Patent Application
20240265674
The present disclosure relates to a system for tracking crop variety in a crop field.
Variety tracking traditionally begins with the planting operation. A farmer will record where seeds are placed by a planter along with linking a field to seed varieties planted. This requires an advanced terminal for the recording and documentation of the planting process. When harvest begins, the farmer needs to pull the crop variety information into their harvest terminal if they want to complete the link of planting to harvesting. This link works under the assumption that the farmer is using recent and new technology that allows this transfer of data effectively. If a farmer is not equipped with such technology, then the farmer has to manually track varieties throughout the harvest. Such tasks are even further complicated with modern precision agriculture.
Precision agriculture or precision farming is a farming management model based on measuring and responding to inter and intra-field variability in crops and farming conditions. The goal of precision agriculture research is to define a decision support system (DSS) for farming management to enhance returns and increase preservation of resources. Specifically, the precision in responses to variability in farming can be improved when known and predetermined farming information is processed and organized to enhance the information and then used to assist in the control and management of farming. Although precision farming can enhance returns and increase preservation of resources, it can complicate farming information systems especially systems tracking crop variety.
Currently, farming management information systems (FMISs) are pervasive in farming and a significant factor in the furthering of precision agriculture. Such information systems can track measuring and responding to inter and intra-field variability in crops and farming conditions as well as enhance DDS for farming management. FMISs allow for new opportunities to improve farming and precision agriculture. However, even though FMISs are improving precision farming, present FMISs have limitations and can be dramatically improved upon considering relatively recent advancements in computer engineering and computer science. One problem with previous systems is the collection and organization of information from farming, including the collection and organization of information on variety tracking. This can be a problem since farming conditions and crop variability can vary greatly in the operations from one field to another.
These are just some of the many issues that can be improved upon in farming, and specifically, in precision agriculture as well as crop variety tracking.
Described herein are technologies for tracking crop variety in a field while harvesting a crop to improve upon some technical problems in tracking crop variety. Also, the techniques disclosed herein provide specific technical solutions to at least overcome the technical problems mentioned in the background section or other parts of the application as well as other technical problems not described herein but recognized by those skilled in the art.
As mentioned in the background section, variety tracking traditionally begins with the planting operation. Such planting requires an advanced terminal for the recording and documentation of the planting process. When harvest begins, the farmer needs to pull the crop variety information into their harvest terminal if they want to complete the link of planting to harvesting. This link works when the farmer is using recent and new technology that allows for such a transfer of data. If a farmer is not equipped with such technology, then the farmer has to manually track varieties throughout the harvest. Such tasks are even further complicated with modern precision agriculture.
In some embodiments, systems use a look forward camera and recognize current crop characteristics (such as height, color, and density) as well as link these characteristics with a known crop variety. As a harvester harvests a field and the recorded characteristics deviate from an original baseline (along with other pass to pass comparisons), a new variety is triggered and recorded. The operator then has the ability to confirm or decline this new trigger. This allows the operator to build a variety map independent of planting information. Also, in some embodiments, the systems pull the satellite imagery and identify the different varieties from different color spectrums in the satellite imagery.
