Corn crop identification method based on new remote sensing index construction and related device

Abstract

The invention provides a corn crop identification method based on new remote sensing index construction and a related device, and relates to the technical field of farmland monitoring mapping. Wherein the electronic equipment obtains a first identification index and a second identification index according to spectral information of a to-be-identified land parcel; wherein the first identification index represents the chlorophyll and moisture content of the crops in the to-be-identified land parcel, and the second identification index represents the low reflection characteristics of the corn crops in the to-be-identified land parcel in the near-infrared band; and if the first identification index and the second identification index meet a preset condition, determining that the crop in the to-be-identified land parcel is the corn crop. Therefore, two specific identification indexes are introduced for identification of the corn crops, so that the identification precision of the corn crops is remarkably improved under a complex agricultural background.

Description

Technical Field

[0001] The present application relates to the technical field of farmland monitoring and mapping, and in particular to a corn crop identification method and related devices based on a new remote sensing index. Background Art

[0002] Corn is one of the three major food crops in the world, accounting for more than 12% of the world's total food production. As an important source of carbohydrates, corn not only meets the basic nutritional needs of humans and animals, but also has a wide range of applications in biofuels, food processing and industrial production. However, with the continuous growth of global food demand and the increasing complexity of agricultural production, how to efficiently and accurately monitor and manage corn planting has become an important issue in the current agricultural field.

[0003] Traditional field survey classification methods have problems such as high cost, long time consumption, and low update frequency, which are difficult to meet the needs of modern agricultural management. With the rapid development of earth observation technology, multi-source, multi-temporal, multi-scale, and high-dimensional remote sensing data continue to emerge, providing new technical means for agricultural monitoring. However, existing remote sensing technology still faces many challenges in identifying corn planting plots.

[0004] During the research, it was found that for remote sensing images, classifier-based recognition methods and dynamic time warping methods were proposed in related technologies. Classifier-based methods include shallow structure models based on support vector machines and random forests, and deep learning models such as long short-term memory networks. Although these methods have shown good recognition effects in some scenarios, the training of the model usually requires sufficient samples, so it is difficult to generalize to a large area. The dynamic time warping method performs well in time series analysis. This method classifies unknown time series by comparing them with known event characteristics, but it is mainly suitable for distinguishing crops with obvious phenological differences, and there are still limitations in the accurate identification of corn planting plots.

[0005] Therefore, there is still a lack of effective methods for accurately identifying and monitoring corn plots using advanced remote sensing technology. Especially in some areas, farmland management units are mainly small farms or family farmers, with small plots and diverse crop types, which increases the complexity of corn plot identification. Summary of the invention

[0006] In order to overcome at least one of the deficiencies in the prior art, the present application provides a corn crop identification method and related device based on a new remote sensing index, including:

[0007] In a first aspect, the present application provides a corn crop identification method based on a new remote sensing index, the method comprising:

[0008] According to the spectral information of the plot to be identified, a first identification index and a second identification index are obtained, wherein the first identification index represents the chlorophyll and moisture content of the crops in the plot to be identified, and the second identification index represents the low reflection characteristics of the corn crops in the plot to be identified in the near-infrared band;

[0009] If the first identification index and the second identification index meet a preset condition, it is determined that the crop in the to-be-identified plot is a corn crop.

[0010] In a second aspect, the present application provides a corn crop identification device based on a new remote sensing index, the device comprising:

[0011] An identification index module, used to obtain a first identification index and a second identification index according to the spectral information of the plot to be identified, wherein the first identification index represents the chlorophyll and moisture content of the crops in the plot to be identified, and the second identification index represents the low reflection characteristics of the corn crops in the plot to be identified in the near-infrared band;

[0012] The crop identification module determines that the crop in the to-be-identified plot is a corn crop if the first identification index and the second identification index meet a preset condition.

[0013] In a third aspect, the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the corn crop identification method based on the new remote sensing index.

[0014] In a fourth aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements the corn crop identification method based on the new remote sensing index.

