contamination, spoilage, infection, and other factors, and USDA grading tables provide quality categories based on the color of rice kernel, milling degree, and maximum damages allowed per grade. Under current methodology, a human inspector needs to visually compare the actual grain with the provided pictures or descriptions as reference, and make qualitative judgment calls regarding disease, damage, and soundness. When using such reference, there is no qualitative number or specification of the color or the minimum area of the “heat damaged” spot. USDA visual references and other sources provide information on a variety of grain defects, disease, damage, contamination, spoilage, infection, and other factors. These visual references can be used to input color information into the system to recognize quantitatively, for example, mold contamination, which can be a health concern. These quantitative characterizations using chromaticity space are far more accurate than human visual inspection, and the results are more repeatable and reliable. A wide variety of grain-related health issues and food safety issues can be identified in this manner.
Kernel analyzer system 1604 is integrated into combine harvest 1600 (typically near or on a driver cabin 1601) by having a sample pull 1603 that allows periodic pulling of samples from a main kernel feed 1602, a sample return 1606, and a link 1605 that either connect to the combine's own network or connects via a wireless uplink directly to the Internet, or both.
Additionally, kernel analyzer system 1604 analyzes and calculates the sorter “rejected grains bin” and provides information related to return on investment (ROI) to recover “good” grains from the rejected bin. In addition, it provides feedback to the sorter machine on its performance, how many good kernels are rejected on each bad kernel and by inspecting the grains before the sorter station as an input to the sorter for optimization of speed vs, performance. This method can recover good grains that can be sold at a premium compared to damaged grains.
User program 1804 performs the actual measurements, classification and reporting. It receives as input a grain sample placed on a working area, and a calibration file that matches the grain type and test requirements. The outputs are summary results, visual classification of all the grains (for example, “broken”, “chalky”, “red”, etc.) and provides tools to further review the results, such as a window where a user may click on a specific grain to view all the grain properties.
A report page 1805 provides additional information such as quantity of grain, distribution, types of grains in the sample, classification of all damaged grains, and statistics such as (for example), number of grains, number of broken grains, average length, or average width. In addition, a test results folder 1806 may be generated and saved, with the folder name including date and time plus optional text if entered by the user as a sample name. Files provide data, on the sample level, grain level, and pixel level for each grain. Images of the sample provide information on the visuals, classifications, and results. Images may be used to upload an image of the sample for re-testing in case of dispute or investigation, and a daily results tile may be updated to maintain daily status. Web-based or local cloud page 1807 includes a set of software applications including (for example) alerts, visualization, reporting, trends, dashboard, and other productivity tools provided to improve quality, yield, and food safety. Wizard 1801 enables a user to set a file name and the method to define grain type, such as by length or length-to-width ratio, and uses USDA other standards as defaults with the option to modify values. The wizard 1801 provides a method to set color value and limits (for example, maximum area to classify “red damages” a grain), and a summary results screen with classification of all detected damages. In addition to the system's capability to measure, classify, and report grain status, it also provides a set of applications that bring new methods to address the grain industry resulting in better quality, safer food, higher yield, and greater profit.
An alert system (not shown) will detect and notify selected users and will perform actions when triggered. User screen sets alert thresholds, for example if the amount of broken grams is higher than 5% or if the level of chalkiness is higher than 20%. Alerts may be via email, voice notification, SMS, or any other electronic communication method with relevant people or systems. A list of alerts is configured by the user and the cloud application monitor it periodically and compares with the test results of the sample. The alert system may notify in different locations of activity, incoming inspection to alert on bad grains in a shipment, processing for quality results, after storage, or before packing. This tool is valuable to improving overall food safety, quality, and operational efficiency,
Hardware ArchitectureGenerally, the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (AMC), or on a network interface card.
Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory. Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols. A general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented. According to specific aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof. In at least some aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments).
Referring now to
In one aspect, computing device 10 includes one or more central processing units (CPU) 12, one or more interfaces 15, and one or more busses 14 (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU 12 may be responsible for implementing specific functions associated with the functions of a. specifically configured computing device or machine. For example, in at least one aspect, a computing device 10 may be configured or designed to function as a server system utilizing CPU 12, local memory 11 and/or remote memory 16, and interface(s) 15. In at least one aspect, CPU 12 may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like.