In some embodiments, a camera (e.g., see camera 302 or 304) mounted to a harvester (e.g., see combine harvester 300) captures images of a crop (e.g., see images 700 and 800) and a computing system (e.g., see computing system 200) determines characteristics of the crop (e.g., see characteristics 702, 704, 706, 708A, 804, 806, and 808A) in the images (such as its height, color, and density) (e.g., see crop feature detection instructions 222 shown in
In some embodiments, a system for tracking varieties of a crop in a field while a harvester (e.g., see combine harvester 300) is moving through the field, includes a camera (e.g., see camera 302 or 304) mounted to the harvester and configured to capture images of the crop (e.g., see images 700 and 800). The system also includes a computing system (e.g., see computing system 200) communicatively coupled to the camera and having instructions executable to detect a first variety of the crop within the images of the crop by identifying a physical characteristic of the crop being within a first range of values of the physical characteristic (e.g., see instructions 222 and 226 shown in
In some embodiments, a method of the technologies includes capturing, by a camera (e.g., see camera 302 or 304) mounted to a harvester (e.g., see combine harvester 300), images of a crop (e.g., see images 700 and 800) while the harvester is moving through a crop field, to track varieties of the crop in the field (e.g., see step 402 shown in
In some embodiments, the method further includes generating, by the computing system (e.g., see computing system 200), a map of the varieties of the crop (e.g., see map 904) based on the geotagged and labeled images, e.g., see geotag 712 in images 700 and 800 (e.g., see step 412 shown in
In some embodiments, the method includes actively and continually monitoring, by the computing system (e.g., see computing system 200), a standard deviation of the characteristics of the crop in the images (e.g., see images 700 and 800) while the harvester (e.g., see harvester 300) is moving through the field (e.g., see step 405 shown in
In some embodiments, the determination of whether the determined characteristics deviate from the known characteristics of the first variety are based on a comparison of characteristics of the crop (e.g., see characteristics 702, 704, 706, 708A, 804, 806, and 808A) in a predetermined sampling of the images (e.g., see images 700 and 800) and the known characteristics of the first variety. In some of such examples, the method further includes actively and continually monitoring, by the computing system (e.g., see computing system 200), a standard deviation of the characteristics of the crop in the images while the harvester (e.g., see combine harvester 300) is moving through the field (e.g., see step 405 shown in
In some of aforementioned embodiments and other embodiments, the method further includes optionally providing, by the computing system, a user interface (e.g., see user interface 216 shown in
In some of aforementioned embodiments and other embodiments, when the computing system (e.g., see computing system 200) determines whether a new variety exists in the field, the computing system considers crop height (e.g., see crop height characteristic 702 shown in
In some embodiments, weights are used in determining characteristics of the crop in the images (such as at step 404) or in determining whether the determined characteristics deviate from known characteristics of a first variety of the crop (such as at step 406). When weights are used for determining characteristics of the crop in the images different weights can be applied to one or more of crop height, crop color, and crop density. Also, different weights can be applied to one or more of the aforementioned secondary factors of the crop, which include yield of the crop, elevation of field, slope of field, measured mass of the crop, seed size of the crop, and seed color of the crop.
In some embodiments of the method, the determination of characteristics of the crop in the images (e.g., see images 700 and 800) is based at least on digital signal processing (e.g., see step 502 shown in
In some examples, the computer vision analysis includes inputting aspects of the images (e.g., see images 700 and 800) or derivatives of aspects of the images into an ANN (e.g., see step 504 as well as instructions 226), and the determination of characteristics of the crop in the images is based at least on the output of the ANN (e.g., see step 508 as well as instructions 226). Also, in such examples, the ANN includes or is part of a deep learning process that determines characteristics of the crop in the images or is a basis for the detection of the different varieties of the crop. And, the deep learning process can include a CNN or a network of CNNs.
In some embodiments, another method of the technologies includes capturing, by a camera (e.g., see camera 302 or 304) mounted to a harvester (e.g., see combine harvester 300), images of a crop (e.g., see images 700 and 800) while the harvester is moving through a crop field, to track varieties of the crop in the field (e.g., see step 1002 shown in
In some embodiments of the method, the identification of the physical characteristic of the crop being within a certain range of values is based at least on digital signal processing. In some instances, the identification of the physical characteristic of the crop being within a certain range of values is based at least on a computer vision analysis. Also, the identification of the physical characteristic of the crop being within a certain range of values can be further based on digital signal processing. The digital signal processing can occur prior to the computer vision analysis as a pre-processing step to generate enhanced input for the computer vision analysis. The computer vision analysis can include inputting the enhanced input into an ANN, and the detecting of the different varieties of the crop can be based at least on the output of the ANN. The ANN can include or be a part of a deep learning process that detects the different varieties of the crop or is a basis for the detection of the different varieties of the crop. The deep learning process can include a CNN or a network of CNNs. In some embodiments, the computer vision analysis includes inputting aspects of the images or derivatives of aspects of the images into an ANN and the detecting of the different varieties of the crop is based at least on the output of the ANN. In such examples, the ANN can include or is part of a deep learning process that detects the different varieties of the crop or is a basis for the detection of the different varieties of the crop. And, the deep learning process includes a CNN or a network of CNNs.
