CN111445346A - Crop growth detection device and method - Google Patents

Crop growth detection device and method Download PDF

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Publication number
CN111445346A
CN111445346A CN202010266242.0A CN202010266242A CN111445346A CN 111445346 A CN111445346 A CN 111445346A CN 202010266242 A CN202010266242 A CN 202010266242A CN 111445346 A CN111445346 A CN 111445346A
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image
matrix
acquiring
crop growth
crop
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CN202010266242.0A
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Inventor
罗斌
李爱学
张晗
周亚男
王成
陈泉
邱朝阳
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Beijing Research Center for Information Technology in Agriculture
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Beijing Research Center for Information Technology in Agriculture
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30188—Vegetation; Agriculture

Abstract

The invention relates to the technical field of crop growth detection, and discloses a crop growth detection device and a method, wherein the crop growth detection device comprises: the system comprises a color comparison card, an image processing mechanism and a crop growth vigor acquisition mechanism; the image processing mechanism is used for acquiring a first image containing a sample image and a colorimetric card image and acquiring a corrected second image according to the first image; the crop growth vigor acquiring mechanism is used for acquiring the crop growth vigor based on a crop recognition model and the second image. The crop growth detection device provided by the invention effectively avoids errors brought by the environment in the image acquisition process; the growth indexes of crops in various aspects can be measured, and the growth vigor of the crops can be comprehensively analyzed; the method can be used for detecting the growth vigor of large-scale crops and the growth vigor of single plants, and is flexible to apply.

Description

Crop growth detection device and method
Technical Field
The invention relates to the technical field of crop growth detection, in particular to a crop growth detection device and method.
Background
In recent years, with the rapid development of agricultural information technology and intelligent equipment, crop information in-situ and nondestructive acquisition and analysis technology is greatly improved, and the method plays a great role in realizing controllable crop growth and increasing crop yield and quality, and the crop growth analysis is an important research direction in crop information research.
The crop growth size, the angle, the growth rate, the chlorophyll content and the like are key indexes in crop growth monitoring, and have important significance for predicting the crop growth condition and the yield.
Disclosure of Invention
The embodiment of the invention provides a crop growth detection device and method, which are used for solving or partially solving the problem that subjective errors are easily introduced in the existing crop growth analysis.
In a first aspect, an embodiment of the present invention provides a crop growth detection apparatus, including: the system comprises a color comparison card, an image processing mechanism and a crop growth vigor acquisition mechanism;
the image processing mechanism is used for acquiring a first image containing a sample image and a colorimetric card image and acquiring a corrected second image according to the first image; the crop growth vigor acquiring mechanism is used for acquiring the crop growth vigor based on a crop recognition model and the second image.
On the basis of the technical scheme, the image processing mechanism comprises an image acquisition unit, and the image acquisition unit comprises a lens and a high-definition imager connected with the lens.
On the basis of the technical scheme, the image processing mechanism comprises an image correction unit, and the image correction unit comprises a matrix acquisition module and an image correction module;
the matrix acquisition module is used for generating a first matrix according to the color gradation value of the colorimetric card image and acquiring a correction matrix based on the first matrix and the second matrix; the image correction module is used for correcting the color gradation value of each pixel point of the sample image according to the correction matrix; and generating the second matrix according to a preset color gradation value of the color chart.
On the basis of the technical scheme, the crop identification model comprises a background image element removing module, a green factor matrix calculating module and a green plant identification module;
the background image element removing module is used for removing other background image elements except the green plant image elements in the second image;
the green factor matrix calculation module is used for calculating a green factor matrix in the image acted by the background image element removal module;
and the green plant identification module is used for performing threshold sectioning on the green factor matrix to obtain a green plant target image.
On the basis of the technical scheme, the other background image elements comprise background image elements which are in one-to-one correspondence with ashes generated by burning soil, wheat straws, corn straws, mulching films and straws.
