CN111783516A - Ploughing quality natural grade evaluation method based on deep learning - Google Patents
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Abstract
The invention provides a farmland quality natural grade evaluation method based on deep learning, which comprises the following steps: obtaining remote sensing image information of an area to be evaluated; calculating a vegetation normalization index of the area to be evaluated; constructing a deep learning model through the grades of the convolutional layers and the pooling layers; cutting the vegetation normalization index of the area to be researched into a fixed size according to the model characteristics, and establishing a training set and a verification set; training the deep learning model through known farmland quality natural grade data and a training set; and inputting the trained model by using the vegetation normalization index of the area to be evaluated to obtain a natural quality grade result of the evaluation area. The method can provide scientific basis for the evaluation of the nature quality of the cultivated land, and objectively and accurately evaluate the nature quality of the cultivated land.
Description
Technical Field
The invention relates to the field of land utilization planning, in particular to a ploughing quality natural grade evaluation method based on deep learning.
Background
The cultivated land is the basis of grain production, is a precious production data, and is an important guarantee for agricultural sustainable development and national grain safety. At present, the management of the cultivated land by the national natural resource department is gradually changed from the management of quantity only to the comprehensive management of three-in-one consideration of quantity, quality and ecological environment effect. Relevant departments and scientific researchers develop a large amount of land resource investigation work, and a large amount of farmland quality data are accumulated. How to effectively utilize the mass data so as to carry out fine evaluation on the quality change of cultivated land becomes a problem to be solved urgently. The traditional method for evaluating the nature grade of the cultivated land quality usually adopts an index weighting method, and has the problems of large calculated amount, low speed, insufficient accuracy and the like.
Disclosure of Invention
Aiming at the problems in the existing method, the invention provides a farmland quality evaluation method based on deep learning.
The invention provides a farmland quality natural grade evaluation method based on deep learning, which is characterized by comprising the following steps:
s1, acquiring Landsat remote sensing image information of a period to be evaluated in a research area and natural grade information of historical farmland quality in an evaluation area;
s2, acquiring Landsat remote sensing images in corresponding time periods according to the natural grade years of the quality of the historical cultivated land in the research area, and respectively calculating the monthly vegetation normalization indexes in the time period to be evaluated and the historical time period;
s3, constructing a full convolution neural network suitable for farmland quality evaluation through convolution layers and pooling layer structures; the convolution kernel of the convolution layer is N x N, N is an odd number larger than 1, the number of the convolution kernels is M, and M is an integer larger than 1; the pooling layer selects a function as maximum pooling or average pooling, and the activation function selects a modified linear unit function:
wherein f (x) is an activation function, x is an input variable, and λ is a linear rectification parameter;
s4, cutting vegetation normalization index results in the historical period of the research area according to the established deep learning network, randomly selecting cutting results from the results, constructing a training set and a verification set, inputting the bimonthly averaged vegetation normalization index as an independent variable and the natural level of the cultivated land quality as a dependent variable into a deep learning model by using the training set, and training the model; testing the evaluation precision of the model through verification, adjusting the parameters of the model according to the test result until the evaluation precision of the model reaches the preset requirement, and obtaining a deep learning model capable of realizing the natural grade evaluation of the farmland quality;
s5, inputting the normalized indexes of the bi-monthly vegetation in the time period to be evaluated of the evaluation area into the model to obtain the corresponding natural grade result of the farmland quality.
The invention has the following beneficial effects: the farmland natural quality evaluation method based on deep learning provided by the invention can quickly and accurately evaluate the farmland natural quality and the like, and is not limited by the evaluation range. The model is trained through historical evaluation data, so that the problem that artificial parameters cannot effectively embody characteristics is avoided by presetting evaluation parameters. A large amount of field work is reduced, and the conditions of farmland natural quality and the like can be acquired more quickly.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the embodiments or drawings used in the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of the method for evaluating the nature quality grade of cultivated land based on deep learning.
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.
As shown in fig. 1, the flowchart of the method for evaluating the natural grade of farmland quality based on deep learning in this embodiment specifically includes the following steps:
step S1: and acquiring Landsat remote sensing image information of a period to be evaluated in a research area and information such as natural quality of historical cultivated land in the evaluation area.
Step S2: and acquiring Landsat remote sensing images in corresponding time periods according to the natural equivalent years of the quality of the historical cultivated land in the research area, and respectively calculating the monthly vegetation normalization indexes of the time period to be evaluated and the historical time period.
Calculating the vegetation normalization index of the obtained remote sensing image of the research area by using the following formula,
the NDVI is the vegetation normalization index obtained after calculation, the NIR is near infrared band data of the remote sensing image, and the Red is Red band data of the remote sensing image.
Step S3: and constructing a full convolution neural network suitable for farmland quality evaluation through structures such as convolution layers, pooling layers and the like.
In this step, the convolution kernel size of the convolution layer is 3 × 3, the number of convolution kernels is 64, the pooling layer adopts a function as maximum pooling, and the activation function selects a modified linear unit function, the function being:
where f (x) is the activation function, x is the input variable, and λ is the linear rectification parameter.
In addition, the size of the convolution kernel can be any odd number, the number of the convolution kernels can be any integer, and the pooling method can also be an average value pooling method.
Step S4: according to the established deep learning network, cutting vegetation normalization index results in the historical period of a research area, randomly selecting cutting results from the cutting results, constructing a training set and a verification set, inputting the monthly vegetation normalization index serving as an independent variable, the natural farmland quality and the like serving as dependent variables into a deep learning model by using the training set, and training the model; and testing the evaluation precision of the model through verification, and adjusting the parameters of the model according to the test result until the evaluation precision of the model reaches the preset requirement to obtain the deep learning model capable of realizing the natural grade evaluation of the farmland quality.
