Crop growth detection method and system based on 5G transmission
A technology for crop growth and detection methods, applied in the field of agricultural machine vision, can solve problems such as poor timeliness and slow data processing, and achieve the effects of improving timeliness, improving timeliness, and reducing computational pressure.
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Embodiment 1
[0061] In view of the above problems, the embodiment of the present application provides a crop growth detection method based on 5G transmission, which can reduce the amount of data to be processed and improve the data processing speed of crop growth detection.
[0062] The technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.
[0063] figure 1 It is a schematic flowchart of a method for detecting crop growth based on 5G transmission shown in the embodiment of the present application.
[0064] see figure 1 , the crop growth detection method based on 5G transmission, comprising:
[0065] 101. Collect hyperspectral images of crops;
[0066] In the embodiment of this application, a hyperspectral image of crops is collected by an intelligent hyperspectral camera. The intelligent hyperspectral camera is equipped with a crop identification model and a characteristic band database. Through the ...
Embodiment 2
[0086] The embodiment of the present application designs step 102 in the first embodiment above.
[0087] figure 2 It is a schematic flowchart of the crop variety type identification method shown in the embodiment of the present application.
[0088] See figure 2 , the identification method of described crop variety type, comprising:
[0089] 201. Using the image local center of gravity algorithm to segment the hyperspectral image to obtain a single crop image;
[0090] In the embodiment of the present application, the hyperspectral images include crop images collected under light in the 450nm, 550nm and 650nm bands.
[0091] In the embodiment of this application, the contour of the image is extracted by using the FindContour function in OpenCV, and then the centroid of the contour is obtained by using the first-order moment of the image to locate, and the hyperspectral image is segmented by combining the image contour and the contour centroid to obtain a single crop ima...
Embodiment 3
[0103] According to the acquisition process of characteristic band types described in the first embodiment above, before step 103, it is necessary to collect characteristic band types of different crops to establish a characteristic band database. The embodiment of the present application designs the establishment of the characteristic band database.
[0104] image 3 It is a schematic diagram of the establishment process of the characteristic band database shown in the embodiment of the present application.
[0105] see image 3 , the establishment process of the characteristic band database, including:
[0106] 301. Collect the spectral image of the crop;
[0107] In the embodiment of the present application, the spectral image of the crop is used as a sample for establishing the characteristic band database, which can be processed by standard normal variable transformation to reduce the influence of uneven particle size and non-specific scattering on the particle surface...
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