CN115511158A - Big data-based intelligent crop breeding analysis method and system - Google Patents
Big data-based intelligent crop breeding analysis method and system Download PDFInfo
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Abstract
The invention discloses a crop intelligent breeding analysis method and system based on big data, which obtains the pest situation of the current farmland area by obtaining the historical image data of the current farmland area and comparing and analyzing the historical image data and the big data pest data, and carries out primary screening on crops, and further carries out secondary screening on the crops by analyzing the crop morbidity situation of the farmland area to obtain the proper preferred crop species. In addition, the invention obtains the scheme information of the optimized crops by matching and combining the optimized crop types and various irrigation technologies, thereby leading a grower to better select and cultivate the crops, reducing the condition of low yield of the crops caused by blindly selecting the crops and further realizing the improvement of the economic benefit of the grower. Meanwhile, the invention scientifically provides accurate crop species suitable for the current farmland environment for growers, and improves the production efficiency of crops.
Description
Technical Field
The invention relates to the field of big data analysis, in particular to a big data-based intelligent crop breeding analysis method and system.
Background
The planting selection of the grain crops relates to the grain safety of countries and regions and the dietary abundance of people, and is also an important way for increasing the yield and income of farmers.
However, when some farmlands select crops for cultivation and production, some unsuitable crops are selected for planting blindly only through simple market consideration, so that the conditions of unfavorable growth of diseases and insect pests and the like of the crops occur, and the economic loss of growers is increased. Therefore, an effective method for intelligently analyzing suitable crops according to the actual environment of the farmland is urgently needed.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides an intelligent crop breeding analysis based on big data.
The invention provides a big data-based intelligent crop breeding analysis method, which comprises the following steps:
according to the scheme, historical image data of a farmland area are obtained, and comparative analysis is carried out on the historical image data and big data pest data to obtain farmland pest data;
screening crops according to farmland pest data to obtain first result crop information;
acquiring crop morbidity information of a specific farmland area, and combining the first result crop information to obtain second result crop information according to the morbidity information;
obtaining farmland region environment data, and performing comprehensive environment analysis according to the farmland region environment data to obtain farmland irrigation information;
and performing combined matching analysis according to the farmland irrigation information and the second result crop information to obtain the optimal crop scheme information.
In this scheme, the regional historical image data of farmland is obtained, and contrastive analysis is carried out with big data pest data according to historical image data, obtains farmland pest data, includes before:
according to the region of the farmland, obtaining farmland pest image data from the big data;
performing image characteristic analysis on the farmland pest image data to obtain farmland pest characteristic data;
performing data association on farmland pest category information and farmland pest characteristic data to obtain associated data;
and performing data integration on the farmland pest characteristic data and the associated data to obtain big data pest data.
In this scheme, the regional historical image data of farmland is obtained, and contrastive analysis is carried out with big data pest data according to historical image data, obtains farmland pest data, specifically does:
pest image recognition and image area segmentation are carried out according to historical image data to obtain pest area image data;
performing feature extraction on the pest region image data to obtain historical pest feature data;
comparing and analyzing historical pest characteristic data and big data pest data to obtain farmland pest species information and farmland pest quantity information;
and performing data integration on the farmland pest species information and the farmland pest quantity information to obtain farmland pest data.
In this scheme, carry out crops screening according to farmland pest information, obtain first result crops information, specifically do:
according to the region range of the farmland area, all crop types meeting the conditions are searched from the big data, and information of crops to be selected is obtained;
obtaining corresponding crop insect resistance information according to the information of the crop to be selected;
and comparing and analyzing the pest resistance information of the crops and the farmland pest information, and screening the crops meeting preset conditions from the crop information to be selected to obtain first result crop information.
In this scheme, the obtaining of the crop disease condition information of the specific farmland area and the obtaining of the second result crop information by combining the first result crop information according to the disease condition information comprise:
acquiring crop disease condition information in a specific farmland area according to preset time;
obtaining crop disease type information and crop disease frequency information according to the crop disease condition information;
according to the crop disease frequency information, three crop disease types with the highest crop disease frequency are obtained to obtain the specific crop disease type information.
