Litchi disease and insect pest identification method based on deep learning

A technology of deep learning and identification method, applied in the field of agricultural pest identification, can solve problems such as poor effect of litchi pest identification model, poor feature extraction ability of pest image, weak generalization ability, etc.

Pending Publication Date: 2022-05-10
SOUTH CHINA AGRI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] At present, there are still many problems to be solved in the existing litchi pest identification technology: for example, there is still a lack of large-scale and complete image data sets of litchi pests and diseases;

Method used

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  • Litchi disease and insect pest identification method based on deep learning
  • Litchi disease and insect pest identification method based on deep learning
  • Litchi disease and insect pest identification method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] Please refer to figure 1 , a method for identifying litchi pests and diseases based on deep learning, comprising the following steps:

[0040] S1, acquiring an image to be recognized;

[0041] S2, inputting the image to be identified into a preset litchi pest identification model, and obtaining a litchi pest identification result of the image to be identified;

[0042] Among them, the litchi pest recognition model consists of litchi pest and disease datasets including sample images, using the lightweight convolutional neural network ShuffleNetV2 as the basic network, introducing the attention mechanism SimAM, using Hardswish as the activation function, and adding The network model training with Dropout regularization processing is obtained.

[0043]Compared with the prior art, the present invention uses the results of deep learning training, can automatically identify various litchi pests and diseases, and solves the problems of low efficiency and poor recognition eff...

Embodiment 2

[0077] A kind of litchi pests and diseases identification system based on deep learning, please refer to Figure 6 , including an image acquisition module 1 and an image recognition module 2 connected to the image acquisition module 1:

[0078] The image acquisition module 1 is used to acquire an image to be identified;

[0079] The image recognition module 2 is used to input the image to be recognized into a preset litchi pest recognition model to obtain the litchi pest recognition result of the image to be recognized;

[0080] Among them, the litchi pest recognition model consists of litchi pest and disease datasets including sample images, using the lightweight convolutional neural network ShuffleNetV2 as the basic network, introducing the attention mechanism SimAM, using Hardswish as the activation function, and adding The network model training with Dropout regularization processing is obtained.

Embodiment 3

[0082] A storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the deep learning-based litchi pest identification method in embodiment 1 are realized.

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Abstract

Aiming at the limitation of the prior art, the invention provides a litchi disease and insect pest identification method based on deep learning. According to the method, a deep learning training result is applied, various litchi diseases and insect pests can be automatically identified, and the problems of low efficiency, poor identification effect and the like of a traditional artificial disease and insect pest identification method are solved; according to a litchi disease and insect pest recognition model used in the scheme, an attention mechanism SimAM is introduced on the basis of a lightweight convolutional neural network ShuffleNetV2, an activation function Hardswitch is used, and a Dropout regularization method is adopted in a full connection layer; the litchi disease and insect pest recognition model can effectively extract important features, suppress interference of non-important features and improve network classification recognition performance, the number of network model parameters is not additionally increased, and storage and calculation expenses of the model are reduced.

Description

technical field [0001] The present invention relates to the technical field of agricultural pest identification, in particular to the application of deep learning and image recognition technology in the technical field of agricultural pest identification, and more specifically, to a method for identifying litchi pests based on deep learning. Background technique [0002] As one of the most important economic forest fruit trees in South my country, litchi has high medicinal value and is known as the "king of fruits". With the large-scale planting of litchi and the increasing number of cultivated varieties, the occurrence of litchi diseases and insect pests is also becoming more and more serious. Litchi pests and diseases not only have a wide variety, but also have a long disease cycle and difficult control, which seriously affect the quality and yield of litchi fruit. [0003] The existing identification method of litchi pests and diseases is mainly through the detection and...

Claims

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Application Information

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IPC IPC(8): G06V20/68G06V10/774G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/048G06N3/045G06F18/24G06F18/214
Inventor 彭红星何慧君
Owner SOUTH CHINA AGRI UNIV
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