CN117496354A - Agricultural production information digital management system and method based on satellite remote sensing image - Google Patents
Agricultural production information digital management system and method based on satellite remote sensing image Download PDFInfo
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Abstract
The invention discloses an agricultural production information digital management system and method based on satellite remote sensing images. The system comprises an image preprocessing module, a planting area dividing module, an area statistics module, an agricultural production resource database and a planting density analysis module; the image preprocessing module is used for acquiring images of the crop planting areas and processing the images; the planting area dividing module is used for extracting and distinguishing the crop characteristics, dividing the crop planting area image into various crop planting subareas and numbering the crop planting subareas; the regional area statistics module acquires the position coordinates of the boundaries of the crop planting subregions and determines the areas of the crop planting subregions; the planting density analysis module counts the total number of crop plants, counts the plant planting density and the crop growth condition and yield of each crop planting subregion, and analyzes according to the historical production statistics condition to obtain a planting density suggestion.
Description
Technical Field
The invention relates to the field of digital management, in particular to an agricultural production information digital management system and method based on satellite remote sensing images.
Background
In 1970, the first artificial satellite of China was successfully launched in Oriental Red No. one, and along with the rising of one satellite and the other satellite, in the 80 th century, china starts satellite remote sensing data application in the agricultural rural area, and through development in the last 40 years, the monitoring field has been expanded to the fields of crop estimation, agricultural disasters, agricultural key engineering, agricultural planting structure adjustment, rural economic development and the like, and satellite remote sensing technology has become an important information source in the agricultural rural area. The satellite remote sensing technology in China has been developed continuously for many years, a series of remote sensing satellites including meteorological satellites, resource satellites and marine satellites are built, the remote sensing satellites play an important role in investigation, monitoring and management of resources such as homeland, natural resources, agriculture, water conservancy and the like and geological disasters and urban planning, and in recent years, the remote sensing technology has been developed to a certain extent when applied to intelligent agriculture, but a plurality of problems still need to be solved, for example: the agricultural management is lagged, and the innovation is insufficient.
The digital management agriculture integrates various information technologies to be rapidly applied in the agricultural development, and the modern agriculture information technology and the production real distance of the agriculture field are gradually integrated, so that a digital management system of agriculture in China is constructed, and the digital management system is a new direction for realizing the information crossing development of agriculture in China. The method is used for solving the new problem found by the digital system in practical operation application by integrating information technology and combining the agricultural management effect of the farm and the application of an evaluation system of the agricultural management effect and mainly taking the farm economy statistical digital system.
Therefore, an agricultural production information digital management system and method based on satellite remote sensing images are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide an agricultural production information digital management system and method based on satellite remote sensing images, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
agricultural production information digital management system based on satellite remote sensing image, this system includes: the system comprises an image preprocessing module, a planting area dividing module, an area statistics module, an agricultural production resource database and a planting density analysis module;
the image preprocessing module is used for collecting images of crop planting areas and carrying out contrast improvement, noise filtering and high-definition filtering processing on the received images of the crop planting areas;
the crop planting area dividing module is used for amplifying the crop planting area image, extracting crop characteristics of the amplified crop planting area image, identifying crop types, dividing the crop planting area image into crop planting subareas according to different appearance characteristics of different crops, respectively corresponding to the crop types, and numbering the crop planting subareas in sequence;
the regional area statistics module acquires the position coordinates of the boundaries of the crop planting subregions by using a GPS (global positioning system) acre measuring instrument for the divided crop planting subregions, so as to determine the areas of the crop planting subregions;
the agricultural production resource database is used for receiving the serial numbers of all crop planting subregions and the crop types corresponding to all the planting subregions sent by the planting region dividing module, receiving and storing the position coordinates of all the crop planting subregion boundaries sent by the region area counting module to form all the crop planting subregion information sets, wherein each crop planting subregion information set comprises all the serial numbers of all the crop planting subregions, the crop types, the position coordinates of the planting subregion boundaries and the historical production statistics of the crops, and simultaneously storing all the appearance feature vectors and the corresponding feature components of different types of crops;
the planting density analysis module is used for receiving the areas of the crop planting subregions sent by the regional area statistics module, counting the total number of crop plants in each crop planting subregion, further counting the plant planting density of each crop planting subregion, and finally analyzing according to the historical production statistics condition to obtain a planting density suggestion;
the output end of the image preprocessing is connected with the input end of the planting area dividing module; the output end of the planting area dividing module is connected with the input end of the area counting module; the output end of the regional area statistics module is connected with the input end of the agricultural production resource database; the output end of the agricultural production resource database is connected with the input end of the planting density analysis module.
