CN105574898A - Method and system for monitoring plant lodging situation based on image detection - Google Patents

Abstract

The invention discloses a method for monitoring a plant lodging situation based on image detection. The method comprises the following steps: S1, obtaining the original image of a plant in a monitoring area, and converting the original image into a gray level image; S2, presetting Gaussian Blur functions, and selecting a plurality of relaxation parameters sigma n; S3, carrying out convolution budgeting on the gray level image and the Gaussian Blur functions corresponding to each relaxation parameter respectively so as to obtain a plurality of blur images gn; S4, presetting a boundary threshold; S5, carrying out binaryzation on the blur images gn according to the boundary threshold so as to obtain a plurality of binaryzation images gbn; S6, substituting the binaryzation images gbn into a preset first calculation model to carry out comprehensive operation so as to obtain a binarization lodging image R(x,y); and S7, calculating the lodging rate Rrite according to a preset second calculation model and the lodging image R(x,y). According to the method, the plant is remotely monitored, so that workers are prevented from traveling to a place personally, the labor intensity is reduced, manual requirement is reduced, and large-scale plant monitoring can be realized.

Description

Plant lodging condition monitoring method and system based on image detection

Technical Field

The invention relates to the technical field of plant growth monitoring, in particular to a plant lodging condition monitoring method and system based on image detection.

Background

Wheat lodging is one of common disasters in agricultural production. Once the wheat falls down, the operation of moisture and nutrients of plants and photosynthesis are reduced, various plant diseases and insect pests can be induced, the grain filling process is seriously influenced, the yield of the wheat and the quality of grains are finally influenced, and the yield loss can reach 27% when the wheat falls down seriously. In addition, because lodging is not beneficial to mechanical harvesting, the increase of manpower harvesting cost can also aggravate the loss of farmland income. The large-area and rapid monitoring of the lodging condition of the wheat is the key for mastering the disaster situation, preventing and controlling in time and evaluating the loss, and has important value for timely acquiring the growth information of the farmland wheat by agricultural departments.

Disclosure of Invention

Based on the technical problems in the background art, the invention provides a plant lodging condition monitoring method and system based on image detection.

The invention provides a plant lodging condition monitoring method based on image detection, which comprises the following steps:

s1, acquiring an original image of the plant in the monitoring area, and converting the original image into a gray image;

s2, presetting a Gaussian fuzzy function, and selecting a plurality of relaxation parameters sigma_n；

S3, respectively connecting the gray-scale image with each relaxation parameter sigma_nCarrying out convolution budget on the corresponding Gaussian blur function to obtain a plurality of blurred images g_n；

S4, presetting a demarcation threshold;

s5, blurring the image g according to the boundary threshold value_nBinarizing to obtain multiple binary images gb_n；

S6, converting the plurality of binary images gb_nSubstituting the binary image into a preset first calculation model to perform comprehensive operation to obtain a binary lodging image R (x, y);

s7, calculating the lodging rate R according to the preset second calculation model and the lodging image R (x, y)_rite。

Preferably, in step S2, the relaxation parameter σ is selected according to the magnitude of the imaging magnification and the actual image_n。

Preferably, in step S2, the plurality of relaxation parameters σ_nThe following relationship is satisfied:

σ_n＝2σ_n-1＝2²σ_n-2＝......＝2ⁿσ₀where σ is₀Is a constant.

Preferably, σ is characterized in that₀＝1。

Preferably, in step S4, the number of the boundary threshold is plural, and the boundary threshold is associated with plural blurred images g_nOne-to-one correspondence is realized; step S5 specifically includes: the blurred image g is subjected to a corresponding demarcation threshold_nBinarizing to obtain multiple binary images gb_n。

Preferably, in step S6, the first calculation model is:

$R(x,y)=\underset{n}{\Σ}({\mathrm{\λ}}_n\×{\mathrm{gb}}_n),{\mathrm{\λ}}_n$ is a preset constant.