In some embodiments of the method, the physical characteristic is crop height, crop color, crop density, crop reflectiveness or any combination thereof. In some embodiments, the physical characteristic is crop height only. In some embodiments, the physical characteristic is crop color only. In some embodiments, the physical characteristic is crop reflectiveness only. In some embodiments, the physical characteristic is crop density only.
In some embodiments, the first range of values of the physical characteristic is predetermined prior to the harvester moving through the field. In such examples, the second range of values of the physical characteristic may not be predetermined prior to the harvester moving through the field and can be determined while the harvester moving through the field.
In some embodiments, an operator of the harvester has the ability to confirm or decline the associating of the first recorded locations to the first variety or the associating of the second recorded locations to the second variety.
In some embodiments, the method further includes identifying the different varieties from different color spectrums in satellite imagery of the crop field to corroborate the generated map of crop varieties as well providing, via a user interface (e.g., see user interface 216), a confidence level of the map that is determined according to a comparison between the map and the satellite imagery.
The systems and methods described herein overcome some technical problems in farming, in general, as well as some technical problems in tracking crop variety, specifically. Also, the techniques disclosed herein provide specific technical solutions to at least overcome the technical problems mentioned in the background section or other parts of the application as well as other technical problems not described herein but recognized by those skilled in the art.
With respect to some embodiments, disclosed herein are computerized methods for tracking crop variety, as well as a non-transitory computer-readable storage medium for carrying out technical operations of the computerized methods. The non-transitory computer-readable storage medium has tangibly stored thereon, or tangibly encoded thereon, computer readable instructions that when executed by one or more devices (e.g., one or more personal computers or servers) cause at least one processor to perform a method for improved systems and methods for tracking crop variety.
With respect to some embodiments, a system is provided that includes at least one computing device configured to provide improved ways for tracking crop variety. And, with respect to some embodiments, a method, such as one of the aforesaid methods, is provided to be performed by at least one computing device. In some example embodiments, computer program code can be executed by at least one processor of one or more computing devices to implement functionality in accordance with at least some embodiments described herein; and the computer program code being at least a part of or stored in a non-transitory computer-readable medium.
These and other important aspects of the invention are described more fully in the detailed description below. The invention is not limited to the particular methods and systems described herein. Other embodiments can be used and changes to the described embodiments can be made without departing from the scope of the claims that follow the detailed description.
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.
The present disclosure will be understood more fully from the detailed description given below and from the accompanying drawings of various embodiments of the disclosure. Embodiments of the invention will now be described, by way of example, with reference to the accompanying drawings, in which:
Details of example embodiments of the invention are described in the following detailed description with reference to the drawings. Although the detailed description provides reference to example embodiments, it is to be understood that the invention disclosed herein is not limited to such example embodiments. But to the contrary, the invention disclosed herein includes numerous alternatives, modifications and equivalents as will become apparent from consideration of the following detailed description and other parts of this disclosure.
Described herein are technologies for tracking crop variety in a field while harvesting a crop to improve upon some technical problems in tracking crop variety. Also, the techniques disclosed herein provide specific technical solutions to at least overcome the technical problems mentioned in the background section or other parts of the application as well as other technical problems not described herein but recognized by those skilled in the art.