On the basis of the technical scheme, the crop growth detection device further comprises a display connected with the green plant identification module.
In a second aspect, an embodiment of the present invention provides a method for detecting crop growth, including: acquiring a first image containing a sample image and a colorimetric card image, and acquiring a corrected second image according to the first image;
and acquiring the crop growth vigor based on the crop recognition model and the second image.
On the basis of the above technical solution, the acquiring a corrected second image according to the first image includes:
generating a first matrix according to the color level value of the color comparison card image, and acquiring a correction matrix based on the first matrix and the second matrix;
correcting the color gradation value of each pixel point of the sample image according to the correction matrix; and generating the second matrix according to a preset color gradation value of the color chart.
On the basis of the above technical solution, the acquiring the growth vigor of the crop based on the crop recognition model and the second image includes: removing other background image elements except the green plant image elements in the second image; calculating a green factor matrix in the image; performing threshold sectioning on the green factor matrix to obtain a green plant target image; and acquiring crop growth vigor based on the green plant target image.
According to the crop growth detection device and method provided by the embodiment of the invention, the first image comprising the sample image and the colorimetric card image is obtained through the image processing mechanism, the first image is corrected, the influence of the ambient light on the color of the sample image is eliminated in the process, and the second image is the real sample image; and the crop growth information is obtained by the crop growth obtaining mechanism based on the crop recognition model and the real sample image. The crop growth detection device provided by the embodiment of the invention effectively avoids errors caused by the environment in the image acquisition process; the growth indexes of crops in various aspects can be measured, and the growth vigor of the crops can be comprehensively analyzed; the method can be used for detecting the growth vigor of large-scale crops and the growth vigor of single plants, and is flexible to apply.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a crop growth detection apparatus according to an embodiment of the present invention.
Reference numerals:
1. a housing; 2. a lens; 3. a high definition imager; 4. a USB interface; 5. a power interface; 6. a data transmission module; 7. a switch; 8. a lithium battery; 9. a display.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the prior art, a plurality of sensitive wave bands which are meaningful for crops are used for collecting spectral images, and color factor operation combination in the images is extracted to seek the optimal combination. And constructing a real-time monitoring model by taking the optimal operation combination as a parameter, and monitoring the area, the chlorophyll content and the like of the crops in real time according to the model.
In the prior art, a large-scale crop satellite remote sensing image is obtained, and according to the space-time characteristics of a remote sensing spectral image and the growth characteristics of different crops, the space distribution maps of the different crops are obtained, so that the classification and growth analysis of the dimensions of the crop plot are completed.
In the crop growth monitoring device based on unmanned aerial vehicle low-altitude remote sensing in the prior art, the detection scale is adjusted by adjusting the height of the unmanned aerial vehicle, and on the basis, the relationship between the leaf area index and the crop coverage is obtained according to the leaf area inversion index model, so that the crop growth is detected.
The crop growth vigor is detected and analyzed by adopting the spectrum or remote sensing image, an additional spectrum acquisition module or a remote sensing device needs to be installed on the detection device, the facility is complex, the cost is high, the portability is poor, and the system portability is weak; the chlorophyll detection method based on absorbance or fluorescence response has single detection content and cannot be combined with crop size and angle detection. Therefore, the research realizes the multi-index detection of the growth vigor of the crops by improving the visible light image, and has important research significance and economic value.
Fig. 1 is a schematic structural diagram of a crop growth detection apparatus according to an embodiment of the present invention, and as shown in fig. 1, the crop growth detection apparatus according to the embodiment of the present invention includes: the system comprises a color comparison card, an image processing mechanism and a crop growth vigor acquisition mechanism;
the image processing mechanism is used for acquiring a first image containing a sample image and a colorimetric card image and acquiring a corrected second image according to the first image; and the crop growth vigor acquiring mechanism is used for acquiring the crop growth vigor based on the crop recognition model and the second image.
