CN109859167A - The appraisal procedure and device of cucumber downy mildew severity - Google Patents
The appraisal procedure and device of cucumber downy mildew severity Download PDFInfo
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Abstract
The embodiment of the present invention provides the appraisal procedure and device of a kind of cucumber downy mildew severity, belongs to technical field of crop cultivation.This method comprises: obtaining collected cucumber leaves image to be assessed under natural environment；Using stochastic gradient descent method, and the neuralnetwork estimating model based on downy mildew severity, the cucumber downy mildew severity of cucumber leaves image is assessed.Method provided in an embodiment of the present invention, by using stochastic gradient descent method, and the neuralnetwork estimating model based on downy mildew severity, the cucumber downy mildew severity of cucumber leaves image is assessed.Due to that can estimate degree of disease automatically, to high degree of automation and recognition efficiency is high, manual intervention bring subjective impact can be effectively reduced, reduce the application cost and complexity of diagnosis process, the accuracy and real-time of disease screening can be effectively improved, also the correlative study for cucumber disease diagnosis provides reliable and accurate data basis.
Description
Technical Field
The embodiment of the invention relates to the technical field of crop cultivation, in particular to a method and a device for evaluating the severity of cucumber downy mildew.
Background
Diseases are caused by various reasons in the planting process of greenhouse cucumbers, so that the yield is reduced and the quality is reduced. Downy mildew is one of the more common diseases in greenhouse cucumber diseases. The accurate diagnosis of the disease is divided into two aspects, namely the identification of the disease type and the estimation of the disease degree. The accurate acquisition of the disease severity is a precondition for scientific disease control of growers, and has important significance for reducing the usage amount of pesticides and improving economic benefits. The traditional disease degree estimation method mainly depends on the experience of a grower, and not only is time and labor consumed, but also the subjectivity is high. In the related art, diagnosis of diseases of greenhouse cucumbers is generally performed using computer vision. Specifically, RGB images of cucumber leaves are adopted to obtain the color, texture and shape characteristics of scabs, and a diagnosis model is established by a machine learning method, so that the diagnosis of diseases is realized. These methods can only identify the type of disease, but cannot evaluate the severity of the disease. Therefore, an effective method for evaluating the severity of cucumber downy mildew is urgently needed.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a method and an apparatus for evaluating cucumber downy mildew severity level for greenhouse crop context awareness, which overcome or at least partially solve the above problems.
According to a first aspect of embodiments of the present invention, there is provided a method for evaluating the severity of cucumber downy mildew, comprising:
acquiring a cucumber leaf image to be evaluated, which is acquired in a natural environment;
and evaluating the severity of the cucumber downy mildew of the cucumber leaf image by adopting a random gradient descent method and based on a neural network estimation model of the severity of the downy mildew.
According to the method provided by the embodiment of the invention, the cucumber downy mildew severity of the cucumber leaf image is evaluated by acquiring the cucumber leaf image to be evaluated, which is acquired in a natural environment, by adopting a random gradient descent method and a neural network evaluation model based on the downy mildew severity. The disease degree can be automatically estimated, so that the degree of automation is high, the identification efficiency is high, the subjective influence caused by manual intervention can be effectively reduced, the application cost and the complexity of the diagnosis process are reduced, the accuracy and the real-time performance of disease diagnosis can be effectively improved, and a reliable and accurate data base is provided for relevant research of cucumber disease diagnosis.
According to a second aspect of embodiments of the present invention, there is provided an apparatus for evaluating the severity of cucumber downy mildew, comprising:
the acquisition module is used for acquiring cucumber leaf images to be evaluated, which are acquired in a natural environment;
and the evaluation module is used for evaluating the cucumber downy mildew severity of the cucumber leaf image by adopting a random gradient descent method and based on the neural network estimation model of the downy mildew severity.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus, including:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, which program instructions are capable of performing the method for assessing the severity of cucumber downy mildew as provided in any of the various possible implementations of the first aspect.