According to the technical scheme, the image preprocessing module comprises an area image acquisition unit and an image processing unit;
the regional image acquisition unit is used for acquiring images of crop planting regions by using satellite remote sensing images and sending the acquired images to the image processing unit;
the image processing unit is used for receiving the crop planting area image sent by the area image acquisition unit, performing contrast improvement, noise filtering and high-definition filtering processing on the received crop planting area image to obtain a preprocessed image, and sending the preprocessed image to the planting area dividing module.
According to the technical scheme, the planting area dividing module comprises a species characteristic extracting unit and a crop area processing unit;
the crop species characteristic extraction unit is used for amplifying the received crop planting area image, extracting crop characteristics from the amplified crop planting area image and identifying crop species;
the crop area processing unit is used for extracting boundary lines of different types of crop planting areas through area scanning according to different appearance characteristics of different crops, the boundary lines of the different types of crops divide a crop planting area image into crop planting subareas, each crop planting subarea corresponds to each crop type respectively, each crop planting subarea is numbered according to a set sequence, the number set is {1,2,3,.. N }, and each crop planting subarea number and each crop type corresponding to each planting subarea are sent to the agricultural production resource database.
According to the technical scheme, the regional area statistics module comprises a regional area acquisition unit and a coordinate position dividing unit;
the regional area acquisition unit is used for acquiring and counting the areas of the planting subregions of various crops;
the coordinate position dividing unit is used for acquiring the position coordinates of the boundaries of the crop planting subregions by utilizing the GPS acre measuring instrument for the divided crop planting subregions.
According to the technical scheme, the agricultural production resource database is used for receiving the serial numbers of all crop planting subareas and the types of crops corresponding to all the planting subareas sent by the planting area dividing module, receiving the position coordinates of all the crop planting subareas boundaries sent by the area counting module, storing the serial numbers of all the crop planting subareas, the types of the crops, the position coordinates of the planting subareas boundaries and the historical production statistics of the crops, and storing all the appearance feature vectors and the corresponding feature components of different types of crops.
According to the technical scheme, the planting density analysis module comprises a data analysis unit and a feedback unit;
the data analysis unit is used for carrying out statistical analysis on the total number of crop plants in each crop planting subregion and the plant planting density of each crop planting subregion;
and the feedback unit is used for analyzing according to the historical production statistics condition to obtain planting density suggestions. The agricultural production information digital management method based on the satellite remote sensing image comprises the following steps:
s1, acquiring an image of a crop planting area, and performing contrast improvement, noise filtering and high-definition filtering treatment on the received image of the crop planting area;
s2, extracting crop characteristics according to the crop planting area image, distinguishing crop types, dividing the crop planting area image into various crop planting subareas, and numbering;
s3, acquiring position coordinates of boundaries of all crop planting subregions by using a GPS (global positioning system) acre measuring instrument, and determining the area of each crop planting subregion;
s4, counting the total number of crop plants in each crop planting subregion, and counting the plant planting density, the crop growth condition and the yield of each crop planting subregion;
and S5, analyzing according to historical production statistics to obtain planting density suggestions.
