CN111602545A - Plant protection operation planning method based on artificial intelligence - Google Patents
Plant protection operation planning method based on artificial intelligence Download PDFInfo
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- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 27
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Abstract
The invention discloses an artificial intelligence-based plant protection operation planning method, which relates to the technical field of artificial intelligence plant protection operation and comprises a large database and an image monitoring unit, wherein the large database is electrically output and connected with an intelligent diagnosis module through a lead, the intelligent diagnosis module is electrically output and connected with a scheme previewing module through a lead, the scheme previewing module comprises a success rate conversion unit, an error scheme collecting unit and a scheme distributing unit, and the success rate conversion unit is electrically output and connected with the error scheme collecting unit through a lead. The invention has the beneficial effects that: this sauce material canning equipment that can compress tightly filler through to the setting through big data integration unit, big data conversion unit and big data classification unit, can be for increasing necessary data in the big database for when any plant abnormal conditions appears, the data in the big database of accessible carries out necessary adaptation to abnormal conditions.
Description
Technical Field
The invention relates to the technical field of artificial intelligence plant protection operation, in particular to an artificial intelligence-based plant protection operation planning method.
Background
Each disease has different disease parts on crops, each insect has different insect states in different growth periods, and the insect shows different in different regions, so that one disease and insect pest are identified, a training set can be formed by two or three thousand photo data, thousands of common disease and insect pests on the crops at present need to be collected, a brain for identifying the disease and insect pest is constructed, disease causes of different crops are collected, and the disease causes are conveniently and quickly found out.
Plant protection operation planning method for artificial intelligence on market is in the use, do not possess mass data according to in the big database, carry out intelligence to the plant that needs plant protection and match, make cultivation personnel or peasant household, can't carry out the picture to the plant that needs to carry out plant protection very first time and upload, be unfavorable for the quick maintenance of plant protection, can't carry out automatic index to the different protection methods of plant, can't carry out the adaptation of effective scheme to the etiology of plant fast, and the result of use is reduced, for this reason, we propose a galvanized steel wire processing with have stop gear around a roll equipment
Disclosure of Invention
The invention aims to provide a plant protection operation planning method based on artificial intelligence, and solves the problems that in the use process of the plant protection operation planning method for artificial intelligence proposed in the background technology, intelligent matching is not carried out on plants needing plant protection according to mass data in a large database, so that cultivators or farmers cannot upload pictures of the plants needing plant protection in the first time, rapid maintenance of plant protection is not facilitated, automatic indexing cannot be carried out on different protection modes of the plants, effective scheme adaptation cannot be carried out on plant etiology rapidly, and the use effect is reduced.
In order to achieve the purpose, the invention provides the following technical scheme: a plant protection operation planning method based on artificial intelligence comprises a big database and an image monitoring unit, wherein the big database is electrically connected with an intelligent diagnosis module through a lead, the intelligent diagnosis module is electrically connected with a scheme preview module through a lead, the scheme preview module comprises a success rate conversion unit, an error scheme collection unit and a scheme distribution unit, the success rate conversion unit is electrically connected with the error scheme collection unit through a lead, the success rate conversion unit is electrically connected with the scheme distribution unit through a lead, the image acquisition module is electrically connected with a data analysis module through a lead, the big database is electrically connected with an intelligent conversion unit through a lead, the scheme distribution unit is electrically connected with an administration implementation unit through a lead, and the administration implementation unit is electrically connected with an administration detection unit through a lead, the image monitoring unit is electrically output and connected with the treatment implementation unit through a wire, the image monitoring unit is electrically output and connected with the timing uploading unit through a wire, the timing uploading unit is electrically output and connected with the abnormality analysis unit through a wire, and the abnormality analysis unit is electrically output and connected with the data uploading unit through a wire.
Optionally, the big database comprises a big data integration unit, a big data conversion unit and a big data classification unit, the big data integration unit is electrically output and connected with the big data conversion unit through a wire, and the big data conversion unit is electrically output and connected with the big data classification unit.