Preferably, in step S7, the second calculation model is:

$R_{rite}=\underset{y}{\Σ}{k}_y\underset{x}{\Σ}R(x,y),$ k_yis a scaling factor.

A plant lodging condition monitoring system based on image detection comprises: the device comprises an ashing processing module, a Gaussian blur module, a binarization processing module and a lodging calculation module; wherein,

the ashing processing module is used for receiving an original image sent by the monitoring area camera device and converting the original image into a gray image;

a series of Gaussian functions are preset in the Gaussian blur module, and relaxation parameters sigma of any two Gaussian functions are different; the Gaussian blur module is connected with the ashing processing module, receives the gray level image and respectively connects the gray level image with each relaxation parameter sigma_nCarrying out convolution budget on the corresponding Gaussian blur function to obtain a plurality of blurred images g_n；

The binarization processing module is connected with the Gaussian blur module, and a plurality of blurred images g are preset in the binarization processing module_nA boundary threshold value corresponding to each other and used for respectively aligning the blurred images g according to the corresponding boundary threshold values_nPerforming binarization processing to obtain multiple binarized images gb_n；

The lodging calculation module is connected with the binarization processing module, and a first calculation model and a second calculation model are preset in the lodging calculation module; the lodging calculation module converts a plurality of binarization results gb_nSubstituting the first calculation model to perform comprehensive operation to obtain a binary lodging image R (x, y), and calculating lodging rate R according to the second calculation model and the lodging image R (x, y)_rite。

Preferably, the first calculation model is:λ_nis a preset constant.

Preferably, the second calculation model is:k_yis a scaling factor.

According to the method and the system for monitoring the plant lodging condition based on image detection, provided by the invention, the plant image of the monitored area is remotely obtained and used as an original image, the original image is subjected to ashing treatment, Gaussian blur treatment and binarization treatment in sequence, then the lodging image is calculated according to the binarization treatment result, and the lodging rate is further calculated. According to the invention, a set of high-flux breeding software can be formed in an automatic image analysis mode, the speed and the accuracy of information acquisition are greatly improved, and the development of subsequent related research work is facilitated.

In addition, the invention avoids the necessity of personnel in person by remotely monitoring the plants, is beneficial to reducing the labor intensity and the manual requirements and can realize large-scale plant monitoring.

Drawings

FIG. 1 is a flow chart of a plant lodging condition monitoring method based on image detection according to the present invention;

fig. 2 is a structural diagram of a plant lodging condition monitoring system based on image detection.

Detailed Description

Referring to fig. 1, the plant lodging condition monitoring method based on image detection provided by the invention comprises the following steps:

and S1, acquiring an original image of the plant in the monitored area, and converting the original image into a gray image.

In the present embodiment, the growth of wheat is monitored. The plant lodging condition monitoring method based on image detection provided by the embodiment is a remote monitoring method, and workers do not need to be in the environment, so that the key point is to remotely acquire the original image of wheat. The original image can be obtained by presetting a camera device in the monitoring area, and the camera device automatically captures the original image and sends the original image to subsequent equipment.

S2, presetting a Gaussian fuzzy function, and selecting a plurality of relaxation parameters sigma_n。

Gaussian functionIt is a commonly used image processing function, and the relaxation parameter σ determines the shape of a two-dimensional gaussian function. In the present embodiment, a plurality of relaxation parameters σ_nThe selection of (A) satisfies the following principles:

σ_n＝2σ_n-1＝2²σ_n-2＝......＝2ⁿσ₀where σ is₀Is a constant.

