CN117788200B - Agricultural product maturity prediction system based on multisource remote sensing data - Google Patents
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Abstract
The invention discloses an agricultural product maturity prediction system based on multisource remote sensing data, which relates to the technical field of remote sensing image processing and comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data processing module, a data analysis module and a maturity prediction module; the method comprises the steps of collecting image data and infrared data of a planting area of agricultural products, processing and analyzing the collected data, judging the growth stage of the agricultural products in the planting area, correcting the expected growth period of the next growth stage according to the difference between the stage growth time of the agricultural products in the previous production stage and the expected growth period, and further enabling the prediction of the ending time of the subsequent growth stage to be more accurate.
Description
Technical Field
The invention relates to the technical field of remote sensing image processing, in particular to an agricultural product maturity prediction system based on multi-source remote sensing data.
Background
The production and sale of agricultural products are important links in agricultural production, and the maturity date of agricultural products is one of the key factors of farmers' selection of picking time and marketing. The traditional agricultural production mode generally depends on the experience and weather conditions of farmers, and the method has great uncertainty and risk, which can cause the problems of improper picking time, reduced product quality, resource waste and the like;
How to monitor the growth stage of the agricultural product by using the remote sensing technology and effectively predict the maturity of the agricultural product in each growth stage is a problem to be solved, and therefore, the agricultural product maturity prediction system based on multi-source remote sensing data is provided.
Disclosure of Invention
The invention aims to provide an agricultural product maturity prediction system based on multisource remote sensing data.
The aim of the invention can be achieved by the following technical scheme: the agricultural product maturity prediction system based on the multi-source remote sensing data comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data processing module, a data analysis module and a maturity prediction module;
The data acquisition module consists of a plurality of data acquisition terminals, is distributed at each position in the planting area of the agricultural products, and acquires remote sensing data in the planting area through the set data acquisition terminals;
The data processing module is used for processing the obtained image data and judging the types of agricultural products in the planting area according to the processing result;
the data analysis module is used for analyzing the obtained remote sensing data and obtaining the growth stage of agricultural products in the planting area according to the analysis result;
the maturity prediction module is used for predicting the maturity of agricultural products in the planting area according to the obtained maturity prediction reference information.
Further, the remote sensing data comprises image data and infrared sensing data;
The planting area consists of a plurality of sub-areas;
Setting a corresponding agricultural product growth stage comparison table according to the types of the agricultural products planted in each subarea, wherein the agricultural product growth stage comparison table also comprises the expected growth period of each growth stage of the agricultural products;
and importing corresponding remote sensing reference data to each growth stage of the agricultural product, wherein the remote sensing reference data comprises image reference characteristics and infrared sensing reference data of each growth stage of the agricultural product.
Further, the process of the data processing module for processing the image data includes:
Converting the obtained image data into image frames, and sorting the obtained image frames according to time;
Constructing a virtual planting area model according to the coverage range of the planting area and the position of each sub-area in the planting area, and setting a two-dimensional plane coordinate system in the virtual planting area model;
Mapping the actual position of each sub-region in the planting region into a virtual planting region model, and obtaining the coordinate range of the coverage range of each sub-region according to the position of each sub-region in the virtual planting region;
Mapping the image frames to the corresponding positions of the virtual planting area model;
Performing image iteration elimination on the mapped image frames, and obtaining global image frames after finishing the image iteration elimination on the image frames;
constructing a UV coordinate system, and mapping the global image frame after rasterization into a two-dimensional plane coordinate system;
And carrying out image feature recognition according to the obtained global image frame, and judging the agricultural product type in the planting area according to the image feature recognition result.
