Embedded Data Acquisition for UAV Analysis Systems

Abstract

The invention discloses an embedded data acquisition unmanned aerial vehicle analysis system, comprising an image acquisition processing module, a calculation analysis module and a control adjustment module, and is characterized in that: the image acquisition processing module is electrically connected to the calculation analysis module, the calculation analysis module is network-connected to the control adjustment module, the image acquisition processing module is used to perform video acquisition and processing on farmland, the calculation analysis module is used to perform calculation analysis on the amount of pesticide to be sprayed on the farmland, and the control adjustment module is used to adjust and control the pesticide spraying device of the unmanned aerial vehicle; the image acquisition processing module comprises a high-definition camera unit, an area selection module, an information transmission module, an image recognition module and a marking module, the high-definition camera unit is electrically connected to the area selection module, and the area selection module is electrically connected to the information transmission module. The invention has the characteristics of good pesticide spraying effect and high insecticide removal efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle analysis, and in particular to an unmanned aerial vehicle analysis system for embedded data acquisition. Background Art

[0002] As a new type of information acquisition carrier, drones have attracted widespread attention from the society. With the gradual maturity of technology, drones have been applied in more and more fields due to their good adaptability and flexible operation, and have gradually become a new type of equipment in agricultural operations.

[0003] Spraying pesticides on crops is one of the important applications of drones in the agricultural field. Currently, the common method of spraying pesticides on crops by drones is to spray a large amount of pesticides on the surface of the leaves of crops to kill or prevent pests; however, the overall shape of most crop leaves is a "V" shape. When a large amount of pesticides are sprayed on the surface of the crop leaves, most of the sprayed pesticides flow into the main stem of the leaves due to the relatively high leaf surfaces on both sides of the main stem of the leaves. Only a small amount of pesticides remain on the leaf surface of the leaves, which seriously affects the effect of pest control and prevention of pests on the leaves of crops, and indirectly wastes a large amount of pesticides, greatly increasing the probability of farmland suffering from insect disasters. Therefore, it is very necessary to design an embedded data acquisition drone analysis system with good pesticide spraying effect and high pest control efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide an unmanned aerial vehicle analysis system with embedded data acquisition to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an embedded data acquisition unmanned aerial vehicle analysis system, comprising an image acquisition and processing module, a calculation and analysis module and a control and adjustment module, characterized in that: the image acquisition and processing module is electrically connected to the calculation and analysis module, the calculation and analysis module is network-connected to the control and adjustment module, the image acquisition and processing module is used to perform video acquisition and processing on farmland, the calculation and analysis module is used to calculate and analyze the amount of pesticide to be sprayed on the farmland, and the control and adjustment module is used to adjust and control the pesticide spraying device of the unmanned aerial vehicle;

[0006] The image acquisition and processing module includes a high-definition camera unit, an area selection module, an information transmission module, an image recognition module and a marking module. The high-definition camera unit is electrically connected to the area selection module, the area selection module is electrically connected to the information transmission module, and the image recognition module is electrically connected to the marking module. The high-definition camera unit is used to capture images of farmland, the area selection module is used to select and capture farmland for image capture, the information transmission module is used to transmit information data, the image recognition module is used to recognize images of crop leaves, and the marking module is used to mark crop leaves.

[0007] According to the above technical solution, the calculation and analysis module includes a cropping and cutting module, an image splicing module, a spraying amount calculation module and a measuring module. The cropping and cutting module is electrically connected to the image splicing module, and the spraying amount calculation module is electrically connected to the measuring module. The cropping and cutting module is used to crop and cut the image of crop leaves, and the image splicing module is used to splice the image of crop leaves. The spraying amount calculation module is used to calculate the pesticide spraying amount, and the measuring module is used to measure the relevant data of crop leaves. The measuring module includes a length and width measurement submodule and an angle measurement submodule. The length and width measurement submodule is electrically connected to the angle measurement submodule, and the length and width measurement submodule is used to measure the length and width data of crop leaves. The angle measurement submodule is used to measure the leaf angle on both sides of the main stem of crop leaves.

[0008] According to the above technical solution, the control and adjustment module includes a data acquisition module electrically connected to an adjustment module, the data acquisition module and the adjustment module are electrically connected to a network, the data acquisition module is used to acquire data information, and the adjustment module is used to adjust the drone pesticide spraying device.

