CN110909652A - Method for dynamically extracting monthly scale of crop planting structure with optimized textural features - Google Patents
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Abstract
The invention discloses a method for dynamically extracting a monthly scale of a crop planting structure with optimal textural features, which comprises the following steps of: s1: determining the space range of an analysis area, preparing data, collecting a time series satellite remote sensing data set not larger than a month scale, uniformly processing the time series satellite remote sensing data set into month scale data in time, and simultaneously completing pre-acquisition of sample data in a research area; s2: utilizing the preprocessed monthly scale satellite remote sensing image data to calculate texture features of the image based on the gray level co-occurrence matrix; s3: calculating the mean value and the variance of different texture characteristic quantities based on the actually measured samples, and calculating the differentiable capacity of the texture characteristic quantities among different samples; the method solves the problems that the traditional method for acquiring the crop planting structure information neglects the screening of the classification characteristic quantity and increases the time complexity.
Description
Technical Field
The invention relates to the field of remote sensing planting structure monitoring, in particular to a monthly scale dynamic extraction method for a crop planting structure with optimal textural features.
Background
The crop planting structure reflects the condition that agricultural production resources are utilized in the space range of human agricultural production, is important information for researching the types, quantity structures and space distribution characteristics of crops, and is also a basis for carrying out crop structure adjustment and optimization and fine management on agricultural water. The traditional method for acquiring the crop planting structure information mainly classifies images based on texture information calculated by a gray level co-occurrence matrix, but neglects screening of classification characteristic quantities, and increases time complexity.
Texture features are widely used in the classification process of high-resolution images. The high-resolution satellite image rapidly occupies the satellite application market of urban planning, regional environment monitoring and evaluation by means of clear and exquisite ground object structure, shape and texture information. For example, Dekker R J.2003 and the like establish texture features based on SAR to analyze urban building areas, find that certain influence exists among texture feature parameters of crops, and need to remove errors caused by the texture feature parameters to classification precision in the identification process; chenjunying et al 2007 acted on the decision tree classification and identification process of agricultural vegetation based on the spectral and textural features of the IKONOS image, and found that only the spectral feature classification precision can reach 83% and the textural feature precision is improved to 91%; the method comprises the following steps of utilizing high-spatial-resolution images to realize land utilization mapping by utilizing Elmqvist B2008, ZHouW 2009 and the like, and verifying analysis results in the field; the yeze horizon 2008 obtains the texture features of Quick Bird images based on the gray level co-occurrence matrix calculation principle, so that the extraction precision of a building area is up to 93%; the Hough et al 2013 utilizes the SPOT-NDVI time sequence to analyze the growth change trend of the farming and pasturing interlaced zone, and the research result is consistent with the existing result; liu Ke Bao etc 2014 utilizes the remote sensing data of 5 meters resolution ratio of RapidEye to extract crops, finds that 5 meters resolution ratio can reduce a large amount of errors in the classification process, and leads the classification precision to reach the highest; liu nationality and other 2015 complete ground remote sensing sampling investigation based on the texture of the high-resolution 2/8 m images, complete crop extraction by using the phenological calendar of crops and the multi-temporal images, and the final precision is superior to 80%; the Piazza GA et al 2016 develop and research high-resolution images, complete forest mapping, and perform classification and comparison based on different classifiers to obtain an optimal classifier and apply the optimal classifier to large-area recognition. 2016 (Song Rubi) and 2016 (Zhang super) extract texture features of specific crops based on PMS (high-grade first-grade), and a planting area change graph of the crops is realized through object-oriented classification; zhengli Juan 2017 is based on the application of high-grade column satellite analysis in agriculture, and provides the advantages of high-grade I and high-grade six in the extraction process. However, these methods do not perform calculation screening on texture features, so that the complexity of classification time is increased due to excessive classification features; meanwhile, the correlation among the characteristics is neglected, the final classification precision is influenced, and the application of remote sensing crop classification is difficult to realize.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a crop planting structure month scale dynamic extraction method with optimal textural features, and solves the problems that the screening of classification feature quantity is neglected and the time complexity is increased in the traditional method for obtaining crop planting structure information.