Further, the process of performing image iterative elimination on the image frame specifically includes:
selecting a reference frame according to the ordering of the image frames;
Acquiring a coordinate range of a reference frame in a two-dimensional plane coordinate system;
rasterizing the obtained reference frame and constructing a UV coordinate system;
Mapping the reference frame after rasterization processing into a UV coordinate system;
Acquiring pixel values of a reference frame in each unit coordinate area in a UV coordinate system;
selecting a next image frame, and acquiring a coordinate range of the next image frame in a two-dimensional plane coordinate system;
Acquiring a coordinate overlapping region of two image frames;
acquiring pixel values of the coordinate overlapping region in each unit coordinate region in the UV coordinate system;
Comparing pixel values of the same coordinates of the two image frames to obtain corresponding comparison results, and completing iterative elimination of the image frames according to the comparison results;
after the elimination and iteration of the image content of all coordinates in the coordinate overlapping area are completed, the obtained new image frame is recorded as a reference frame, then the next image frame is obtained, and the like, the image iteration elimination of all the image frames is completed, and then the global image frame is obtained.
Further, the process of image feature recognition according to the obtained global image frame comprises the following steps:
Mapping the obtained global image frame into a virtual planting area model, and dividing the global image frame into a plurality of sub-image frames according to the positions of all sub-areas in the virtual planting area;
extracting image features of each sub-image frame, and matching the extracted image features with image reference features of each growth stage of the agricultural product;
and obtaining the types of agricultural products planted in each subarea according to the matching result.
Further, the process of analyzing the growth stage of the agricultural products in the planting area by the data analysis module comprises the following steps:
Summarizing the infrared sensing data in the acquired remote sensing data, and generating a corresponding infrared spectrogram according to the acquired infrared sensing data;
According to the obtained agricultural product types, obtaining infrared sensing reference data of each growth stage of the agricultural product corresponding to the agricultural product types to generate corresponding comparison spectrograms;
extracting infrared spectrum characteristics of the generated control spectrograms in each growth stage and the obtained infrared spectrograms, obtaining the corresponding control spectrograms in each growth stage and the infrared spectrum characteristics in the infrared spectrograms, summarizing the obtained infrared spectrum characteristics, marking the infrared spectrum characteristics in the summarized control spectrograms as a control spectrum characteristic set, and marking the infrared spectrum characteristics in the summarized infrared spectrograms as a spectrum characteristic set to be identified;
And matching the obtained spectral feature set to be identified with the comparison spectral feature set to obtain the similarity of the spectral feature set to be identified and the comparison spectral feature set corresponding to each growth stage, and obtaining the growth stage of the agricultural products in each subarea of the planting area according to the obtained similarity result.
Further, the process for obtaining the similarity between the spectral feature set to be identified and the contrast spectral feature set corresponding to each growth stage includes:
Classifying each infrared spectrum feature in the spectrum feature set to be identified according to the subarea where the corresponding infrared spectrogram is located, and marking the infrared spectrum feature which is classified as the subarea infrared spectrum feature;
constructing a comparison matrix, wherein the comparison matrix comprises matrix areas with the same quantity as that of the comparison spectrum feature sets, and each matrix area corresponds to one comparison spectrum feature set;
Comparing the infrared spectrum features of the subareas with the infrared spectrum features in each comparison spectrum feature set, and mapping corresponding comparison parameters into the corresponding areas of the comparison matrix according to the comparison result, wherein the comparison parameters comprise 0 and 1;
When the infrared spectrum characteristics of the sub-region are consistent with the infrared spectrum characteristics in the comparison spectrum characteristic set, mapping a comparison parameter '1' in a matrix region corresponding to the comparison spectrum characteristic set in the comparison matrix, and otherwise, mapping '0';
Acquiring the number of comparison parameters '1' in each matrix region in the comparison matrix, and acquiring the ratio between the comparison parameters and the total number of infrared spectrum features of the subareas, and marking the ratio as the similarity between the spectrum feature set to be identified and the comparison spectrum feature set;
And marking the growth stage of the agricultural product corresponding to the control spectrum characteristic set with the highest similarity as maturity prediction reference information.