According to the technical scheme, in S2, crop features in the crop planting area image are extracted, crop profile feature vectors in the crop mixed area image are extracted, the profile feature vectors comprise five types of roots, stems, leaves, flowers and fruits, feature components of various feature vectors are further extracted, the feature components comprise colors and shapes, the feature components corresponding to the extracted crop profile feature vectors are compared with the profile components corresponding to the various crop profile feature vectors in the agricultural production resource database one by one, the similarity of the feature components corresponding to the various profile feature vectors and the profile component similarity corresponding to the various crop profile feature vectors stored in the agricultural production resource database is counted, the similarity of the feature components corresponding to the counted various profile feature vectors and the profile component similarity corresponding to the various crop profile feature vectors stored in the crop resource database is compared with a preset similarity threshold, and when each feature component similarity corresponding to each type of profile feature vector is greater than a set similarity threshold, the crop type with the largest similarity is output as the crop type.
According to the above technical scheme, in S3, the position coordinates of the boundary of the crop planting subregion are obtained by using the GPS positioning system, the longitude and latitude coordinates of the boundary end point of the obtained crop sub-planting region are converted into plane coordinates, the longitude direction is regarded as the Y axis, the latitude direction is regarded as the X axis, and the formula for converting the longitude and latitude coordinates of the boundary end point j of the crop planting subregion at any point into plane coordinates is as follows:
wherein X is j ，Y j The plane coordinate of the point j, R is the earth radius, L j 、B j The longitude and the latitude of the point j are respectively, the crop planting subregion is approximately an irregular polygon, each end point of the boundary of the crop planting subregion is connected with the origin of coordinates, each side of the crop planting subregion and the origin form a triangle, thus dividing the irregular polygon into a plurality of triangles, calculating the area of the triangle corresponding to each side, and summing the areas of all the triangles divided to obtain the crop speciesImplant area.
According to the above technical scheme, in S4, the total number of crop plants in each crop planting subregion is counted, and the total number of crop plants Q is calculated, where the formula is:
wherein L is the distance between the extracted single plant and the single plant in the crop planting subregion, H is the distance between the extracted unit plant planting row and the unit plant planting row in the crop planting subregion, S is the area of the crop planting subregion, the plant planting density of each crop planting subregion is ρ, and the formula is:
q is expressed as the total number of each crop plant in each crop planting subregion, S is expressed as the area of each crop planting subregion, and trend checking is carried out according to the historical production statistical yield condition and the plant planting density, so that the planting density suggestion is obtained.
Compared with the prior art, the invention has the following beneficial effects:
1. the image preprocessing module is used for acquiring the image of the crop planting area and processing the image; the planting area dividing module is used for extracting and distinguishing the crop characteristics, dividing the crop planting area image into various crop planting subareas and numbering the crop planting subareas; the regional area statistics module acquires the position coordinates of the boundaries of the crop planting subregions and determines the areas of the crop planting subregions; the planting density analysis module counts the total number of crop plants, counts the plant planting density and the crop growth condition and yield of each crop planting subregion, analyzes according to the historical production statistics condition, obtains a planting density suggestion, and promotes the development of crop production.
