Crop phenotypic parameter extraction method and device

A technology of parameter extraction and crops, applied in image data processing, instruments, calculations, etc., can solve the problems of difficulty in extracting parameters such as length, width, and area of ​​leaves, affecting the final recognition accuracy, and inability to extract leaf inclination. Achieve the effect of realizing automatic measurement, reducing the image of the extraction result, and improving the detection accuracy

Pending Publication Date: 2020-09-22
BEIJING GREEN VALLEY TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] First of all, the efficiency of manual measurement is low, and at the same time, due to the irregular shape of the blade, the error caused by naked eye observation is also relatively large;
[0004] Secondly, the extraction of crop phenotypic parameters by taking pictures will seriously affect the classification effect due to the diversity, similarity, illumination difference, background factors and other problems of leaf shape.
It would take a lot of time and effort to manua

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  • Crop phenotypic parameter extraction method and device
  • Crop phenotypic parameter extraction method and device
  • Crop phenotypic parameter extraction method and device

Examples

Experimental program
Comparison scheme
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Example Embodiment

[0054] Example 1

[0055] figure 1 A schematic flowchart of a method for extracting phenotypic parameters of crops provided by the first embodiment of the present invention is shown.

[0056] The method for extracting phenotypic parameters of crops includes the following steps:

[0057] In step S110, group point cloud data of crops are acquired.

[0058] Specifically, the three-dimensional point cloud data of crops can be collected through lidar. Since lidar will collect all point cloud data in the scanning range, the collected three-dimensional point cloud data not only includes surrounding environment, ground vegetation, etc. Point cloud data also includes point cloud data of multiple crops. Since this technical solution mainly deals with point cloud data of crops, all point cloud data of the scanning range collected by the lidar is collectively referred to as the group point cloud data of crops here.

[0059] In this embodiment, the crops may include crops with stem and leaf charac...

Example Embodiment

[0106] Example 2

[0107] Figure 4 It shows a schematic flowchart of a method for extracting phenotypic parameters of crops according to the second embodiment of the present invention.

[0108] The method for extracting phenotypic parameters of crops includes the following steps:

[0109] In step S210, group point cloud data of crops are acquired.

[0110] This step is the same as step S110, and will not be repeated here.

[0111] In step S220, denoising and normalizing the group point cloud data are performed to obtain preprocessed group point cloud data.

[0112] Specifically, in order to further improve the accuracy of extraction of leaf parameters, the group point cloud data may also be preprocessed to remove noise point clouds, filter out point cloud data such as surface vegetation environment, and retain effective group point cloud data.

[0113] Further, the preprocessing operation may include denoising and normalization processing.

[0114] Specifically, the three-dimensional grou...

Example Embodiment

[0122] Example 3

[0123] Figure 5 It shows a schematic structural diagram of a crop phenotype parameter extraction device provided by the third embodiment of the present invention. The crop phenotypic parameter extraction device 300 corresponds to the crop phenotypic parameter extraction method in Embodiment 1. The crop phenotypic parameter extraction method in Embodiment 1 is also applicable to the crop phenotype parameter extraction device 300, which will not be repeated here. Go into details.

[0124] The crop phenotypic parameter extraction device 300 includes an acquisition module 310, an identification module 320, an extraction module 330, a segmentation module 340, and a fitting module 350.

[0125] The obtaining module 310 is used to obtain group point cloud data of crops.

[0126] The identification module 320 is configured to identify the root positions of all crops according to the group point cloud data.

[0127] The extraction module 330 is configured to extract single...

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Abstract

The invention discloses a crop phenotypic parameter extraction method and device. The method comprises the steps of obtaining group point cloud data of crops; identifying root positions of all crops according to the group point cloud data; extracting single plant point cloud data corresponding to a single plant crop from the group point cloud data according to the root position; performing stem and leaf segmentation based on the single plant point cloud data, and segmenting each leaf and stem into independent point cloud data; respectively fitting the segmented independent point cloud data toobtain crop phenotypic parameters which comprise stalk height, stalk diameter, leaf length, leaf width, leaf inclination angle and leaf area. According to the technical scheme, the automation degree is high, the crop phenotypic parameter extraction method and device can adapt to crop phenotypic parameter extraction under the condition of irregular leaf shapes, and the extraction precision is high.

Description

technical field [0001] The invention relates to the technical field of agricultural meteorology, in particular to a method and device for extracting crop phenotypic parameters. Background technique [0002] Informatization is an important feature of modern agriculture, and information technology is becoming more and more important to the development of agricultural economy. Efficient use of agricultural resources requires a full understanding of the growth of crops. To study crop growth, parameters such as leaf length, leaf width, leaf inclination, and leaf area are usually obtained. In the early days, the leaves were extracted by manual measurement. In recent years, it has been developed to take pictures of crops and use image processing algorithms to process leaf images, and combine contour tracking, seed filling and other methods to complete leaf extraction. However, the above extraction methods of crop phenotype parameters have the following problems: [0003] First of...

Claims

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Application Information

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IPC IPC(8): G06T7/11
CPCG06T7/11G06T2207/10028G06T2207/30188
Inventor 高上
Owner BEIJING GREEN VALLEY TECH CO LTD
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