Further, the process of the maturity prediction module for predicting the maturity of agricultural products within the planting area includes:
sequencing the maturity prediction reference information in the same production stage according to the acquisition time of acquiring the remote sensing data;
acquiring the starting time and the ending time of a previous growth stage, and acquiring the stage duration of the previous growth stage;
Comparing the period length of the previous growth period with the expected growth period of the growth period to obtain a period length optimization coefficient;
Correcting the predicted growth period of the current growth stage according to the time length optimization coefficient of the previous growth stage;
And (3) acquiring the growing time of the current growth stage, comparing the corrected predicted growth period of the current growth stage to obtain a time difference value, and then, predicting the mature period of the agricultural product by the predicted time of the agricultural product entering the next growth stage and the like.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of collecting image data and infrared data of a planting area of agricultural products, processing and analyzing the collected data, judging the growth stage of the agricultural products in the planting area, correcting the expected growth period of the next growth stage according to the difference between the stage growth time of the agricultural products in the previous production stage and the expected growth period, and further enabling the prediction of the ending time of the subsequent growth stage to be more accurate.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the present invention.
Detailed Description
As shown in fig. 1, an agricultural product maturity prediction system based on multi-source remote sensing data comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data processing module, a data analysis module and a maturity prediction module;
The data acquisition module consists of a plurality of data acquisition terminals, is distributed at each position in the planting area of the agricultural products, and acquires remote sensing data in the planting area through the set data acquisition terminals;
it should be further noted that, in the implementation process, the remote sensing data includes image data and infrared sensing data;
The planting area consists of a plurality of sub-areas, each sub-area is marked with i, wherein i=1, 2, … … and n, wherein n is an integer and n is more than 1;
Setting a corresponding agricultural product growth stage comparison table according to the types of the agricultural products planted in each subarea, wherein the agricultural product growth stage comparison table also comprises the expected growth period of each growth stage of the agricultural products;
and importing corresponding remote sensing reference data to each growth stage of the agricultural product, wherein the remote sensing reference data comprises image reference characteristics and infrared sensing reference data of each growth stage of the agricultural product.
The data processing module is used for processing the obtained image data, judging the types of agricultural products in the planting area according to the processing result, and the specific process comprises the following steps:
Converting the obtained image data into image frames, and sorting the obtained image frames according to time;
Constructing a virtual planting area model according to the coverage range of the planting area and the position of each sub-area in the planting area, and setting a two-dimensional plane coordinate system in the virtual planting area model; the virtual planting area model is a two-dimensional plane model constructed according to the coverage range of the planting area and the positions of all the subareas in the planting area in a certain proportion;
mapping the actual position of each subarea in the planting area into a virtual planting area model, and obtaining the coordinate range of the coverage range of each subarea according to the position of each subarea in the virtual planting area, which is marked as C i;
According to the obtained positions of the planting areas corresponding to the image frames, associating the image frames with the corresponding positions of the virtual planting area models;
mapping the image frames to the corresponding positions of the virtual planting area models according to the association relation between the image frames and the virtual planting area models;
Performing image iteration elimination on the mapped image frames, and obtaining global image frames after finishing the image iteration elimination on the image frames;
constructing a UV coordinate system, and mapping the global image frame after rasterization into a two-dimensional plane coordinate system;
And carrying out image feature recognition according to the obtained global image frame, and judging the agricultural product type in the planting area according to the image feature recognition result.