In this scheme, the obtaining of the crop disease condition information in the specific farmland area and the obtaining of the second result crop information by combining the first result crop information according to the disease condition information specifically include:
generating a crop type label according to the first result crop information;
searching disease resistance information corresponding to the crop species from the big data according to the crop species label to obtain the crop disease resistance information;
and analyzing dominant species according to the disease resistance information of the crops and the disease type information of the specific crops, and screening preferred crop species from the first result crop information to obtain second result crop information.
In this scheme, acquire regional environmental data of farmland, carry out environment integrated analysis according to regional environmental data of farmland, obtain the farmland irrigation information, specifically do:
obtaining farmland region size data and farmland water source point data;
performing irrigation point analysis according to the farmland region size data and farmland water source point data to obtain farmland water source point density information, farmland water source point distance information and farmland soil spraying area information;
and analyzing irrigation conditions by combining farmland region environmental data according to farmland water source point density information, farmland water source point distance information and farmland soil spraying area information to obtain farmland irrigation information.
In this scheme, the crop information is combined, matched and analyzed according to the farm irrigation information and the second result to obtain the preferred crop scheme information, which specifically comprises:
acquiring irrigation mode information in farmland irrigation information;
acquiring crop type information in the second result crop information;
matching and combining the crop type information and the irrigation mode information to obtain combined information of various crops and irrigation;
and (4) performing information arrangement on the combined information of the various crops and irrigation to obtain the information of the optimal crop scheme.
The second aspect of the invention also provides a crop intelligent breeding analysis system based on big data, which comprises: the device comprises a memory and a processor, wherein the memory comprises a crop intelligent breeding analysis method program based on big data, and when the crop intelligent breeding analysis method program based on big data is executed by the processor, the following steps are realized:
obtaining historical image data of a farmland area, and performing comparative analysis on the historical image data and big data pest data to obtain farmland pest data;
screening crops according to farmland pest data to obtain first result crop information;
acquiring crop morbidity information of a specific farmland area, and combining the first result crop information to obtain second result crop information according to the morbidity information;
obtaining farmland region environment data, and performing comprehensive environment analysis according to the farmland region environment data to obtain farmland irrigation information;
and performing combined matching analysis according to the farmland irrigation information and the second result crop information to obtain the preferred crop scheme information.
In this scheme, the crop information is combined, matched and analyzed according to the farm irrigation information and the second result to obtain the preferred crop scheme information, which specifically comprises:
acquiring irrigation mode information in farmland irrigation information;
acquiring crop type information in the second result crop information;
matching and combining the crop type information and the irrigation mode information to obtain combined information of various crops and irrigation;
and (4) performing information arrangement on the combined information of the various crops and irrigation to obtain the information of the optimal crop scheme.
The invention discloses a crop intelligent breeding analysis method and system based on big data, which obtains the pest situation of the current farmland area by obtaining the historical image data of the current farmland area and comparing and analyzing the historical image data and the big data pest data, and carries out primary screening on crops, and further carries out secondary screening on the crops by analyzing the crop morbidity situation of the farmland area to obtain the proper preferred crop species. In addition, the invention obtains the scheme information of the optimized crops by matching and combining the optimized crop types and various irrigation technologies, thereby leading a grower to better select and cultivate the crops, reducing the condition of low yield of the crops caused by blindly selecting the crops and further realizing the improvement of the economic benefit of the grower. Meanwhile, the invention scientifically provides accurate crop species suitable for the current farmland environment for growers, and improves the production efficiency of crops.
Drawings
FIG. 1 is a flow chart of an intelligent crop breeding analysis method based on big data according to the present invention;
FIG. 2 shows a flow chart of the present invention for obtaining farmland pest data;
FIG. 3 is a flow chart illustrating the process of obtaining first result crop information according to the present invention;
FIG. 4 shows a block diagram of an intelligent crop breeding analysis system based on big data according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
FIG. 1 shows a flow chart of an intelligent crop breeding analysis method based on big data.
