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A vegetation coverage detection method and device thereof in a grazing sheep feeding path of a grassland

A technology of vegetation coverage and detection method is applied in the field of vegetation coverage detection in the feeding path of grassland grazing sheep, which can solve the problems of high labor intensity and low detection accuracy of vegetation coverage.

Active Publication Date: 2019-02-12
INNER MONGOLIA UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method and device for detecting vegetation coverage in the feeding path of grazing sheep on grasslands, which has the advantages of convenience and accuracy, and solves the problem of vegetation coverage in the feeding path of grazing sheep on grasslands. The problems of low detection accuracy and high labor intensity

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  • A vegetation coverage detection method and device thereof in a grazing sheep feeding path of a grassland
  • A vegetation coverage detection method and device thereof in a grazing sheep feeding path of a grassland
  • A vegetation coverage detection method and device thereof in a grazing sheep feeding path of a grassland

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Embodiment 1

[0127] see figure 1 , the method for detecting vegetation coverage in the feeding path of grassland grazing sheep in this embodiment is used to detect the vegetation coverage in the feeding path of grassland grazing sheep. In this example, the vegetation coverage is defined as the coverage of a single plant, that is, the ratio of the plant canopy shaded ground area to the sample plot area at the feeding point of grazing sheep, and the vegetation is only pasture. Monitoring the coverage of each type of grazing sheep's favorite pasture will affect its feeding path and the residence time of the feeding plants. Therefore, it is necessary to monitor the vegetation coverage of the feeding plants to correlate with grazing behavior. The analysis of experimental data can also invert the feeding path and feeding time of grazing sheep to infer the plant coverage and vegetation quality of the feeding area.

[0128] According to the definition of coverage, the vegetation coverage can be o...

Embodiment 2

[0248] The device for detecting vegetation coverage in the feeding path of grassland grazing sheep in this embodiment adopts the detection method of vegetation coverage in the feeding path of grassland grazing sheep in Embodiment 1. The detection device includes an image acquisition mechanism, an image preprocessing module, a feature library training module, and a vegetation segmentation module, wherein:

[0249] The image acquisition mechanism is used to collect vegetation images on the feeding path of grazing sheep; the image acquisition mechanism includes a vehicle body with multiple wheels at the bottom, a camera mounted on the vehicle body, and a motor that drives the wheels to rotate; the rotation of the motor drives the wheels to rotate and make The vehicle body moves on the grazing path of grazing sheep, and the camera captures image 1 of the vegetation on the grazing path of grazing sheep.

[0250] The image preprocessing module includes a calibration unit that first ...

Embodiment 3

[0253] The detection method of vegetation coverage in the feeding path of grassland grazing sheep in this embodiment is similar to that of Embodiment 1, the difference is that this embodiment does not use the gray level co-occurrence matrix method to extract the texture features of the image, but uses the gray level-gradient co-occurrence Matrix algorithm. The gray-gradient co-occurrence matrix algorithm divides the image into sub-regions of the same size, and counts the gray-gradient average feature quantity of each region, and then solves the error caused by image rotation.

[0254] Grayscale-gradient co-occurrence matrix implementation method: the grayscale matrix F(m, n) and the gradient matrix G(m, n) jointly count the occurrence of pixels of F(m, n)=i and G(m, n)=j , and normalize it to get the value of the (i, j)th element. There are many secondary features that can be extracted. The present invention selects seven invariant rotation quantities for texture feature extr...

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Abstract

The invention discloses a vegetation coverage detection method and device thereof in a grazing sheep feeding path of a grassland. The vegetation coverage detection method comprises the following stepsof 1 collecting and pre-processing a vegetation image; 2 establishing a vegetation characteristic database; 3 segmenting the vegetation image and calculating the coverage degree of various vegetation. The detection device comprises an image acquisition mechanism, an image preprocessing module, a feature library training module and a vegetation segmentation module. The method and the system of theinvention can correct the focal length of the camera and the coefficient when the image pixel is calculated by comparing the vegetation image with the calibration image of the artificial method, thereby improving the accuracy of the vegetation image. By adopting the principal component analysis method to reduce the dimension of the image, the redundancy of the characteristic parameters can be reduced. The method and the system of the invention conveniently divide a vegetation image into a plurality of single vegetation images by establishing a BP neural network training model, thereby calculating the coverage of each vegetation and improving the detection efficiency of the vegetation coverage in the grazing sheep feeding path.

Description

technical field [0001] The invention relates to a detection method and a device for vegetation coverage in the technical field of detection, in particular to a detection method and a device for vegetation coverage in the feeding path of grazing sheep on grasslands. Background technique [0002] my country is particularly rich in grassland resources. The total area of ​​grassland in the country is nearly 6 billion mu, accounting for about 41.7% of the total area of ​​my country. In order to maintain regional and ecological balance, grassland resources play an extremely important role. Grassland resources are the basis of grazing. Grazing activities directly affect grassland ecosystems. Grazing livestock not only eat grassland plants, but also trample plants by walking, which will seriously affect grassland vegetation and grassland resources. [0003] With the development of animal welfare breeding and intelligent animal husbandry, the detection and identification of vegetatio...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/40G06K9/46G06K9/62G06T7/194G06T7/45
CPCG06T7/194G06T7/45G06T2207/30188G06T2207/20084G06T2207/20081G06V20/188G06V10/30G06V10/267G06V10/56G06F18/2135G06F18/23213
Inventor 韩丁翁智王俊林仲兆楠武佩王忠武韩国栋杜德鹏马子寅
Owner INNER MONGOLIA UNIVERSITY
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