It should be further noted that, in the implementation process, the process of performing image iterative elimination on the image frame specifically includes:
Selecting the image frame which is the forefront according to the sequence of the image frames, and marking the image frame as a reference frame;
Acquiring a coordinate range of a reference frame in a two-dimensional plane coordinate system, and marking the coordinate range as C Datum ;
rasterizing the obtained reference frame and constructing a UV coordinate system;
Mapping the reference frame after rasterization processing into a UV coordinate system;
Acquiring pixel values of a reference frame in each unit coordinate area in a UV coordinate system;
Selecting a next image frame, and acquiring a coordinate range of the next image frame in a two-dimensional plane coordinate system, which is marked as C next;
acquiring a coordinate overlapping region of C next and C Datum ;
mapping the coordinate overlapping region of the next image frame into a UV coordinate system, and obtaining pixel values of the coordinate overlapping region in each unit coordinate region in the UV coordinate system;
comparing the pixel values of the same coordinate in the coordinate overlapping area of the reference frame and the next image frame to obtain a corresponding comparison result;
When the pixel value of the reference frame is not smaller than the pixel value of the same coordinate of the next image frame, iterating the image content of the coordinate of the next image frame by the image content of the coordinate of the reference frame, and eliminating the image content of the coordinate of the next image frame;
when the pixel value of the reference frame is smaller than the pixel value of the same coordinate of the next image frame, iterating the image content of the coordinate of the reference frame by the image content of the coordinate of the next image frame, and eliminating the image content of the coordinate of the reference frame;
after the elimination and iteration of the image content of all coordinates in the coordinate overlapping area are completed, the obtained new image frame is recorded as a reference frame, then the next image frame is obtained, and the like, the image iteration elimination of all the image frames is completed, and then the global image frame is obtained.
It should be further noted that, in the implementation process, the process of performing image feature recognition according to the obtained global image frame includes:
Mapping the obtained global image frame into a virtual planting area model, and dividing the global image frame into a plurality of sub-image frames according to the positions of all sub-areas in the virtual planting area;
extracting image features of each sub-image frame, and matching the extracted image features with image reference features of each growth stage of the agricultural product;
and obtaining the types of agricultural products planted in each subarea according to the matching result.
The data analysis module is used for analyzing the obtained remote sensing data and obtaining the growth stage of agricultural products in the planting area according to the analysis result, and the specific process comprises the following steps:
Summarizing the infrared sensing data in the acquired remote sensing data, and generating a corresponding infrared spectrogram according to the acquired infrared sensing data;
According to the obtained agricultural product types, obtaining infrared sensing reference data of each growth stage of the agricultural product corresponding to the agricultural product types to generate corresponding comparison spectrograms;
extracting infrared spectrum characteristics of the generated control spectrograms in each growth stage and the obtained infrared spectrograms, obtaining the corresponding control spectrograms in each growth stage and the infrared spectrum characteristics in the infrared spectrograms, summarizing the obtained infrared spectrum characteristics, marking the infrared spectrum characteristics in the summarized control spectrograms as a control spectrum characteristic set, and marking the infrared spectrum characteristics in the summarized infrared spectrograms as a spectrum characteristic set to be identified;
And matching the obtained spectral feature set to be identified with the comparison spectral feature set to obtain the similarity of the spectral feature set to be identified and the comparison spectral feature set corresponding to each growth stage, and obtaining the growth stage of the agricultural products in each subarea of the planting area according to the obtained similarity result.
It should be further noted that, in the implementation process, the process of obtaining the similarity between the spectral feature set to be identified and the reference spectral feature set corresponding to each growth stage includes:
Classifying each infrared spectrum feature in the spectrum feature set to be identified according to the subarea where the corresponding infrared spectrogram is located, and marking the infrared spectrum feature which is classified as the subarea infrared spectrum feature;
constructing a comparison matrix, wherein the comparison matrix comprises matrix areas with the same quantity as that of the comparison spectrum feature sets, and each matrix area corresponds to one comparison spectrum feature set;
Comparing the infrared spectrum features of the subareas with the infrared spectrum features in each comparison spectrum feature set, and mapping corresponding comparison parameters into the corresponding areas of the comparison matrix according to the comparison result, wherein the comparison parameters comprise 0 and 1;
When the infrared spectrum characteristics of the sub-region are consistent with the infrared spectrum characteristics in the comparison spectrum characteristic set, mapping a comparison parameter '1' in a matrix region corresponding to the comparison spectrum characteristic set in the comparison matrix, and otherwise, mapping '0'; it should be further noted that, in the implementation process, the sub-region infrared spectrum features may be simultaneously consistent with the infrared spectrum features in the multiple reference spectrum feature sets;
Acquiring the number of comparison parameters '1' in each matrix region in the comparison matrix, and acquiring the ratio between the comparison parameters and the total number of infrared spectrum features of the subareas, and marking the ratio as the similarity between the spectrum feature set to be identified and the comparison spectrum feature set;
And marking the growth stage of the agricultural product corresponding to the control spectrum characteristic set with the highest similarity as maturity prediction reference information.
