CN111679274B - Method and device for predicting and evaluating crop growth by using ultra-wideband radar - Google Patents
Method and device for predicting and evaluating crop growth by using ultra-wideband radar Download PDFInfo
- Publication number
- CN111679274B CN111679274B CN202010720933.3A CN202010720933A CN111679274B CN 111679274 B CN111679274 B CN 111679274B CN 202010720933 A CN202010720933 A CN 202010720933A CN 111679274 B CN111679274 B CN 111679274B
- Authority
- CN
- China
- Prior art keywords
- data
- ultra
- growth
- wideband radar
- crops
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
Abstract
A method and a device for predicting and evaluating the growth vigor of crops by using an ultra-wideband radar comprise the following steps: acquiring ultra-wideband radar data and growth data of a plurality of groups of growth processes in the growth process of crops; slicing the recorded ultra-wideband radar data and the growth data according to a time schedule to obtain slice monomers, comparing differences of echo data among the slice monomers, and if the differences are larger than a first threshold value and the growth data are abnormal, performing exception marking on the ultra-wideband radar data in the time schedule; recording the measured ultra-wideband radar data of the crops by using an ultra-wideband radar; and comparing the measured ultra-wideband radar data with previously acquired ultra-wideband radar data with growth data to obtain growth information. The characteristics of inner structure can be deeply observed to this application utilization ultra wide band radar, and it is too big to avoid surperficial branch and leaf to influence the observation result.
Description
Technical Field
The application relates to a method and a device for predicting and evaluating crop growth by using an ultra-wideband radar.
Background
Ultra-wideband radar refers to radar that transmits signals with a fractional bandwidth greater than 0.25. Ultra-wideband technology is to perform a series of processing and processing, including generation, transmission, reception, processing, etc., on a very short single pulse to achieve the functions of communication, detection, remote sensing, etc. Ultra-wideband refers to a major feature of the technology, i.e. the occupied bandwidth is very large. Based on the characteristics, compared with other common radar systems, the radar has better anti-jamming capability and certain penetrating capability and target identification capability.
In the agricultural field, ultra-wideband radar is mostly used for measuring soil moisture in the CN105116399A and CN104062654A by utilizing the penetration capability of the ultra-wideband radar. For crop planting, soil moisture is only one side, and more meaningful monitoring and prediction of the growth of the crops are achieved. The existing monitoring and prediction for the growth mostly adopts aerial photography or other modes to obtain remote sensing images, and corresponding growth conditions are obtained by analyzing the remote sensing images, but the observation accuracy and the result accuracy in prediction of the remote sensing images are insufficient to a certain extent.
Disclosure of Invention
In order to solve the above problems, the present application discloses a method for predicting and evaluating growth vigor of crops by using an ultra-wideband radar, which includes an original database and growth vigor data, obtains similar growth vigor data in the original database by evaluating the growth vigor data, and then obtains a prediction result, and includes the following steps:
acquiring an original database: acquiring ultra-wideband radar data and growth data of a plurality of groups of growth processes in the growth process of crops;
slicing the recorded ultra-wideband radar data and the growth data according to a time schedule to obtain slice monomers, comparing differences of echo data among the slice monomers, and if the differences are larger than a first threshold value and the growth data are abnormal, performing exception marking on the ultra-wideband radar data in the time schedule;
acquiring growth data: recording the measured ultra-wideband radar data of the crops by using an ultra-wideband radar;
and comparing the measured ultra-wideband radar data with previously acquired ultra-wideband radar data with growth data to obtain growth information. This application utilizes the ultra wide band radar can go deep into to observe the characteristic of inner structure, avoid surperficial branch and leaf to influence too big to the observation result, this application combines ultra wide band radar data and growth data, the ultra wide band radar data composition database of growth process in the corresponding earlier stage, then utilize current growth state characterization parameter, search the database through this characterization parameter, then obtain its corresponding growth state and refer to, thereby predict its growth vigor, it is of course to explain, the degree of accuracy of prediction is directly relevant with the abundance of original database, new monitoring data of course can be deposited in the database in order to strengthen the database as the new dataflow of database, and the referability of reinforcing database.
