A pine wilt tree detection and positioning method based on deep learning

A technology of pine wood nematode disease and deep learning is applied in the field of detection and positioning of dead trees of pine wood nematode disease based on deep learning, which can solve the problems of low efficiency, waste of manpower and material resources, etc. Effect

Inactive Publication Date: 2019-06-28
SOUTH CHINA AGRI UNIV
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AI Technical Summary

Problems solved by technology

At present, the method of detecting and controlling pine wood nematode disease in my country is usually to go to the pine forest manually to find the diseased pine trees, and then apply pest

Method used

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  • A pine wilt tree detection and positioning method based on deep learning
  • A pine wilt tree detection and positioning method based on deep learning
  • A pine wilt tree detection and positioning method based on deep learning

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Embodiment Construction

[0024] The present invention will be further described below in conjunction with the examples and drawings, but the embodiments of the present invention are not limited thereto.

[0025] see figure 1 , the method for detecting and locating dead trees with pine wood nematode disease based on deep learning of the present embodiment comprises the following steps:

[0026] (1) Collection, preprocessing and labeling of training samples. Use an aerial photography drone equipped with a visible light camera to go to a pine forest known to have dead trees of pine wood nematode disease to take high-altitude shooting to collect sample images of pine wood nematode disease, and perform preprocessing such as denoising, splicing, and cutting on the captured sample images (Existing matlab and python tools can be used specifically), and the size of the sample image is normalized to 224mm × 224mm × 3mm to form a training sample; the training samples that have been collected and preprocessed ar...

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Abstract

The invention discloses a pine wood nematode disease dead tree detection positioning method based on deep learning, and the method comprises the following steps: (1) collecting a sample image, carrying out the preprocessing of the sample image, and carrying out the marking of a pine tree, and obtaining a training sample; (2) training the pine wood nematode disease dead tree training sample by using a deep learning framework and a convolutional neural network to obtain a detection model; (3) enabling the unmanned aerial vehicle to carry out high-altitude fixed-point shooting on the target areaand acquires images and position information; and (4) transmitting the acquired image into a detection model, performing dead tree identification on the acquired image by the detection model, outputting the detected detection image, and finally obtaining a prescription chart of geographic position information of the dead tree of the pine wood nematode disease according to the coordinate position of the dead tree in the image. According to the method, the sick pine tree can be rapidly, efficiently and accurately detected, and the position of the sick pine tree is judged, so that subsequent treatment is facilitated.

Description

technical field [0001] The present invention relates to a dead tree detection method of pine wood nematode disease, in particular to a deep learning-based detection and positioning method of dead tree disease of pine wood nematode disease. Background technique [0002] Pine wood nematode disease is a devastating epidemic of pine trees that is extremely harmful to pine forests in my country. The external symptoms of infected pine trees are that the needles gradually turn yellowish brown, and the needles of severely diseased pine trees will turn reddish brown. , eventually wilting and dying. Pine wood nematode disease is highly pathogenic and has a fatal impact on the host. Often the infected pine tree will die soon and will quickly spread to other pine trees. The spread leads to the death of a large number of pine trees. Therefore, if the pine trees with pine wood nematode disease cannot be detected and treated in time, it will bring huge economic losses and certain damage to...

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Application Information

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IPC IPC(8): G06K9/00G06T7/70
Inventor 兰玉彬童泽京邓小玲杨炜光黄梓效曾国亮杨佳诚巫昌盛成胜南
Owner SOUTH CHINA AGRI UNIV
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