The technical scheme adopted by the invention is that the method for dynamically extracting the monthly scale of the crop planting structure with the optimized textural features comprises the following steps:
s1: determining the space range of an analysis area, preparing data, collecting a time series satellite remote sensing data set not larger than a month scale, uniformly processing the time series satellite remote sensing data set into month scale data in time, and simultaneously completing pre-acquisition of sample data in a research area;
s2: calculating image texture characteristics based on the gray level co-occurrence matrix according to the preprocessed satellite remote sensing image data, and describing texture characteristics of crops by using eight characteristic quantities;
s3: calculating the mean value and the variance of different texture characteristic quantities based on the actually measured samples, and calculating the differentiable capacity of the texture characteristic quantities among different samples;
s4: establishing an optimal formula based on the distinguishable capability of each characteristic quantity, determining the optimal number of the texture characteristic quantities participating in classification by using the formula, and constructing the optimal number as a new image;
s5: and finely identifying the crop types in the research area by using a random forest classifier, realizing fine management of the crops, generating a space-time distribution thematic map of the crops with a complete time sequence and verifying the precision.
Preferably, S1 includes the steps of:
s11: selecting GF-1WFV data with high temporal resolution and high spatial resolution according to the location and extent of the study area, considering the use of the presence-2, high score two, landsat8 or HJ-1A/B replacement if the data source is not completely covered, while investigating crop types and respective growth climatic stages within the scope of the examples;
s12: processing the collected data by remote sensing images, and if substitute data appears, resampling the uniform spatial resolution;
s13: the representativeness, the typicality and the timeliness of the samples need to be considered for collecting the samples, the research area is divided into n areas with the same area by establishing a regular grid, and different crop samples are selected in each area.
Preferably, S2 includes the steps of:
s21: calculating texture characteristic information quantity based on a gray level co-occurrence matrix, and counting a gray level correlation coefficient between two pixel points at a certain distance according to a gray level co-occurrence matrix GLCM, wherein the expression is as follows:
p(i,j)＝[p(i,j,d,θ)]
where P (i, j) is the frequency at which the same pixel pair occurs with a determined distance and direction; d is the distance from the pixel point, the angle of the connecting line vector of the two pixels is theta, and the theta is usually 0 degree, 45 degrees, 90 degrees and 135 degrees;
s22, when calculating the texture by utilizing the gray level co-occurrence matrix, selecting eight characteristic quantities to characterize the characteristics of the texture:
average value: the calculation formula of the regular degree of the gray level average value and the texture in the window is as follows:
variance: reflecting the degree of matrix element deviation from the mean value and the size of gray level change, the calculation formula is as follows:
where μ is the mean of p (i, j),
and (3) comparison: the degree of the definition of the image and the depth of the texture groove is reflected, and the calculation formula is as follows:
reverse difference: the smoothness of the image distribution is reflected, and is a measure of the uniformity degree of the image, and the calculation formula is as follows:
degree of difference: the calculation formula for detecting the difference degree of the images is as follows:
amount of information contained in the image: the randomness of the image texture is measured, the characteristic parameters of the randomness of the gray level distribution are measured, the chaotic degree of the gray level of the image is represented, and the calculation formula is as follows:
uniformity of image gray level distribution: the image gray level distribution uniformity and texture thickness are reflected, and the calculation formula is as follows:
degree of similarity: the similarity degree of the elements of the space gray level co-occurrence matrix in the row or column direction is reflected, and the calculation formula is as follows:
in the formula:
preferably, S3 includes the steps of:
s31: and (3) counting the mean value and the variance of different samples on each characteristic quantity, wherein the calculation formula is as follows:
s32: the method comprises the following steps of constructing separable degree calculation of samples based on the Pasteur distance, and calculating the differentiable capacity between different samples aiming at different texture characteristic quantities, wherein the calculation formula is as follows:
in the formula, the average value of the same feature amount in 2 different classes on the μ image, and σ is the standard deviation of the same feature amount in 2 different classes.