[0009] According to the above technical solution, the operation method of the drone analysis system mainly includes the following steps:

[0010] Step S1: Before spraying pesticides on the farmland, the drone takes off from the farmland and collects video of the farmland;

[0011] Step S2: After completing the video collection of the farmland, the video collection results are further sent to the UAV console for processing;

[0012] Step S3: The computer on the drone console calculates and analyzes the amount of pesticide to be sprayed on the farmland based on the video acquisition results;

[0013] Step S4: The console computer controls the drone to spray pesticides on the farmland according to the calculation and analysis results.

[0014] According to the above technical solution, step S2 further includes the following steps:

[0015] Step S21: The drone collects images of the farmland through a high-definition camera unit, and selects farmland with an area threshold P for image collection through an electrical signal-activated area selection module, and further sends the collection results to the drone control platform computer for analysis and processing through a network signal;

[0016] Step S22: the image recognition module matches the collected result image with the crop leaf image data information stored and entered in the computer system of the UAV control platform, and extracts and marks the images in the collected result image that are larger than the matching threshold T;

[0017] Step S23: The marking module marks the extracted crop leaf image, marks the position of the main stem of the crop leaf, that is, the part with a color difference from the leaf surface of the crop leaf, as B, and marks the edge of the leaf as A.

[0018] According to the above technical solution, step S3 further includes the following steps:

[0019] Step S31: the angle measurement submodule measures the leaf angles on both sides of the main stem in each extracted crop leaf image through the fitted angle processing instrument, and obtains the average leaf angle of the crop leaf image contained in the collected result image as θ;

[0020] Step S32: The cropping module cuts the crop leaf image contained in the collected result image into two parts with the main stem of the crop leaf as the center line, and cuts one of the parts with equal width, and arranges the cropped half of the crop leaves from large to small according to the height value, and the arrangement result is {H1, H2, H3...H n-1 , H n};

[0021] Step S33: The image stitching module stitches the cropped crop leaf images into n , H2 and H n-1 ...then stitch them one by one to form an approximate regular image, and calculate the area of ​​the image after stitching.

[0022] According to the above technical solution, step S33 further includes the following steps:

[0023] Step S331: the length and width measurement submodule measures the length and width of the stitched image, and after obtaining the area of ​​the image, further obtains the area of ​​the entire image of the cropped leaves;

[0024] Step S332: According to the above steps, the area of ​​all the crop leaf images contained in the collected result image is further measured to obtain an average area S of the crop leaves contained in the collected result image;

[0025] Step S333: After completing the collection and detection of crop leaf image-related data, the spraying amount calculation module calculates the amount of pesticide to be sprayed on the farmland represented by the collected result image, and obtains the amount of pesticide to be sprayed as Z.

[0026] According to the above technical solution, the calculation formula of the amount of pesticide Z to be sprayed in step S333 is as follows:

[0027]

[0028] Among them, Z is the amount of pesticide to be sprayed, K is the conversion coefficient of the amount of pesticide to be sprayed, S is the average area value of the crop leaves contained in the collected result image, and θ is the average leaf angle value of the crop leaves contained in the collected result image.

[0029] According to the above technical solution, step S4 further includes the following steps:

[0030] Step S41: After the data acquisition module obtains the calculation result of the amount of pesticide to be sprayed through the electrical signal, the data information is sent to the drone control end;

[0031] Step S42: The drone adjustment module adjusts the pesticide spraying device according to the received calculation result data and starts spraying pesticides on the farmland.

[0032] Compared with the prior art, the beneficial effects achieved by the present invention are: the present invention, by providing an image acquisition and processing module, a calculation and analysis module and a control and adjustment module, can make the calculation result of the pesticide spraying amount more accurate and scientific, thereby improving the pesticide spraying effect on farmland and improving the insect removal rate of farmland. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0034] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] See also Figure 1 , the present invention provides a technical solution: an embedded data acquisition unmanned aerial vehicle analysis system, including an image acquisition and processing module, a calculation and analysis module and a control and adjustment module, characterized in that: the image acquisition and processing module is electrically connected to the calculation and analysis module, the calculation and analysis module is network-connected to the control and adjustment module, the image acquisition and processing module is used to perform video acquisition and processing on the farmland, the calculation and analysis module is used to calculate and analyze the amount of pesticide to be sprayed on the farmland, and the control and adjustment module is used to adjust and control the pesticide spraying device of the unmanned aerial vehicle;

[0037] The image acquisition and processing module includes a high-definition camera unit, an area selection module, an information transmission module, an image recognition module and a marking module. The high-definition camera unit is electrically connected to the area selection module, the area selection module is electrically connected to the information transmission module, and the image recognition module is electrically connected to the marking module. The high-definition camera unit is used to capture images of farmland, the area selection module is used to select and capture farmland for image capture, the information transmission module is used to transmit information data, the image recognition module is used to recognize images of crop leaves, and the marking module is used to mark crop leaves.