[0015] Compared with the prior art, this application has the following beneficial effects:

[0016] The present application provides a corn crop identification method and related devices based on a new remote sensing index. The electronic device obtains a first identification index and a second identification index based on the spectral information of the plot to be identified; the first identification index characterizes the chlorophyll and moisture content of the crops in the plot to be identified, and the second identification index characterizes the low reflection characteristics of the corn crops in the plot to be identified in the near-infrared band; if the first identification index and the second identification index meet the preset conditions, it is determined that the crops in the plot to be identified are corn crops. In this way, two specific identification indexes are introduced for the identification of corn crops, which significantly improves the identification accuracy of corn crops in complex agricultural backgrounds. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 One of the flow charts of the corn crop identification method based on the new remote sensing index provided in the embodiment of the present application;

[0019] Figure 2 The second flowchart of the corn crop identification method based on the new remote sensing index provided in the embodiment of the present application;

[0020] Figure 3 A schematic diagram of the structure of a corn crop identification device based on a new remote sensing index provided in an embodiment of the present application;

[0021] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0024] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0025] In the description of the present application, it should be noted that the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. In addition, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0026] Based on the above statement, as introduced in the background technology, there is still a lack of effective methods for accurately identifying and monitoring corn-growing plots using advanced remote sensing technology. Especially in some areas, farmland management units are mainly small farms or family farmers, with small plots and diverse crop types, which increases the complexity of corn-growing plot identification.

[0027] Based on the discovery of the above technical problems, the inventors have proposed the following technical solutions to solve or improve the above problems through creative work. It should be noted that the defects in the solutions in the above prior art are the results obtained by the inventors after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed in the embodiments of the present application for the above problems below should all be the contributions made by the inventors to the present application in the process of invention and creation, and should not be understood as technical contents known to those skilled in the art.

[0028] In view of the above problems, an embodiment of the present application (hereinafter referred to as the present embodiment) provides a corn crop identification method based on a new remote sensing index. The index threshold-based method of this method can effectively classify specific crop types from existing remote sensing data by enhancing the spectral differences between the target crop type and other crops. Since this method is simple to calculate and highly practical, it is easier to automate, thus simplifying the classification process and being suitable for a wide range of crop monitoring and classification tasks. In addition, by combining dynamic time warping and remote sensing indices, accurate identification of corn can be achieved in a heterogeneous agricultural context. Figure 1 As shown, the method includes:

[0029] S2, obtaining a first identification index and a second identification index according to the spectral information of the land parcel to be identified.

[0030] Among them, the first identification index characterizes the chlorophyll and moisture content of the crops in the to-be-identified plot, and the second identification index characterizes the low reflectivity characteristics of the corn crops in the to-be-identified plot in the near-infrared band.

[0031] S3: If the first identification index and the second identification index meet the preset conditions, it is determined that the crop in the to-be-identified plot is a corn crop.

[0032] In this way, two specific recognition indexes are introduced for the identification of corn crops, which significantly improves the recognition accuracy of corn crops in complex agricultural backgrounds.

[0033] In addition, it should be understood that for the corn crop identification method based on the new remote sensing index provided in this embodiment, the electronic device implementing the method may be, but is not limited to, a mobile terminal, a tablet computer, a laptop computer, a desktop computer, and a server, etc. The server may be a single server or a server group. The server group may be centralized or distributed (for example, the server may be a distributed system). In some embodiments, the server may be local or remote relative to the user terminal. In some embodiments, the server may be implemented on a cloud platform; as an example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, etc., or any combination thereof. In some embodiments, the server may be implemented on an electronic device having one or more components. Therefore, it means that the method can be run on a cloud or a local server in the form of software, which is convenient for large-scale application and promotion.

[0034] To make the solution provided by this embodiment clearer, the following uses a server as an electronic device to implement the method. Figure 1 Each step in the method shown is described in detail. However, it should be understood that the operations of the flowchart may not be implemented in order, and steps without logical contextual relationship may be reversed or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowchart, or remove one or more operations from the flowchart under the guidance of the content of this application. Figure 1 , the method comprising:

[0035] S2, obtaining a first identification index and a second identification index according to the spectral information of the land parcel to be identified.