Preferably, S4 includes the steps of:
s41: calculating the total separation capacity of each characteristic quantity in different crop categories, wherein the specific calculation formula is as follows:
in the formula, DijThe degree of separability between different samples;
s42: sorting the characteristic quantities according to the sequence of the distinguishing capacity values corresponding to the characteristic quantities from large to small;
s43: and calculating the cumulative sum of the separability of the characteristic quantities, wherein the calculation formula is as follows:
s44: constructing a corresponding function expression based on the accumulated separation value corresponding to the texture characteristic quantity and the corresponding precision, finding that the function expression meets the basic rule of a logarithmic function, and acquiring a derivative corresponding to the function, namely the slope of each point, wherein the calculation formula is as follows:
y＝0.0625ln(x)+0.642
R2＝0.9191
wherein x represents the accumulated D value corresponding to each characteristic quantity;
s45: and calculating the difference of the slopes, if the difference is less than 0.0001, the calculated point corresponds to the optimal number of the texture feature quantity, outputting the result, and acquiring a specific optimal calculation formula as follows:
D＝|y'i-y'i-1|
s46: and combining the screened texture characteristic quantities to form a new image.
Preferably, S5 includes the steps of:
s51: carrying out planting structure identification on the research area by taking a month as a unit to generate a time-space distribution thematic map of crops with a complete time sequence;
s52: and verifying the result according to the verification sample to obtain the overall classification precision and the Kappa coefficient.
The method for dynamically extracting the monthly scale of the crop planting structure with the optimized textural features has the following beneficial effects:
1. the invention preferably and finally serves the identification of the crop planting structure based on the image texture, not only can effectively identify the crop planting structure, but also can save time and reduce the workload of a computer.
2. The technology has the characteristics of quick calculation and strong applicability, effectively improves the classification limitation of medium and high resolution data, improves the classification speed, improves the precision and efficiency of the high-grade first number in classification, and has important significance for the business popularization of the crop planting structure identification technology.
Drawings
Fig. 1 is a general flow chart of a monthly scale dynamic extraction method of a crop planting structure with preferred textural features according to the invention.
Fig. 2 is a flowchart of the substep S1 of the monthly scale dynamic extraction method of a crop planting structure with preferred textural features of the invention.
Fig. 3 is a flowchart of the substep S2 of the method for dynamically extracting the monthly scale of the crop planting structure with the preferred textural features of the invention.
Fig. 4 is a flowchart of the substep S3 of the monthly scale dynamic extraction method of a crop planting structure with preferred textural features of the invention.
Fig. 5 is a flowchart of the substep S4 of the method for dynamically extracting the monthly scale of the crop planting structure with the preferred textural features of the invention.
Fig. 6 is a flowchart of the substep S5 of the method for dynamically extracting the monthly scale of the crop planting structure with the preferred textural features of the invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, a method for dynamically extracting a monthly scale of a crop planting structure comprises the following steps:
s1: determining the space range of an analysis area, preparing data, collecting a time series satellite remote sensing data set not larger than a month scale, uniformly processing the time series satellite remote sensing data set into month scale data in time, and simultaneously completing pre-acquisition of sample data in a research area;
s2: calculating image texture characteristics based on the gray level co-occurrence matrix according to the preprocessed satellite remote sensing image data, and describing texture characteristics of crops by using eight characteristic quantities;
s3: calculating the mean value and the variance of different texture characteristic quantities based on the actually measured samples, calculating the distinguishing capability of the texture characteristic quantities among different samples, and finally evaluating the distinguishing capability of the different characteristic quantities among the samples;
s4: establishing an optimal formula based on the distinguishable capability of each characteristic quantity, determining the optimal number of the texture characteristic quantities participating in classification by using the formula, and constructing the optimal number as a new image;
s5: and finely identifying the crop types in the research area by using a random forest classifier, realizing fine management of the crops, generating a space-time distribution thematic map of the crops with a complete time sequence and verifying the precision.
As shown in fig. 2, S1 of the present embodiment includes the steps of:
s11: selecting GF-1WFV data which is independently developed in China and has high time resolution and high spatial resolution according to the position and the range of a research area, and considering to use the position-2, the high mark two, the landsat8 or the HJ-1A/B for replacement if a data source cannot completely cover, and simultaneously investigating the types of crops and respective growth climatic periods within the range of an embodiment;
s12: processing the collected data by remote sensing images, and if substitute data appears, resampling the uniform spatial resolution;
s13: the representativeness, the typicality and the timeliness of the samples need to be considered for collecting the samples, the research area is divided into n areas with the same area by establishing a regular grid, and crop samples are selected in each area.