Step S4 includes the following steps:
s41, randomly cutting the data of the research area into 224 × 224 pixel-size images, wherein each image comprises a dependent variable layer and 6 independent variable layers;
s42, randomly selecting 90% of the acquired image sets as training sets, and using the remaining 10% as verification sets;
s43, performing model training on the model established in the step S3 by using the training set data and taking the dependent variable as input and the natural quality grade as output; and testing the precision of the trained model by using a verification set, and adjusting the model parameters according to the test precision in the random gradient descending direction until the model precision meets the preset requirement, thereby completing the training of the model.
In the step, an accuracy evaluation function Loss of the model is constructed,
wherein M represents the number of cultivated land levels; y iscMarking an indication variable, wherein if the prediction result is the same as the actual situation, the indication variable is 1, otherwise, the indication variable is 0; p is a radical ofcThe prediction probability for class c is predicted for the model. And adjusting and optimizing the model parameters by adopting a random gradient descent method. Namely, the model parameter when Loss takes the minimum value is the final parameter.
Data from the study area was randomly sliced into 224 x 224 pixel size images. Each time, 40% of images from the training set were randomly selected as a batch of images to be input into the model process, and the learning rate was set to 1 × 10-6.
Step S5: and inputting the biennial average vegetation normalization index of the evaluation area in the period to be evaluated into the model to obtain a corresponding farmland quality natural grade result.
In addition to the above embodiments, the present invention may have other embodiments. All technical schemes of the quality evaluation of nature and the like of the cultivated land in the form of deep learning algorithm and the like fall within the protection scope required by the invention.
Claims (6)
1. A farmland quality natural grade evaluation method based on deep learning is characterized by comprising the following steps:
s1, acquiring Landsat remote sensing image information of a period to be evaluated in a research area and natural grade information of historical farmland quality in an evaluation area;
s2, acquiring Landsat remote sensing images in corresponding time periods according to the natural grade years of the quality of the historical cultivated land in the research area, and respectively calculating the monthly vegetation normalization indexes in the time period to be evaluated and the historical time period;
s3, constructing a full convolution neural network suitable for farmland quality evaluation through convolution layers and pooling layer structures; the convolution kernel of the convolution layer is N x N, N is an odd number larger than 1, the number of the convolution kernels is M, and M is an integer larger than 1; the pooling layer selects a function as maximum pooling or average pooling, and the activation function selects a modified linear unit function:
wherein f (x) is an activation function, x is an input variable, and λ is a linear rectification parameter;
s4, cutting vegetation normalization index results in the historical period of the research area according to the established deep learning network, randomly selecting cutting results from the results, constructing a training set and a verification set, inputting the bimonthly averaged vegetation normalization index as an independent variable and the natural level of the cultivated land quality as a dependent variable into a deep learning model by using the training set, and training the model; testing the evaluation precision of the model through verification, adjusting the parameters of the model according to the test result until the evaluation precision of the model reaches the preset requirement, and obtaining a deep learning model capable of realizing the natural grade evaluation of the farmland quality;
s5, inputting the normalized indexes of the bi-monthly vegetation in the time period to be evaluated of the evaluation area into the model to obtain the corresponding natural grade result of the farmland quality.
2. The KNN-based village land reclamation planning simulation method of claim 1, wherein: the S2 includes:
calculating the vegetation normalization index of the obtained remote sensing image of the research area by using the following formula,
the NDVI is the vegetation normalization index obtained after calculation, the NIR is near infrared band data of the remote sensing image, and the Red is Red band data of the remote sensing image.
3. The KNN-based village land reclamation planning simulation method of claim 1, wherein: in step S3, the convolution kernel size of the convolution layer is 3 × 3, and the number of convolution kernels is 64.
4. The KNN-based village land reclamation planning simulation method of claim 1, wherein: the step S4 includes the following steps:
s41, randomly cutting the data of the research area into 224 × 224 pixel-size images, wherein each image comprises a dependent variable layer and 6 independent variable layers;
s42, randomly selecting 90% of the acquired image sets as training sets, and using the remaining 10% as verification sets;
s43, performing model training on the model established in the step S3 by using the training set data and taking the dependent variable as input and the natural quality grade as output; and testing the precision of the trained model by using a verification set, and adjusting the model parameters according to the test precision until the model precision reaches the preset requirement, thereby completing the training of the model.
5. The KNN-based village land reclamation planning simulation method of claim 1, wherein: in step S43, adjusting and optimizing the model parameters by adopting a random gradient descent method; randomly cutting the data of the research area into 224 × 224 pixel images, randomly selecting 40% of the images from the training set as a batch of image input models for processing each time, and setting the learning rate to 1 × 10-6。
6. The KNN-based village land reclamation planning simulation method of claim 1, wherein: in the step, an accuracy evaluation function Loss of the model is constructed,
wherein M represents the number of cultivated land levels; y iscMarking an indicator variable, and if the predicted result is the same as the actual situation1 if not, 0 if not; p is a radical ofcPredicting a prediction probability for class c for the model; and adjusting and optimizing the model parameters by adopting a random gradient descent method, wherein the model parameters when the Loss takes the minimum value are final parameters.
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| CN114943900A (en) * | 2022-05-13 | 2022-08-26 | 南方海洋科学与工程广东省实验室(广州) | Marine environment quality grade evaluation method, device and medium based on deep learning |
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