CN113111950B - Wheat rust classification method based on ensemble learning - Google Patents
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Abstract
The invention discloses a wheat rust classification method based on ensemble learning, which comprises the following steps of 1: collecting wheat rust images with a plurality of devices after the wheat turning green period; step 2: preprocessing the collected images, and performing sample expansion by using a data enhancement technology; and step 3: constructing an integrated model, integrating various convolutional neural networks with different structures, and training the model by using a snapshot integration method; and 4, step 4: selecting an optimal model and model parameters according to the loss and the used fusion algorithm; and 5: and using the optimal model for the identification and classification of the wheat rust image. The wheat rust images can be classified by combining the advantages of different convolutional neural network architectures, the best model can be selected more quickly by utilizing the bagging integration algorithm, the snapshot integration method and the fusion algorithm, the accuracy of wheat rust classification is improved, and the problem that leaf rust, stem rust and stripe rust are difficult to distinguish is solved.
Description
Technical Field
The invention belongs to the field of crop disease classification, and particularly relates to a wheat rust classification method based on ensemble learning.
Background
Wheat rust is a disease with extremely strong harmfulness, can reduce the total wheat yield, influence the livelihood of farmers, threaten the grain safety of the whole country under severe conditions, and is difficult to monitor, control and eradicate the wheat rust on a large scale. The three wheat rusts, namely stem rust, leaf rust and stripe rust, have similar disease characteristics, the disease positions are crossed, and people in more professionals are needed to distinguish the wheat rusts from each other, but much time and labor are consumed, so that the three wheat rusts are classified in a time-saving and labor-saving mode, the identification accuracy can be guaranteed, and then the wheat rusts are treated with medicines according to the diseases, so that the yield loss is avoided. With the continuous development of machine learning and deep learning, more and more researchers in agriculture adopt the method for research, but the diseases with higher similarity cannot be well distinguished.
Aiming at the problems, the research integrates various mainstream convolutional neural network models by utilizing ensemble learning instead of relying on one network model, trains the integrated model by using a snapshot integration mode, and screens the optimal model by using a fusion algorithm twice. Finally, the method can better classify the three rust diseases of wheat. At present, few scholars study the classification problem of similar diseases in crops, and no method for specially and quantitatively studying three rust classifications of wheat exists.
Disclosure of Invention
In order to overcome the defects of the technology, the invention provides a wheat rust classification method based on ensemble learning.
In order to solve the technical problems, the invention adopts the technical scheme that:
a wheat rust classification method based on ensemble learning is characterized by comprising the following steps:
step 1: after the wheat is in the green turning stage, leaf rust, stem rust and stripe rust appear in succession, the onset characteristics of the three diseases are similar, and images of the wheat rust are collected by various devices, such as: unmanned aerial vehicles, cameras, mobile phones, and the like;
step 2: preprocessing an image, and specifically comprises the following steps:
step 201: making labels on the collected images, and dividing the labels into three categories of leaf rust wheat, stem rust wheat and stripe rust wheat;
step 202: sample expansion is carried out on the three types of images by utilizing various data enhancement technologies, the data volume required by training is increased, and the generalization capability and robustness of the model are improved;
and step 3: constructing an integrated model, and training the model by using a snapshot integration method, wherein the method comprises the following specific steps:
step 301: selecting four models of VGG, ResNet, DenseNet and inclusion V3 with different convolutional layer connection modes for integration;
step 302: the four models for integration are placed in a model library, a bagging integration mode is adopted, one model is randomly selected for training by utilizing hyper-parameter optimization search during each training, and enough iteration times N are set to ensure that each model is selected. The formula of the Bagging integration algorithm is as follows:
wherein, yn(x) Representing the output of the model, this integration is a random sampling with a put back;
step 303: the method comprises the steps of training a model by adopting a snapshot integration mode, storing the model and parameters of each point reaching a local optimal point, and selecting the optimal model in the current training process through the loss of a verification set after one training is finished;
and 4, step 4: selecting the optimal model by adopting a fusion algorithm according to the loss in the training process, and specifically comprising the following steps of:
step 401: the loss magnitude is calculated using a weighted cross entropy loss function, as follows:
wherein N represents the number of categories, here 3, representing respectively stem rust, leaf rust and stripe rust; p is a radical ofiRepresenting the probability of the sample belonging to the ith class; y ═ y0,…,yN-1]Is the onehot representation of the sample label, when the sample belongs to category i, yi1, the rest is 0; w ═ W0,…,wN-1]A weight representing the category i;
step 402: after the optimal model in the current training process is selected after one training, a fusion algorithm is generated by utilizing the 'replacement' idea, and the results of all models obtained by the current training are processed. Multiplying the verification set loss of the optimal model in the current training process with the verification set losses of the other models in the current training process, and then performing evolution, if the obtained loss value is smaller, taking the smaller value as an optimal value, and obtaining the corresponding model weight through the same calculation, and taking the model as a new optimal model in the current training process;
step 403: after the iteration times reach the upper limit, the optimal model obtained by each iteration is fused and screened again by using the fusion algorithm to obtain the optimal model in the whole training process;
and 5: and using the obtained optimal model for identifying and classifying the wheat rust images.