As shown in fig. 3, S2 of the present embodiment includes the steps of:
s21: calculating texture characteristic information quantity based on a gray level co-occurrence matrix, counting a gray level correlation coefficient between two pixel points at a certain distance according to a gray level co-occurrence matrix GLCM, and expressing the probability distribution of repeated occurrence of gray levels, wherein the expression is as follows:
p(i,j)＝[p(i,j,d,θ)]
where P (i, j) is the frequency at which the same pixel pair occurs with a determined distance and direction; d is the distance from the pixel point, the angle of the connecting line vector of the two pixels is theta, and the theta is usually 0 degree, 45 degrees, 90 degrees and 135 degrees;
when the texture is calculated by utilizing the gray level co-occurrence matrix, eight feature quantities which are commonly used are selected to characterize the characteristics of the texture.
Mean: and reflecting the gray level average value and the rule degree of the texture in the window.
Variance: reflecting the degree of matrix element deviation from the mean and the magnitude of gray scale change. When the gray scale change is large, the larger the deviation from the mean value is, the larger the value is.
Where μ is the mean of p (i, j). This value is large when the gradation change in the image is large.
Contrast: reflecting the definition of the image and the depth of the texture groove. The deeper the texture grooves, the greater its value. A larger value indicates a higher degree of sharpness of the texture.
Homogeneity: the inverse difference reflects the smoothness of the image distribution and is a measure of the uniformity of the image. If the local gray scale of the image is uniform, the value of the inverse difference moment is large.
Dissimuity: for detecting the degree of difference in the images. If the variation in the local area is large, the value is large.
Encopy: the randomness of the image texture, i.e. the amount of information contained in the image, is a characteristic parameter for measuring the randomness of the gray level distribution, and represents the degree of disorder of the gray level of the image. The larger the entropy, the larger the class uncertainty of the sample. Otherwise, the gray scale in the image is uniform, so the entropy value is small.
Angular Second Moment: the uniformity degree and the texture thickness of the image gray level distribution are reflected, and the method is a measure of the image gray level distribution uniformity. When the element distribution in the gray level co-occurrence matrix is more concentrated near the main diagonal line, the gray level distribution of the image in the local area is more uniform, the image presents thicker texture, and the value of the angular second moment is correspondingly larger.
Correction: reflecting the degree of similarity of the elements of the spatial gray level co-occurrence matrix in the row or column direction. If the matrix elements are uniform and equal, the correlation degree is higher and the value is larger; conversely, if the element difference is large, the correlation is small.
In the formula:
as shown in fig. 4, S3 of the present embodiment includes the steps of:
s31: and (3) counting the mean value and the variance of different samples on each characteristic quantity, wherein the calculation formula is as follows:
s32: the separable degree calculation of the samples is constructed based on Bhattacharyya distance, the distinguishable capability between different samples is calculated aiming at different texture characteristic quantities, and the calculation formula is as follows:
in the formula, the average value of the same feature amount in 2 different classes on the μ image, and σ is the standard deviation of the same feature amount in 2 different classes.
As shown in fig. 5, S4 of the present embodiment includes the steps of:
s41: calculating the total separating capacity of each characteristic quantity in different crop categories, wherein a specific calculation formula is as follows:
in the formula, DijThe degree of separability between different samples;
s42: sorting the characteristic quantities according to the sequence of the distinguishing capacity values corresponding to the characteristic quantities from large to small;
s43: and calculating the cumulative sum of the separability of the characteristic quantities, wherein the calculation formula is as follows:
s44: constructing a corresponding function expression based on the accumulated separation value corresponding to the texture characteristic quantity and the corresponding precision, and finding that the function expression meets the basic rule of a logarithmic function, thereby obtaining a derivative corresponding to the function, namely the slope of each point, specifically as follows:
y＝0.0625ln(x)+0.642
R2＝0.9191
wherein x represents the accumulated D value corresponding to each characteristic quantity;
s45: and calculating the difference of the slopes, wherein if the difference is less than 0.0001, the point corresponds to the optimal number of the texture feature quantity. And outputs the result. The specific preferred calculation formula is obtained as follows:
D＝|y'i-y'i-1|
s46: and combining the screened texture characteristic quantities to form a new image.