The maturity prediction module is used for predicting the maturity of agricultural products in the planting area according to the obtained maturity prediction reference information, and the specific process comprises the following steps:
sequencing the maturity prediction reference information in the same production stage according to the acquisition time of acquiring the remote sensing data;
acquiring the starting time and the ending time of a previous growth stage, and acquiring the stage duration of the previous growth stage;
Comparing the period length of the previous growth period with the expected growth period of the growth period to obtain a period length optimization coefficient; it should be further noted that in the implementation, each production stage is provided with a corresponding predicted growth cycle
Illustrating:
the period length of the previous growth period is T1, and the expected growth period of the previous growth period is T0;
the duration optimization factor is the ratio of T1 to T0.
Correcting the predicted growth period of the current growth stage according to the time length optimization coefficient of the previous growth stage;
And (3) acquiring the growing time of the current growth stage, comparing the corrected predicted growth period of the current growth stage to obtain a time difference value, and then, predicting the mature period of the agricultural product by the predicted time of the agricultural product entering the next growth stage and the like.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (3)
1. The agricultural product maturity prediction system based on the multi-source remote sensing data comprises a monitoring center, and is characterized in that the monitoring center is in communication connection with a data acquisition module, a data processing module, a data analysis module and a maturity prediction module;
The data acquisition module consists of a plurality of data acquisition terminals, is distributed at each position in the planting area of the agricultural products, and acquires remote sensing data in the planting area through the set data acquisition terminals;
The data processing module is used for processing the obtained image data and judging the types of agricultural products in the planting area according to the processing result;
the data analysis module is used for analyzing the obtained remote sensing data and obtaining the growth stage of agricultural products in the planting area according to the analysis result;
The maturity prediction module is used for predicting the maturity of agricultural products in the planting area according to the obtained maturity prediction reference information;
The remote sensing data comprises image data and infrared sensing data;
The planting area consists of a plurality of sub-areas;
Setting a corresponding agricultural product growth stage comparison table according to the types of the agricultural products planted in each subarea, wherein the agricultural product growth stage comparison table also comprises the expected growth period of each growth stage of the agricultural products;
Importing corresponding remote sensing reference data to each growth stage of the agricultural product, wherein the remote sensing reference data comprises image reference characteristics and infrared sensing reference data of each growth stage of the agricultural product;
the process of the data processing module for processing the image data comprises the following steps:
Converting the obtained image data into image frames, and sorting the obtained image frames according to time;
Constructing a virtual planting area model according to the coverage range of the planting area and the position of each sub-area in the planting area, and setting a two-dimensional plane coordinate system in the virtual planting area model;
Mapping the actual position of each sub-region in the planting region into a virtual planting region model, and obtaining the coordinate range of the coverage range of each sub-region according to the position of each sub-region in the virtual planting region;
Mapping the image frames to the corresponding positions of the virtual planting area model;
Performing image iteration elimination on the mapped image frames, and obtaining global image frames after finishing the image iteration elimination on the image frames;
constructing a UV coordinate system, and mapping the global image frame after rasterization into a two-dimensional plane coordinate system;
Image feature recognition is carried out according to the obtained global image frame, and the agricultural product type in the planting area is judged according to the image feature recognition result;
The process of performing image iterative elimination on the image frame specifically comprises the following steps:
selecting a reference frame according to the ordering of the image frames;
Acquiring a coordinate range of a reference frame in a two-dimensional plane coordinate system;
rasterizing the obtained reference frame and constructing a UV coordinate system;
Mapping the reference frame after rasterization processing into a UV coordinate system;