As shown in fig. 1, the first aspect of the present invention provides a big data-based intelligent crop breeding analysis method, which includes:
s102, obtaining historical image data of a farmland area, and performing comparative analysis on the historical image data and big data pest data to obtain farmland pest data;
s104, screening crops according to the farmland pest data to obtain first result crop information;
s106, acquiring crop morbidity condition information of a specific farmland area, and combining the first result crop information to obtain second result crop information according to the morbidity condition information;
s108, obtaining farmland region environment data, and performing comprehensive environment analysis according to the farmland region environment data to obtain farmland irrigation information;
and S110, performing combined matching analysis according to the farmland irrigation information and the second result crop information to obtain the preferred crop scheme information.
According to the embodiment of the invention, the obtaining of the historical image data of the farmland area and the comparative analysis of the historical image data and the big data pest data to obtain the farmland pest data comprises the following steps:
according to the region of the farmland, obtaining farmland pest image data from the big data;
performing image characteristic analysis on the farmland pest image data to obtain farmland pest characteristic data;
performing data association on farmland pest category information and farmland pest characteristic data to obtain associated data;
and performing data integration on the farmland pest characteristic data and the associated data to obtain big data pest data.
It should be noted that, because the conditions of climate and environment are different in different regions, the conditions of the pests in the corresponding farmland are different, so that the analysis of the pest information in the local region has a better reference value. According to the region of the farmland, obtaining farmland pest image data from the big data, specifically obtaining pest image data of the local region of the farmland from the big data, wherein the image data has a good reference effect. The pests comprise harmful insects such as diamondback moths, noctuids, cabbage aphids, flea beetles and the like.
FIG. 2 shows a flow chart for obtaining farmland pest data according to the present invention.
According to the embodiment of the invention, the obtaining of the historical image data of the farmland area and the comparative analysis of the historical image data and the big data pest data to obtain the farmland pest data specifically comprise:
s202, pest image recognition and image area segmentation are carried out according to historical image data to obtain pest area image data;
s204, performing feature extraction on the pest region image data to obtain historical pest feature data;
s206, comparing and analyzing the historical pest characteristic data and the big data pest data to obtain farmland pest species information and farmland pest quantity information;
and S208, performing data integration on the farmland pest species information and the farmland pest quantity information to obtain farmland pest data.
It should be noted that the historical image data of the farmland area is acquired by an image acquisition camera device. The method comprises the steps of comparing and analyzing historical pest feature data and big data pest data to obtain farmland pest species information and farmland pest quantity information, specifically, performing feature comparison on the farmland pest feature data in the historical pest feature data and the big data pest data to screen out farmland pest species information with high feature comparison similarity, and obtaining quantity information of corresponding pests according to historical image data. And through analyzing historical image data, can obtain the accurate pest data in current farmland region to analyze suitable planting crops more effectively.
FIG. 3 is a flow chart illustrating the process of obtaining first result crop information according to the present invention.
According to the embodiment of the invention, the crop screening is carried out according to the farmland pest information to obtain the first result crop information, which specifically comprises the following steps:
s302, searching all crop types meeting the conditions from the big data according to the region range of the farmland region to obtain the information of the crop to be selected;
s304, obtaining corresponding crop insect resistance information according to the information of the crop to be selected;
s306, comparing and analyzing the crop pest resistance information and the farmland pest information, and screening out crops meeting preset conditions from the crop information to be selected to obtain first result crop information.
It should be noted that, in the region range where the farmland region is located, the region range refers to a region range near the current farmland region, and the region range and the farmland region have similar climatic environment conditions. And searching all crop types meeting the conditions from the big data to obtain the crop information to be selected, wherein the crop types included in the crop information to be selected are all crop types meeting the climate conditions of the farmland region. The crops that accord with the preset condition specifically for this crops can have certain insect resistance to the pest kind in the farmland pest information.
According to the embodiment of the invention, the obtaining of the crop disease condition information of the specific farmland area and the obtaining of the second result crop information by combining the first result crop information according to the disease condition information comprise:
acquiring crop disease condition information in a specific farmland area according to preset time;
according to the crop disease condition information, crop disease category information and crop disease frequency information are obtained;
according to the crop disease frequency information, three crop disease types with the highest crop disease frequency are obtained to obtain the specific crop disease type information.