As shown in fig. 6, S5 of the present embodiment includes the steps of:
s51: identifying ground objects in the research area according to a random forest principle;
s52: and setting various parameters required by the classifier, inputting classification samples to identify and classify the crop planting structure in the research area, and finishing dynamic identification.
S53: identifying crops in a research area to generate a complete time sequence crop space-time distribution thematic map;
s54: and verifying the result according to the verification sample to obtain the overall classification precision and the Kappa coefficient.
In the implementation of the embodiment, the method provided by the invention is based on the principle that different crops have different texture characteristics on images, the types of the crops in an experimental area are analyzed and summarized, then the optimal texture characteristic quantity is screened based on the calculation principle of the separability and the precision of the texture characteristic quantity, a new image is formed, and finally the crop planting structure information is identified and obtained by using a random forest classification method. The technical scheme has the characteristics of simplicity, effectiveness and strong applicability, can quickly and accurately acquire the spatial distribution information of crops in a large range, improves the reduction of classification speed and the poor classification precision caused by excessive characteristic quantity participating in classification in the traditional method, improves the calculation efficiency and precision of the remote sensing monitoring of the crop planting structure, and is beneficial to the business popularization of the remote sensing technology monitoring of the crop planting structure.
Claims (6)
1. The method for dynamically extracting the monthly scale of the crop planting structure with the optimized textural features is characterized by comprising the following steps of:
s1: determining the space range of an analysis area, preparing data, collecting a time series satellite remote sensing data set not larger than a month scale, uniformly processing the time series satellite remote sensing data set into month scale data in time, and simultaneously completing pre-acquisition of sample data in a research area;
s2: calculating image texture characteristics based on the gray level co-occurrence matrix according to the preprocessed satellite remote sensing image data, and describing texture characteristics of crops by using eight characteristic quantities;
s3: calculating the mean value and the variance of different texture characteristic quantities based on the actually measured samples, and calculating the differentiable capacity of the texture characteristic quantities among different samples;
s4: establishing an optimal formula based on the distinguishable capability of each characteristic quantity, determining the optimal number of the texture characteristic quantities participating in classification by using the formula, and constructing the optimal number as a new image;
s5: and finely identifying the crop types in the research area by using a random forest classifier, realizing fine management of the crops, generating a space-time distribution thematic map of the crops with a complete time sequence and verifying the precision.
2. The method for dynamically extracting the monthly scale of the crop planting structure with the preferable textural features according to claim 1, wherein the step S1 comprises the following steps:
s11: selecting GF-1WFV data with high temporal resolution and high spatial resolution according to the location and extent of the study area, considering the use of the presence-2, high score two, landsat8 or HJ-1A/B replacement if the data source is not completely covered, while investigating crop types and respective growth climatic stages within the scope of the examples;
s12: processing the collected data by remote sensing images, and if substitute data appears, resampling the uniform spatial resolution;
s13: the representativeness, the typicality and the timeliness of the samples need to be considered for collecting the samples, the research area is divided into n areas with the same area by establishing a regular grid, and different crop samples are selected in each area.
3. The method for dynamically extracting the monthly scale of the crop planting structure with the preferable textural features according to claim 1, wherein the step S2 comprises the following steps:
s21: calculating texture characteristic information quantity based on a gray level co-occurrence matrix, and counting a gray level correlation coefficient between two pixel points at a certain distance according to a gray level co-occurrence matrix GLCM, wherein the expression is as follows:
p(i,j)＝[p(i,j,d,θ)]
where P (i, j) is the frequency at which the same pixel pair occurs with a determined distance and direction; d is the distance from the pixel point, the angle of the connecting line vector of the two pixels is theta, and the theta is usually 0 degree, 45 degrees, 90 degrees and 135 degrees;
s22, when calculating the texture by utilizing the gray level co-occurrence matrix, selecting eight characteristic quantities to characterize the characteristics of the texture:
average value: the calculation formula of the regular degree of the gray level average value and the texture in the window is as follows:
variance: reflecting the degree of matrix element deviation from the mean value and the size of gray level change, the calculation formula is as follows:
wherein μ is the mean of p (i, j);
and (3) comparison: the degree of the definition of the image and the depth of the texture groove is reflected, and the calculation formula is as follows:
reverse difference: the smoothness of the image distribution is reflected, and is a measure of the uniformity degree of the image, and the calculation formula is as follows:
degree of difference: the calculation formula for detecting the difference degree of the images is as follows:
amount of information contained in the image: the randomness of the image texture is measured, the characteristic parameters of the randomness of the gray level distribution are measured, the chaotic degree of the gray level of the image is represented, and the calculation formula is as follows:
uniformity of image gray level distribution: the image gray level distribution uniformity and texture thickness are reflected, and the calculation formula is as follows:
degree of similarity: the similarity degree of the elements of the space gray level co-occurrence matrix in the row or column direction is reflected, and the calculation formula is as follows:
in the formula:
4. the method for dynamically extracting the monthly scale of the crop planting structure with the preferable textural features according to claim 1, wherein the step S3 comprises the following steps:
s31: and (3) counting the mean value and the variance of different samples on each characteristic quantity, wherein the calculation formula is as follows:
s32: the method comprises the following steps of constructing separable degree calculation of samples based on the Pasteur distance, and calculating the differentiable capacity between different samples aiming at different texture characteristic quantities, wherein the calculation formula is as follows:
in the formula, the average value of the same feature quantity in 2 different classes on the mu image, and the standard deviation of the same feature quantity in 2 different classes.
