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A method for identifying crop diseases and insect pests based on mixed attention module

A recognition method and crop technology, applied in the field of computer vision, can solve problems such as difficulty in improving the accuracy rate and unstable effect of the attention module, and achieve the effect of low application cost and reduced mapping interval

Active Publication Date: 2022-04-08
SOUTH CHINA AGRI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Especially in fine-grained classification tasks, this kind of interference due to "serial connection" will make the effect of the attention module unstable, and it is difficult to guarantee the improvement of accuracy

Method used

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  • A method for identifying crop diseases and insect pests based on mixed attention module
  • A method for identifying crop diseases and insect pests based on mixed attention module
  • A method for identifying crop diseases and insect pests based on mixed attention module

Examples

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Embodiment 1

[0048] Such as figure 1 and 2 As shown, the present embodiment provides a method for identifying crop diseases and insect pests based on a mixed attention module, comprising the following steps:

[0049] S1), input the crop disease and insect pest picture of RGB three channels, it is preprocessed, specifically comprises the following steps:

[0050] S101), randomly adopt four methods of bilinear interpolation, nearest neighbor method, bicubic interpolation and area interpolation to process the pictures of crop diseases and insect pests, and scale them to 224×224 operations;

[0051] S102), flipping the picture randomly horizontally and vertically, so as to achieve the purpose of data enhancement.

[0052] S2), the image processed in step S1) is extracted through the convolutional layer features, and then the maximum pooling layer with a small scale of 3×3 is used for further feature abstraction, namely:

[0053]

[0054]

[0055] where x out Indicates the output feat...

Embodiment 2

[0078] The data set used in this example selects pictures of some crop diseases and insect pests jointly created by Shanghai Xinke Technology and Innovation Works. Pests and diseases are further divided into two types: "general" and "serious" (for example: "apple scab is normal" and "apple scab is severe"), with a total of 61 categories. The data set used in this example has a total of 36,258 pictures of crop diseases and insect pests, and the complete labels of the data set are shown in Table 1.

[0079] Table 1 Classification labels of crop diseases and insect pests dataset

[0080]

[0081]

[0082]

[0083] On this basis, this embodiment divides the pictures into a training set and a test set, wherein the training set contains 31718 pictures, the test set contains 4540 pictures, and the pixel size of most pictures in the data set is about 300×400. Some pixels are pictures with a resolution of 2kd; therefore, in order to meet the input size of the convolutional ne...

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Abstract

The invention discloses a fine-grained recognition method of crop diseases and insect pests based on the improved mixed attention module. First, input the pictures of crop diseases and insect pests, perform feature extraction through the convolution layer after preprocessing, and use the Inception idea combined with the residual connection in the forward propagation process structure, the feature map F obtained by the convolutional layer is used as the input of the attention I_CBAM, and the weight M is obtained C (F) and M S (F); finally get the feature map F 2 , using the softmax function to produce the final predicted probabilities. In order to improve the accuracy of the pest identification model and detect pests in time, the present invention improves the mixed attention CBAM, and solves the serial connection channel attention and spatial attention through the parallel connection structure of channel attention and spatial attention. The problem of interference generated, while making the improvement of the accuracy of the fine-grained pest recognition model after adding attention more stable, also ensures the direct generalization of I_CBAM in different models.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for identifying crop diseases and insect pests based on a mixed attention module. Background technique [0002] Among the various natural disasters of plants in our country, crop diseases and insect pests occupy a very important position. Therefore, it is necessary to predict and monitor them in time to prevent the occurrence of major disasters. [0003] However, the occurrence of pest and disease disasters is closely related to factors such as planting system, crop layout, and climate trends. It is very difficult for ordinary farmers to predict pests and diseases in small-scale planting. There are often more than ten kinds of pesticides to choose from for the control of a disease and insect pest, and the environmental pollution and greenhouse gas emissions caused by each pesticide are also different. At the same time, the different stages and disease procedures...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/13G06V10/774G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/0409G06N3/08G06V20/13G06N3/045G06F18/2415G06F18/214
Inventor 王美华吴振鑫周祖光
Owner SOUTH CHINA AGRI UNIV
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