In the present embodiment, first, the relaxation parameter σ is selected based on the magnitude of the imaging magnification and the actual image₀Then according to σ⁰And the above formula obtains the sigma in sequence₁、σ₂、σ₃……σ_n-1、σ_nAnd sequentially mixing σ₁、σ₂、σ₃……σ_n-1、σ_nSubstituting sigma in the Gaussian function to obtain a series of Gaussian functions. In particular, σ may be selected₀＝1。

S3, respectively connecting the gray-scale image with each relaxation parameter sigma_nCarrying out convolution budget on the corresponding Gaussian blur function to obtain a plurality of blurred images g_n。

In this step, a series of Gaussian functions obtained in the previous step are respectively applied to perform Gaussian blur on the gray level image, namely, sigma is respectively used₀、σ₁、σ₂、σ₃……σ_n-1、σ_nThe corresponding Gaussian function performs Gaussian blur on the gray level image to obtain a series of blurred images g₀、g₁、g₂、g₃……g_n-1、g_n。

And S4, presetting a demarcation threshold value. In the present embodiment, the number of the boundary threshold values is plural, and the boundary threshold values are associated with plural blurred images g_nAnd correspond to each other.

S5, according to the corresponding boundary threshold value, respectively aligning the blurred image g₀、g₁、g₂、g₃……g_n-1、g_nBinarizing to obtain multiple binary images gb₀、gb₁、gb₂、gb₃……gb_n-1、gb_n. Specifically, in the binarization, all the dot color values having color values lower than the boundary threshold value in the blurred image are converted into 0, and all the dot color values having color values higher than the boundary threshold value in the blurred image are converted into 255.

S6, a plurality of binarization results gb_nAnd substituting the binary image into a preset first calculation model to perform comprehensive operation to obtain a binary lodging image R (x, y).

The first calculation model is:

$R(x,y)=\underset{n}{\Σ}({\mathrm{\λ}}_n\×{\mathrm{gb}}_n),$ λ_nis a preset constant.

In specific practice, λ_nThe constant, i.e. λ, can be chosen_n＝λ_n-1＝λ_n-2＝......＝λ₀；

λ_nIt is also possible to follow the formula: lambda [ alpha ]_n＝ω₁λ_n-1＝ω₂λ_n-2＝......＝ω_nλ₀Taking values, wherein omega₁、ω₂、ω_nAnd λ₀Are all constants.

S7, calculating the lodging rate R according to the preset second calculation model and the lodging image R (x, y)_rite。

The second calculation model is:

$R_{rite}=\underset{y}{\Σ}{k}_y\underset{x}{\Σ}R(x,y),$ k_yis a scaling factor.

The above method is further explained below in conjunction with an image detection-based plant lodging condition monitoring system.

Referring to fig. 2, the plant lodging condition monitoring system based on image detection comprises: the device comprises an ashing processing module, a Gaussian blur module, a binarization processing module and a lodging calculation module.

The ashing processing module is used for receiving the original image sent by the monitoring area camera device and converting the original image into a gray image.

A series of Gaussian functions are preset in the Gaussian blur moduleThe relaxation parameters σ of any two gaussian functions are different, and the choice of relaxation parameter σ satisfies the following formula:

σ_n＝2σ_n-1＝2²σ_n-2＝......＝2ⁿσ₀where σ is₀Is constant and is determined by the magnitude of the imaging magnification and the actual image.

The Gaussian blur module is connected with the ashing processing module, receives the gray level image and respectively connects the gray level image with each relaxation parameter sigma_nCarrying out convolution budget on the corresponding Gaussian blur function to obtain a plurality of blurred images g_n。

The binarization processing module is connected with the Gaussian blur module, and a plurality of blurred images g are preset in the binarization processing module_nA boundary threshold value corresponding to each other and used for respectively aligning the blurred images g according to the corresponding boundary threshold values_nPerforming binarization processing to obtain multiple binarized images gb_n。

The lodging calculation module is preset with a first calculation model and a second calculation model.

The first calculation model is:

$R(x,y)=\underset{n}{\Σ}({\mathrm{\λ}}_n\×{\mathrm{gb}}_n),$ λ_nis a preset constant.

The second calculation model is:

k_yis a scaling factor.