It should be noted that the preset time is generally 1 to 3 years before, and the obtaining of the crop disease occurrence information in the specific farmland area is specifically to obtain the crop disease occurrence information in the area where the current farmland area is located. The disease category information of crops comprises gray mold, wheat stripe rust, powdery mildew, root rot, damping off, downy mildew and the like.
According to the embodiment of the invention, the obtaining of the crop disease condition information of the specific farmland area and the obtaining of the second result crop information by combining the first result crop information according to the disease condition information specifically comprise:
generating a crop type label according to the first result crop information;
searching disease resistance information corresponding to the crop species from the big data according to the crop species label to obtain the crop disease resistance information;
and analyzing dominant species according to the disease resistance information of the crops and the disease type information of the specific crops, and screening preferred crop species from the first result crop information to obtain second result crop information.
In the above-described method, the preferred crop type is selected from the first resultant crop information, and the crop type included in the second resultant crop information obtained by obtaining the second resultant crop information is a crop type having disease resistance corresponding to the disease type of the specific crop.
According to the embodiment of the invention, the obtaining of the farmland regional environment data and the comprehensive environmental analysis according to the farmland regional environment data to obtain the farmland irrigation information specifically comprise:
obtaining farmland region size data and farmland water source point data;
carrying out irrigation point analysis according to the farmland region size data and the farmland water source point data to obtain farmland water source point density information, farmland water source point distance information and farmland soil spraying area information;
and analyzing irrigation conditions by combining farmland region environmental data according to farmland water source point density information, farmland water source point distance information and farmland soil spraying area information to obtain farmland irrigation information.
It should be noted that the farmland region size data includes actual farmland area size and farmland planting soil area size, the farmland water source point data includes specific position information of farmland water source points in the farmland region, and the farmland water source point distance information includes shortest distances from the water source points to the farmland. The farmland irrigation information comprises various irrigation modes which accord with farmland conditions, and the irrigation modes comprise channel irrigation, pipeline irrigation, well irrigation and the like. The farmland regional environment data comprises environment data such as illumination time, air temperature, humidity and the like of a farmland.
According to the embodiment of the invention, the combined matching analysis is performed according to the farmland irrigation information and the second result crop information to obtain the preferred crop scheme information, and the method specifically comprises the following steps:
acquiring irrigation mode information in farmland irrigation information;
acquiring crop type information in the second result crop information;
matching and combining the crop type information and the irrigation mode information to obtain combined information of various crops and irrigation;
and (4) performing information arrangement on the combined information of the various crops and irrigation to obtain the information of the optimal crop scheme.
The preferred crop plan information includes preferred crop type information, crop irrigation method information, crop-corresponding biological characteristics, and the like.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring preferred crop type information according to the preferred crop scheme information;
generating a preferred crop label according to the preferred crop type information;
and searching the growth data from the big data according to the preferred crop label to obtain preferred crop growth process data.
It should be noted that the growth process data includes growth state information of each growth cycle of the preferred crop and environmental data suitable for growth.
According to the embodiment of the invention, the method further comprises the following steps:
constructing a crop growth prediction model;
obtaining the information of the types of the preferred crops and the corresponding crop irrigation mode information according to the information of the scheme of the preferred crops;
obtaining current farmland region environment data, and importing the farmland region environment data, preferred crop type information, crop irrigation mode information, farmland region environment data and preferred crop growth process data into a crop growth prediction model for growth prediction to obtain crop growth state prediction information;
and comparing and analyzing the growth state prediction information and the preset state information to obtain crop planting scheme correction information.
It should be noted that the preset state information is the preferable state information of the normal growth of the crops, and has good reference contrast.
It should be noted that when the deviation between the predicted growth state information and the preset state information is too large, the planting conditions of the crops need to be corrected appropriately to ensure the normal growth of the crops. The crop planting scheme correction information comprises crop fertilizer application correction information and irrigation moisture correction information.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring information of a preferable crop scheme;
carrying out crop production and planting according to the information of the optimized crop scheme, and acquiring a plurality of farmland soil humidity data according to a preset time interval;
carrying out numerical value equalization processing on the plurality of farmland soil humidity data to obtain average soil humidity data;
acquiring preferred crop type information in the preferred crop scheme information;
obtaining seed coating mode information of the corresponding crop to be selected according to the information of the preferred crop types;
and comprehensively analyzing the average soil humidity data and the seed coating mode information of the crops to be selected to obtain the preferred seed coating mode information.