[0038] The calculation and analysis module includes a cropping and cutting module, an image stitching module, a spraying amount calculation module and a measuring module. The cropping and cutting module is electrically connected to the image stitching module, and the spraying amount calculation module is electrically connected to the measuring module. The cropping and cutting module is used to crop and cut the image of crop leaves, the image stitching module is used to stitch the image of crop leaves, the spraying amount calculation module is used to calculate the pesticide spraying amount, and the measuring module is used to measure the relevant data of crop leaves. The measuring module includes a length and width measurement submodule and an angle measurement submodule. The length and width measurement submodule is electrically connected to the angle measurement submodule, the length and width measurement submodule is used to measure the length and width data of crop leaves, and the angle measurement submodule is used to measure the leaf angle on both sides of the main stem of crop leaves.

[0039] The control and adjustment module includes a data acquisition module electrically connected to the adjustment module, the data acquisition module and the adjustment module are electrically connected to the network, the data acquisition module is used to acquire data information, and the adjustment module is used to adjust the drone pesticide spraying device.

[0040] The operation method of the drone analysis system mainly includes the following steps:

[0041] Step S1: Before spraying pesticides on the farmland, the drone takes off from the farmland and collects video of the farmland;

[0042] Step S2: After completing the video collection of the farmland, the video collection results are further sent to the UAV console for processing;

[0043] Step S3: The computer on the drone console calculates and analyzes the amount of pesticide to be sprayed on the farmland based on the video acquisition results;

[0044] Step S4: The console computer controls the drone to spray pesticides on the farmland according to the calculation and analysis results.

[0045] Step S2 further comprises the following steps:

[0046] Step S21: The drone collects images of the farmland through a high-definition camera unit, and selects farmland with an area threshold P for image collection through an electrical signal-activated area selection module, and further sends the collection results to the drone control platform computer for analysis and processing through a network signal;

[0047] Step S22: the image recognition module matches the collected result image with the crop leaf image data information stored and entered in the computer system of the UAV control platform, and extracts and marks the image greater than the matching threshold T in the collected result image; the extracted image greater than the matching threshold T is the crop leaf image contained in the collected result image;

[0048] Step S23: The marking module marks the extracted crop leaf image, marks the position of the main stem of the crop leaf, that is, the part with a color difference from the leaf surface of the crop leaf, as B, and marks the edge of the leaf as A.

[0049] Step S3 further comprises the following steps:

[0050] Step S31: the angle measurement submodule measures the leaf angles on both sides of the main stem in each extracted crop leaf image through the fitted angle processing instrument, and obtains the average leaf angle of the crop leaf image contained in the collected result image as θ;

[0051] Step S32: The cropping module cuts the crop leaf image contained in the collected result image into two parts with the main stem of the crop leaf as the center line, and cuts one of the parts with equal width, and arranges the cropped half of the crop leaves from large to small according to the height value, and the arrangement result is {H1, H2, H3...H n-1 , H n}; Since the leaves of crops grow symmetrically with the main stem as the midline, the crop leaf image is cut into two parts with the main stem as the midline, and the areas of the two cut images are the same, which is convenient for the subsequent calculation of the area of ​​the entire crop leaf;

[0052] Step S33: The image stitching module stitches the cropped crop leaf images into n , H2 and H n-1 ...they are stitched in sequence to form an approximate regular image, and the area of ​​the stitched image is calculated; the crop leaves are not complete regular shapes, which is not conducive to the calculation of the area. Stitching them into a regular image is conducive to the area calculation of the crop leaves and makes the calculation result data more accurate.

[0053] Step S33 further includes the following steps:

[0054] Step S331: the length and width measurement submodule measures the length and width of the stitched image, and after obtaining the area of ​​the image, further obtains the area of ​​the entire image of the cropped leaves;

[0055] Step S332: According to the above steps, the area of ​​all the crop leaf images contained in the collected result image is further measured to obtain an average area S of the crop leaves contained in the collected result image;

[0056] Step S333: After completing the collection and detection of crop leaf image-related data, the spraying amount calculation module calculates the amount of pesticide to be sprayed on the farmland represented by the collected result image, and obtains the amount of pesticide to be sprayed as Z.