According to a fourth aspect of the present invention, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method for assessing the severity of cucumber downy mildew as provided in any one of the various possible implementations of the first aspect.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of embodiments of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for evaluating severity of cucumber downy mildew according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a neural network estimation model for downy mildew severity according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an apparatus for evaluating the severity of cucumber downy mildew according to an embodiment of the present invention;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Diseases are caused by various reasons in the planting process of greenhouse cucumbers, so that the yield is reduced and the quality is reduced. Downy mildew is one of the more common diseases in greenhouse cucumber diseases. The accurate diagnosis of the disease is divided into two aspects, namely the identification of the disease type and the estimation of the disease degree. The accurate acquisition of the disease severity is a precondition for scientific disease control of growers, and has important significance for reducing the usage amount of pesticides and improving economic benefits. The traditional disease degree estimation method mainly depends on the experience of a grower, and not only is time and labor consumed, but also the subjectivity is high. In the related art, diagnosis of diseases of greenhouse cucumbers is generally performed using computer vision. Specifically, RGB images of cucumber leaves are adopted to obtain the color, texture and shape characteristics of scabs, and a diagnosis model is established by a machine learning method, so that the diagnosis of diseases is realized. These methods can only identify the type of disease, but cannot evaluate the severity of the disease. Therefore, an effective method for evaluating the severity of cucumber downy mildew is urgently needed.
Although a small amount of research is carried out on constructing an estimation model by using a shallow machine learning method aiming at the disease degree, although the estimation model has a certain effect, the early stage lesion segmentation and the artificial setting of image characteristics are required, and the accuracy of segmentation is difficult to ensure due to the influence of complex background and illumination under the natural environment, so that the methods are difficult to expand and use under the natural environment. The convolutional neural network is an unsupervised learning method with self-learning capability and is considered to be one of the most effective ways for image recognition at present. The convolutional neural network has been widely applied in the agricultural field, and the obvious advantage of the convolutional neural network in image recognition provides a thought for people. Therefore, the research on the method for quantitatively estimating the severity of the downy mildew of the greenhouse cucumber based on the convolutional neural network can provide support for accurately diagnosing the cucumber diseases.
Based on the above description, the embodiment of the present invention provides a method for evaluating the severity of cucumber downy mildew. Referring to fig. 1, the method includes:
101. and acquiring an image of the cucumber leaf to be evaluated, which is acquired in a natural environment.
102. And evaluating the severity of the cucumber downy mildew of the cucumber leaf image by adopting a random gradient descent method and based on a neural network estimation model of the severity of the downy mildew.
According to the method provided by the embodiment of the invention, the cucumber downy mildew severity of the cucumber leaf image is evaluated by acquiring the cucumber leaf image to be evaluated, which is acquired in a natural environment, by adopting a random gradient descent method and a neural network evaluation model based on the downy mildew severity. The disease degree can be automatically estimated, so that the degree of automation is high, the identification efficiency is high, the subjective influence caused by manual intervention can be effectively reduced, the application cost and the complexity of the diagnosis process are reduced, the accuracy and the real-time performance of disease diagnosis can be effectively improved, and a reliable and accurate data base is provided for relevant research of cucumber disease diagnosis.
Based on the content of the foregoing embodiment, as an alternative embodiment, before the evaluating the severity of cucumber downy mildew of the cucumber leaf image based on the neural network estimation model of the severity of downy mildew, the method further includes: preprocessing a sample image of a cucumber downy mildew leaf collected in a natural environment, and constructing to obtain an original data set based on the preprocessed sample image; and training the original neural network model based on the sample image in the original data set and the lesion degree value of the sample image to obtain a neural network estimation model of the downy mildew severity. The original neural network model may be a convolutional neural network model, which is not specifically limited in this embodiment of the present invention.