It should be noted that, by wrapping the seed coating agent with various components on the surface of the seeds, crop coated seeds with certain insect damage prevention can be formed, and due to the factors such as the duration of illumination of farmland, climate environment and the like, in the crop irrigation process, the humidity of soil is also changed, and different soil humidities can influence the seed coating agent effect on the surface of the seeds. According to the invention, a proper coating mode is analyzed through average soil humidity data, so that the pest and disease prevention capability of crops can be improved. The coating methods include mechanical and manual coating methods. The predetermined time interval is typically 2 to 4 hours before and after irrigation.
FIG. 4 shows a block diagram of an intelligent crop breeding analysis system based on big data according to the present invention.
The second aspect of the present invention also provides a big data-based intelligent crop breeding and analyzing system 4, which comprises: a memory 41 and a processor 42, wherein the memory includes a big data-based intelligent crop breeding analysis method program, and when executed by the processor, the big data-based intelligent crop breeding analysis method program implements the following steps:
obtaining historical image data of a farmland area, and performing comparative analysis on the historical image data and big data pest data to obtain farmland pest data;
screening crops according to farmland pest data to obtain first result crop information;
acquiring crop morbidity information of a specific farmland area, and acquiring second result crop information by combining first result crop information according to the morbidity information;
obtaining farmland region environment data, and performing comprehensive environment analysis according to the farmland region environment data to obtain farmland irrigation information;
and performing combined matching analysis according to the farmland irrigation information and the second result crop information to obtain the preferred crop scheme information.
According to the embodiment of the invention, the obtaining of the historical image data of the farmland area and the comparative analysis of the historical image data and the big data pest data to obtain the farmland pest data comprises the following steps:
according to the region of the farmland, obtaining farmland pest image data from the big data;
performing image characteristic analysis on the farmland pest image data to obtain farmland pest characteristic data;
performing data association on the farmland pest category information and farmland pest characteristic data to obtain associated data;
and performing data integration on the farmland pest characteristic data and the associated data to obtain big data pest data.
It should be noted that, because the conditions of climate and environment in different regions are different, and the corresponding pest conditions in farmland are different, the analysis of the pest information in farmland in local regions has a better reference value. According to the region of the farmland, obtaining farmland pest image data from the big data, specifically obtaining pest image data of the local region of the farmland from the big data, wherein the image data has a good reference effect. The pests comprise pests such as plutella xylostella, noctuids, cabbage aphids, flea beetles and the like.
According to the embodiment of the invention, the obtaining of the farmland region historical image data and the comparative analysis of the historical image data and the big data pest data to obtain the farmland pest data specifically comprise:
pest image recognition and image area segmentation are carried out according to historical image data to obtain pest area image data;
performing feature extraction on the pest region image data to obtain historical pest feature data;
comparing and analyzing the historical pest characteristic data and the big data pest data to obtain farmland pest species information and farmland pest quantity information;
and performing data integration on the farmland pest species information and the farmland pest quantity information to obtain farmland pest data.
It should be noted that the historical image data of the farmland area is acquired by an image acquisition camera device. The method comprises the steps of comparing and analyzing historical pest feature data and big data pest data to obtain farmland pest species information and farmland pest quantity information, specifically, performing feature comparison on the farmland pest feature data in the historical pest feature data and the big data pest data to screen out farmland pest species information with high feature comparison similarity, and obtaining quantity information of corresponding pests according to historical image data. And through analyzing historical image data, can obtain the accurate pest data in current farmland region to analyze suitable planting crops more effectively.
According to the embodiment of the invention, the crop screening is carried out according to the farmland pest information to obtain the first result crop information, which specifically comprises the following steps:
according to the region range of the farmland area, all crop types meeting the conditions are searched from the big data, and information of crops to be selected is obtained;
obtaining corresponding crop insect resistance information according to the information of the crop to be selected;
and comparing and analyzing the pest resistance information of the crops and the farmland pest information, and screening the crops meeting preset conditions from the crop information to be selected to obtain first result crop information.