[0036] Among them, the first identification index characterizes the chlorophyll and moisture content of the crops in the plot to be identified, and the second identification index characterizes the low reflectance characteristics of the corn crops in the plot to be identified in the near-infrared band. The calculation basis of the above-mentioned first identification index and the second identification index is the significant difference between the spectral characteristics of corn crops during the maximum growth period and the spectral characteristics of other crops. It can be understood that the first identification index and the second identification index are identification parameters specially proposed for identifying corn crops after a lot of research, and their principles are based on the spectral characteristics unique to corn crops. Therefore, these two indices can be used to effectively determine whether the crops in the plot are corn crops. The following is a detailed description of the methods for obtaining these two identification indexes.

[0037] (1) First, the surface reflectivity data of the study area is obtained from the Sentinel-2 satellite through the open GEE (Google Earth Engine) platform. It should be understood that the selected study area includes plots of land planted with corn crops and plots of land planted with other crops. In this embodiment, the plot of land planted with corn crops is referred to as a reference corn field. Of course, the surface reflectivity data of the study area can also be obtained from other open remote sensing satellites, and this embodiment does not specifically limit the source of the surface reflectivity data of the study area.

[0038] It should be understood that the Sentinel-2 satellite images contain reflectances of 13 different bands, including 5 visible and near-infrared bands, 3 red-edge bands, 2 short-wave infrared bands, and 3 other bands. In order to build a corn recognition model, this scheme pays special attention to 5 key bands, namely: B4 red band (Red), B3 green band (Green), B5 red edge band (Re1), B8 near-infrared band (NIR) and B11 short-wave infrared band (SWIR1).

[0039] In addition, considering the update frequency of remote sensing images during crop growth, this embodiment adopts a median synthetic image with an interval of 15 days, that is, in each 15-day period, all available images during this period are collected, and then the median of all images in each band is taken to generate a median synthetic image. Based on this method, a total of 24 median synthetic images can be obtained in one year. The selection of this time interval and frequency helps to capture the spectral characteristics of crops at different growth stages, thereby more accurately reflecting the growth status of crops.

[0040] (2) Then, the Normalized Difference Vegetation Index (NDVI) is calculated based on the B4 red band (Red) and the B8 near-infrared band (NIR) in the Sentinel-2 median composite image. The expression is as follows:

[0041] NDVI=(NIR-Red) / (NIR+Red)

[0042] Next, the NDVI sequence of the selected reference cornfield is used as the sample to calculate the standard. To distinguish it from the NDVI sequence of the plot to be identified, the NDVI sequence of the plot to be identified is called the first vegetation sequence in this embodiment, and the NDVI sequence obtained by the reference cornfield is called the second vegetation sequence. Each NDVI sequence includes multiple NDVIs, which are sorted by time.

[0043] It should be understood that due to differences in climate, management methods and corn varieties, the corn time series curves are different. Therefore, a certain number of corn samples need to be collected. These samples should cover different climatic conditions, management methods and corn varieties to ensure the diversity and representativeness of the samples. Due to the influence of different climate, management and variety factors, each NDVI sequence curve will show certain differences. In the end, these slightly different NDVI sequences are summarized as the second vegetation sequence. Therefore, the second vegetation sequence constitutes a standard growth curve that characterizes the growth law of corn crops, which is used to subsequently identify plots suspected of being planted with corn crops and to study the maximum growth period of corn crops.

[0044] (3) In order to study the spectral differences between corn and other crops, this embodiment needs to further find the maximum growth period of corn crops through the second vegetation sequence. To this end, the standard growth curve corresponding to the second vegetation sequence is smoothed, and then a peak detection algorithm based on continuous wavelet transform (CWT) is used to determine the maximum growth point of the corn crop during its entire growth period. It should be understood that the continuous wavelet transform algorithm can identify peaks of different scales and amplitudes, and the specific calculation formula is as follows:

[0045] P raw (t)=P(t)+B(t)+C,t∈[t1,t2],

[0046] C(a,b)=∫ R P(t)ψ a,b (t)dt+∫ R B(t)ψ a,b (t)dt+∫ R Cψ a,b (t)dt,

[0047] Where P(t) is the true peak, B(t) is the baseline function with a mean of 0, C is the two-dimensional matrix of wavelet coefficients, [t1, t2] is the interval where the peak may exist; ψ a,b(t) is the scaled and translated wavelet. Since it is assumed that the baseline is slowly changing and monotonous within the peak support region, the baseline at the peak can be locally approximated as a constant C plus a function B(t) defined within the peak support region with the peak center as the origin. a,b (t) has zero mean, and the expression ∫ R Cψ a,b (t)dt will be zero. For a symmetric wavelet function, ∫ R B(t)ψ a,b (t)dt will also be approximately zero. Therefore, the equation only contains the term containing the true peak value P(t). When calculating specifically, the SciPy.Signal module provided by the Python language can be used to perform calculations to find the maximum growth point of corn, and expand 15 days around the maximum growth point as the time window of the maximum growth period of corn.