It should be noted that, the color comparison card and the sample are placed at one place, and the first image needs to include the sample image and the color comparison card image at the same time. Wherein the first image is stored in an RGB mode.
In the embodiment of the invention, a first image containing a sample image and a colorimetric card image is obtained through an image processing mechanism, the first image is corrected, the influence of ambient light on the color of the sample image is eliminated in the process, and a second image at the moment is a real sample image; and the crop growth information is obtained by the crop growth obtaining mechanism based on the crop recognition model and the real sample image. The crop growth detection device provided by the embodiment of the invention effectively avoids errors caused by the environment in the image acquisition process; the growth indexes of crops in various aspects can be measured, and the growth vigor of the crops can be comprehensively analyzed; the method can be used for detecting the growth vigor of large-scale crops and the growth vigor of single plants, and is flexible to apply.
On the basis of the above-described embodiment, the image processing mechanism includes an image acquisition unit including the lens 2 and the high-definition imager 3 connected to the lens 2.
In the embodiment of the invention, the colorimetric card and the sample are placed under the same lens 2 for shooting and imaging. Lens 2 and high definition imager 3 can separate with the device, uploads through wireless or wired equipment and carries out image processing, improves the device convenience, satisfies different scene demands.
On the basis of the above embodiment, the image processing mechanism includes an image correction unit including a matrix acquisition module and an image correction module;
the matrix acquisition module is used for generating a first matrix according to the color gradation value of the colorimetric card image and acquiring a correction matrix based on the first matrix and the second matrix;
it should be noted that, the color level value of the image of the color chart is extracted, a first matrix is generated, and a second matrix is generated according to the preset color level value of the color chart; calculating a correction matrix by utilizing the operation of the pseudo-inverse matrix;
the image correction module is used for correcting the color gradation value of each pixel point of the sample image according to the correction matrix;
it should be noted that, the grayscale value of each pixel point of the sample image is extracted, and a plurality of matrices are generated. And obtaining a plurality of corrected matrixes of each pixel point according to the correction matrix and the matrixes so as to obtain a real sample image.
On the basis of the embodiment, the crop identification model comprises a background image element removing module, a green factor matrix calculating module and a green plant identification module;
the background image element removing module is used for removing other background image elements except the green plant image elements in the second image;
it should be noted that the background image element removing module includes a green plant distinguishing unit, a hue filtering unit and a saturation filtering unit;
the green plant distinguishing unit is used for obtaining the minimum hue value h of the green plant image elements different from other background image elements according to the color distribution of the image elements in the real sample image in the HSV color spaceminMaximum hue value hmaxMinimum saturation value sminAnd a maximum saturation value smax. Other background image elements include background image elements such as soil, wheat straw, corn straw, mulching film, and ash generated after straw combustion.
The tone filtering unit is used for filtering image elements with tones not in accordance with the tones of the green plant image elements from the real sample image through a band-pass filter BPH (H) to obtain an image I with the tones in accordance with the tones of the green plant image elements1。
After the color tone filtering unit acts, background image elements such as soil, corn straws, mulching films and ashes generated after straw combustion can be filtered from the real sample image, because the color tones of the background image elements are not consistent with the color tones of green plants, or the color tones of the background image elements are not in the color tone range of the green plants.
The saturation filtering unit is used for filtering the image I through a band-pass filter BPS (S)1Filtering out image elements with saturation degree different from that of green plant image elements to obtain image I with hue and saturation degree consistent with those of green plant image elements2。
After the action of the saturation filtering unit, the image I can be obtained1The background image elements such as wheat straw are filtered out, because the saturation of the background elements such as wheat straw is not consistent with that of green plants, the image I obtained in such a way2Namely, the image after filtering background image elements such as soil, wheat straws, corn straws, mulching films, ashes generated after the straws are burnt and the like.