Preferably, the ultra-wideband radar data is acquired radar electromagnetic scattering echo data, and the echo data is echo delay time and vibration amplitude corresponding to the echo delay time.
Preferably, the echo delay times are sorted according to a time schedule, and the slices are obtained as follows: the method comprises the steps of dividing echo data according to time progress, wherein the divided time length is a first time length, drawing the divided data, and taking echo delay time as a horizontal coordinate and vibration amplitude as a line graph of a vertical coordinate, wherein the line graph is a meta graph which is arranged in a sequencing mode according to the time progress. In the application, the data is finally actually patterned, so that the data is patterned, the data comparison is found in the processing process to be easily influenced by single data or environment, but on the contrary, after the data comparison, the relevant noise is greatly reduced, and the referential performance of consistency comparison is better.
Preferably, the growth data is used for performing one-to-one label difference processing on the decomposed metagraph.
Preferably, the method for comparing the measured ultra-wideband radar data with the previously acquired ultra-wideband radar data with growth data is performed as follows:
obtaining measured ultra-wideband radar data;
the method comprises the steps of dividing ultra-wideband radar data, drawing the divided data with the divided time length as a first time length, and taking echo delay time as a horizontal coordinate and vibration amplitude as a line graph of a vertical coordinate, wherein the line graph is a monitoring graph;
comparing the graph similarity of the monitoring graph and the meta graph according to the time progress, calculating the similarity, accumulating the front similarities to obtain an average first similarity, sequencing according to the first similarity, wherein the growth trends of the front similarities are the prediction trend of the monitored crops. By acquiring the similarity of the latest slices, it should be noted that the total time of the related slices is generally 24h-72h, so that the related slices have better representativeness, and the first slices with high similarity are the future growth of crops corresponding to possible monitoring graphs.
Preferably, the first threshold is 85-90%.
Preferably, when the difference is greater than the first threshold value but the growth data is normal, objective data of crop growth is recorded and environmental abnormality labeling processing is performed.
Preferably, the first time length is 1ms to 5 ms.
Preferably, the first degree of similarity is 75-85%.
On the other hand, this application still discloses an utilize ultra wide band radar to carry out crops growth situation prediction evaluation device, includes following module:
the basic data acquisition module is used for acquiring ultra-wideband radar data and growth data of a plurality of groups of growth processes in the growth process of crops;
the data processing module is used for slicing the recorded ultra-wideband radar data and the growth data according to the time progress to obtain slice monomers, comparing differences of echo data among the slice monomers, and performing exception processing on the ultra-wideband radar data in the time progress if the differences are larger than a first threshold and the growth data are abnormal;
the detection module is used for recording the measured ultra-wideband radar data of the crops by using an ultra-wideband radar;
and the judging module compares the measured ultra-wideband radar data with the previously acquired ultra-wideband radar data with the growth data to acquire growth information.
This application can bring following beneficial effect:
1. the characteristics of the internal structure can be observed deeply by using the ultra-wideband radar, and the influence of surface branches and leaves on the observation result is avoided; the method also combines the ultra-wideband radar data with the growth data, namely the ultra-wideband radar data in the growth process as reference forms a database, then the characterization parameters of the ultra-wideband radar corresponding to the existing growth state are utilized, the database is searched through the characterization parameters, and then the corresponding growth state reference is obtained, so that the growth vigor of the database is predicted, it is needless to say that the accuracy degree of prediction is directly related to the richness degree of the original database, and certainly, new monitoring data can be stored in the database as new data flow of the database to strengthen the database and enhance the referential performance of the database;
2. in the application, the data is finally actually patterned, so that the data is patterned, the fact that the data is easily influenced by single data or environment if data comparison is carried out in the processing process is found, but on the contrary, after the data is patterned, the noise in the relevant comparison aspect is greatly reduced, and the referential property of consistency comparison is better than before;
3. by acquiring the similarity of the latest slices, the future growth vigor corresponding to the growth data with high first similarity as reference is the future growth vigor of the crop being monitored corresponding to the possible monitoring graph.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart of a first embodiment;
FIG. 2 is a schematic flow chart of a second embodiment.