It should be noted that, in the region range where the farmland region is located, the region range refers to a region range near the current farmland region, and the region range and the farmland region have similar climatic environment conditions. And searching all crop types meeting the conditions from the big data to obtain the crop information to be selected, wherein the crop types included in the crop information to be selected are all the crop types meeting the climate conditions of the farmland region. The crops that accord with the preset condition specifically for this crops can have certain insect resistance to the pest kind in the farmland pest information.
According to the embodiment of the invention, the obtaining of the crop disease condition information of the specific farmland area and the obtaining of the second result crop information by combining the first result crop information according to the disease condition information comprise the following steps:
acquiring crop disease condition information in a specific farmland area according to preset time;
obtaining crop disease type information and crop disease frequency information according to the crop disease condition information;
according to the crop disease frequency information, three crop disease types with the highest crop disease frequency are obtained to obtain the specific crop disease type information.
It should be noted that the preset time is generally 1 to 3 years before, and the obtaining of the crop disease occurrence information in the specific farmland area is specifically to obtain the crop disease occurrence information in the area where the current farmland area is located. The disease type information of the crops comprises gray mold, wheat stripe rust, powdery mildew, root rot, damping off, downy mildew and the like.
According to the embodiment of the invention, the obtaining of the crop disease condition information of the specific farmland area and the obtaining of the second result crop information by combining the first result crop information according to the disease condition information specifically comprise:
generating a crop type label according to the first result crop information;
searching disease resistance information corresponding to the crop species from the big data according to the crop species label to obtain the crop disease resistance information;
and analyzing dominant species according to the disease resistance information of the crops and the disease type information of the specific crops, and screening preferred crop species from the first result crop information to obtain second result crop information.
In the above-described method, the preferred crop type is selected from the first resultant crop information, and the crop type included in the second resultant crop information obtained by obtaining the second resultant crop information is a crop type having disease resistance corresponding to the disease type of the specific crop.
According to the embodiment of the invention, the obtaining of the farmland regional environment data and the comprehensive environmental analysis according to the farmland regional environment data to obtain the farmland irrigation information specifically comprise:
obtaining farmland region size data and farmland water source point data;
performing irrigation point analysis according to the farmland region size data and farmland water source point data to obtain farmland water source point density information, farmland water source point distance information and farmland soil spraying area information;
according to farmland water source point density information, farmland water source point distance information and farmland soil spraying area information, irrigation condition analysis is carried out by combining farmland regional environmental data, and farmland irrigation information is obtained.
It should be noted that the farmland region size data includes actual farmland area size and farmland planting soil area size, the farmland water source point data includes specific position information of farmland water source points in the farmland region, and the farmland water source point distance information includes shortest distances from the water source points to the farmland. The farmland irrigation information comprises various irrigation modes which accord with farmland conditions, and the irrigation modes comprise channel irrigation, pipeline irrigation, well irrigation and the like. The farmland regional environment data comprises environment data such as illumination time, air temperature, humidity and the like of a farmland.
According to the embodiment of the invention, the combined matching analysis is performed according to the farmland irrigation information and the second result crop information to obtain the preferred crop scheme information, and the method specifically comprises the following steps:
acquiring irrigation mode information in farmland irrigation information;
acquiring crop type information in the second result crop information;
matching and combining the crop type information and the irrigation mode information to obtain combined information of various crops and irrigation;
and (4) performing information arrangement on the combined information of the various crops and the irrigation to obtain the preferred crop scheme information.
The preferred crop plan information includes information such as preferred crop type information, crop irrigation method information, and crop-corresponding biological characteristics.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring information of the preferred crop species according to the information of the preferred crop scheme;
generating a preferred crop label according to the preferred crop type information;
and searching the growth data from the big data according to the preferred crop label to obtain the preferred crop growth process data.
It should be noted that the growth process data includes growth state information of each growth cycle of the preferred crop and environmental data suitable for growth.
According to the embodiment of the invention, the method further comprises the following steps:
constructing a crop growth prediction model;
obtaining the information of the preferred crop types and the corresponding crop irrigation mode information according to the information of the preferred crop scheme;
obtaining current farmland region environment data, and importing the farmland region environment data, preferred crop type information, crop irrigation mode information, farmland region environment data and preferred crop growth process data into a crop growth prediction model for growth prediction to obtain crop growth state prediction information;
and comparing and analyzing the growth state prediction information and the preset state information to obtain crop planting scheme correction information.