[0048] (4) Finally, the spectrum of corn during its maximum growth period and the spectrum of other major crops were analyzed to determine the spectral characteristics unique to corn crops. After comparative studies, it was found that the spectral values ​​of corn in the green band (Green), red edge band (RE1) and short-wave infrared band (SWIR1) were lower than those of grains, small bean crops and potatoes; in the near-infrared band, the reflectivity of sunflowers and soybeans was significantly higher than that of corn.

[0049] Based on the above findings, since the spectral values ​​of corn in the green band (Green), red edge band (RE1) and short wave infrared band (SWIR1) are low, this implementation proposes the following expression for the first identification index:

[0050] MCCWI=1 / (1000*green*re1*swir1)

[0051] In the formula, MCWCI represents the first recognition index, green represents the reflectivity of green light, re1 represents the reflectivity of the red edge band, and swir1 represents the reflectivity of the short-wave infrared band. It can be understood that the first recognition index reflects the chlorophyll and moisture content of crops to a certain extent.

[0052] Similarly, in the near-infrared band, the reflectivity of sunflowers and soybeans is significantly higher than that of corn. Therefore, this embodiment proposes the following expression for the second identification index:

[0053]

[0054] Wherein, MNDVI represents the second identification index, NIR represents the reflectivity of the near-infrared band, and red represents the reflectivity of the red light band. Since there are certain similarities between the calculation method of the second identification index and the calculation method of NDVI, and since NDVI actually reflects the growth of vegetation, the second identification index in this embodiment is also called the improved normalized vegetation index, which reflects the reflectivity difference of crops in the near-infrared band. Moreover, it is not difficult to see that as the parameters NIR and red decrease, the MNDVI values ​​all show an increasing trend. Therefore, compared with other crops, corn shows a higher MNDVI value.

[0055] Based on the expressions of the first identification index and the second identification index, for the land parcel to be identified, the server can obtain the reflectivity of green light, the reflectivity of the red edge band, the reflectivity of the short-wave infrared band, the reflectivity of the red light band and the reflectivity of the near-infrared band in the land parcel to be identified according to the spectral information of the land parcel to be identified; then, obtain the first identification index according to the reflectivity of green light, the reflectivity of the red edge band and the reflectivity of the short-wave infrared band; and obtain the second identification index according to the reflectivity of the red light band and the reflectivity of the near-infrared band.

[0056] Based on the introduction of the first identification index and the second identification index in the above embodiment, Figure 1 Step S3 in the following is described:

[0057] S3: If the first identification index and the second identification index meet the preset conditions, it is determined that the crop in the to-be-identified plot is a corn crop.

[0058] In this embodiment, the preset condition is that the first recognition index is greater than the first threshold value, and the second recognition index is greater than the second threshold value. For example, the first threshold value can be set to 1.0, and the second threshold value can be set to 0.1. Of course, when practicing this method, those skilled in the art can adjust these two threshold values ​​adaptively according to the variety of corn.

[0059] During the research process, it was also found that if the first identification index and the second identification index were calculated for each plot to be identified, and then the two indices were used to determine whether the main crop in the plot to be identified was corn, it would consume a lot of computing resources. Figure 2 As shown, before step S2, the method further includes:

[0060] S1-1, obtaining a first vegetation sequence of a plot to be identified.

[0061] The first vegetation sequence includes a plurality of normalized vegetation indices of the plot to be identified.

[0062] S1-2, calculating the similarity between the first vegetation sequence and the second vegetation sequence.

[0063] The second vegetation sequence includes a plurality of normalized vegetation indices obtained by referring to corn fields.