Based on the content of the foregoing embodiments, as an alternative embodiment, regarding a manner of preprocessing a sample image of a cucumber downy mildew leaf collected in a natural environment, the embodiment of the present invention is not particularly limited to this, and includes but is not limited to: removing sample images with resolution lower than a preset threshold value from the acquired sample images; the background pattern of each sample image is removed, and each sample image is adjusted to a preset size.
Specifically, after a sample image acquired in a greenhouse environment is acquired, an image with low quality, such as an image with low resolution or definition, may be removed. In addition, the sample image may also be normalized, that is, the sample image is adjusted to the same color space and the same size, for example, 128 × 128 pixels, which is not specifically limited in this embodiment of the present invention. Note that the larger the size of the sample label image, the higher the calculation cost. In addition, the background patterns of the remaining sample images can be removed to reduce irrelevant information in the images.
Based on the content of the foregoing embodiment, as an optional embodiment, before training the original neural network model based on the original data set to obtain the neural network estimation model of the downy mildew severity, the method further includes: processing the sample image in the original data set according to a preset processing mode to expand the original data set, wherein the preset processing mode at least comprises any one of the following three modes, namely color dithering, horizontal turning and vertical turning.
Specifically, the sample image may be subjected to color dithering, horizontal and vertical flipping, data enhancement in 90 °, 180 °, 270 ° rotation, and the like, so as to expand the sample. It should be noted that the sample image is expanded here mainly to improve the estimation effect of the subsequent network model. In the network model training process, the sample images obtained in the previous step and expanded in the step can be used as a training set for training, and the training set can be divided into two parts according to functions, namely a training set and a test set. And when the data sets are divided, the data sets are divided according to a proper proportion, so that the data amount of each type of data sets is ensured to be relatively balanced.
Based on the content of the above embodiment, as an alternative embodiment, the neural network estimation model for downy mildew severity includes an input layer, 5 convolution layers, 4 pooling layers, 4 batch normalization layers, 2 full-connection layers, and an output layer. Of course, a Dropout layer may be further included, and the Dropout layer may be placed before the 2 full-connection layers, which is not specifically limited in this embodiment of the present invention.
Based on the above description of the embodiment, as an optional embodiment, the input layer is connected to the first convolution layer and the first batch normalization layer, the first convolution layer and the first batch normalization layer are connected to the first pooling layer, the first pooling layer is connected to the second convolution layer and the second batch normalization layer, the second convolution layer and the second batch normalization layer are connected to the second pooling layer, the second pooling layer is connected to the third convolution layer and the third batch normalization layer, the third convolution layer and the third batch normalization layer are connected to the third pooling layer, the third pooling layer is connected to the fourth convolution layer and the fourth batch normalization layer, the fourth convolution layer and the fourth batch normalization layer are connected to the fourth pooling layer, the fourth pooling layer is connected to the fifth convolution layer, and the fifth convolution layer is connected to the 2 full-connection layers and the output layer in sequence. The connection relationship between the layers can refer to fig. 2.
Specifically, the input size of the model is 128 × 128 pixels, the sizes of convolution kernels in the convolutional layers are 5 × 5, the number of the convolution kernels in the convolutional layers can be 32, 64, 128, 256 and 512 respectively, and each time convolution operation is performed, the network can effectively extract the features in the image to generate a corresponding number of feature maps. And the pooling layer adopts 2 multiplied by 2 convolution kernels to carry out average pooling so as to realize the down-sampling of the feature map. The weight parameters in the network structure can be greatly reduced through 4 pooling layers, and the calculation cost is reduced. The last convolutional layer is followed by 2 fully-connected layers, which vectorize all the feature maps and represent the features of the entire image with one-dimensional vectors. A dropout layer is added in front of a full connection layer, and a neural network unit is temporarily discarded from the network according to a certain probability, so that an overfitting phenomenon is prevented, and the model identification accuracy is improved. And finally, a regressonlayer regression layer.