Detailed Description
In order to clearly explain the technical features of the present invention, the present application will be explained in detail by the following embodiments in combination with the accompanying drawings.
In a first embodiment, as shown in fig. 1, a method for predicting and evaluating growth of crops by using ultra-wideband radar comprises the following steps:
s101, establishing a reference database:
the establishment basis of the method is that a database system is needed, and the database system needs to comprise ultra-wideband radar data and growth data of the growth process as much as possible; the ultra-wideband radar data is obtained radar electromagnetic scattering echo data, and the echo data is echo delay time and vibration amplitude corresponding to the echo delay time;
s102, data slicing is carried out to obtain a metagraph, and growth data of the metagraph are subjected to different processing:
the database also includes the following data: slicing the recorded ultra-wideband radar data and the growth data according to a time schedule to obtain slice monomers, comparing differences of echo data among the slice monomers, and if the differences are larger than a first threshold value and the growth data are abnormal, performing exception marking on the ultra-wideband radar data in the time schedule; the echo delay time is sequenced according to time progress, and the slices are obtained according to the following modes: dividing echo data according to time progress, wherein the divided time length is a first time length, drawing the divided data, and taking echo delay time as a horizontal coordinate and vibration amplitude as a line graph of a vertical coordinate, wherein the line graph is a metagraph which is arranged in a sequence according to the time progress; the first threshold value is 85-90%, growth data are combined, for example, insect damage, drought and waterlogging, lodging and the like are displayed on the growth data, the metagrams obtained corresponding to the time are marked one by one according to information on the growth data, and the abnormal state at the time is mapped, namely, different marking processing is carried out;
s103, crop monitoring:
recording the measured ultra-wideband radar data of the crops by using an ultra-wideband radar;
s104, monitoring data processing:
dividing ultra-wideband radar data, wherein the divided time length is a first time length, drawing the divided data, and taking echo delay time as a horizontal coordinate and vibration amplitude as a line graph of a vertical coordinate, wherein the line graph is a monitoring graph; the first time length is 1ms-5ms, and the first similarity is 75-85%;
and S105, comparing the monitoring data with the data of the database to obtain a predicted trend.
Comparing the graph similarity of the monitoring graph and the meta graph according to the time progress, calculating the similarity, accumulating the front similarities to obtain an average first similarity, and sequencing according to the first similarity, wherein the growth trends of the front two are the predicted trends of the monitored crops.
For the two examples, the two test fields are respectively subjected to monitoring tests, namely test 1 and test 2, in test 1, the following 5 abnormal states are met, namely irregular emergence, excessive weeds, drought, insect pests and lodging caused by strong wind, wherein the growth trends of the two fields before the irregular emergence stage are accurate, the excessive weeds are predicted accurately, the other field is predicted to have excessive cultivation density, the drought and insect pests are predicted accurately, but the lodging caused by strong wind has sporadic property, so that neither field is predicted accurately, one field is identified as the end of growth, and the other field is identified as waterlogging.
In a second embodiment, as shown in fig. 2, a method for predicting and evaluating growth of crops by using ultra-wideband radar comprises the following steps:
s201, establishing a reference database:
the establishment basis of the method is that a database system is needed, and the database system needs to comprise ultra-wideband radar data and growth data of the growth process as much as possible; the ultra-wideband radar data is obtained radar electromagnetic scattering echo data, and the echo data is echo delay time and vibration amplitude corresponding to the echo delay time;
s202, data slicing is conducted to obtain a metagraph, and the growing data of the metagraph and the standard and different processing of environmental abnormity are conducted:
the database also includes the following data: slicing the recorded ultra-wideband radar data and the growth data according to a time schedule to obtain slice monomers, comparing differences of echo data among the slice monomers, and if the differences are larger than a first threshold value and the growth data are abnormal, performing exception marking on the ultra-wideband radar data in the time schedule; the echo delay time is sequenced according to time progress, and the slices are obtained according to the following modes: dividing echo data according to time progress, wherein the divided time length is a first time length, drawing the divided data, and taking echo delay time as a horizontal coordinate and vibration amplitude as a line graph of a vertical coordinate, wherein the line graph is a metagraph which is arranged in a sequence according to the time progress; the first threshold value is 85-90%, growth data are combined, for example, insect damage, drought and waterlogging, lodging and the like are displayed on the growth data, the metagrams obtained corresponding to the time are marked one by one according to information on the growth data, and the abnormal state at the time is mapped, namely, different marking processing is carried out; when the difference is larger than a first threshold value but the growth data is normal, recording objective data of crop growth, and performing environmental anomaly labeling processing;
s203, crop monitoring:
recording the measured ultra-wideband radar data of the crops by using an ultra-wideband radar;
s204, monitoring data processing:
dividing ultra-wideband radar data, wherein the divided time length is a first time length, drawing the divided data, and taking echo delay time as a horizontal coordinate and vibration amplitude as a line graph of a vertical coordinate, wherein the line graph is a monitoring graph; the first time length is 1ms-5ms, and the first similarity is 75-85%;
s205, comparing the monitoring data with the data of the database to obtain a prediction trend.