[0064] In this embodiment, the server may obtain the curve similarity between the first curve formed by the first vegetation sequence and the second curve formed by the second vegetation sequence; and use the curve similarity as the similarity between the first vegetation sequence and the second vegetation sequence.

[0065] For example, a dynamic time warping (DTW) algorithm can be used to calculate the curve similarity between the first curve and the second curve pixel by pixel, so as to preliminarily screen the corn planting area. The implementation process of the dynamic time warping algorithm is as follows:

[0066] (1) Create a distance matrix

[0067] Calculate the Euclidean distance between any two points in the two sequences and store these distances in a matrix. This matrix is ​​called a distance matrix, and its size is m×n, where m and n represent the lengths of the two sequences respectively. In this embodiment, the lengths of the two sequences are 24.

[0068] (2) Calculate the cumulative distance matrix

[0069] Starting from the upper left corner of the distance matrix, the minimum cumulative distance to each point is calculated step by step using the dynamic programming method. Specifically, for each point (i, j) in the distance matrix, its cumulative distance D(i, j) can be calculated by the following formula:

[0070] D(i,j)=d(i,j)+min(D(i-1,j),D(i,j-1),D(i-1,j-1))

[0071] Where D(i,j) is the distance between the i-th point and the j-th point in the reference time series and the actual time series, and min(D(i-1,j),D(i,j-1),D(i-1,j-1)) represents the minimum cumulative distance among the three possible paths before reaching point (i,j).

[0072] (3) Select the best path

[0073] Starting from the lower right corner of the cumulative distance matrix, backtrack to find an optimal path, that is, the path with the smallest cumulative distance.

[0074] (4) Calculate DTW distance

[0075] The cumulative distance on the best path is the DTW distance between the two sequences. The smaller the DTW distance is, the more similar the two sequences are. Therefore, in this embodiment, the reciprocal of the DTW distance can be used as the curve similarity between the two sequences.

[0076] Continue to see Figure 2 , based on the obtained similarity, the method further comprises:

[0077] S1-3, determine whether the similarity is greater than a similarity threshold, if so, execute step S2, otherwise, execute step S4.

[0078] S4, determining that the crops in the to-be-identified plot are not corn crops.

[0079] In this way, by comparing the first vegetation sequence of the plot to be identified with the standard second vegetation sequence, plots that obviously do not have corn crops can be quickly excluded.

[0080] For the first recognition index and the second recognition index of this embodiment, this embodiment also evaluates the classification accuracy evaluation to ensure the reliability and accuracy of the recognition result. Four important indicators are used in the evaluation process: producer accuracy (PA), user accuracy (UA), overall accuracy (OA) and F1 score. These indicators are calculated using the data in the confusion matrix.

[0081] Among them, producer accuracy (PA) refers to the proportion of all real corn samples that are correctly classified as corn. The calculation formula is:

[0082] PA=TP / (TP+FN)

[0083] Where TP represents the number of true corn samples correctly classified as corn, and FN represents the number of corn samples incorrectly classified as non-corn.

[0084] User accuracy (UA) refers to the proportion of samples that are actually corn among all samples classified as corn. Its calculation formula is:

[0085] UA=TP / (TP+FP)

[0086] Where FP represents the number of non-corn samples incorrectly classified as corn.

[0087] Overall accuracy (OA) refers to the proportion of all samples that are correctly classified, regardless of whether they are corn or non-corn. The calculation formula is:

[0088] OA=(TP+TN) / (TP+FP+TN+FN)

[0089] Where TN represents the number of non-corn samples correctly classified as non-corn.

[0090] The F1 score is an indicator that takes PA and UA into consideration and is used to measure the overall performance of the classification system. Its calculation formula is:

[0091] F1=2*(UA*PA) / (UA+PA)

[0092] The higher the F1 score in the formula, the better the accuracy of the classification system. Therefore, through the above four indicators, the accuracy of the corn field recognition method can be comprehensively evaluated to ensure the reliability and effectiveness of the technology in practical applications.