2. The application of the agricultural digital management system can promote the development of agricultural modernization, is favorable for acquiring agricultural market information, reduces investment cost, reduces investment transaction risk, simultaneously combines agricultural information with agricultural technology knowledge, greatly improves the internationally output quantity of agricultural products in China, and when talents of advanced agricultural technology are broadcast, the agricultural science technology knowledge is widely spread and utilized in farmers, and the application of the advanced technology of the agricultural digital management system drives sustainable development strategy implementation of the agricultural industry.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic structural diagram of an agricultural production information digital management system based on satellite remote sensing images;
fig. 2 is a schematic diagram of steps of a method for digitally managing agricultural production information based on satellite remote sensing images.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: agricultural production information digital management system based on satellite remote sensing image, this system includes: the system comprises an image preprocessing module, a planting area dividing module, an area statistics module, an agricultural production resource database and a planting density analysis module;
the image preprocessing module is used for collecting images of crop planting areas and carrying out contrast improvement, noise filtering and high-definition filtering processing on the received images of the crop planting areas;
the crop planting area dividing module is used for amplifying the crop planting area image, extracting crop characteristics of the amplified crop planting area image, identifying crop types, dividing the crop planting area image into crop planting subareas according to different appearance characteristics of different crops, respectively corresponding to the crop types, and numbering the crop planting subareas in sequence;
the regional area statistics module acquires the position coordinates of the boundaries of the crop planting subregions by using a GPS (global positioning system) acre measuring instrument for the divided crop planting subregions, so as to determine the areas of the crop planting subregions;
the agricultural production resource database is used for receiving the serial numbers of all crop planting subregions and the crop types corresponding to all the planting subregions sent by the planting region dividing module, receiving and storing the position coordinates of all the crop planting subregion boundaries sent by the region area counting module to form all the crop planting subregion information sets, wherein each crop planting subregion information set comprises all the serial numbers of all the crop planting subregions, the crop types, the position coordinates of the planting subregion boundaries and the historical production statistics of the crops, and simultaneously storing all the appearance feature vectors and the corresponding feature components of different types of crops;
the planting density analysis module is used for receiving the areas of the crop planting subregions sent by the regional area statistics module, counting the total number of crop plants in each crop planting subregion, further counting the plant planting density of each crop planting subregion, and finally analyzing according to the historical production statistics condition to obtain a planting density suggestion;
the output end of the image preprocessing is connected with the input end of the planting area dividing module; the output end of the planting area dividing module is connected with the input end of the area counting module; the output end of the regional area statistics module is connected with the input end of the agricultural production resource database; the output end of the agricultural production resource database is connected with the input end of the planting density analysis module.
The image preprocessing module comprises an area image acquisition unit and an image processing unit;
the regional image acquisition unit is used for acquiring images of crop planting regions by using satellite remote sensing images and sending the acquired images to the image processing unit;
the image processing unit is used for receiving the crop planting area image sent by the area image acquisition unit, performing contrast improvement, noise filtering and high-definition filtering processing on the received crop planting area image to obtain a preprocessed image, and sending the preprocessed image to the planting area dividing module.
The planting area dividing module comprises a species characteristic extracting unit and a crop area processing unit;
the crop species characteristic extraction unit is used for amplifying the received crop planting area image, extracting crop characteristics from the amplified crop planting area image and identifying crop species;
the crop area processing unit is used for extracting boundary lines of different types of crop planting areas through area scanning according to different appearance characteristics of different crops, the boundary lines of the different types of crops divide a crop planting area image into crop planting subareas, each crop planting subarea corresponds to each crop type respectively, each crop planting subarea is numbered according to a set sequence, the number set is {1,2,3,.. N }, and each crop planting subarea number and each crop type corresponding to each planting subarea are sent to the agricultural production resource database.
The regional area statistics module comprises a regional area acquisition unit and a coordinate position dividing unit;
the regional area acquisition unit is used for acquiring and counting the areas of the planting subregions of various crops;
the coordinate position dividing unit is used for acquiring the position coordinates of the boundaries of the crop planting subregions by utilizing the GPS acre measuring instrument for the divided crop planting subregions.
The agricultural production resource database is used for receiving the serial numbers of all crop planting subareas and the crop types corresponding to all the planting subareas sent by the planting area dividing module, receiving the position coordinates of all the crop planting subarea boundaries sent by the area counting module, storing the serial numbers, the crop types, the position coordinates of the planting subarea boundaries and the historical production statistics of the crops, and storing all the appearance feature vectors and the corresponding feature components of different types of crops.
