CN108960310A - A kind of agricultural pest recognition methods based on artificial intelligence - Google Patents
A kind of agricultural pest recognition methods based on artificial intelligence Download PDFInfo
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- CN108960310A CN108960310A CN201810660699.2A CN201810660699A CN108960310A CN 108960310 A CN108960310 A CN 108960310A CN 201810660699 A CN201810660699 A CN 201810660699A CN 108960310 A CN108960310 A CN 108960310A
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- G06F18/24—Classification techniques
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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Abstract
Step 1 a kind of agricultural pest recognition methods based on artificial intelligence pre-processes collected leaf image, leaf image is transformed to frequency domain using Fast Fourier Transform (FFT), removes high frequency section, reverts to spatial domain picture；Step 2, obtained image is subjected to the processing of fragment multiscalization, obtains the image slice of different scale, forms the training dataset of different scale；Step 3, building deep neural network model carries out parameter initialization and tuning, it is made to be more suitable for crops data；Step 4, Analysis On Multi-scale Features depth network model obtained carry out MLP Fusion Features, are identified using the feature training classifier of fusion.
Description
Technical field
The present invention relates to a kind of recognition methods more particularly to a kind of agricultural pest recognition methods based on artificial intelligence.
Background technique
In currently available technology, there are following three kinds for the Intelligent prevention and cure method of pest and disease damage, first is that based on deep learning
Crop pest control scheme recommender system, but this system not only needs to input cultivar name, but also there is faces
To small data set, the problems such as deep neural network is difficult to train, frequent over-fitting.Second is that the crop of fusion spectrum and image information
Pest and disease damage identifies and distinguishes between method, but the method needs the high spectrum image using EO-1 hyperion camera herborization blade, therefore
This method higher cost, can not promote civilian；Third is that the sunflower leaf diseases judgment method based on support vector machines, but its
It is voted using multiple two classifiers, needs to train a fairly large number of classifier when in face of a variety of diseases, therefore work as disease
This method is difficult to work normally when class is more.
Summary of the invention
The present invention proposes a kind of agricultural pest recognition methods based on artificial intelligence.Acquired image is carried out first
Leaf image is transformed to frequency domain using Fast Fourier Transform (FFT) by pretreatment, and setting average threshold removes high frequency section, then
Spatial domain picture is reverted to, the background of the noise of leaf image and complexity is removed at this time, only the remaining blade for needing to be concerned
Main body.Then obtained image is subjected to the processing of fragment multiscalization, obtains the image slice of different scale, carries out forming difference
The training dataset of scale.Use 16 network model of vgg crossed via million image data set Image net pre-training
Parameter carries out parameter initialization to the deep neural network model that we construct.Then using above-mentioned training dataset to obtaining
Deep neural network carries out tuning, it is made to be more suitable for crops data.The multiple dimensioned spy that finally depth network model is obtained
Sign carries out MLP Fusion Features, using feature one classifier of training of fusion, completes pest and disease damage identification mission using classifier.
Detailed description of the invention
Fig. 1 is the overall flow figure of recognition methods of the present invention；
The step of Fig. 2 is recognition methods of the present invention in an embodiment is schemed；
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
Not constituting a conflict with each other can be combined with each other.
The present invention proposes a kind of agricultural pest recognition methods based on artificial intelligence, is as shown in Figure 1 present invention identification
The overall flow figure of method；
Acquired image is pre-processed first, leaf image is transformed into frequency domain using Fast Fourier Transform (FFT),
Setting average threshold removes high frequency section, then reverts to spatial domain picture, at this time the noise of leaf image and the back of complexity
Scape is removed, only the remaining blade body for needing to be concerned.
Then obtained image is subjected to the processing of fragment multiscalization, obtains the image slice of different scale, is formed
The training dataset of different scale.
Using the 16 network model parameter of vgg crossed via million image data set Image net pre-training to us
The deep neural network model of building carries out parameter initialization.Then using above-mentioned training dataset to obtained depth nerve net
Network carries out tuning, it is made to be more suitable for crops data.
The Analysis On Multi-scale Features that finally depth network model is obtained carry out MLP Fusion Features, use the feature training of fusion
One classifier completes pest and disease damage identification mission using classifier.
