Forest growth modeling and predicting method and device based on machine learning

A machine learning and growth modeling technology, applied in forest measurement and forest industry, it can solve problems such as unreliable accuracy, and achieve the effect of strong adaptability and high prediction accuracy.

Inactive Publication Date: 2019-07-26
BEIJING WOOD AI TECH LTD
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Problems solved by technology

However, the accuracy of telemetry and remote sensing methods is af

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  • Forest growth modeling and predicting method and device based on machine learning
  • Forest growth modeling and predicting method and device based on machine learning
  • Forest growth modeling and predicting method and device based on machine learning

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[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. . Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of this application.

[0039] In the prior art, destructive sampling has better accuracy than other methods. This method requires cutting down a certain number of trees in the forest area, and then bringing them into the laboratory for various measurements, and finally a model with higher accuracy can be obtained. Such as figure 1 As shown, the x-axis represents the diameter at breast height of trees, the y-axis represents total biomass, and the discrete points i...

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Abstract

The invention discloses a forest growth modeling and predicting method and device based on machine learning, and relates to the field of forest industry. The method comprises the following steps: collecting destructive sampling image data of a tree through at least one image sensing device; labeling annual rings in the image data to obtain labeled data; obtaining basic parameters of a tree corresponding to the image data, wherein the basic parameters at least comprise tree biological quality data; inputting the image data, the annotation data and the basic parameters as training data into a machine learning model, and performing training to generate a biological quality model, wherin the biological quality model is used for automatically identifying the subsequently collected destructive sampling image data and outputting current and predicted tree biological quality data. By adopting the technical scheme of the invention, the multi-dimensional training data can be input into the machine learning model to generate the biological quality model, so that the biological quality model is higher in adaptability and higher in prediction accuracy.

Description

technical field [0001] The invention relates to the fields of forest industry and forest measurement, in particular to a method and device for forest growth modeling and prediction based on machine learning. Background technique [0002] In the field of forestry industry, the measurement, estimation and prediction of wood biomass (Biomass) is an important work. This is because the value of the forest directly depends on the stock of the forest, and the biomass is an important indicator of its stock. For example, the total carbon sequestration of forests and the total commercial value of economic forests can be calculated from the biomass of forests. [0003] In the prior art, there are already a variety of methods for measuring, estimating and predicting biomass. These methods are summarized by using fixed parameters such as diameter at breast height, and estimating the biomass of individual trees or regional forests based on mathematical models. . For example, the method...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N20/00
CPCG06N20/00G06V20/188G06V10/44G06F18/214
Inventor 丁磊
Owner BEIJING WOOD AI TECH LTD
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