CPU 12 may include one or more processors 13 such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some aspects, processors 13 may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device 10. In a particular aspect, a local memory 11 (such as non-volatile random access memory (RANI) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU 12. However, there are many different ways in which memory may be coupled to system 10. Memory 11 may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU 12 may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices.
As used herein, the “term processor” is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit,
In one aspect, interfaces 15 are provided as network interface cards (NICs). Generally, NICs control the sending and receiving of data packets over a computer network; other types of interfaces 15 may for example support other peripherals used with computing device 10. Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USW, Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radio frequency (RF), BLUETOOTH™, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP), ISDN, fast Ethernet interfaces, Gigabit Ethernet Interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally, such interfaces 15 may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM).
Although the system shown in
Regardless of network device configuration, the system of an aspect may employ one or more memories or memories modules (such as, for example, remote memory block 16 and local memory 11) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the aspects described herein (or any combinations of the above). Program instructions may control execution of or comprise an operating system and/or one or more applications, for example. Memory 16 or memories 11, 16 may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein.
Because such information and program instructions may be employed to implement one or more systems or methods described herein, at least some network device aspects may include nontransitory machine-readable storage media, which, for example, limy be configured or designed to store program instructions, state information, and the like for performing various operations described herein. Examples of such nontransitory machine-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and “hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like. It should be appreciated that such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), “hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably. Examples of program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVA™ compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language).
In some aspects, systems may be implemented on a standalone computing system. Referring now to
In some aspects, systems may be implemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to
In addition, in some aspects, servers 32 may call external services 37 when needed to obtain additional information, or to refer to additional data, concerning a particular call. Communications with external services 37 may take place, for example, via one or more networks 31. In various aspects, external services 37 may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect, where client applications 24 are implemented on a smartphone or other electronic device, client applications 24 may obtain information stored in a server system 32 in the cloud or on an external service 37 deployed on one or more of a particular enterprise's or user's premises.
In some aspects, clients 33 or servers 32 (or both) may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks 31. For example, one or more databases 34 may be used or referred to by one or more aspects. It should be understood by one having ordinary skill in the art that databases 34 may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means. For example, in various aspects one or more databases 34 may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and so forth). In some aspects, variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect. It will be appreciated by one having ordinary skill the art that any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular aspect described herein. Moreover, it should be appreciated that the term “database” as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system. Unless a specific meaning is specified for a given use of the term “database”, it should be construed to mean any of these senses of the word, all of which are understood as a plain meaning of the term “database” by those having ordinary skill in the art,
Similarly, some aspects may make use of one or more security systems 36 and configuration systems 35. Security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with aspects without limitation, unless a specific security 36 or configuration system 35 or approach is specifically required by the description of any specific aspect.
In various aspects, functionality for implementing systems or methods of various aspects may be distributed among any number of client and/or server components. for example, various software modules may be implemented for perform n various functions iii connection with the system of any particular aspect, and such modules may be variously implemented to run on server and/or client components.
The skilled person will be aware of a range of possible modifications of the various aspects described above. Accordingly, the present invention is defined by the claims and their equivalents,
Claims
1. A system for automated grain inspection during harvest, comprising:
- a sample inlet, mounted in or on a harvester, and configured to divert samples of grain being harvested to an imaging system; and
- the imaging system, mounted in or on the harvester, and configured to take digital images or video of the samples of grain diverted by the sample inlet;
- a humidity sensor, configured to capture data from air surrounding the samples of grain; and
- a sample outlet, mounted in or on the harvester, and configured to either return the samples of grain to the grain being harvested after imaging and moisture data capture or discard the samples after imaging and moisture data capture: and
- a computing deuce comprising a memory and a processor, and configured to receive the digital images or video from the imaging system and the moisture data from the humidity sensor;
- an image processor comprising a first plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the first plurality of programming instructions, when operating on the processor, causes the computing device to: receive the ages or video of the samples of grain; identify in each image or video a plurality of grains of the samples of grain; for each of the plurality of grains identified, determine a color value of each pixel representing that grain; and create a histogram of the color values of the plurality of rains grains identified from the determined color values; and
- a parametric evaluator comprising a second plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the second plurality of programming instructions, when operating on the processor, causes the computing device to: receive the histogram from the image processor; determine whether the histogram falls within an expected histogram parameter; receive the moisture data from the humidity sensor: determine whether the moisture data kills within an expected moisture parameter; and if the histogram falls within the expected histogram parameter and the moisture data falls within the expected moisture parameter, indicate acceptability of the sample of grain; and
- a hierarchical histogram evaluator comprising a third plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the third plurality of programming instructions, when operating on the processor, cause die computing device to: if the parametric evaluator has not indicated acceptability of the samples of grain, receive the histogram from the parametric evaluator; and compare the histogram to a hierarchy of histograms to identify' an abnormality in the samples of grain that is a cause of unacceptability; and
- a geolocation device configured to track a location of the harvester; and
- a wireless communication device configured to: receive either the indication of acceptability of the samples of grain from the parametric evaluator or the identification of the abnormality in the samples of grain;
- receive the location of the harvester from the geolocation device; and
- transmit the indication of acceptability of the samples of grain from the parametric evaluator or the identification oldie abnormality in the samples of grain, along with the location of the harvester, wirelessly to a computer or network of computers located remotely from the harvester.