Comparing the graph similarity of the monitoring graph and the meta graph according to the time progress, calculating the similarity, accumulating the front similarities to obtain an average first similarity, and sequencing according to the first similarity, wherein the growth trends of the front two are the predicted trends of the monitored crops.
For the two embodiments, two test fields are respectively subjected to monitoring tests, namely test 3 and test 4, in the test 3, the following 5 abnormal states are met, namely excessive weeds, waterlogging disasters, nutrition deficiency, insect damage and lodging caused by strong wind, and the excessive weeds, the waterlogging disasters and the nutrition deficiency are all accurately predicted; one pest is accurately predicted, and the other pest is predicted to lack nutrition; for the lodging caused by strong wind, although the prediction function is not performed, in the later state display, the lodging phenomenon is identified by two growth trends.
In a third embodiment, the device for predicting and evaluating the growth of crops by using the ultra-wideband radar comprises the following modules:
the basic data acquisition module is used for acquiring ultra-wideband radar data and growth data of a plurality of groups of growth processes in the growth process of crops;
the data processing module is used for slicing the recorded ultra-wideband radar data and the growth data according to the time progress to obtain slice monomers, comparing differences of echo data among the slice monomers, and performing exception processing on the ultra-wideband radar data in the time progress if the differences are larger than a first threshold and the growth data are abnormal;
the detection module is used for recording the measured ultra-wideband radar data of the crops by using an ultra-wideband radar;
and the judging module compares the measured ultra-wideband radar data with the previously acquired ultra-wideband radar data with the growth data to acquire growth information.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to one or more embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The above description is merely one or more embodiments of the present disclosure and is not intended to limit the present disclosure. Various modifications and alterations to one or more embodiments of the present description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of one or more embodiments of the present specification should be included in the scope of the claims of the present specification.
Claims (9)
1. A crop growth prediction evaluation method by using an ultra-wideband radar comprises an original database and growth data, and is characterized in that: evaluating the growth data to obtain similar growth data in an original database, and then obtaining a prediction result;
the method comprises the following steps:
acquiring an original database: ultra-wideband radar data and growth data of a plurality of groups of growth processes in the growth process of crops;
slicing the recorded ultra-wideband radar data and the growth data according to a time schedule to obtain slice monomers, comparing differences of echo data among the slice monomers, and if the differences are larger than a first threshold value and the growth data are abnormal, performing exception marking on the ultra-wideband radar data in the time schedule;
acquiring growth data: recording the measured ultra-wideband radar data of the crops by using an ultra-wideband radar;
and comparing the measured ultra-wideband radar data with previously acquired ultra-wideband radar data with growth data to obtain growth information.
2. The method for predicting and evaluating the growth of crops by using the ultra-wideband radar as claimed in claim 1, wherein the method comprises the following steps: the ultra-wideband radar data is radar electromagnetic scattering echo data obtained by acquisition, and the echo data is echo delay time and vibration amplitude corresponding to the echo delay time; the echo delay time is sequenced according to time progress, and the slices are obtained according to the following modes: the method comprises the steps of dividing echo data according to time progress, wherein the divided time length is a first time length, drawing the divided data, and taking echo delay time as a horizontal coordinate and vibration amplitude as a line graph of a vertical coordinate, wherein the line graph is a meta graph which is arranged in a sequencing mode according to the time progress.