[0093] Based on the same inventive concept as the corn crop identification method based on the new remote sensing index provided in this embodiment, this embodiment also provides a corn crop identification device based on the new remote sensing index, which includes at least one software function module that can be stored in a memory or fixed in an electronic device in the form of software. The processor in the electronic device is used to execute the executable module stored in the memory. For example, the software function module and computer program included in the device. Please refer to Figure 3 , functionally speaking, the device may include:

[0094] The identification index module 11 is used to obtain a first identification index and a second identification index according to the spectral information of the plot to be identified, wherein the first identification index represents the chlorophyll and moisture content of the crops in the plot to be identified, and the second identification index represents the low reflection characteristics of the corn crops in the plot to be identified in the near infrared band;

[0095] The crop identification module 12 determines that the crop in the to-be-identified plot is a corn crop if the first identification index and the second identification index meet a preset condition.

[0096] In this embodiment, the identification index module 11 is used to implement Figure 1 In step S2, the crop identification module 12 is used to identify Figure 1 Therefore, for the detailed description of each of the above modules, please refer to the specific implementation methods of the corresponding steps.

[0097] In addition, since the invention concept is the same as that of the corn crop identification method based on the new remote sensing index, the corn crop identification device based on the new remote sensing index can also implement other steps or sub-steps of the method through the above modules.

[0098] Optionally, the identification index module 11 is further specifically used for:

[0099] According to the spectral information, the reflectivity of green light, the reflectivity of the red edge band, the reflectivity of the short-wave infrared band, the reflectivity of the red light band and the reflectivity of the near-infrared band in the land to be identified are obtained;

[0100] A first identification index is obtained according to the reflectivity of green light, the reflectivity of the red edge band, and the reflectivity of the short-wave infrared band;

[0101] A second identification index is obtained according to the reflectivity of the red light band and the reflectivity of the near infrared band.

[0102] Optionally, the identification index module 11 is further used for:

[0103] Acquire a first vegetation sequence of the to-be-identified land parcel, wherein the first vegetation sequence includes a plurality of normalized vegetation indices of the to-be-identified land parcel;

[0104] Calculating a similarity between the first vegetation sequence and a second vegetation sequence, wherein the second vegetation sequence includes a plurality of normalized vegetation indices obtained by referring to a corn field;

[0105] If the similarity is greater than the similarity threshold, a first recognition index and a second recognition index are obtained according to the spectral information of the land parcel to be recognized.

[0106] Optionally, the identification index module 11 is further specifically used for:

[0107] Calculating a curve similarity between a first curve formed by a first vegetation sequence and a second curve formed by a second vegetation sequence;

[0108] The curve similarity is taken as the similarity between the first vegetation sequence and the second vegetation sequence.

[0109] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0110] It should also be understood that if the above implementation is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0111] Therefore, this embodiment further provides a storage medium, which stores a computer program, and when the computer program is executed by a processor, the corn crop identification method based on the new remote sensing index provided in this embodiment is implemented. The storage medium can be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc., which can store program codes.

[0112] This embodiment provides an electronic device for implementing a corn crop identification method based on a new remote sensing index. Figure 4 As shown, the electronic device may include a processor 22 and a memory 21. In addition, the memory 21 stores a computer program, and the processor implements the corn crop identification method based on the new remote sensing index provided in this embodiment by reading and executing the computer program corresponding to the above implementation in the memory 21.

[0113] Continue to see Figure 4 The electronic device further includes a communication unit 23. The memory 21, the processor 22 and the communication unit 23 are electrically connected to each other directly or indirectly through a system bus 24 to achieve data transmission or interaction.

[0114] The memory 21 may be an information recording device based on any electronic, magnetic, optical or other physical principle, used to record execution instructions, data, etc. In some embodiments, the memory 21 may be, but is not limited to, a volatile memory, a non-volatile memory, a storage drive, etc.

[0115] In some embodiments, the volatile memory may be a random access memory (RAM); in some embodiments, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), a flash memory, etc.; in some embodiments, the storage drive may be a disk drive, a solid-state drive, any type of storage disk (such as a CD, a DVD, etc.), or a similar storage medium, or a combination thereof, etc.

[0116] The communication unit 23 is used to send and receive data through a network. In some embodiments, the network may include a wired network, a wireless network, a fiber optic network, a telecommunication network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, or a near field communication (NFC) network, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include a wired or wireless network access point, such as a base station and / or a network switching node, through which one or more components of the service request processing system may be connected to the network to exchange data and / or information.