The size of the convolution operation output characteristic graph can be expressed by the following formula:
Wi+1＝(Wi-F+2P)/S+1
in the above formula, WiRepresenting the input image size, F the size of the convolution kernel, P and S the fill pixel and step size, respectively. For a convolution operation, the input-output relationship can be expressed by the following formula:
in the above formula, l denotes a layer index, i denotes an input feature map index, k denotes an output feature map index,the representation takes the ith feature map on the l-1 th layer as input.Showing the output of the kth feature map at the l-th layer. W represents a convolution weight tensor, b represents a bias parameter, and f (·) represents an activation function.
The pooling layer performs size reduction on the received result, and the following formula can be specifically referred to:
where down (-) is the down-sampling function, F is the down-sampling filter size, and S is the down-sampling step.
Based on the content of the foregoing embodiments, the embodiments of the present invention further provide an apparatus for evaluating the severity of cucumber downy mildew, which is used for executing the method for evaluating the severity of cucumber downy mildew provided in the foregoing method embodiments. Referring to fig. 3, the apparatus includes: an acquisition module 301 and an evaluation module 302; wherein,
an obtaining module 301, configured to obtain a cucumber leaf image to be evaluated, acquired in a natural environment;
the evaluation module 302 is configured to evaluate the severity of cucumber downy mildew of the cucumber leaf image by using a random gradient descent method and based on a neural network estimation model of the severity of downy mildew.
Based on the content of the foregoing embodiment, as an alternative embodiment, the apparatus further includes:
the system comprises a preprocessing module, a data processing module and a data processing module, wherein the preprocessing module is used for preprocessing a sample image of a cucumber downy mildew leaf collected in a natural environment and constructing an original data set based on the preprocessed sample image;
and the training module is used for training the original neural network model based on the sample images in the original data set and the lesion degree values of the sample images to obtain a neural network estimation model of the downy mildew severity.
Based on the content of the above embodiment, as an optional embodiment, the preprocessing module is configured to remove a sample image with a resolution lower than a preset threshold from the acquired sample images; the background pattern of each sample image is removed, and each sample image is adjusted to a preset size.
Based on the content of the foregoing embodiment, as an alternative embodiment, the apparatus further includes:
the expansion module is used for processing the sample image in the original data set according to a preset processing mode so as to expand the original data set, wherein the preset processing mode at least comprises any one of the following three modes, namely color dithering, horizontal overturning and vertical overturning.
Based on the content of the above embodiment, as an alternative embodiment, the neural network estimation model for downy mildew severity includes an input layer, 5 convolution layers, 4 pooling layers, 4 batch normalization layers, 2 full-connection layers, and an output layer.
Based on the above description of the embodiment, as an optional embodiment, the input layer is connected to the first convolution layer and the first batch normalization layer, the first convolution layer and the first batch normalization layer are connected to the first pooling layer, the first pooling layer is connected to the second convolution layer and the second batch normalization layer, the second convolution layer and the second batch normalization layer are connected to the second pooling layer, the second pooling layer is connected to the third convolution layer and the third batch normalization layer, the third convolution layer and the third batch normalization layer are connected to the third pooling layer, the third pooling layer is connected to the fourth convolution layer and the fourth batch normalization layer, the fourth convolution layer and the fourth batch normalization layer are connected to the fourth pooling layer, the fourth pooling layer is connected to the fifth convolution layer, and the fifth convolution layer is connected to the 2 full-connection layers and the output layer in sequence.