It should be noted that the preset state information is the preferable state information of the normal growth of the crops, and has good reference contrast.
It is worth mentioning that when the deviation between the predicted growth state information and the preset state information is too large, the planting conditions of the crops need to be properly corrected to ensure the normal growth of the crops. The crop planting scheme correction information comprises crop fertilizer application correction information and irrigation moisture correction information.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring information of a preferable crop scheme;
carrying out crop production and planting according to the information of the optimized crop scheme, and acquiring a plurality of farmland soil humidity data according to a preset time interval;
carrying out numerical value equalization processing on the soil humidity data of the multiple farmlands to obtain average soil humidity data;
acquiring preferred crop type information in the preferred crop scheme information;
obtaining seed coating mode information of the corresponding crop to be selected according to the information of the preferred crop types;
and comprehensively analyzing the average soil humidity data and the seed coating mode information of the crops to be selected to obtain the preferred seed coating mode information.
It should be noted that, by wrapping the seed coating agent with various components on the surface of the seeds, crop coated seeds with certain insect damage prevention can be formed, and due to the factors such as the duration of illumination of farmland, climate environment and the like, in the crop irrigation process, the humidity of soil is also changed, and different soil humidities can influence the seed coating agent effect on the surface of the seeds. According to the invention, a proper coating mode is analyzed through average soil humidity data, so that the pest and disease prevention capability of crops can be improved. The coating methods include mechanical and manual coating methods. The predetermined time interval is typically 2 to 4 hours before and after irrigation.
The invention discloses a crop intelligent breeding analysis method and system based on big data, which obtains the pest situation of the current farmland area by obtaining the historical image data of the current farmland area and comparing and analyzing the historical image data and the big data pest data, and carries out primary screening on crops, and further carries out secondary screening on the crops by analyzing the crop morbidity situation of the farmland area to obtain the proper preferred crop species. In addition, the invention obtains the scheme information of the optimized crops by matching and combining the optimized crop types and various irrigation technologies, thereby leading a grower to better select and cultivate the crops, reducing the condition of low yield of the crops caused by blindly selecting the crops and further realizing the improvement of the economic benefit of the grower. Meanwhile, the invention scientifically provides accurate crop species suitable for the current farmland environment for growers, and improves the production efficiency of crops.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The intelligent crop breeding analysis method based on the big data is characterized by comprising the following steps:
obtaining historical image data of a farmland area, and performing comparative analysis on the historical image data and big data pest data to obtain farmland pest data;
screening crops according to farmland pest data to obtain first result crop information;
acquiring crop morbidity information of a specific farmland area, and combining the first result crop information to obtain second result crop information according to the morbidity information;
obtaining farmland region environment data, and performing comprehensive environment analysis according to the farmland region environment data to obtain farmland irrigation information;
and performing combined matching analysis according to the farmland irrigation information and the second result crop information to obtain the preferred crop scheme information.
2. The intelligent crop breeding analysis method based on big data as claimed in claim 1, wherein the obtaining of the historical image data of the farmland area and the comparative analysis of the historical image data and the big data pest data to obtain the farmland pest data comprises:
according to the region of the farmland, obtaining farmland pest image data from the big data;
performing image characteristic analysis on the farmland pest image data to obtain farmland pest characteristic data;
performing data association on the farmland pest category information and farmland pest characteristic data to obtain associated data;
and performing data integration on the farmland pest characteristic data and the associated data to obtain big data pest data.
3. The intelligent crop breeding analysis method based on big data as claimed in claim 2, wherein the historical image data of the farmland area is obtained, and comparative analysis is performed according to the historical image data and the big data pest data to obtain farmland pest data, and the method specifically comprises the following steps:
pest image recognition and image area segmentation are carried out according to historical image data to obtain pest area image data;
performing feature extraction on the pest region image data to obtain historical pest feature data;
comparing and analyzing historical pest characteristic data and big data pest data to obtain farmland pest species information and farmland pest quantity information;
and performing data integration on the farmland pest species information and the farmland pest quantity information to obtain farmland pest data.