The planting density analysis module comprises a data analysis unit and a feedback unit;
the data analysis unit is used for carrying out statistical analysis on the total number of crop plants in each crop planting subregion and the plant planting density of each crop planting subregion;
and the feedback unit is used for analyzing according to the historical production statistics condition to obtain planting density suggestions. The agricultural production information digital management method based on the satellite remote sensing image comprises the following steps:
s1, acquiring an image of a crop planting area, and performing contrast improvement, noise filtering and high-definition filtering treatment on the received image of the crop planting area;
s2, extracting crop characteristics according to the crop planting area image, distinguishing crop types, dividing the crop planting area image into various crop planting subareas, and numbering;
s3, acquiring position coordinates of boundaries of all crop planting subregions by using a GPS (global positioning system) acre measuring instrument, and determining the area of each crop planting subregion;
s4, counting the total number of crop plants in each crop planting subregion, and counting the plant planting density, the crop growth condition and the yield of each crop planting subregion;
and S5, analyzing according to historical production statistics to obtain planting density suggestions.
In S2, extracting crop features in the crop planting area image, extracting crop appearance feature vectors in the crop mixed area image, classifying the crop appearance feature vectors, wherein the appearance feature vectors comprise roots, stems, leaves, flowers and fruits, further extracting feature components of various feature vectors, wherein the feature components comprise colors and shapes, comparing the feature components corresponding to the extracted crop appearance feature vectors with the appearance components corresponding to the various appearance feature vectors of various crops in the agricultural production resource database one by one, counting the similarity of the feature components corresponding to the various appearance feature vectors of various crops stored in the agricultural production resource database, comparing the similarity of the feature components corresponding to the counted various appearance feature vectors with a preset similarity threshold, and outputting the crop with the largest similarity as the crop type when the similarity of each feature component corresponding to each appearance feature vector is larger than the set similarity threshold.
In S3, a GPS positioning system is utilized to acquire the position coordinates of the boundary of the crop planting subregion, longitude and latitude coordinates of the boundary end point of the acquired crop sub-planting region are converted into plane coordinates, the longitude direction is taken as a Y axis, the latitude direction is taken as an X axis, and the formula for converting the longitude and latitude coordinates of the boundary end point j of the crop planting subregion at any point into the plane coordinates is as follows:
wherein X is j ，Y j The plane coordinate of the point j, R is the earth radius, L j 、B j The longitude and latitude of the point j are respectively, the crop planting subregion is approximately an irregular polygon, and the crop planting subregion is provided with a crop planting subregionEach end point of the boundary of the region is connected with the origin of coordinates, each side of the crop planting subregion and the origin form a triangle, so that the irregular polygon is divided into a plurality of triangles, the area of the triangle corresponding to each side is calculated, and the areas of all the triangles which are divided are summed to obtain the area of the crop planting subregion.
In S4, counting the total number of crop plants in each crop planting subregion, and calculating the total number of crop plants Q, wherein the formula is as follows:
wherein L is the distance between the extracted single plant and the single plant in the crop planting subregion, H is the distance between the extracted unit plant planting row and the unit plant planting row in the crop planting subregion, S is the area of the crop planting subregion, the plant planting density of each crop planting subregion is ρ, and the formula is:
q is expressed as the total number of each crop plant in each crop planting subregion, S is expressed as the area of each crop planting subregion, and trend checking is carried out according to the historical production statistical yield condition and the plant planting density, so that the planting density suggestion is obtained.