3. The method for predicting and evaluating the growth of crops by using the ultra-wideband radar as claimed in claim 2, wherein the method comprises the following steps: and the growth data is used for carrying out one-to-one marking processing on the decomposed metagraphs.
4. The method for predicting and evaluating the growth of crops by using the ultra-wideband radar as claimed in claim 2, wherein the method comprises the following steps: the method for determining the comparison of the ultra-wideband radar data with the previously acquired ultra-wideband radar data with growth data is performed as follows:
obtaining measured ultra-wideband radar data;
the method comprises the steps of dividing ultra-wideband radar data, drawing the divided data with the divided time length as a first time length, and taking echo delay time as a horizontal coordinate and vibration amplitude as a line graph of a vertical coordinate, wherein the line graph is a monitoring graph;
comparing the graph similarity of the monitoring graph and the meta graph according to the time progress, calculating the similarity, accumulating the front similarities to obtain an average first similarity, sequencing according to the first similarity, wherein the growth trends of the front similarities are the prediction trend of the monitored crops.
5. The method for predicting and evaluating the growth of crops by using the ultra-wideband radar as claimed in claim 1, wherein the method comprises the following steps: the first threshold is 85-90%.
6. The method for predicting and evaluating the growth of crops by using the ultra-wideband radar as claimed in claim 5, wherein the method comprises the following steps: and when the difference is larger than the first threshold value but the growth data is normal, recording objective data of crop growth and carrying out environmental anomaly marking processing.
7. The method for predicting and evaluating the growth of crops by using the ultra-wideband radar as claimed in claim 2, wherein the method comprises the following steps: the first time length is 1ms-5 ms.
8. The method for predicting and evaluating the growth of crops by using the ultra-wideband radar as claimed in claim 4, wherein the method comprises the following steps: the first similarity is 75-85%.
9. The utility model provides an utilize ultra wide band radar to carry out crops growth prediction evaluation device which characterized in that: the system comprises the following modules:
the basic data acquisition module is used for acquiring ultra-wideband radar data and growth data of a plurality of groups of growth processes in the growth process of crops;
the data processing module is used for slicing the recorded ultra-wideband radar data and the growth data according to the time progress to obtain slice monomers, comparing differences of echo data among the slice monomers, and performing exception processing on the ultra-wideband radar data in the time progress if the differences are larger than a first threshold and the growth data are abnormal;
the detection module is used for recording the measured ultra-wideband radar data of the crops by using an ultra-wideband radar;
and the judging module compares the measured ultra-wideband radar data with the previously acquired ultra-wideband radar data with the growth data to acquire growth information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010720933.3A CN111679274B (en) | 2020-07-24 | 2020-07-24 | Method and device for predicting and evaluating crop growth by using ultra-wideband radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010720933.3A CN111679274B (en) | 2020-07-24 | 2020-07-24 | Method and device for predicting and evaluating crop growth by using ultra-wideband radar |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111679274A CN111679274A (en) | 2020-09-18 |
| CN111679274B true CN111679274B (en) | 2021-05-14 |
Family
ID=72438324
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010720933.3A Active CN111679274B (en) | 2020-07-24 | 2020-07-24 | Method and device for predicting and evaluating crop growth by using ultra-wideband radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111679274B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117151477B (en) * | 2023-10-31 | 2024-01-23 | 中化现代农业有限公司 | Crop anomaly identification method, device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9037414B1 (en) * | 2011-01-14 | 2015-05-19 | University Of Notre Dame Du Lac | Methods and apparatus for electromagnetic signal polarimetry sensing |
