Plant disease detection method based on residual network

A plant disease and detection method technology, applied in the field of image recognition, can solve the problems of slow recognition speed, high work intensity, low accuracy rate, etc., and achieve the effect of deep network depth and fast training speed

Pending Publication Date: 2019-07-19
TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0004] For many years, the diagnosis of plant diseases has been mainly based on the experience of plant disease workers, which has the disadvantages of high work intensity, the influence of subjective factors, and the dependence on agricultural experts
In recent years, the use of artificial neural networks and deep lear...

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  • Plant disease detection method based on residual network
  • Plant disease detection method based on residual network
  • Plant disease detection method based on residual network

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Embodiment Construction

[0024] The examples of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0025] In this embodiment, a powerful PC is used for the whole process of training and testing of the plant disease detection model, and the CNN is performed in GPU mode. The configuration of the PC is as follows:

[0026] Table 1 Configuration of PC

[0027] CPU Intel Core i7-8700k 3.7GHz GPU Nvidia 1080Ti 11GB RAM 16 GB OS Windows 10 1803

[0028] The plant disease detection method based on residual network of the present invention comprises the following steps:

[0029] Step 1. Establish a plant disease detection data set

[0030] At present, since there are not many studies on plant leaf disease detection and related data sets are relatively scarce, how to obtain a good set of data sets plays an important role. A good training set plays a key role in both the training phase and the validation of tra...

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Abstract

The invention relates to a plant disease detection method based on a residual network, and the method comprises the following steps: building a plant disease detection data set which adopts a brand new leaf disease image data set of AI Challenger; carrying out statistical analysis on the categories and the number of the data sets, separating each type of images from the complete data set, storingthe images in corresponding category folders, and then carrying out image preprocessing; performing enhancement processing on the image by using random angle rotation, random horizontal mirror rotation, random vertical mirror rotation and random affine transformation methods; setting hyper-parameters of deep convolutional neural network training; using pyTorch as a deep learning framework, using aresidual network as a deep learning model, and training the residual network deep learning model to obtain a plant disease detection result. The residual error network ResNet is used for training thedeep convolutional neural network, 27 diseases of up to 10 crops can be accurately identified, the speed is high, the precision is high, and the method has important significance for agricultural production.

Description

technical field [0001] The invention belongs to the technical field of image recognition, in particular to a plant disease detection method based on a residual network ResNet. Background technique [0002] Plants play a vital role in human production, life and even survival. However, due to the continuous expansion of human production and life, excessive hunting and gathering behavior of humans, pollution from many factories, and the development of large-scale land vegetation, the natural ecosystem has been greatly changed, making the environmental adaptability of plants lower and self-regulation disorder , causing plant diseases. [0003] Plant diseases are mainly caused by biological factors (biological pathogens) and abiotic factors (unsuitable living environment), which make plants grow abnormally and can cause plants to die within a certain period of time. Long-term illness can lead to the extinction of plant species or infect other species to extinction. This effect...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06N3/044G06F18/241
Inventor 张传雷武大硕李建荣刘丽欣任雪飞刘璞张善文
Owner TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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