The invention has the following beneficial effects: the three rust diseases of the wheat can be classified from the wheat rust images obtained by different equipment, so that the different rust diseases of the wheat can be prevented and controlled in time, the yield reduction is avoided, and the technical support is provided for further pathological research on the different rust diseases of the wheat.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a comparison of classification results of different methods according to an embodiment of the present invention.
FIG. 3 is a partial classification effect display according to an embodiment of the present invention; (a) stem rust disease; (b) leaf rust disease; (c) stripe rust.
Detailed Description
The present invention will be described in detail with reference to specific examples.
Referring to fig. 1, the invention is a flow chart of a wheat rust classification method based on ensemble learning, and the method comprises the following steps:
step 1: the classification process of wheat rust is shown in fig. 1, firstly, after the wheat is in the green turning stage, images of leaf rust, stem rust and stripe rust begin to be collected, the onset characteristics of the three diseases are similar, and the images of wheat rust are collected by paying attention to distinguishing and supporting the collection of the images of wheat rust by using various devices, such as: unmanned aerial vehicles, cameras, mobile phones, and the like;
step 2: the image is preprocessed in order to increase the data amount required by training, and even if the number of images is too small due to the problems of insufficient professionals and the like when the images are collected, the training amount is not necessary to be worried about, and the image preprocessing method comprises the following specific steps:
step 201: making labels on the collected images, and dividing the labels into three categories of leaf rust wheat, stem rust wheat and stripe rust wheat;
step 202: sample expansion is carried out on the three types of images by sequentially utilizing five data enhancement technologies of random cutting, horizontal turning, vertical turning, contrast enhancement and aliasing enhancement, the data quantity required by training is increased, and the generalization capability and robustness of the model are improved;
and 3, step 3: the method comprises the following steps of constructing an integrated model, and training the model by using a snapshot integration method, wherein the method can obtain a plurality of models reaching local optimal points in one training process, and the local optimal points contain useful information, and the method comprises the following specific steps:
step 301: selecting four models of VGG, ResNet, DenseNet and inclusion V3 with different convolutional layer connection modes for integration;
step 302: putting four models for integration into a model library, adopting a bagging integration mode, randomly selecting one model for training by utilizing hyper-parameter optimization search during each training, and setting enough iteration times N to ensure that each model is selected; the Bagging integration algorithm is as follows:
wherein, yn(x) Representing the output of the model, and the integration mode is a random sampling with a replace;
step 303: the model training is carried out by adopting a snapshot integration mode, the method saves the model and parameters of each point reaching the local optimum point, and selects the optimum model in the current training process through the loss of a verification set after one training is finished, FIG. 2 shows the accuracy of wheat rust classification by using four single models, namely VGG, ResNet, DenseNet and inclusion V3 in the existing research and the classification accuracy of the method, and the classification accuracy of the method reaches 96 percent as can be seen from FIG. 2 and Table 1, thereby showing the effectiveness of the method;
TABLE 1
And 4, step 4: selecting an optimal model by adopting a fusion algorithm according to the loss in the training process, wherein the fusion algorithm is used twice, one time is used for screening the optimal model in the current training process in each training process, and the other time is used for selecting the optimal model in the whole training process after the number of arrival iterations ensures that all convolutional neural network models are randomly selected, and the method comprises the following specific steps:
step 401: the loss magnitude is calculated using a weighted cross entropy loss function, as follows:
wherein N represents the number of categories, here 3, representing respectively stem rust, leaf rust and stripe rust; pi represents the probability that the sample belongs to class i; y ═ y0,…,yN-1]Is the onehot representation of the sample label, when the sample belongs to category i, yi1, the rest is 0; w ═ W0,…,wN-1]A weight representing a category i;
step 402: after the optimal model in the current training process is selected after one training, a fusion algorithm is generated by utilizing the 'replacement' idea, and the results of all models obtained by the current training are processed. Multiplying the verification set loss of the optimal model in the current training process with the verification set losses of the other models in the current training process, and then performing evolution, if the obtained loss value is smaller, taking the smaller value as an optimal value, and obtaining the corresponding model weight through the same calculation, and taking the model as a new optimal model in the current training process;
step 403: after the iteration times reach the upper limit, the optimal model obtained by each iteration is fused and screened again by using the fusion algorithm to obtain the optimal model in the whole training process;
and 5: and using the obtained optimal model for identifying and classifying the wheat rust images. After the optimal model is provided, the wheat images containing stem rust, leaf rust and stripe rust can be identified in any wheat image, and partial classification effects of three types of rust wheat are shown in fig. 3, which shows that the method can better extract disease characteristics of different rust for identification, thereby completing effective classification.