The step of being illustrated in figure 2 recognition methods of the present invention in embodiment figure, first to collected leaf image into
Row pretreatment, leaf image is transformed into frequency domain using Fast Fourier Transform (FFT), to having a size of M*N leaf image function f (x,
Y) discrete Fourier transform is
U and v is the frequency variable of leaf image, and M and N are positive number, and F (u, v) is the frequency distribution of leaf image
Map, wherein the position frequency closer to center is lower, the position of brighter (gray value is higher) represents the signal amplitude of the frequency
It is bigger, thus using centre be it is true, remaining be that false masking-out is multiplied with frequency distribution map and to filter high frequency.
Masking-out
Spatial domain picture f ' (x, y) is converted by frequency distribution map using inverse Fourier transform
Wherein
F '=F × G
Then the image f'(x, y that will be obtained) multiple dimensioned fragmentation processing is carried out, it is that size is cut with (M/2, N/2)
Piece obtains 4 (M/2 × N/2) sectioning images；It is that size is sliced with (M/3, N/3), obtains 9 (M/3 × N/3) slices
Image；All images are all done into above-mentioned processing, obtain the training set of three kinds of scales, scale is respectively (M × N) (M/2 × N/2)
(M/3×N/3)。
Then neural network is constructed according to deep neural network VGG-16 network model, and trained using Image-Net
The parameter arrived is its initiation parameter, the data set handled using multiple dimensioned fragmentation and its label training network model, study
Model parameter.Freeze convolution layer parameter in learning process, convolutional layer learning rate is set to 0, only full connection layer parameter is carried out
It optimizes and revises, learning rate is set to 0.001.If former label is y, the output that deep neural network model obtains is a, then obtain
Cross entropy is C, and wherein x indicates that sample, n indicate total sample number.
Then tuning is carried out to obtained deep neural network, the optimization method is
Wherein, σ ' (z)=σ (z) (1- σ (z)), σ (z) are activation primitives, and exporting as a=σ (z), z is neural network node
Input XjWith network weight ωjInner product, j is positive integer.
After deep neural network study convergence, network the last layer is removed, image input network is obtained into 4096 dimensions
Sparse features vector.Due to having input the image of three groups of different scales, multiple dimensioned image characteristics of image is obtained.By three kinds of rulers
The merging features of degree are to the vector for obtaining 4096*3=12288 dimension together, and using this vector as input, building MLP feature is melted
Close frame.
MLP Fusion Features frame includes an input layer, two hidden layers and an output layer.Wherein input layer includes
12288 neurons, first hidden layer include the full articulamentum of 4096 neurons, and second hidden layer is to include 4096
The full articulamentum of neuron, output layer are Softmax layers, and neuron number is output classification number.Finally using under stochastic gradient
It drops algorithm and carries out parameter update, the feature merged after convergence and recognition result.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify to technical solution documented by previous embodiment or equivalent replacement of some of the technical features；And
These are modified or replaceed, the spirit and model of technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution
It encloses.
Claims (5)
1. a kind of agricultural pest recognition methods based on artificial intelligence, which is characterized in that step 1, to collected leaf figure
As being pre-processed, leaf image is transformed into frequency domain using Fast Fourier Transform (FFT), removes high frequency section, reverts to airspace figure
Picture；Step 2, the obtained spatial domain picture is subjected to the processing of fragment multiscalization, obtains the image slice of different scale, formed
The training dataset of different scale；Step 3, building deep neural network model carries out parameter initialization and tuning；Step 4, will
The Analysis On Multi-scale Features that depth network model obtains carry out MLP Fusion Features, are carried out using the feature training classifier of the fusion
Identification.
