Pecan common pest recognition method based on deep learning

A deep learning, pecan technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as insufficient data sample size, large fluctuations in model fit, low recognition accuracy, etc., to achieve automatic Recognition and classification, improving recognition accuracy, and the effect of high recognition accuracy

Inactive Publication Date: 2019-10-08
ZHEJIANG FORESTRY UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0017] Purpose of the invention: In order to overcome the deficiencies in the prior art, the methods adopted in the prior art have insufficient data sample size, complex data preprocessing, insufficient feature extraction, large fluctuations in model fitting, and no specificity and complexity of pests. To optimize and adjust the model, all lead to low recognition accuracy

Method used

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  • Pecan common pest recognition method based on deep learning
  • Pecan common pest recognition method based on deep learning
  • Pecan common pest recognition method based on deep learning

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Experimental program
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Effect test

Embodiment 1

[0067] A kind of method for identifying and classifying hickory pests based on deep learning of the present embodiment, refer to figure 2 , including the following steps:

[0068] Collect image data of hickory pest samples;

[0069] The hickory pest sample image data is processed to obtain the processed hickory pest sample image data set;

[0070] Use the VGG convolutional neural network model and optimize the VGG convolutional neural network model:

[0071] VGG convolutional neural network model reference figure 1 , mainly composed of input layer, convolutional layer, pooling layer, fully connected layer, and Softmax layer. VGGNet can be divided into 5 layer groups, including Conv1 convolutional layer group 1, Conv2 convolutional layer group 2, Conv3 convolutional layer group 3, Conv4 convolutional layer group 4 and Conv5 convolutional layer group 5, fully connected layer including FC6 Fully connected layer 6 and FC7 fully connected layer 7. Each layer group contains 1 ...

Embodiment 2

[0078] A kind of hickory pest identification and classification method based on deep learning of the present embodiment, based on embodiment one, collects the image data of different growth stages of hickory pest, for example collects the image data of pest from egg to larva to adult stage, is used for training The model avoids the error in model detection caused by the large difference in the body characteristics of pests in different growth stages. Of course, the types of pests are different and the growth stages of pests are different. In addition, the color of the hickory pest sample image is reserved according to the situation, because the pests not only have differences in shape and texture, but color is also an important factor affecting the recognition accuracy. Therefore, in the process of data processing, the input image data is not converted to grayscale. image, but retains the values ​​of the RGB three color channels of the image.

Embodiment 3

[0080] A kind of hickory pest identification classification method based on deep learning of the present embodiment, based on embodiment 2, hickory pest sample image data is processed, obtains the hickory pest sample image data set after processing, comprises the following steps:

[0081] Dealing with erroneous and duplicate data in hickory pest sample image data;

[0082] Using data augmentation to expand the image data of hickory pest samples.

[0083]Use data enhancement to expand the image data of hickory pest samples: After screening, the number of samples of each insect is different. In order to improve the classification accuracy of the network, make the performance of the network better, and prevent problems such as overfitting, this implementation For example, data enhancement is used to expand the amount of image data of the above-mentioned hickory pest sample. By operating the image data of the above-mentioned hickory pest sample, for example, in this embodiment, th...

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Abstract

The invention discloses a hickory nut common pest recognition method based on deep learning, and belongs to the technical field of agricultural and forestry pest recognition and classification, the method comprises the following steps: collecting hickory nut pest sample image data; processing the pecan pest sample image data to obtain a processed pecan pest sample image data set; adopting a VGG convolutional neural network model, and optimizing the VGG convolutional neural network model; carrying out target pest identification and classification by utilizing the optimized VGG convolutional neural network model. Based on the VGGNet network structure, a set of pest identifying and classifying method suitable for hickory is developed, rapid identification and classification of common hickorypests can be achieved, automatic identification and classification can be achieved, and the identification accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of identification and classification of agricultural and forestry pests, and in particular relates to a method for identifying and classifying hickory pests based on deep learning. Background technique [0002] Our country is a large agricultural country. During the cultivation of crops, different kinds of pests are encountered every year, which makes the yield and quality of crops decline to varying degrees. When the disaster is severe, it may even lead to large-scale failure of crops. Accurate and effective classification, identification and identification of insects is an important prerequisite for timely pest control and avoiding huge economic losses of crops. Insects are the most diverse animals in the natural environment, and it is very difficult to identify them due to their varied shapes and rich textures. Traditional insect taxonomy and identification mainly rely on insect experts or insect taxonom...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/241G06F18/214
Inventor 冯海林任丽锦方益明杜晓晨刘兴泉周国鑫
Owner ZHEJIANG FORESTRY UNIVERSITY
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