The lodging calculation module is connected with the binarization processing module and used for converting a plurality of binarization results gb_nSubstituting the first calculation model to perform comprehensive operation to obtain a binary lodging image R (x, y), and calculating lodging rate R according to the second calculation model and the lodging image R (x, y)_rite。

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (10)

1. A plant lodging condition monitoring method based on image detection is characterized by comprising the following steps:

s1, acquiring an original image of the plant in the monitoring area, and converting the original image into a gray image;

s2, presetting a Gaussian fuzzy function, and selecting a plurality of relaxation parameters sigma_n；

S3, respectively connecting the gray-scale image with each relaxation parameter sigma_nCarrying out convolution budget on the corresponding Gaussian blur function to obtain a plurality of blurred images g_n；

S4, presetting a demarcation threshold;

s5, blurring the image g according to the boundary threshold value_nBinarizing to obtain multiple binary images gb_n；

S6, converting the plurality of binary images gb_nSubstituting the binary image into a preset first calculation model to perform comprehensive operation to obtain a binary lodging image R (x, y);

s7, calculating the lodging rate R according to the preset second calculation model and the lodging image R (x, y)_rite。

2. The method for monitoring plant lodging conditions based on image detection as claimed in claim 1, wherein in step S2, a relaxation parameter σ is selected according to the magnitude of imaging magnification and the actual image_n。

3. The method for monitoring plant lodging based on image detection as claimed in claim 2, wherein in step S2, a plurality of relaxation parameters σ_nThe following relationship is satisfied:

σ_n＝2σ_n-1＝2²σ_n-2＝......＝2ⁿσ₀where σ is₀Is a constant.

4. The image detection-based plant lodging condition monitoring method of claim 3, wherein σ₀＝1。

5. The image detection-based plant lodging monitoring method of claim 1, wherein in step S4, the number of demarcation thresholds is multiple and is associated with a plurality of blurred images g_nOne-to-one correspondence is realized; step S5 specifically includes: the blurred image g is subjected to a corresponding demarcation threshold_nBinarizing to obtain multiple binary images gb_n。

6. The method for monitoring plant lodging based on image detection as claimed in claim 1, wherein in step S6, the first calculation model is:

$R(x,y)=\underset{n}{\Σ}({\mathrm{\λ}}_n\×{\mathrm{gb}}_n),$ λ_nis a preset constant.

7. The method for monitoring plant lodging based on image detection as claimed in claim 1, wherein in step S7, the second calculation model is:

$R_{rite}=\underset{y}{\Σ}{k}_y\underset{x}{\Σ}R(x,y),$ k_yis a scaling factor.

8. A plant lodging condition monitoring system based on image detection is characterized by comprising: the device comprises an ashing processing module, a Gaussian blur module, a binarization processing module and a lodging calculation module; wherein,

the ashing processing module is used for receiving an original image sent by the monitoring area camera device and converting the original image into a gray image;

a series of Gaussian functions are preset in the Gaussian blur moduleThe relaxation parameters sigma of any two Gaussian functions are different; the Gaussian blur module is connected with the ashing processing module, receives the gray level image and respectively connects the gray level image with each relaxation parameter sigma_nCarrying out convolution budget on the corresponding Gaussian blur function to obtain a plurality of blurred images g_n；

The binarization processing module is connected with the Gaussian blur module, and a plurality of blurred images g are preset in the binarization processing module_nA boundary threshold value corresponding to each other and used for respectively aligning the blurred images g according to the corresponding boundary threshold values_nPerforming binarization processing to obtain multiple binarized images gb_n；

The lodging calculation module is connected with the binarization processing module, and a first calculation model and a second calculation model are preset in the lodging calculation module; the lodging calculation module converts a plurality of binarization results gb_nSubstituting the first calculation model to perform comprehensive operation to obtain a binary lodging image R (x, y), and calculating lodging rate R according to the second calculation model and the lodging image R (x, y)_rite。

9. The image detection-based plant lodging monitoring system of claim 8, wherein the first computational model is:λ_nis a preset constant.

10. The image detection-based plant lodging monitoring system of claim 8, wherein the second computational model is:k_yis a scaling factor.