The green factor matrix calculation module is used for calculating a green factor matrix in the image acted by the background image element removal module;
it should be noted that the green factor matrix calculation module is used for calculating the image I acted by the background image element removal module2Green factor matrix M ing。
The green plant identification module is used for carrying out threshold sectioning on the green factor matrix to obtain a green plant target image.
It should be noted that the green plant identification module is used for identifying the green factor matrix MgPerforming threshold segmentation to obtain a green plant target image Og。
On the basis of the above embodiment, the crop growth detection device further comprises a display 9 connected with the green plant identification module.
In the embodiment of the invention, the image of the sample can be clearly and simply seen through the display 9.
The crop growth detection device provided by the embodiment of the invention further comprises a shell 1, wherein the lens 2 is arranged at a lens frame reserved on the shell 1, and the high-definition imager 3 connected with the lens 2 is positioned in the shell 1; the display 9 connected with the green plant identification module is positioned on the side surface of the shell 1; a battery module for providing electric quantity for the matrix acquisition module, the image correction module, the background image element removal module, the green factor matrix calculation module and the green plant identification module is arranged in the shell 1, and the battery module can be a lithium battery 8; the modules realize data communication through a data transmission module 6 positioned in the shell 1; a USB interface 4 and a power interface 5 are reserved on the shell 1; the shell 1 is also provided with a switch 7 for starting the crop growth detection device.
It should be noted that the battery module can adopt a solar power supply module, and solar energy is used for charging the battery module, so that the endurance time and the service life of the crop growth detection device are prolonged.
Another embodiment of the present invention provides a method for detecting growth of a crop, including:
acquiring a first image containing a sample image and a colorimetric card image, and acquiring a corrected second image according to the first image;
and acquiring the crop growth vigor based on the crop recognition model and the second image.
It should be noted that, the color comparison card and the sample are placed at one place, and the first image needs to include the sample image and the color comparison card image at the same time. Wherein the first image is stored in an RGB mode.
In the embodiment of the invention, a first image comprising a sample image and a colorimetric card image is obtained, the first image is corrected, the influence of ambient light on the color of the sample image is eliminated in the process, and a second image at the moment is a real sample image; and acquiring crop growth information based on the crop identification model and the real sample image. The crop growth detection method provided by the embodiment of the invention effectively avoids errors brought by the environment in the image acquisition process; the growth indexes of crops in various aspects can be measured, and the growth vigor of the crops can be comprehensively analyzed; the method can be used for detecting the growth vigor of large-scale crops and the growth vigor of single plants, and is flexible to apply.
On the basis of the above embodiment, acquiring the corrected second image from the first image includes:
generating a first matrix according to the color gradation value of the color card image, and acquiring a correction matrix based on the first matrix and the second matrix;
it should be noted that, the color level value of the image of the color chart is extracted, a first matrix is generated, and a second matrix is generated according to the preset color level value of the color chart; calculating a correction matrix by utilizing the operation of the pseudo-inverse matrix;
and correcting the color gradation value of each pixel point of the sample image according to the correction matrix.
It should be noted that, the grayscale value of each pixel point of the sample image is extracted, and a plurality of matrices are generated. And obtaining a plurality of corrected matrixes of each pixel point according to the correction matrix and the matrixes so as to obtain a real sample image.
On the basis of the above embodiment, acquiring the crop growth vigor based on the crop recognition model and the second image includes:
removing other background image elements except the green plant image elements in the second image;
it should be noted that, according to the color distribution of the image elements in the real sample image in the HSV color space, the minimum hue value h of the green plant image elements different from the other background image elements is obtainedminMaximum hue value hmaxMinimum saturation value sminAnd a maximum saturation value smax. Other background image elements include background image elements such as soil, wheat straw, corn straw, mulching film, and ash generated after straw combustion.