Embodiment one: the method comprises the steps of collecting images of crop planting areas, carrying out contrast improvement, noise filtering and high-definition filtering treatment on the received crop planting area images, amplifying the crop planting area images, carrying out crop feature extraction on the amplified crop planting area images, identifying crop types, dividing the crop planting area images into crop planting subareas according to different appearance features of different crops, respectively corresponding to the crop types, numbering the crop planting subareas in sequence, acquiring position coordinates of boundaries of the crop planting subareas by utilizing a GPS acreage measuring instrument, further determining areas of the crop planting subareas, receiving plant total number of the crop planting subareas sent by an area statistics module, further counting plant planting density of the crop planting subareas, and finally analyzing according to historical production statistics conditions to obtain planting density suggestions.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. Agricultural production information digital management system based on satellite remote sensing image, its characterized in that: the system comprises: the system comprises an image preprocessing module, a planting area dividing module, an area statistics module, an agricultural production resource database and a planting density analysis module;
the image preprocessing module is used for collecting images of crop planting areas and carrying out contrast improvement, noise filtering and high-definition filtering processing on the received images of the crop planting areas;
the crop planting area dividing module is used for amplifying the crop planting area image, extracting crop characteristics of the amplified crop planting area image, identifying crop types, dividing the crop planting area image into crop planting subareas according to different appearance characteristics of different crops, respectively corresponding to the crop types, and numbering the crop planting subareas in sequence;
the regional area statistics module acquires the position coordinates of the boundaries of the crop planting subregions by using a GPS (global positioning system) acre measuring instrument for the divided crop planting subregions, so as to determine the areas of the crop planting subregions;
the agricultural production resource database is used for receiving the serial numbers of all crop planting subregions and the crop types corresponding to all the planting subregions sent by the planting region dividing module, receiving and storing the position coordinates of all the crop planting subregion boundaries sent by the region area counting module to form all the crop planting subregion information sets, wherein each crop planting subregion information set comprises all the serial numbers of all the crop planting subregions, the crop types, the position coordinates of the planting subregion boundaries and the historical production statistics of the crops, and simultaneously storing all the appearance feature vectors and the corresponding feature components of different types of crops;
the planting density analysis module is used for receiving the areas of the crop planting subregions sent by the regional area statistics module, counting the total number of crop plants in each crop planting subregion, further counting the plant planting density of each crop planting subregion, and finally analyzing according to the historical production statistics condition to obtain a planting density suggestion;
the output end of the image preprocessing is connected with the input end of the planting area dividing module; the output end of the planting area dividing module is connected with the input end of the area counting module; the output end of the regional area statistics module is connected with the input end of the agricultural production resource database; the output end of the agricultural production resource database is connected with the input end of the planting density analysis module.
2. The satellite remote sensing image-based agricultural production information digital management system according to claim 1, wherein: the image preprocessing module comprises an area image acquisition unit and an image processing unit;
the regional image acquisition unit is used for acquiring images of crop planting regions by using satellite remote sensing images and sending the acquired images to the image processing unit;
the image processing unit is used for receiving the crop planting area image sent by the area image acquisition unit, performing contrast improvement, noise filtering and high-definition filtering processing on the received crop planting area image to obtain a preprocessed image, and sending the preprocessed image to the planting area dividing module.
3. The satellite remote sensing image-based agricultural production information digital management system according to claim 1, wherein: the planting area dividing module comprises a species characteristic extracting unit and a crop area processing unit;
the crop species characteristic extraction unit is used for amplifying the received crop planting area image, extracting crop characteristics from the amplified crop planting area image and identifying crop species;
the crop area processing unit is used for extracting boundary lines of different types of crop planting areas through area scanning according to different appearance characteristics of different crops, the boundary lines of the different types of crops divide a crop planting area image into crop planting subareas, each crop planting subarea corresponds to each crop type respectively, each crop planting subarea is numbered according to a set sequence, the number set is {1,2,3,.. N }, and each crop planting subarea number and each crop type corresponding to each planting subarea are sent to the agricultural production resource database.
4. The satellite remote sensing image-based agricultural production information digital management system according to claim 1, wherein: the regional area statistics module comprises a regional area acquisition unit and a coordinate position dividing unit;
the regional area acquisition unit is used for acquiring and counting the areas of the planting subregions of various crops;
the coordinate position dividing unit is used for acquiring the position coordinates of the boundaries of the crop planting subregions by utilizing the GPS acre measuring instrument for the divided crop planting subregions.
5. The satellite remote sensing image-based agricultural production information digital management system according to claim 1, wherein: the agricultural production resource database is used for receiving the serial numbers of all crop planting subareas and the crop types corresponding to all the planting subareas sent by the planting area dividing module, receiving the position coordinates of all the crop planting subarea boundaries sent by the area counting module, storing the serial numbers, the crop types, the position coordinates of the planting subarea boundaries and the historical production statistics of the crops, and storing all the appearance feature vectors and the corresponding feature components of different types of crops.