Acquiring pixel values of a reference frame in each unit coordinate area in a UV coordinate system;
selecting a next image frame, and acquiring a coordinate range of the next image frame in a two-dimensional plane coordinate system;
Acquiring a coordinate overlapping region of two image frames;
acquiring pixel values of the coordinate overlapping region in each unit coordinate region in the UV coordinate system;
Comparing pixel values of the same coordinates of the two image frames to obtain corresponding comparison results, and completing iterative elimination of the image frames according to the comparison results;
After the elimination and iteration of the image content of all coordinates in the coordinate overlapping area are completed, the obtained new image frame is recorded as a reference frame, then the next image frame is obtained, and the like, the image iteration elimination of all the image frames is completed, and then the global image frame is obtained;
the process of image feature recognition according to the obtained global image frame comprises the following steps:
Mapping the obtained global image frame into a virtual planting area model, and dividing the global image frame into a plurality of sub-image frames according to the positions of all sub-areas in the virtual planting area;
extracting image features of each sub-image frame, and matching the extracted image features with image reference features of each growth stage of the agricultural product;
Obtaining the types of agricultural products planted in each subarea according to the matching result;
the process of analyzing the growth stage of agricultural products in the planting area by the data analysis module comprises the following steps:
Summarizing the infrared sensing data in the acquired remote sensing data, and generating a corresponding infrared spectrogram according to the acquired infrared sensing data;
According to the obtained agricultural product types, obtaining infrared sensing reference data of each growth stage of the agricultural product corresponding to the agricultural product types to generate corresponding comparison spectrograms;
extracting infrared spectrum characteristics of the generated control spectrograms in each growth stage and the obtained infrared spectrograms, obtaining the corresponding control spectrograms in each growth stage and the infrared spectrum characteristics in the infrared spectrograms, summarizing the obtained infrared spectrum characteristics, marking the infrared spectrum characteristics in the summarized control spectrograms as a control spectrum characteristic set, and marking the infrared spectrum characteristics in the summarized infrared spectrograms as a spectrum characteristic set to be identified;
And matching the obtained spectral feature set to be identified with the comparison spectral feature set to obtain the similarity of the spectral feature set to be identified and the comparison spectral feature set corresponding to each growth stage, and obtaining the growth stage of the agricultural products in each subarea of the planting area according to the obtained similarity result.
2. The agricultural product maturity prediction system based on multi-source remote sensing data of claim 1, wherein the process of obtaining the similarity between the set of spectral features to be identified and the set of control spectral features corresponding to each growth stage comprises:
Classifying each infrared spectrum feature in the spectrum feature set to be identified according to the subarea where the corresponding infrared spectrogram is located, and marking the infrared spectrum feature which is classified as the subarea infrared spectrum feature;
constructing a comparison matrix, wherein the comparison matrix comprises matrix areas with the same quantity as that of the comparison spectrum feature sets, and each matrix area corresponds to one comparison spectrum feature set;
Comparing the infrared spectrum features of the subareas with the infrared spectrum features in each comparison spectrum feature set, and mapping corresponding comparison parameters into the corresponding areas of the comparison matrix according to the comparison result, wherein the comparison parameters comprise 0 and 1;
When the infrared spectrum characteristics of the sub-region are consistent with the infrared spectrum characteristics in the comparison spectrum characteristic set, mapping a comparison parameter '1' in a matrix region corresponding to the comparison spectrum characteristic set in the comparison matrix, and otherwise, mapping '0';
Acquiring the number of comparison parameters '1' in each matrix region in the comparison matrix, and acquiring the ratio between the comparison parameters and the total number of infrared spectrum features of the subareas, and marking the ratio as the similarity between the spectrum feature set to be identified and the comparison spectrum feature set;
And marking the growth stage of the agricultural product corresponding to the control spectrum characteristic set with the highest similarity as maturity prediction reference information.