| CN108445483A (en) * | 2018-03-16 | 2018-08-24 | 成都锦江电子系统工程有限公司 | Water floats plant radar sensing system |
| CN110287457A (en) * | 2019-07-02 | 2019-09-27 | 吉林大学 | Corn Biomass inverting measuring method based on satellite military systems data |
| CN110377961A (en) * | 2019-06-25 | 2019-10-25 | 北京百度网讯科技有限公司 | Crop growth environment control method, device, computer equipment and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9857316B2 (en) * | 2012-01-13 | 2018-01-02 | University Of Notre Dame Du Lac | Methods and apparatus for electromagnetic signal polarimetry sensing |
-
2020
- 2020-07-24 CN CN202010720933.3A patent/CN111679274B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9037414B1 (en) * | 2011-01-14 | 2015-05-19 | University Of Notre Dame Du Lac | Methods and apparatus for electromagnetic signal polarimetry sensing |
| CN108445483A (en) * | 2018-03-16 | 2018-08-24 | 成都锦江电子系统工程有限公司 | Water floats plant radar sensing system |
| CN110377961A (en) * | 2019-06-25 | 2019-10-25 | 北京百度网讯科技有限公司 | Crop growth environment control method, device, computer equipment and storage medium |
| CN110287457A (en) * | 2019-07-02 | 2019-09-27 | 吉林大学 | Corn Biomass inverting measuring method based on satellite military systems data |
Non-Patent Citations (2)
| Title |
|---|
| STUDY ON THE INFLUENCE OF DROUGHT TO CROP GROWTH BASED ON SAR REMOTE SENSING;Aimin Cai et al.;《IGARSS 2009》;20091231;第IV-382至IV-385页 * |
| 作物生长过程检测技术研究与实现;张帅亮;《中国优秀硕士学位论文全文数据库 农业科技辑》;20180715(第7期);正文第29-40页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111679274A (en) | 2020-09-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106779188A (en) | Plant pest Forecasting Methodology and device in a kind of plantation equipment | |
| CN116012720B (en) | Crop growth condition monitoring method, device and medium based on high-resolution remote sensing image | |
| CN111679274B (en) | Method and device for predicting and evaluating crop growth by using ultra-wideband radar | |
| Marsland et al. | AviaNZ: A future‐proofed program for annotation and recognition of animal sounds in long‐time field recordings | |
| KR102145617B1 (en) | System for selecting plants based on phenotypes and method thereof | |
| An et al. | Real-time monitoring method of strawberry fruit growth state based on YOLO improved model | |
| Latif et al. | Integration of google play content and frost prediction using cnn: scalable iot framework for big data | |
| Galera et al. | Analysis of airborne Olea pollen in Cartagena (Spain) | |
| Ruparelia et al. | Real-time tomato detection, classification, and counting system using deep learning and embedded systems | |
| Arshad et al. | Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia | |
| CN115082803B (en) | Cultivated land abandoned land monitoring method and device based on vegetation season change and storage medium | |
| CN116703638A (en) | Greenhouse vegetable planting management system and method thereof | |
| Parker et al. | Evaluating long-term monitoring of temperate reef fishes: a simulation testing framework to compare methods | |
| Sundar et al. | Flower counting in yield approximation using digital image processing techniques | |
| CN113628253A (en) | Method and system for accurately detecting individual health of animal and storage medium | |
| Tan et al. | Anchor-free deep convolutional neural network for plant and plant organ detection and counting | |
| CN111797783A (en) | Intelligent pulsar screening system based on two-channel convolutional neural network | |
| Dinca et al. | Halyomorpha Halys Detection in Orchard from UAV Images Using Convolutional Neural Networks | |
| CN112528880B (en) | WiFi CSI-based small sample countermeasure learning action recognition method, system and terminal | |
| CN113627834B (en) | Intelligent monitoring and early warning method and system for fruit and vegetable full chain | |
| CN115514621B (en) | Fault monitoring method, electronic device and storage medium | |
| CN117390110B (en) | Method and system for generating electronic map of plant diseases and insect pests | |
| Mondal et al. | Editorial for Special Issue:“Global Vegetation and Land Surface Dynamics in a Changing Climate”. Land 2021, 10, 45 | |
| CN116912698B (en) | Land coverage change monitoring method and device, electronic equipment and storage medium | |
| Vergni et al. | Influence of the rainfall Time Step on the Thresholds for Separating Erosive and Non-Erosive Events |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |