CN112907547A - Tropical crop pest risk assessment method and system - Google Patents
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Abstract
The invention provides a tropical crop pest risk assessment method and system, wherein the method comprises the following steps: dividing a crop planting area into a plurality of areas, and collecting the number of pests in each area to obtain the density of the pests in each area; acquiring M image information of crops in each area within time T, acquiring N scab images according to the M image information, and acquiring X scab images according to the N scab images of the areas; establishing a pest risk neural model, taking pest density data, crop scab image quantity data and crop planting area temperature data as input vectors of the pest risk neural model, and taking output vectors of the pest risk neural model as pest risk grades.
Description
Technical Field
The invention relates to the technical field of insect pest prediction, in particular to a tropical crop pest risk assessment method and system.
Background
With the development of agricultural technology, people have more and more experience on the occurrence of crop pests. However, the prior art has at least the following problems: although the purpose of preventing diseases and insect pests can be achieved to a certain extent in the prior art, the risk of diseases and insect pests which may occur in the future cannot be predicted, and corresponding plant protection measures cannot be taken in advance to prevent and treat the diseases and insect pests.
Disclosure of Invention
The invention aims to provide a tropical crop disease and pest risk assessment method and system to solve the problems in the background technology.
The invention is realized by the following technical scheme: the invention provides a tropical crop disease and pest risk assessment method in a first aspect, which comprises the following steps:
dividing a crop planting area into a plurality of areas, and collecting the number of pests in each area to obtain the density of the pests in each area;
acquiring M image information of crops in each area within time T, acquiring N scab images according to the M image information, and acquiring X scab images according to the N scab images of the areas;
establishing a pest risk neural model, taking pest density data, crop scab image quantity data and crop planting area temperature data as input vectors of the pest risk neural model, and taking output vectors of the pest risk neural model as pest risk grades.
Preferably, the step of collecting the number of pests in each area to obtain the pest density in each area comprises the following steps:
setting K traps in each area, and counting the number of pests trapped in each trap within time T;
and calculating the total number of the pests in the K traps, wherein the ratio of the total number of the pests in the K traps to the area of the area is the pest density A in each area.
Preferably, the acquiring M image information of the crop in each area within the time T, and obtaining N lesion images from the M image information includes:
performing image conversion on the M pieces of image information to obtain M gray level images;
converting the M gray level images into M binary images;
carrying out image segmentation on the M binary images to obtain N scab images;
x lesion images are obtained from the N lesion images of the plurality of regions.
Preferably, the taking pest density data, crop scab image quantity data and crop planting area temperature data as input vectors of the pest risk neural model comprises the following steps:
setting density intervals Ai { A1, A2, A3 and A4}, and setting a characteristic weight Bi { B1, B2, B3 and B4} corresponding to each density interval Ai, wherein if the pest density A is one of the density intervals Ai, calculating a risk weight of the pest density A: a is Bi;
setting quantity intervals Xi { X1, X2, X3 and X4}, and setting feature weights Ci { C1, C2, C3 and C4} corresponding to each quantity interval Xi, and if the quantity X of the lesion images is one of the quantity intervals Xi, calculating the risk weight of the quantity of the lesion images: x Ci;
setting temperature intervals Di { D1, D2 and D3}, and simultaneously setting feature weights Fi { F1, F2 and F3} corresponding to each temperature interval Di, and if the temperature is one of the temperature intervals Di, calculating the risk weight of the temperature: d Fi;
and taking the risk weight of the pest density A, the risk weight of the number of lesion images and the risk weight of the temperature as input vectors of a pest risk neural model.
Preferably, the insect pest risk neural model comprises a three-layer neural network model, wherein the neural network model comprises an input layer, a hidden layer and an output layer, at least three input vectors are input into the input layer, a sigmoid function is selected as the activation function, and a mean square error function is selected as the error function.
The invention provides a tropical crop disease and pest risk assessment system, which comprises a trap arranged in a crop planting area, a central processing unit used for remote data processing and analysis, and an unmanned aerial vehicle used for crop image acquisition, wherein the central processing unit comprises a data receiving unit, an image analysis processing unit and a risk assessment unit, and the data receiving unit is connected with a signal of the unmanned aerial vehicle;
the image analysis processing unit is in signal connection with the data receiving unit and is used for storing, comparing, analyzing and processing the real-time image data received by the data receiving unit and then sending the real-time image data to the risk assessment unit.
Preferably, the system further comprises a plurality of cameras, temperature sensors and wireless communication modules, wherein the cameras, the temperature sensors and the wireless communication modules are arranged in the crop planting area, the cameras and the temperature sensors are connected with the wireless communication modules through signals, and the wireless communication modules are connected with the data receiving unit through signals.
Preferably, the system further comprises a policy library, wherein the policy library comprises a plurality of risk policies, and the risk policies correspond to different evaluation results of the risk evaluation unit.
Compared with the prior art, the invention has the following beneficial effects:
according to the tropical crop disease and pest risk assessment method and system provided by the invention, the real data is used as the training data to build the disease and pest risk neural model, so that the prediction result is more credible; the adopted neural network method can process data more effectively, has better expansibility, is not limited to insect pest prediction of a certain area, and can be applied to various areas in the country under the condition of enough data; in addition, the training data can be updated continuously, and newly acquired data can be incorporated into a data training set after each application, so that the model has stronger generalization.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a flow chart of a tropical crop pest risk assessment method provided by the invention.
Detailed Description
In order to better understand the technical content of the invention, specific embodiments are provided below, and the invention is further described with reference to the accompanying drawings.
Referring to fig. 1, the first aspect of the present invention provides a method for evaluating the risk of diseases and insect pests of tropical crops, comprising the following steps:
step 101: dividing a crop planting area into a plurality of areas, and collecting the number of pests in each area to obtain the density of the pests in each area;
specifically, when dividing the crop planting area into a plurality of areas, the crop planting area may be divided into a plurality of grids in the form of a grid, each grid is provided with a plurality of crops, and in acquiring the number of pests in each area and obtaining the pest density in each area, the method further includes:
setting K traps in each area, and counting the number of pests trapped in each trap within time T;
and calculating the total number of the pests in the K traps, wherein the ratio of the total number of the pests in the K traps to the area of the area is the pest density A in each area.
Step 102: acquiring M image information of crops in each area within time T, acquiring N scab images according to the M image information, and acquiring X scab images according to the N scab images of the areas;
acquiring M image information of crops in each area within time T, and acquiring N scab images according to the M image information, wherein the method comprises the following steps:
the M pieces of image information are image-converted to obtain M pieces of grayscale images, which are generally obtained by measuring the brightness of each pixel in a single electromagnetic spectrum. The gray scale image for display is typically stored with a non-linear scale of 8 bits per sampled pixel, so that 256 levels of gray scale are possible. This precision just avoids visible banding distortion and is very easy to program;
and converting the M gray level images into M binary images, wherein the binarization processing of the images is to set the gray level value of a point on the image to be 0 or 255, namely to make the whole image show obvious black and white effect. That is, a gray scale image with 256 brightness levels is selected by a proper threshold value to obtain a binary image which can still reflect the whole and local features of the image. In digital image processing, binary images are very important, and particularly in practical image processing, many systems are configured by binary image processing, and in order to perform processing and analysis of binary images, a grayscale image is first binarized to obtain a binarized image, which is advantageous in that when an image is further processed, the collective property of the image is only related to the positions of points with pixel values of 0 or 255, and the multi-level values of the pixels are not related, so that the processing is simplified, and the processing and compression amount of data is small. In order to obtain an ideal binary image, a non-overlapping region is generally defined by closed and connected boundaries. All pixels with the gray levels larger than or equal to the threshold are judged to belong to the specific object, the gray level of the pixels is 255 for representation, otherwise the pixels are excluded from the object area, the gray level is 0 for representing the background or the exceptional object area;
the M binary images are subjected to image segmentation to obtain N scab images, and since the gray levels of the binary images are only two, namely any pixel in the images is not 0 or 1 and has no other transitional gray level, which regions are scab regions can be visually seen according to the binary images;
x lesion images are obtained from the N lesion images of the plurality of regions.
Step 103: establishing a pest risk neural model, taking pest density data, crop scab image quantity data and crop planting area temperature data as input vectors of the pest risk neural model, and taking output vectors of the pest risk neural model as pest risk grades.
Specifically, the insect pest risk neural model comprises a three-layer neural network model, wherein the neural network model comprises an input layer, a hidden layer and an output layer, at least three input vectors of the input layer are provided, one output vector of the output layer is provided, namely the insect pest risk grade of a planting area, a sigmoid function is selected as an activation function, and a mean square error function is selected as an error function.
When risk assessment is carried out, pest density data, crop scab image quantity data and crop planting area temperature data are required to be used as input vectors of a pest risk neural model;
and the input vector thereof is obtained by the following way:
setting density intervals Ai { A1, A2, A3 and A4}, and setting a characteristic weight Bi { B1, B2, B3 and B4} corresponding to each density interval Ai, wherein if the pest density A is one of the density intervals Ai, calculating a risk weight of the pest density A: a is Bi;
for example, when pest density a belongs to a1 interval, the characteristic weight of a1 is B1, the risk weight of pest density a is a × B1, and the risk weight of pest density a is used as one of the input vectors of the pest risk neural model.
Setting quantity intervals Xi { X1, X2, X3 and X4}, and setting feature weights Ci { C1, C2, C3 and C4} corresponding to each quantity interval Xi, and if the quantity X of the lesion images is one of the quantity intervals Xi, calculating the risk weight of the quantity of the lesion images: x Ci;
similarly, when the number X of lesion images belongs to the X3 interval, the characteristic weight of X3 is C3, the risk weight of pest density a is X × C3, and the risk weight of the number X of lesion images is used as one of the input vectors of the pest risk neural model.
Setting temperature intervals Di { D1, D2 and D3}, and simultaneously setting feature weights Fi { F1, F2 and F3} corresponding to each temperature interval Di, and if the temperature is one of the temperature intervals Di, calculating the risk weight of the temperature: d Fi;
when the temperature data D of the species planting area belongs to D2, the characteristic weight of D2 is F2, the risk weight of the temperature data D is D x F2, and the risk weight of the temperature data D is used as one of the input vectors of the insect pest risk neural model.
And obtaining an output value Y through the insect pest risk neural model, wherein when the output value Y is a value between 0.5 and 1, the probability of insect pest occurrence is high, and if the value a is a value between 0.5 and 0, the probability of insect pest occurrence in the forest is low.
The invention provides a tropical crop disease and pest risk assessment system, which comprises a trap arranged in a crop planting area, a central processing unit used for remote data processing and analysis, and an unmanned aerial vehicle used for crop image acquisition, wherein the central processing unit comprises a data receiving unit, an image analysis processing unit and a risk assessment unit, and the data receiving unit is connected with a signal of the unmanned aerial vehicle;
the image analysis processing unit is in signal connection with the data receiving unit and is used for storing, comparing, analyzing and processing the real-time image data received by the data receiving unit and then sending the real-time image data to the risk assessment unit.
Specifically, the system further comprises a plurality of cameras, temperature sensors and wireless communication modules, wherein the cameras, the temperature sensors and the wireless communication modules are arranged in the crop planting area, the cameras and the temperature sensors are connected with the wireless communication modules through signals, and the wireless communication modules are connected with the data receiving unit through signals.
Specifically, the system further comprises a policy library, wherein the policy library comprises a plurality of risk policies, and the risk policies correspond to different evaluation results of the risk evaluation units.
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 made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A tropical crop pest risk assessment method is characterized by comprising the following steps:
dividing a crop planting area into a plurality of areas, and collecting the number of pests in each area to obtain the density of the pests in each area;
acquiring M image information of crops in each area within time T, acquiring N scab images according to the M image information, and acquiring X scab images according to the N scab images of the areas;
establishing a pest risk neural model, taking pest density data, crop scab image quantity data and crop planting area temperature data as input vectors of the pest risk neural model, and taking output vectors of the pest risk neural model as pest risk grades.
2. The tropical crop pest risk assessment system according to claim 1, wherein collecting the number of pests in each region and obtaining the pest density in each region comprises:
setting K traps in each area, and counting the number of pests trapped in each trap within time T;
and calculating the total number of the pests in the K traps, wherein the ratio of the total number of the pests in the K traps to the area of the area is the pest density A in each area.
3. The method for evaluating the risk of diseases and insect pests of tropical crops according to claim 1, wherein M pieces of image information of crops in each area are acquired within a time T, and N pieces of lesion images are obtained from the M pieces of image information, and the method comprises the following steps:
performing image conversion on the M pieces of image information to obtain M gray level images;
converting the M gray level images into M binary images;
carrying out image segmentation on the M binary images to obtain N scab images;
x lesion images are obtained from the N lesion images of the plurality of regions.
4. The method for evaluating the risk of diseases and insect pests of the tropical crops according to any one of claims 2 or 3, wherein the step of using pest density data, crop lesion image quantity data and crop planting area temperature data as input vectors of a pest risk neural model comprises the following steps:
setting density intervals Ai { A1, A2, A3 and A4}, and setting a characteristic weight Bi { B1, B2, B3 and B4} corresponding to each density interval Ai, wherein if the pest density A is one of the density intervals Ai, calculating a risk weight of the pest density A: a is Bi;
setting quantity intervals Xi { X1, X2, X3 and X4}, and setting feature weights Ci { C1, C2, C3 and C4} corresponding to each quantity interval Xi, and if the quantity X of the lesion images is one of the quantity intervals Xi, calculating the risk weight of the quantity of the lesion images: x Ci;
setting temperature intervals Di { D1, D2 and D3}, and simultaneously setting feature weights Fi { F1, F2 and F3} corresponding to each temperature interval Di, and if the temperature is one of the temperature intervals Di, calculating the risk weight of the temperature: d Fi;
and taking the risk weight of the pest density A, the risk weight of the number of lesion images and the risk weight of the temperature as input vectors of a pest risk neural model.
5. The method for assessing the risk of diseases and insect pests of tropical crops according to claim 4, wherein the insect pest risk neural model comprises a three-layer neural network model, the neural network model comprises an input layer, a hidden layer and an output layer, the number of input vectors of the input layer is at least three, the activation function is a sigmoid function, and the error function is a mean square error function.
6. A tropical crop disease and pest risk assessment system is characterized by comprising a trap arranged in a crop planting area, a central processing unit used for remote data processing and analysis and an unmanned aerial vehicle used for crop image acquisition, wherein the central processing unit comprises a data receiving unit, an image analysis processing unit and a risk assessment unit, and the data receiving unit is connected with a signal of the unmanned aerial vehicle;
the image analysis processing unit is in signal connection with the data receiving unit and is used for storing, comparing, analyzing and processing the real-time image data received by the data receiving unit and then sending the real-time image data to the risk assessment unit.
7. The system for assessing risk of diseases and insect pests of tropical crops according to claim 6, further comprising a plurality of cameras, temperature sensors and wireless communication modules, wherein the cameras, the temperature sensors and the wireless communication modules are arranged in the crop planting area, the cameras and the temperature sensors are connected with the wireless communication modules through signals, and the wireless communication modules are connected with the data receiving unit through signals.
8. The tropical crop pest and disease risk assessment system according to claim 6, further comprising a strategy library, wherein the strategy library comprises a plurality of risk strategies, and the risk strategies correspond to different assessment results of the risk assessment units.
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| CN114693898A (en) * | 2022-05-11 | 2022-07-01 | 山东师范大学 | Pancreas and tumor three-dimensional image segmentation system and method |
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Application publication date: 20210604 |