3. The system for predicting the maturity of agricultural products based on multi-source remote sensing data of claim 2, wherein said maturity prediction module predicts the maturity of agricultural products within the area of planting comprising:
sequencing the maturity prediction reference information in the same production stage according to the acquisition time of acquiring the remote sensing data;
acquiring the starting time and the ending time of a previous growth stage, and acquiring the stage duration of the previous growth stage;
Comparing the period length of the previous growth period with the expected growth period of the growth period to obtain a period length optimization coefficient;
Correcting the predicted growth period of the current growth stage according to the time length optimization coefficient of the previous growth stage;
And (3) acquiring the growing time of the current growth stage, comparing the corrected predicted growth period of the current growth stage to obtain a time difference value, and then, predicting the mature period of the agricultural product by the predicted time of the agricultural product entering the next growth stage and the like.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BRPI0204276B1 (en) * | 2002-10-02 | 2018-02-14 | Universidade De São Paulo - Usp | METHOD FOR PRODUCTIVITY PREDICTION AND MONITORING OF AGRICULTURAL CROPS BY MULTIESPECTRAL REMOTE SENSING AND HISTORICAL DATA |
| CN108363949A (en) * | 2017-12-27 | 2018-08-03 | 二十世纪空间技术应用股份有限公司 | A kind of cotton remote-sensing monitoring method based on phenology analysis |
| CN109614891A (en) * | 2018-11-27 | 2019-04-12 | 北京师范大学 | Crops recognition methods based on phenology and remote sensing |
| CN114066033A (en) * | 2021-11-10 | 2022-02-18 | 青岛农业大学 | Intelligent agriculture optimization method and system |
| CN116227758A (en) * | 2023-05-10 | 2023-06-06 | 江西师范大学 | Agricultural product maturity prediction method and system based on remote sensing technology and deep learning |
| CN116821630A (en) * | 2023-07-20 | 2023-09-29 | 荆州谷德农业科技发展有限公司 | Agricultural product maturity prediction method based on fusion data analysis |
-
2024
- 2024-02-28 CN CN202410217867.6A patent/CN117788200B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BRPI0204276B1 (en) * | 2002-10-02 | 2018-02-14 | Universidade De São Paulo - Usp | METHOD FOR PRODUCTIVITY PREDICTION AND MONITORING OF AGRICULTURAL CROPS BY MULTIESPECTRAL REMOTE SENSING AND HISTORICAL DATA |
| CN108363949A (en) * | 2017-12-27 | 2018-08-03 | 二十世纪空间技术应用股份有限公司 | A kind of cotton remote-sensing monitoring method based on phenology analysis |
| CN109614891A (en) * | 2018-11-27 | 2019-04-12 | 北京师范大学 | Crops recognition methods based on phenology and remote sensing |
| CN114066033A (en) * | 2021-11-10 | 2022-02-18 | 青岛农业大学 | Intelligent agriculture optimization method and system |
| CN116227758A (en) * | 2023-05-10 | 2023-06-06 | 江西师范大学 | Agricultural product maturity prediction method and system based on remote sensing technology and deep learning |
| CN116821630A (en) * | 2023-07-20 | 2023-09-29 | 荆州谷德农业科技发展有限公司 | Agricultural product maturity prediction method based on fusion data analysis |
Non-Patent Citations (1)
| Title |
|---|
| 基于卫星遥感预测作物成熟期的可行性分析;蒙继华;吴炳方;;遥感技术与应用;20130430(第02期);第165-173页 * |
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