6. The satellite remote sensing image-based agricultural production information digital management system according to claim 1, wherein: the planting density analysis module comprises a data analysis unit and a feedback unit;
the data analysis unit is used for carrying out statistical analysis on the total number of crop plants in each crop planting subregion and the plant planting density of each crop planting subregion;
and the feedback unit is used for analyzing according to the historical production statistics condition to obtain planting density suggestions.
7. The agricultural production information digital management method based on the satellite remote sensing image is characterized by comprising the following steps of: the method comprises the following steps:
s1, acquiring an image of a crop planting area, and performing contrast improvement, noise filtering and high-definition filtering treatment on the received image of the crop planting area;
s2, extracting crop characteristics according to the crop planting area image, distinguishing crop types, dividing the crop planting area image into various crop planting subareas, and numbering;
s3, acquiring position coordinates of boundaries of all crop planting subregions by using a GPS (global positioning system) acre measuring instrument, and determining the area of each crop planting subregion;
s4, counting the total number of crop plants in each crop planting subregion, and counting the plant planting density, the crop growth condition and the yield of each crop planting subregion;
and S5, analyzing according to historical production statistics to obtain planting density suggestions.
8. The method for digitally managing agricultural production information based on satellite remote sensing images according to claim 7, wherein the method comprises the steps of: in S2, extracting crop features in the crop planting area image, extracting crop appearance feature vectors in the crop mixed area image, classifying the crop appearance feature vectors, wherein the appearance feature vectors comprise roots, stems, leaves, flowers and fruits, further extracting feature components of various feature vectors, wherein the feature components comprise colors and shapes, comparing the feature components corresponding to the extracted crop appearance feature vectors with the appearance components corresponding to the various appearance feature vectors of various crops in the agricultural production resource database one by one, counting the similarity of the feature components corresponding to the various appearance feature vectors of various crops stored in the agricultural production resource database, comparing the similarity of the feature components corresponding to the counted various appearance feature vectors with a preset similarity threshold, and outputting the crop with the largest similarity as the crop type when the similarity of each feature component corresponding to each appearance feature vector is larger than the set similarity threshold.
9. The method for digitally managing agricultural production information based on satellite remote sensing images according to claim 7, wherein the method comprises the steps of: in S3, a GPS positioning system is utilized to acquire the position coordinates of the boundary of the crop planting subregion, longitude and latitude coordinates of the boundary end point of the acquired crop sub-planting region are converted into plane coordinates, the longitude direction is taken as a Y axis, the latitude direction is taken as an X axis, and the formula for converting the longitude and latitude coordinates of the boundary end point j of the crop planting subregion at any point into the plane coordinates is as follows:
wherein X is j ，Y j The plane coordinate of the point j, R is the earth radius, L j 、B j The longitude and the latitude of the point j are respectively, the crop planting subregion is approximately an irregular polygon, each end point of the boundary of the crop planting subregion is connected with the origin of coordinates, each side of the crop planting subregion and the origin form a triangle, so that the irregular polygon is divided into a plurality of triangles, the area of the triangle corresponding to each side is calculated, and the areas of all the triangles which are divided are summed to obtain the area of the crop planting subregion.
10. The method for digitally managing agricultural production information based on satellite remote sensing images according to claim 7, wherein the method comprises the steps of: in S4, counting the total number of crop plants in each crop planting subregion, and calculating the total number of crop plants Q, wherein the formula is as follows:
wherein L is the distance between the extracted single plant and the single plant in the crop planting subregion, H is the distance between the extracted unit plant planting row and the unit plant planting row in the crop planting subregion, S is the area of the crop planting subregion, the plant planting density of each crop planting subregion is ρ, and the formula is:
q is expressed as the total number of each crop plant in each crop planting subregion, S is expressed as the area of each crop planting subregion, and trend checking is carried out according to the historical production statistical yield condition and the plant planting density, so that the planting density suggestion is obtained.
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