2. the method as described in claim 1, which is characterized in that in the step 1, first to collected leaf image into
Row pretreatment, leaf image is transformed into frequency domain using Fast Fourier Transform (FFT), to having a size of M*N leaf image function f (x,
Y) discrete Fourier transform is
The u and v is the frequency variable of leaf image, and F (u, v) is the frequency distribution map of leaf image, wherein closer to
The position frequency at center is lower, and the signal amplitude that the higher position of gray value represents the frequency is bigger, using centre be it is true, remaining
It is that false masking-out is multiplied with frequency distribution map and filters high frequency, the masking-out is
Spatial domain picture f ' (x, y) is converted by frequency distribution map using inverse Fourier transform,
Wherein
F '=F × G
3. method according to claim 2, which is characterized in that in the step 2, the image f'(x, y that will obtain) it carries out
Multiple dimensioned fragmentation processing is that size is sliced with (M/2, N/2), obtains 4 (M/2 × N/2) sectioning images；With (M/3,
N/3 it) is sliced for size, obtains 9 (M/3 × N/3) sectioning images；All images are all done into above-mentioned processing, obtain three kinds
The training set of scale, scale are respectively (M × N) (M/2 × N/2) (M/3 × N/3).
4. method as claimed in claim 3, which is characterized in that in the step 3, be based on deep neural network VGG-16 net
Network model construction neural network is the neural network initiation parameter using the parameter that Image-Net training obtains, using more
The data set and its label training network model, learning model parameter of scale fragmentation processing freeze in the learning process
Convolution layer parameter only optimizes adjustment to full connection layer parameter, if former label is y, deep neural network model is obtained defeated
It is out a, wherein x indicates that sample, n indicate total sample number, and obtained cross entropy is C
Then tuning is carried out to obtained deep neural network, the optimization method is
Wherein, σ ' (z)=σ (z) (1- σ (z)), σ (z) are activation primitives, and z is the input X of neural network nodejWith network weight
ωjInner product.
5. method as claimed in claim 4, which is characterized in that in the step 4, after deep neural network study convergence,
Network the last layer is removed, the image input network of three groups of different scales is obtained into the sparse features vector of 4096 dimensions, by institute
Vector is stated as input, constructs MLP Fusion Features frame, the MLP Fusion Features frame includes an input layer, and two are hidden
Layer and an output layer, wherein input layer includes 12288 neurons, and first hidden layer includes connecting entirely for 4096 neurons
Layer is connect, second hidden layer is the full articulamentum comprising 4096 neurons, and output layer is flexible maximum value transfer function layer, mind
It is output classification number through first number, uses the cross entropy as loss function, carried out more using stochastic gradient descent algorithm
Newly, convergence obtains fusion feature and recognition result.
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| CN110084145A (en) * | 2019-04-08 | 2019-08-02 | 华北水利水电大学 | A kind of multiple dimensioned identifying system of pest and disease damage time-frequency domain and operating method based on TensorFlow |
| CN110263644A (en) * | 2019-05-21 | 2019-09-20 | 华南师范大学 | Classifying Method in Remote Sensing Image, system, equipment and medium based on triplet's network |
| CN110414615A (en) * | 2019-08-02 | 2019-11-05 | 中国科学院合肥物质科学研究院 | Image is repaired based on interim depth and improves the corn Spodopterafrugiperda detection method of Double-DQN technology |
| CN111783901A (en) * | 2020-07-13 | 2020-10-16 | 征图新视(江苏)科技股份有限公司 | Printing quality classification method based on deep learning |
| CN112861712A (en) * | 2021-02-06 | 2021-05-28 | 郑州师范学院 | Agricultural pest and disease monitoring method based on artificial intelligence and multi-temporal remote sensing |
| CN117576467A (en) * | 2023-11-22 | 2024-02-20 | 安徽大学 | Crop disease image identification method integrating frequency domain and spatial domain information |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110084145A (en) * | 2019-04-08 | 2019-08-02 | 华北水利水电大学 | A kind of multiple dimensioned identifying system of pest and disease damage time-frequency domain and operating method based on TensorFlow |
| CN110084145B (en) * | 2019-04-08 | 2023-05-12 | 华北水利水电大学 | TensorFlow-based pest and disease damage time-frequency domain multi-scale identification system and operation method |
| CN110263644A (en) * | 2019-05-21 | 2019-09-20 | 华南师范大学 | Classifying Method in Remote Sensing Image, system, equipment and medium based on triplet's network |
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| CN112861712A (en) * | 2021-02-06 | 2021-05-28 | 郑州师范学院 | Agricultural pest and disease monitoring method based on artificial intelligence and multi-temporal remote sensing |
| CN117576467A (en) * | 2023-11-22 | 2024-02-20 | 安徽大学 | Crop disease image identification method integrating frequency domain and spatial domain information |
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