As mentioned in the background section, variety tracking traditionally begins with the planting operation. Such planting requires an advanced terminal for the recording and documentation of the planting process. When harvest begins, the farmer needs to pull the crop variety information into their harvest terminal if they want to complete the link of planting to harvesting. This link works when the farmer is using recent and new technology that allows for such a transfer of data. If a farmer is not equipped with such technology, then the farmer has to manually track varieties throughout the harvest. Such tasks are even further complicated with modern precision agriculture.
In some embodiments, systems use a look forward camera and recognize current crop characteristics (such as height, color, and density) as well as link these characteristics with a known crop variety. As a harvester harvests a field and the recorded characteristics deviate from an original baseline (along with other pass to pass comparisons), a new variety is triggered and recorded. The operator then has the ability to confirm or decline this new trigger. This allows the operator to build a variety map independent of planting information. Also, in some embodiments, the systems pull the satellite imagery and identify the different varieties from different color spectrums in the satellite imagery.
In some embodiments, using existing image processing techniques and slight modification to existing hardware, the camera can capture images as the crop is harvested and each image can be linked with a geospatial location. The attributes of the crop then can be compared to a defined number of samples with the standard deviation actively being monitored. At any point which the standard deviation is greater than a defined threshold, a new variety could be triggered as long as the standard deviation returned within the allowable threshold.
In some embodiments, a simple method for capturing a standard deviation can be considered from pass to pass. Thus, as the combined transitioned from a harvest state to headland state to harvest state, the sum of an entire run can be averaged and a recorded as a single data point. As multiple runs occur and more data points are recorded, the standard deviation from run to run is compared actively checking for a new variety. Once a new variety is detected, the operator can either confirm and name the new variety or decline. In some embodiments, in a more complex method or system, the computing system will not only consider the various crop properties (such as height, density, reflectiveness, color, or grain moisture) but also consider yield, elevation, slope, measured mass, seed size, and seed color. The computing system can also actively monitor the different properties while comparing each property independently regardless of a harvest or a headland state change. When a significant deviation is detected from the most adjacent or sequential data point, the system will automatically flag a new variety.
As shown in
In some embodiments, the farming machine (e.g., see farming machine 106, 108, or 110) includes a vehicle. In some embodiments, the farming machine is a combine harvester (e.g., see combine harvester 300 show in
The communications network 104 includes one or more local area networks (LAN(s)) and/or one or more wide area networks (WAN(s)). In some embodiments, the communications network 104 includes the Internet and/or any other type of interconnected communications network. The communications network 104 can also include a single computer network or a telecommunications network. More specifically, in some embodiments, the communications network 104 includes a local area network (LAN) such as a private computer network that connects computers in small physical areas, a wide area network (WAN) to connect computers located in different geographical locations, and/or a middle area network (MAN) to connect computers in a geographic area larger than that covered by a large LAN but smaller than the area covered by a WAN.
At least each shown component of the network 100 (including remote computing system 102, communications network 104, and farming machines 106, 108, and 110) can be or include a computing system which includes memory that includes media. The media includes or is volatile memory components, non-volatile memory components, or a combination of thereof. In general, in some embodiments, each of the computing systems includes a host system that uses memory. For example, the host system writes data to the memory and read data from the memory. The host system is a computing device that includes a memory and a data processing device. The host system includes or is coupled to the memory so that the host system reads data from or writes data to the memory. The host system is coupled to the memory via a physical host interface. The physical host interface provides an interface for passing control, address, data, and other signals between the memory and the host system.
The computing system 200 includes a processing device 202, a main memory 204 (e.g., read-only memory (ROM), flash memory, dynamic random-access memory (DRAM), etc.), a static memory 206 (e.g., flash memory, static random-access memory (SRAM), etc.), and a data storage system 210, which communicate with each other via a bus 218. The processing device 202 represents one or more general-purpose processing devices such as a microprocessor, a central processing unit, or the like. More particularly, the processing device can include a microprocessor or a processor implementing other instruction sets, or processors implementing a combination of instruction sets. Or, the processing device 202 is one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 202 is configured to execute instructions 214 for performing the operations discussed herein performed by a computing system. In some embodiments, the computing system 200 includes a network interface device 208 to communicate over the communications network 104 shown in
The data storage system 210 includes a machine-readable storage medium 212 (also known as a computer-readable medium) on which is stored one or more sets of instructions 214 or software embodying any one or more of the methodologies or functions described herein performed by a computing system. The instructions 214 also reside, completely or at least partially, within the main memory 204 or within the processing device 202 during execution thereof by the computing system 200, the main memory 204 and the processing device 202 also constituting machine-readable storage media.