The invention has the following beneficial effects: the three rust diseases of the wheat can be well identified and classified, so that the model can be used for classifying stem rust diseases, leaf rust diseases and stripe rust diseases of wheat rust disease images obtained from different devices, and timely prevention and control of the different rust diseases of the wheat are researched according to the classification, and yield reduction is avoided. In addition, the method can be further used for providing technical support for the pathological research of different wheat rusts.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (3)
1. A wheat rust classification method based on ensemble learning is characterized by comprising the following steps:
step 1: collecting an image of wheat rust after the wheat is in a green turning period;
step 2: preprocessing the image;
and step 3: constructing an integrated model, and training the model by using a snapshot integration method, wherein the method comprises the following specific steps:
step 301: selecting four models of VGG, ResNet, DenseNet and inclusion V3 with different convolutional layer connection modes for integration;
step 302: putting four models for integration into a model library, adopting a bagging integration mode, randomly selecting one model for training by utilizing hyper-parameter optimization search during each training, and setting enough iteration times N to ensure that each model is selected;
step 303: the method comprises the steps of training a model by adopting a snapshot integration mode, storing the model and parameters of each point reaching a local optimal point, and selecting the optimal model in the current training process through the loss of a verification set after one training is finished;
and 4, step 4: selecting the optimal model by adopting a fusion algorithm according to the loss in the training process, and specifically comprising the following steps of:
step 401: the loss magnitude is calculated using a weighted cross entropy loss function, as follows:
wherein N represents the number of categories, here 3, representing respectively stem rust, leaf rust and stripe rust; p is a radical ofiRepresenting the probability of the sample belonging to the ith class; y ═ y0,…,yN-1]Is a sample markOnehot representation of the label, y when the sample belongs to category ii1, the rest is 0; w ═ W0,…,wN-1]A weight representing a category i;
step 402: after the optimal model in the current training process is selected after one-time training is finished, a fusion algorithm is generated by using a 'replacement' thought, and results of all models obtained by current training are processed; multiplying the verification set loss of the optimal model in the current training process with the verification set losses of the other models in the current training process, and then performing evolution, if the obtained loss value is smaller, taking the smaller value as an optimal value, and obtaining the corresponding model weight through the same calculation, and taking the model as a new optimal model in the current training process;
step 403: after the iteration times reach the upper limit, the optimal model obtained by each iteration is fused and screened again by using the fusion algorithm to obtain the optimal model in the whole training process;
and 5: and using the obtained optimal model for identifying and classifying the wheat rust images.
2. The method according to claim 1, wherein the step 2 comprises the following specific steps:
step 201: making labels on the collected images, and dividing the labels into three categories of leaf rust wheat, stem rust wheat and stripe rust wheat;
step 202: and sample expansion is carried out on the three types of images by utilizing various data enhancement technologies, so that the data volume required by training is increased, and the generalization capability and robustness of the model are improved.
3. The method of claim 1, wherein in step 302, the Bagging integration algorithm is as follows:
wherein, yn(x) Representing the output of the model in an integrated manner with a drop-backAnd (4) random sampling.
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