2. The system of claim 1, further comprising the computer or network of computers located remotely from the harvester. configured to:
- receive the digital images, video, or analyses from the wireless communication device on a plurality of harvesters;
- receive location data from the wireless communication device on the plurality of harvesters; and
- track variations in grain quality by location of harvest.
3. The system of claim 1, Wherein the samples of grain diverted for inspection are held until at least one analysis is performed by the computing device, and then either returned to the grain being harvested or discarded, depending on a result of the at least one analysis.
4. A method for auto ed grain inspection during harvest comprising the steps of:
- diverting samples of grain being harvested to an imaging system via a sample inlet, mounted in or on a harvester;
- taking digital images or video of the samples of grain diverted by the sample inlet a imaging system, mounted in or on the harvester;
- capturing moisture data from air surrounding the samples of grain using a humidity sensor;
- returning the samples of grain to the grain being harvested after imaging and moisture data capture or discarding the samples of grain after imaging and moisture data capture via a sample outlet, mounted in or on the harvester;
- receiving the digital images or video into an image processor operating on a computing device rom the imaging system.;
- identifying in each image or video a plurality of grains of the samples of grain;
- for each of the plurality of grains identified, determining a color value of each pixel representing that grain; and
- creating a histogram of the color values of the plurality of grains identified from the determined color values;
- receiving, into a parametric evaluator operating on the computing device, the histogram from the image processor;
- determining whether the histogram falls within an expected histogram parameter;
- receiving the moisture data from the humidity sensor;
- determining whether the moisture data falls within an expected moisture parameter;
- if the histogram falls within the expected histogram parameter and the moisture data falls within the expected moisture parameter, indicating acceptability of the samples of grain; if the parametric evaluator has not indicated acceptability of the samples of grain, receiving, into a hierarchical histogram evaluator operating on the computing device, the histogram from the parametric evaluator, and comparing the histogram to a hierarchy of histograms to identify an abnormality in the samples of grain that is a cause of unacceptability;
- tracking a location of the harvester via a geolocation device;
- receiving either the indication of acceptability of the samples of grain from the parametric evaluator or the identification of the abnormality in the samples of grain into a wireless communication device;
- receiving the location of the harvester from the geolocation device into the wireless communication device; and
- transmitting the indication of acceptability of the samples of grain from the parametric evaluator or the identification of the abnormality in the samples of grain, along with the location of the harvester, wirelessly from the wireless communication device to a computer or network of computers located remotely from the harvester.
5. The method of claim 4, further comprising the steps of:
- receiving into the computer or network of computers located remotely from the harvester the digital images, video, or analyses from the wireless communication device On a plurality of harvesters;
- receiving into the computer or network of computers located remotely from the bars ester location data from the wireless communication device on a plurality of harvesters; and
- tracking variations in grain quality by location of harvest.
6. The method of claim 4, wherein the samples of grain diverted for inspection are held until at least one analysis is performed by the computing device, and then either returned to the grain being harvested or discarded, depending on a result of die at least one analysis.
Type: Application
Filed: Nov 9, 2021
Publication Date: Mar 3, 2022
Inventor: Ron Hadar (Capitola, CA)
Application Number: 17/522,161