In some embodiments, the instructions 214 include specific instructions to implement functionality described herein related to the methods described herein and that can correspond to any one of the computing devices, data processors, user interface devices, and I/O devices described herein related to a computing system. For example, the instructions 222 include crop feature detection instructions 222, data linking and recording instructions 224, data enhancement instructions 226, and map generation instructions 228. In some embodiments, the data enhancement instructions include different types of data analysis libraries as well different types of data processing libraries--including various mathematical and statistical modeling and operations libraries and machine learning, artificial intelligence, and deep learning libraries as well as specific libraries for ANN and CNN data processing and for training ANNs, CNNs and other types of computing schemes or systems.
While the machine-readable storage medium 212 is shown in an example embodiment to be a single medium, the term “machine-readable storage medium” should be taken to include a single medium or multiple media that store the one or more sets of instructions. The term “machine-readable storage medium” shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure performed a computing system. The term “machine-readable storage medium” shall accordingly be taken to include solid-state memories, optical media, or magnetic media.
Also, as shown, the computing system 200 includes user interface 216 that includes a display, in some embodiments, and, for example, implements functionality corresponding to any one of the user interface devices disclosed herein. A user interface, such as user interface 216, or a user interface device described herein includes any space or equipment where interactions between humans and machines occur. A user interface described herein allows operation and control of the machine from a human user, while the machine simultaneously provides feedback information to the user. Examples of a user interface (UI), or user interface device include the interactive aspects of computer operating systems (such as graphical user interfaces), machinery operator controls, and process controls. A UI described herein includes one or more layers, including a human-machine interface (HMI) that interfaces machines with physical input hardware and output hardware.
Also, as shown, the computing system 200 includes farming machine electronics 220 that includes sensors, cameras, or other types of electrical and/or mechanical feedback devices, one or more user interfaces (e.g., any one of the UI described herein), and any type of computer hardware and software configured to interface and communicatively couple to operational components of a farming machine (e.g., see electronics 126, 128, and 130). Also, in some embodiments, the farming machine electronics 220 as well as the electronics 126, 128, and 130 include any one of the cameras described herein for capturing images of crop (e.g., see cameras 302 and 304 show in
In some systems of the technologies disclosed herein, any steps of embodiments of the methods described herein are implementable by executing instructions corresponding to the steps, which are stored in memory (e.g., see instructions 214, 222, 224, 226, and 228 shown in
The combine harvester 300 has processing system 312 that extends generally parallel with the path of travel of the harvester. It is to be understood that such a harvester is being used to illustrate principals herein and the subject matter described herein is not limited to harvesters with processing systems designed for axial flow, nor to axial flow harvesters having only a single processing system. The combine harvester 300 also includes a harvesting header (not shown) at the front of the machine that delivers collected crop materials to the front end of a feeder house 314. Such materials are moved upwardly and rearwardly within feeder house 314 by a conveyer 316 until reaching a beater 318 that rotates about a transverse axis. Beater 318 feeds the material upwardly and rearwardly to a rotary processing device, in the illustrated instance to a rotor 322 having an infeed auger 320 on the front end thereof. Infeed auger 320, in turn, advances the materials axially into the processing system 312 for threshing and separating. The processing system 312 is housed by processing system housing 313. In other types of systems, conveyer 316 may deliver the crop directly to a threshing cylinder.