[0117] The processor 22 may be an integrated circuit chip having a signal processing capability, and the processor may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the processor may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), or a microprocessor, or any combination thereof.

[0118] Understandably, Figure 4The structure shown is for illustration only. The electronic device may also have Figure 4 More or fewer components than shown, or with Figure 4 Different configurations are shown. Figure 4 The components shown may be implemented in hardware, software or a combination thereof.

[0119] It should be understood that the apparatus and method disclosed in the above-mentioned embodiments can also be implemented in other ways. The apparatus embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0120] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A corn crop identification method based on a new remote sensing index, characterized in that: The method comprises: According to the spectral information of the plot to be identified, a first identification index and a second identification index are obtained, wherein the first identification index represents the chlorophyll and moisture content of the crops in the plot to be identified, and the second identification index represents the low reflection characteristics of the corn crops in the plot to be identified in the near-infrared band; If the first identification index and the second identification index meet a preset condition, it is determined that the crop in the to-be-identified plot is a corn crop.

2. The corn crop identification method based on the new remote sensing index according to claim 1 is characterized in that: According to the spectral information of the land parcel to be identified, a first identification index and a second identification index are obtained, including: According to the spectral information, the reflectivity of green light, the reflectivity of the red edge band, the reflectivity of the short-wave infrared band, the reflectivity of the red light band and the reflectivity of the near infrared band in the land block to be identified are obtained; Obtaining the first identification index according to the reflectivity of the green light, the reflectivity of the red edge band, and the reflectivity of the short-wave infrared band; The second identification index is obtained according to the reflectivity of the red light band and the reflectivity of the near infrared band.

3. The corn crop identification method based on the new remote sensing index according to claim 2 is characterized in that: The relationship between the reflectivity of the green light, the reflectivity of the red edge band, the reflectivity of the short-wave infrared band and the first recognition index is: MCCWI=1 / (1000*green*re1*swir1) In the formula, MCWCI represents the first identification index, green represents the reflectivity of green light, re1 represents the reflectivity of the red edge band, and swir1 represents the reflectivity of the short-wave infrared band.

4. The corn crop identification method based on the new remote sensing index according to claim 2 is characterized in that: The relationship between the reflectivity of the red light band, the reflectivity of the near infrared band and the second identification index is: In the formula, MNDVI represents the second identification index, NIR represents the reflectivity of the near-infrared band, and red represents the reflectivity of the red light band.

5. The corn crop identification method based on a new remote sensing index according to any one of claims 1 to 4, characterized in that: The preset condition is that the first recognition index is greater than a first threshold, and the second recognition index is greater than a second threshold.

6. The corn crop identification method based on the new remote sensing index according to claim 1 is characterized in that: The method further comprises: Acquire a first vegetation sequence of the to-be-identified land parcel, wherein the first vegetation sequence includes a plurality of normalized vegetation indices of the to-be-identified land parcel; Calculating a similarity between the first vegetation sequence and a second vegetation sequence, wherein the second vegetation sequence includes a plurality of normalized vegetation indices obtained by referring to a corn field; If the similarity is greater than the similarity threshold, a first recognition index and a second recognition index are obtained according to the spectral information of the land parcel to be recognized.

7. The corn crop identification method based on the new remote sensing index according to claim 6 is characterized in that: Calculating the similarity between the first vegetation sequence and the second vegetation sequence includes: calculating a curve similarity between a first curve formed by the first vegetation sequence and a second curve formed by the second vegetation sequence; The curve similarity is used as the similarity between the first vegetation sequence and the second vegetation sequence.

8. A corn crop identification device based on a new remote sensing index, characterized in that: The device comprises: An identification index module, used to obtain a first identification index and a second identification index according to the spectral information of the plot to be identified, wherein the first identification index represents the chlorophyll and moisture content of the crops in the plot to be identified, and the second identification index represents the low reflection characteristics of the corn crops in the plot to be identified in the near-infrared band; The crop identification module determines that the crop in the to-be-identified plot is a corn crop if the first identification index and the second identification index meet a preset condition.

9. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program, when executed by a processor, implements the corn crop identification method based on a new remote sensing index as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements the corn crop identification method based on the new remote sensing index as described in any one of claims 1 to 7.

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