4. The intelligent crop breeding analysis method based on big data as claimed in claim 3, wherein the crop screening is performed according to farmland pest information to obtain first result crop information, specifically:
according to the region range of the farmland area, all crop types meeting the conditions are searched from the big data, and information of crops to be selected is obtained;
obtaining corresponding crop insect resistance information according to the information of the crop to be selected;
and comparing and analyzing the pest resistance information of the crops and the farmland pest information, and screening the crops meeting preset conditions from the crop information to be selected to obtain first result crop information.
5. The intelligent crop selective breeding analysis method based on big data as claimed in claim 1, wherein the obtaining of crop morbidity information in a specific farmland area, and the obtaining of second result crop information by combining the first result crop information according to the morbidity information, comprises:
acquiring crop disease condition information in a specific farmland area according to preset time;
obtaining crop disease type information and crop disease frequency information according to the crop disease condition information;
according to the crop disease frequency information, three crop disease types with the highest crop disease frequency are obtained to obtain the specific crop disease type information.
6. The intelligent crop selective breeding analysis method based on big data as claimed in claim 5, wherein the obtaining of the disease condition information of the crops in the specific farmland area is performed in combination with the first result crop information to obtain the second result crop information according to the disease condition information, and specifically comprises:
generating a crop type label according to the first result crop information;
searching disease resistance information corresponding to the crop species from the big data according to the crop species label to obtain the crop disease resistance information;
and analyzing dominant species according to the disease resistance information of the crops and the disease type information of the specific crops, and screening preferred crop species from the first result crop information to obtain second result crop information.
7. The intelligent crop breeding analysis method based on big data as claimed in claim 1, wherein the farmland region environment data is obtained, and the comprehensive environment analysis is performed according to the farmland region environment data to obtain farmland irrigation information, specifically:
obtaining farmland region size data and farmland water source point data;
carrying out irrigation point analysis according to the farmland region size data and the farmland water source point data to obtain farmland water source point density information, farmland water source point distance information and farmland soil spraying area information;
according to farmland water source point density information, farmland water source point distance information and farmland soil spraying area information, irrigation condition analysis is carried out by combining farmland regional environmental data, and farmland irrigation information is obtained.
8. The intelligent crop breeding analysis method based on big data as claimed in claim 1, wherein the combination matching analysis is performed according to the farm irrigation information and the second result crop information to obtain the preferred crop scheme information, and the specific steps are as follows:
acquiring irrigation mode information in farmland irrigation information;
acquiring crop type information in the second result crop information;
matching and combining the crop type information and the irrigation mode information to obtain combined information of various crops and irrigation;
and (4) performing information arrangement on the combined information of the various crops and irrigation to obtain the information of the optimal crop scheme.
9. The utility model provides a crops intelligence breeding analytic system based on big data which characterized in that, this system includes: the device comprises a memory and a processor, wherein the memory comprises a crop intelligent breeding analysis method program based on big data, and when the crop intelligent breeding analysis method program based on big data is executed by the processor, the following steps are realized:
obtaining historical image data of a farmland area, and performing comparative analysis on the historical image data and big data pest data to obtain farmland pest data;
screening crops according to farmland pest data to obtain first result crop information;
acquiring crop morbidity information of a specific farmland area, and acquiring second result crop information by combining first result crop information according to the morbidity information;
obtaining farmland region environment data, and performing comprehensive environment analysis according to the farmland region environment data to obtain farmland irrigation information;
and performing combined matching analysis according to the farmland irrigation information and the second result crop information to obtain the preferred crop scheme information.
10. The intelligent crop breeding analysis system based on big data as claimed in claim 9, wherein the combined matching analysis is performed according to the farm irrigation information and the second result crop information to obtain the preferred crop scheme information, specifically:
acquiring irrigation mode information in farmland irrigation information;
acquiring crop type information in the second result crop information;
matching and combining the crop type information and the irrigation mode information to obtain combined information of various crops and irrigation;
and (4) performing information arrangement on the combined information of the various crops and the irrigation to obtain the preferred crop scheme information.
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