The crop materials entering processing system 312 can move axially and helically therethrough during threshing and separating. During such travel, the crop materials are threshed and separated by rotor 322 operating in chamber 323 which concentrically receives the rotor 322. The lower part of the chamber 323 contains concave assembly 324 and a separator grate assembly 326. Rotation of the rotor 322 impels the crop material rearwardly in a generally helical direction about the rotor 322. A plurality of rasp bars and separator bars (not shown) mounted on the cylindrical surface of the rotor 322 cooperate with the concave assembly 324 and separator grate assembly 326 to thresh and separate the crop material, with the grain escaping laterally through concave assembly 324 and separator grate assembly 326 into cleaning mechanism 328. Bulkier stalk and leaf materials are retained by the concave assembly 324 and the separator grate assembly 326 and are impelled out the rear of processing system 312 and ultimately out of the rear of the combine harvester 300.
A blower 330 forms part of the cleaning mechanism 328 and provides a stream of air throughout the cleaning region below processing system 312 and directed out the rear of the combine harvester 300 so as to carry lighter chaff particles away from the grain as it migrates downwardly toward the bottom of the machine to a clean grain auger 332. Since the grain is cleaned by the blower 330 by the time it reaches the auger 332, in some embodiments the camera for capturing images of the crop is mounted near the auger 332 facing a section that conveys the cleaned grain (e.g., see camera 304). Clean grain auger 332 delivers the clean grain to an elevator (not shown) that elevates the grain to a storage bin 334 on top of the combine harvester 300, from which it is ultimately unloaded via an unloading spout 336. A returns auger 337 at the bottom of the cleaning region is operable in cooperation with other mechanism (not shown) to reintroduce partially threshed crop materials into the front of processing system 312 for an additional pass through the processing system 312.
As shown in
Also, the method 400, at step 405, includes actively and continually monitoring, by the computing system (e.g., see computing system 200), a standard deviation of the characteristics of the crop in the images (e.g., see images 700 and 800) while the harvester (e.g., see combine harvester 300) is moving through the field. In such examples, the threshold changes according to changes in monitored standard deviation. In some embodiments, as shown in
Also shown in
In some of aforementioned embodiments and other embodiments, when the computing system (e.g., see computing system 200) determines whether a new variety exists in the field, the computing system considers crop height (e.g., see crop height characteristic 702 shown in
In some embodiments, weights are used in determining characteristics of the crop in the images (such as at step 404) or in determining whether the determined characteristics deviate from known characteristics of a first variety of the crop (such as at step 406). When weights are used for determining characteristics of the crop in the images different weights can be applied to one or more of crop height, crop color, and crop density. Also, different weights can be applied to one or more of the aforementioned secondary factors of the crop, which include yield of the crop, elevation of field, slope of field, measured mass of the crop, seed size of the crop, and seed color of the crop.
In some embodiments of the method 400, the determination of characteristics of the crop in the images (e.g., see images 700 and 800) is based at least on digital signal processing (e.g., see step 502 shown in
In some examples, the computer vision analysis includes inputting aspects of the images (e.g., see images 700 and 800) or derivatives of aspects of the images into an ANN (e.g., see step 504 as well as instructions 226), and the detecting of the different varieties of the crop is based at least on the output of the ANN (e.g., see step 508 as well as instructions 226). Also, in such examples, the ANN includes or is part of a deep learning process that detects the different varieties of the crop or is a basis for the detection of the different varieties of the crop. And, the deep learning process can include a CNN or a network of CNNs.