Filtering out image elements with the color tone not in accordance with the color tone of the green plant image elements from the real sample image by a band-pass filter BPH (H) to obtain an image I with the color tone in accordance with the color tone of the green plant image elements1. Background image elements such as soil, corn stalks, mulching films and ashes generated after straw combustion can be filtered from the real sample image, because the color tone of the background image elements is not consistent with that of the green plants, or the color tone of the background image elements is not in the range of the color tone of the green plants.
From the image I by means of a band-pass filter BPS (S)1Filtering out image elements with saturation degree different from that of green plant image elements to obtain image I with hue and saturation degree consistent with those of green plant image elements2. Can be derived from image I1The background image elements such as wheat straw are filtered out, because the saturation of the background elements such as wheat straw is not consistent with that of green plants, the image I obtained in such a way2Namely, the image after filtering background image elements such as soil, wheat straws, corn straws, mulching films, ashes generated after the straws are burnt and the like.
Calculating a green factor matrix in the image;
in addition, the image I is calculated2Green factor matrix M ing。
Performing threshold segmentation on the green factor matrix to obtain a green plant target image; and acquiring the growth vigor of the crops based on the green plant target image.
Need to make sure thatIt is illustrated that for the green factor matrix MgPerforming threshold segmentation to obtain a green plant target image Og。
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (9)

1. A crop growth detection device, comprising: the system comprises a color comparison card, an image processing mechanism and a crop growth vigor acquisition mechanism;
the image processing mechanism is used for acquiring a first image containing a sample image and a colorimetric card image and acquiring a corrected second image according to the first image; the crop growth vigor acquiring mechanism is used for acquiring the crop growth vigor based on a crop recognition model and the second image.
2. The crop growth detection apparatus of claim 1, wherein the image processing mechanism comprises an image acquisition unit, the image acquisition unit comprising a lens and a high-definition imager connected to the lens.
3. The crop growth detection apparatus of claim 1, wherein the image processing mechanism comprises an image correction unit comprising a matrix acquisition module and an image correction module;
the matrix acquisition module is used for generating a first matrix according to the color gradation value of the colorimetric card image and acquiring a correction matrix based on the first matrix and the second matrix; the image correction module is used for correcting the color gradation value of each pixel point of the sample image according to the correction matrix; and generating the second matrix according to a preset color gradation value of the color chart.
4. The crop growth detection apparatus according to claim 1, wherein the crop identification model comprises a background image element removal module, a green factor matrix calculation module, and a green plant identification module;
the background image element removing module is used for removing other background image elements except the green plant image elements in the second image;
the green factor matrix calculation module is used for calculating a green factor matrix in the image acted by the background image element removal module;
and the green plant identification module is used for performing threshold sectioning on the green factor matrix to obtain a green plant target image.
5. The apparatus of claim 4, wherein the other background image elements include background image elements corresponding to soil, wheat straw, corn straw, mulching film, and ash generated by burning straw.
6. The apparatus according to claim 4, further comprising a display connected to the green plant identification module.
7. A crop growth detection method using the apparatus according to any one of claims 1 to 6, comprising:
acquiring a first image containing a sample image and a colorimetric card image, and acquiring a corrected second image according to the first image;
and acquiring the crop growth vigor based on the crop recognition model and the second image.
8. The method according to claim 7, wherein the obtaining the corrected second image according to the first image comprises:
generating a first matrix according to the color level value of the color comparison card image, and acquiring a correction matrix based on the first matrix and the second matrix;
correcting the color gradation value of each pixel point of the sample image according to the correction matrix; and generating the second matrix according to a preset color gradation value of the color chart.
9. The method according to claim 7, wherein the acquiring of the crop growth based on the crop recognition model and the second image comprises:
removing other background image elements except the green plant image elements in the second image; calculating a green factor matrix in the image; performing threshold sectioning on the green factor matrix to obtain a green plant target image; and acquiring crop growth vigor based on the green plant target image.
CN202010266242.0A 2020-04-07 2020-04-07 Crop growth detection device and method Pending CN111445346A (en)

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