[0057] The calculation formula for the amount of pesticide Z to be sprayed in step S333 is as follows:

[0058]

[0059] Among them, Z is the amount of pesticide to be sprayed, K is the conversion coefficient of the amount of pesticide to be sprayed, S is the average area value of the crop leaves contained in the acquisition result image, and θ is the average leaf angle value of the crop leaves contained in the acquisition result image. It can be seen from the formula that the smaller the average leaf angle value of the crop leaves is, the higher the leaf surfaces on both sides of the main stem of the leaves are. The more pesticides sprayed on the leaves, the easier it is for them to flow to the main stem of the leaves. Therefore, the smaller the leaf area of ​​the crop and the smaller the leaf angle value, the smaller the amount of pesticide required to be sprayed.

[0060] Step S4 further comprises the following steps:

[0061] Step S41: After the data acquisition module obtains the calculation result of the amount of pesticide to be sprayed through the electrical signal, the data information is sent to the drone control end;

[0062] Step S42: The drone adjustment module adjusts the pesticide spraying device according to the received calculation result data and starts spraying pesticides on the farmland.

[0063] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" 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 other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0064] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An embedded data acquisition drone analysis system, wherein the operation method of the drone analysis system mainly comprises the following steps: Step S1: Before spraying pesticides on the farmland, the drone takes off from the farmland and collects video of the farmland; Step S2: After completing the video collection of the farmland, the video collection results are further sent to the UAV console for processing; Step S3: The computer on the drone console calculates and analyzes the amount of pesticide to be sprayed on the farmland based on the video acquisition results; Step S4: the console computer controls the drone to spray pesticides on the farmland according to the calculation and analysis results; The step S2 further comprises the following steps: Step S21: The drone collects images of the farmland through a high-definition camera unit, and activates area selection through electrical signals, selects farmland with an area threshold P for image collection, and further sends the collection results to the drone control platform computer for analysis and processing through network signals; Step S22: matching the collected result image with the crop leaf image data information stored and recorded in the computer system of the UAV control platform, extracting and marking the images in the collected result image that are greater than the matching threshold T; Step S23: marking the extracted crop leaf image, marking the position of the main stem of the crop leaf, i.e., the part with different color from the leaf surface of the crop leaf, as B, and marking the edge of the leaf as A; The step S3 further comprises the following steps: Step S31: fitting the angles, measuring the leaf angles on both sides of the main stem in each extracted crop leaf image, and obtaining an average leaf angle of the crop leaf image contained in the collected result image as θ; Step S32: The crop leaf image contained in the collected result image is cut into two parts with the main stem of the crop leaf as the center line, and one of the parts is cropped with equal width, and the cropped half of the crop leaves are arranged from large to small according to the height value, and the arrangement result is {H1, H2, H3...H n-1 , H n }; Step S33: The crop leaf image is cut according to H1 and H n , H2 and H n-1 ... stitching is performed sequentially to form an approximately regular image, and the area of ​​the image after stitching is calculated; The step S33 further comprises the following steps: Step S331: measuring the length and width of the stitched image to obtain the area of ​​the image, and then further obtaining the area of ​​the entire image of the crop leaves to be cut; Step S332: According to step S331, the area of ​​all crop leaf images contained in the collected result image is further measured to obtain an average area S of the crop leaves contained in the collected result image; Step S333: After completing the collection and detection of crop leaf image-related data, the amount of pesticide to be sprayed on the farmland represented by the collection result image is calculated to obtain the amount of pesticide to be sprayed as Z; The calculation formula of the amount of pesticide Z to be sprayed in step S333 is as follows: Wherein, Z is the amount of pesticide to be sprayed, K is the conversion coefficient of the amount of pesticide to be sprayed, S is the average area value of the crop leaves contained in the collected result image, and θ is the average leaf angle value of the crop leaves contained in the collected result image; The step S4 further comprises the following steps: Step S41: After obtaining the calculation result of the amount of pesticide to be sprayed through the electrical signal, the data information is sent to the drone control end; Step S42: The UAV adjusts the pesticide spraying device according to the received calculation result data and starts spraying pesticides on the farmland.

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