As shown in
In some embodiments of the method 1000, the identification of the physical characteristic of the crop being within a certain range of values is based at least on digital signal processing. In some instances, the identification of the physical characteristic of the crop being within a certain range of values is based at least on a computer vision analysis. Also, the identification of the physical characteristic of the crop being within a certain range of values can be further based on digital signal processing. The digital signal processing can occur prior to the computer vision analysis as a pre-processing step to generate enhanced input for the computer vision analysis. The computer vision analysis can include inputting the enhanced input into an ANN, and the detecting of the different varieties of the crop can be based at least on the output of the ANN. The ANN can include or be a part of a deep learning process that detects the different varieties of the crop or is a basis for the detection of the different varieties of the crop. The deep learning process can include a CNN or a network of CNNs. In some embodiments, the computer vision analysis includes inputting aspects of the images or derivatives of aspects of the images into an ANN and the detecting of the different varieties of the crop is based at least on the output of the ANN. In such examples, the ANN can include or is part of a deep learning process that detects the different varieties of the crop or is a basis for the detection of the different varieties of the crop. And, the deep learning process includes a CNN or a network of CNNs.
In some embodiments of the method 1000, the physical characteristic is crop height, crop color, crop density, crop reflectiveness or any combination thereof. In some embodiments, the physical characteristic is crop height only. In some embodiments, the physical characteristic is crop color only. In some embodiments, the physical characteristic is crop reflectiveness only. In some embodiments, the physical characteristic is crop density only. In some embodiments of the method 1000, the first range of values of the physical characteristic is predetermined prior to the harvester moving through the field. In such examples, the second range of values of the physical characteristic may not be predetermined prior to the harvester moving through the field and can be determined while the harvester moving through the field. In some embodiments of the method 1000, an operator of the harvester has the ability to confirm or decline the associating of the first recorded locations to the first variety or the associating of the second recorded locations to the second variety. In some embodiments of the method 1000, the method further includes identifying the different varieties from different color spectrums in satellite imagery of the crop field to corroborate the generated map of crop varieties as well providing, via a user interface (e.g., see user interface 216), a confidence level of the map that is determined according to a comparison between the map and the satellite imagery.
Also, similar to image 700, image 800 has been derived from image data that has been through many image processing stages (such as the stages shown in
As shown in
Also, in some embodiments, a crop variety map (e.g., see map 904) can be combined with a yield map. The advantage of the crop variety map or the crop variety map combined with the yield map over the yield map alone is that the crop variety map provides additional information on the factors for the yields represented in a yield map. The crop variety map, with or without being combined with the yield map, can also be combined with different types of agriculture informational maps such as a soil quality map, a soil moisture map, a soil pH-level map, and/or a crop or carbon density map. Such combined maps can then be used to analyze a crop and its field and possibly improve farming practices or some other variance that may affect quality of a crop for different crop varieties in different situations.
Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a predetermined result. The operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. It should be borne in mind, however, that these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. The present disclosure can refer to the action and processes of a computing system, or similar electronic computing device, which manipulates and transforms data represented as physical (electronic) quantities within the computing system's registers and memories into other data similarly represented as physical quantities within the computing system memories or registers or other such information storage systems.
The present disclosure also relates to an apparatus for performing the operations herein. This apparatus can be specially constructed for the intended purposes, or it can include a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program can be stored in a computer readable storage medium, such as any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, each coupled to a computing system bus.
The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems can be used with programs in accordance with the teachings herein, or it can prove convenient to construct a more specialized apparatus to perform the methods. The structure for a variety of these systems will appear as set forth in the description herein. In addition, the present disclosure is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages can be used to implement the teachings of the disclosure as described herein.
The present disclosure can be provided as a computer program product, or software, which can include a machine-readable medium having stored thereon instructions, which can be used to program a computing system (or other electronic devices) to perform a process according to the present disclosure. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). In some embodiments, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium such as a read only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory components, etc.
While the invention has been described in conjunction with the specific embodiments described herein, it is evident that many alternatives, combinations, modifications and variations are apparent to those skilled in the art. Accordingly, the example embodiments of the invention, as set forth herein are intended to be illustrative only, and not in a limiting sense. Various changes can be made without departing from the spirit and scope of the invention.
| Number | Date | Country | |
|---|---|---|---|
| 63483675 | Feb 2023 | US |
