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Plant disease and insect pest identification method and system based on deep learning

A technology of deep learning and recognition methods, applied in the field of computer vision, can solve problems such as small amount of data, failure to meet model training requirements, difficulty in model training, etc., and achieve the effect of improving accuracy

Inactive Publication Date: 2019-11-26
SHANDONG AGRICULTURAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, at present, in order to improve the accuracy of the deep learning model, a large number of data sets are needed, but the inventors found that in terms of plant diseases and insect pests, the acquisition of data sets is limited and the amount of data obtained is small, which cannot meet the requirements of model training. The deep learning model training of plant diseases and insect pests under the set has brought great difficulties

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  • Plant disease and insect pest identification method and system based on deep learning
  • Plant disease and insect pest identification method and system based on deep learning

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Embodiment 1

[0030] This embodiment discloses a method for identifying plant diseases and insect pests based on deep learning, comprising the following steps:

[0031] Step 1: Receive image data of various types of plant diseases and insect pests, and each type contains multiple images.

[0032] Step 2: Carry out data enhancement processing to the image, obtain the partial image of plant diseases and insect pests based on the above image, and perform random image preprocessing. The image preprocessing method includes: image transformation (such as zooming, cropping, rotating and flipping images, etc.), and Image quality adjustment (such as brightness, contrast adjustment and blur processing, etc.).

[0033] Described step 2 specifically comprises:

[0034] Step 2.1: All original image data are firstly partially enlarged for the diseased part.

[0035] Step 2.2: Randomly tilt, distort, randomly cut, crop, mirror flip, Gaussian distortion, adjust brightness, adjust contrast, rotate 90 degr...

Embodiment 2

[0049] The purpose of this embodiment is to provide a plant disease and pest identification system based on deep learning.

[0050] In order to achieve the above object, the present embodiment provides a plant disease and pest identification system based on deep learning, including:

[0051] The training data acquisition module receives the original training image, and the original training image includes image data of various plant diseases and insect pests;

[0052] The training data preprocessing module receives the image preprocessing method selected by the user for the training image, and performs image preprocessing;

[0053] The training data expansion module inputs the image output of the training data preprocessing module into a pre-built deep learning model based on the attention mechanism according to the pest category, and the deep learning model includes a sequentially connected convolution layer, a residual attention mechanism model and Fully connected layer; visu...

Embodiment 3

[0057] The purpose of this embodiment is to provide an electronic device.

[0058] An electronic device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the program, the following steps are implemented, including:

[0059] (1) receiving the original training image, which includes various image data of plant diseases and insect pests in the original training image;

[0060] (2) Receive the image preprocessing method selected by the user for the training image, and perform image preprocessing;

[0061] (3) input the pre-processed image into the pre-built deep learning model based on the attention mechanism by the pest category, and the deep learning model includes successively connected convolutional layers, residual attention mechanism models and fully connected layers; The output of the residual attention mechanism model is visualized as a new training image, and returns to step (2) until...

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Abstract

The invention discloses a plant disease and insect pest identification method and system based on deep learning. The plant disease and insect pest identification method comprises the steps: (1) receiving an original training image which comprises various types of plant disease and insect pest image data; (2) preprocessing based on an image preprocessing mode selected by a user for the training image; (3) inputting the preprocessed image into a deep learning model according to disease and pest categories, wherein the deep learning model comprises a convolution layer, a residual attention mechanism model and a full connection layer which are connected in sequence; and visualizing the output of the residual attention mechanism model to serve as a new training image, and returning to the step(2); (4) training a deep learning model based on all the training images; and (5) identifying diseases and insect pests. According to the invention, targeted enhancement and expansion processing are carried out on the small data set by using a method of combining data enhancement and an attention mechanism, so that the plant disease and insect pest identification accuracy can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method and system for identifying plant diseases and insect pests based on deep learning. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] my country has been a big agricultural country since ancient times. At present, with the increasing demand for food in our country, the scale of agriculture is constantly expanding, but the insufficient control of plant diseases and insect pests has become more and more revealed. According to statistics, in recent years, my country has an average annual occurrence of billions of plant diseases and insect pests. Mu times, tens of millions of tons of grain loss will be caused every year. One aspect of causing such a huge time is that it is impossible to accurately distinguish plant diseases and insec...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/10G06V10/44G06N3/045G06F18/24
Inventor 孙晓勇韩金玉魏庆功吴澍辰
Owner SHANDONG AGRICULTURAL UNIVERSITY
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