Method and system for ground point fitting and crop height extraction based on point cloud data

Abstract

The invention discloses a ground point fitting and crop height extraction method and system based on point cloud data. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a plant region; then determining plant canopy data information; determining ground point cloud data in combination with the ground fluctuation information; and finally, according to the canopy top point and the fitted ground point, calculating the height of the plant in the sampling area. According to the method provided by the invention, the ground points actually existing in the point cloud are extracted, the effective degree of each part of the point cloud is analyzed, the ground points are extracted based on the elevation and the gradient, and the elevation of the missing ground points is fitted based on the real ground points, so that errors generated by topographic relief are eliminated, and the applicability is higher; the crop height extraction can be carried out by filtering out the effective point clouds from the original point clouds, and at the same time, the interference of the invalid point clouds is eliminated, the complexity of subsequent processing is reduced, and the error of crop height extraction caused by topographic relief is overcome.

Description

technical field [0001] The invention relates to the technical field of point cloud data processing, in particular to a method and system for ground point fitting and crop height extraction based on point cloud data. Background technique [0002] Crop height plays an important role in accurately monitoring crop growth, predicting crop yield and optimizing agricultural production management. The crop height in the present invention refers to the distance from the top to the ground. The traditional crop height estimation methods are mainly based on layer-by-layer statistics and large-scale field manual measurement, but there are major problems such as waste of measurement time and human resources, and lack of spatial distribution information. In the past, with the rapid development of remote sensing technology, there were many large-area vegetation studies based on satellite remote sensing images. For example, by calculating different vegetation indices and extracting crop inf...

AI Technical Summary

Problems solved by technology

With the diversification of filtering methods and application scenarios, the selection of thresholds is becoming more and more complicated

Although the above method is suitable for most application scenarios, for the point cloud of crops generated based on SfM, the narrow spacing between crops leads to different degrees of occlusion between different leaves, which makes some crops The soil below cannot be captured by drone imagery

Therefore, in the case of incomplete ground points, the point cloud cannot meet the sample requirements of the above methods.

[0005] Although there are currently many researches on extracting crop parameters based on UAV point cloud, most of them focus on removing outliers as the main idea to improve the extraction accuracy of crop height, but in fact, in a wider crop area, terrain fluctuations often It will bring more obvious errors to the extraction of crop height. Therefore, the method of improving the accuracy of extracting crop height by removing outliers is unable to avoid the error caused by terrain fluctuations.

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