The device provided by the embodiment of the invention evaluates the cucumber downy mildew severity of the cucumber leaf image by acquiring the cucumber leaf image to be evaluated, which is acquired under the natural environment, by adopting a random gradient descent method and a neural network estimation model based on the downy mildew severity. The disease degree can be automatically estimated, so that the degree of automation is high, the identification efficiency is high, the subjective influence caused by manual intervention can be effectively reduced, the application cost and the complexity of the diagnosis process are reduced, the accuracy and the real-time performance of disease diagnosis can be effectively improved, and a reliable and accurate data base is provided for relevant research of cucumber disease diagnosis.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor)410, a communication Interface 420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may call logic instructions in the memory 430 to perform the following method: acquiring a cucumber leaf image to be evaluated, which is acquired in a natural environment; and evaluating the severity of the cucumber downy mildew of the cucumber leaf image by adopting a random gradient descent method and based on a neural network estimation model of the severity of the downy mildew. It should be noted that in actual implementation, the form of the electronic device may be a PC or a tablet computer, and the PC or the tablet computer may collect data and may have a decision control function, and the like.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the method provided in the foregoing embodiments when executed by a processor, and the method includes: acquiring a cucumber leaf image to be evaluated, which is acquired in a natural environment; and evaluating the severity of the cucumber downy mildew of the cucumber leaf image by adopting a random gradient descent method and based on a neural network estimation model of the severity of the downy mildew.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A method for evaluating the severity of cucumber downy mildew, which is characterized by comprising the following steps:
acquiring a cucumber leaf image to be evaluated, which is acquired in a natural environment;
and evaluating the cucumber downy mildew severity of the cucumber leaf image by adopting a random gradient descent method and based on a neural network estimation model of the downy mildew severity.
2. The method of claim 1, wherein before evaluating the severity of cucumber downy mildew for the images of cucumber leaves, the neural network estimation model based on the severity of downy mildew further comprises:
preprocessing a sample image of a cucumber downy mildew leaf collected in a natural environment, and constructing to obtain an original data set based on the preprocessed sample image;
and training an original neural network model based on the sample image in the original data set and the lesion degree value of the sample image to obtain the neural network estimation model of the downy mildew severity.
3. The method of claim 2, wherein the preprocessing of the sample image of the leaves of cucumber downy mildew collected in natural environment comprises:
removing sample images with resolution lower than a preset threshold value from the acquired sample images;
the background pattern of each sample image is removed, and each sample image is adjusted to a preset size.
4. The method of claim 2, wherein before training the original neural network model based on the original data set to obtain the neural network estimation model of downy mildew severity, the method further comprises:
processing the sample image in the original data set according to a preset processing mode to expand the original data set, wherein the preset processing mode at least comprises any one of the following three modes, namely color dithering, horizontal turning and vertical turning.
5. The method of claim 1, wherein said neural network estimation model of downy mildew severity comprises an input layer, 5 convolutional layers, 4 pooling layers, 4 batch normalization layers, 2 fully-connected layers, and an output layer.
6. The method of claim 5, wherein the input layer is coupled to a first convolutional layer and a first batch of normalization layers, the first convolutional layer and the first batch of normalization layers are coupled to a first pooling layer, the first pooling layer is coupled to a second convolutional layer and a second batch of normalization layers, the second convolutional layer and the second batch of normalization layers are coupled to a second pooling layer, the second pooling layer is coupled to a third convolutional layer and a third batch of normalization layers, the third convolutional layer and the third batch of normalization layers are coupled to a third pooling layer, the third pooling layer is coupled to a fourth convolutional layer and a fourth batch of normalization layers, the fourth convolutional layer and the fourth batch of normalization layers are coupled to a fourth pooling layer, and the fourth pooling layer is coupled to a fifth convolutional layer, the fifth convolution layer is connected with the 2 full-connection layers and the output layer in sequence.
7. An apparatus for evaluating the severity of cucumber downy mildew, comprising:
the acquisition module is used for acquiring cucumber leaf images to be evaluated, which are acquired in a natural environment;
and the evaluation module is used for evaluating the cucumber downy mildew severity of the cucumber leaf image by adopting a random gradient descent method and based on a neural network estimation model of the downy mildew severity.
8. An electronic device, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 6.
9. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112699941A (en) * | 2020-12-31 | 2021-04-23 | 浙江科技学院 | Plant disease severity image classification method and device, computer equipment and storage medium |
| CN112699941B (en) * | 2020-12-31 | 2023-02-14 | 浙江科技学院 | Plant disease severity image classification method, device, equipment and storage medium |
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