5. The method for dynamically extracting the monthly scale of the crop planting structure with the preferable textural features according to claim 1, wherein the step S4 comprises the following steps:
s41: calculating the total separation capacity of each characteristic quantity in different crop categories, wherein the specific calculation formula is as follows:
in the formula, DijThe degree of separability between different samples;
s42: sorting the characteristic quantities according to the sequence of the distinguishing capacity values corresponding to the characteristic quantities from large to small;
s43: and calculating the cumulative sum of the separability of the characteristic quantities, wherein the calculation formula is as follows:
s44: constructing a corresponding function expression based on the accumulated separation value corresponding to the texture characteristic quantity and the corresponding precision, finding that the function expression meets the basic rule of a logarithmic function, and acquiring a derivative corresponding to the function, namely the slope of each point, wherein the calculation formula is as follows:
y＝0.0625ln(x)+0.642
R2＝0.9191
wherein x represents the accumulated D value corresponding to each characteristic quantity;
s45: and calculating the difference of the slopes, if the difference is less than 0.0001, the calculated point corresponds to the optimal number of the texture feature quantity, outputting the result, and acquiring a specific optimal calculation formula as follows:
D＝|y'i-y'i-1|
s46: and combining the screened texture characteristic quantities to form a new image.
6. The method for dynamically extracting the monthly scale of the crop planting structure with the preferable textural features according to claim 1, wherein the step S5 comprises the following steps:
s51: identifying ground objects in the research area according to a random forest principle;
s52: setting various parameters required by a classifier, inputting classification samples to identify and classify crop planting structures in a research area, and completing dynamic identification;
s53: identifying crops in a research area to generate a complete time sequence crop space-time distribution thematic map;
s54: and verifying the result according to the verification sample to obtain the overall classification precision and the Kappa coefficient.
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| CN111950530A (en) * | 2020-09-08 | 2020-11-17 | 中国水利水电科学研究院 | Multi-feature optimization and fusion method for extracting crop planting structure |
| CN112949607A (en) * | 2021-04-15 | 2021-06-11 | 辽宁工程技术大学 | Wetland vegetation feature optimization and fusion method based on JM Relief F |
| CN114219847A (en) * | 2022-02-18 | 2022-03-22 | 清华大学 | Method and system for determining crop planting area based on phenological characteristics and storage medium |
| CN114490449A (en) * | 2022-04-18 | 2022-05-13 | 飞腾信息技术有限公司 | Memory access method and device and processor |
| CN114519721A (en) * | 2022-02-16 | 2022-05-20 | 广东皓行科技有限公司 | Crop lodging identification method, device and equipment based on remote sensing image |
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| CN114519721A (en) * | 2022-02-16 | 2022-05-20 | 广东皓行科技有限公司 | Crop lodging identification method, device and equipment based on remote sensing image |
| CN114219847A (en) * | 2022-02-18 | 2022-03-22 | 清华大学 | Method and system for determining crop planting area based on phenological characteristics and storage medium |
| CN114219847B (en) * | 2022-02-18 | 2022-07-01 | 清华大学 | Method and system for determining crop planting area based on phenological characteristics and storage medium |
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