Optionally, the big data integration unit, the big data conversion unit and the big data classification unit are electrically connected in parallel through a wire, and the big data integration unit, the big data conversion unit and the big data classification unit are electrically connected in series with the intelligent diagnosis module through wires.
Optionally, the intelligent diagnosis module comprises a data matching unit, a scheme matching unit, a medication regulation and control unit and a scheme uploading unit, the data matching unit is electrically output and connected with the scheme matching unit through a wire, the scheme matching unit is electrically output and connected with the medication regulation and control unit through a wire, and the medication regulation and control unit is electrically output and connected with the scheme uploading unit through a wire.
Optionally, the data matching unit, the scheme matching unit, the medication regulation and control unit and the scheme uploading unit are electrically connected in parallel through a wire, and the data matching unit, the scheme matching unit, the medication regulation and control unit and the scheme uploading unit are electrically connected in series with the scheme preview module through a wire.
Optionally, the image acquisition module comprises a camera, an image processing unit and a digital-to-analog conversion unit, the camera is electrically output and connected with the image processing unit through a wire, and the image processing unit is electrically output and connected with the digital-to-analog conversion unit through a wire.
Optionally, the camera, the image processing unit and the digital-to-analog conversion unit are electrically connected in parallel through a wire, and the camera, the image processing unit and the digital-to-analog conversion unit are electrically connected in series with the data analysis module through wires.
Optionally, the data analysis module includes a data detection unit, a data analysis unit and a data preprocessing unit, the data detection unit is electrically connected to the data analysis unit through a wire, and the data analysis unit is electrically connected to the data preprocessing unit through a wire.
Optionally, the data detection unit, the data analysis unit and the data preprocessing unit are electrically connected in parallel through a wire, and the data detection unit, the data analysis unit and the data preprocessing unit are electrically connected in series with the intelligent diagnosis module through wires.
Optionally, the specific implementation steps are as follows:
s1, the large database is connected with the internet, a user downloads plant protection research schemes required to be used in the later period, various schemes are stored in the large data integration unit, the schemes are transmitted to the inside of the large data conversion unit through a wire to be converted in a centralized manner, and the schemes are transmitted to the inside of the large data classification unit through the wire to be classified in a unified manner, so that later-period query is facilitated;
s2, cultivating personnel or farmers can use the camera of the handheld terminal to shoot abnormal plants if the plants are found to be abnormal, the shot pictures are transmitted to the inside of the image processing unit, and after independent matting processing is carried out on abnormal areas of the plants in the slices, the processed pictures are transmitted to the digital-to-analog conversion unit to carry out data conversion on the pictures, so that the later-stage systems can conveniently recognize the pictures;
s3, enabling the digitized picture to enter a data analysis module, after uniformly detecting data by a data detection unit in the data analysis module, transmitting the detected data to the inside of a data analysis unit, analyzing the internal data by the data analysis unit, and after analyzing, sending useful information to a data preprocessing unit for necessary preprocessing, so that the data is converted into a document with an individual format, and the later-stage archiving is facilitated;
s4, after data preprocessing, the data are transmitted to the inside of the intelligent diagnosis module through a lead, a data matching unit in the intelligent diagnosis module can match data for abnormal phenomena in the file through data in a big database, after matching is completed, the data are transmitted to a scheme matching unit, the scheme matching unit transmits the data to the inside of a big data classification unit to perform matching operation of an effective treatment scheme, and after the matching of the effective scheme is completed, the data are transmitted to a big data conversion unit through a lead;
s5, converting the data, transmitting the converted data to an intelligent conversion unit, wherein the intelligent conversion unit can convert the effective scheme and the data scheme to be solved in a direct proportion mode, and then transmitting the converted data to the inside of a medicine use regulation and control unit, and the medicine use regulation and control unit can intelligently convert the medicine use measurement and range in the same proportion mode according to the size of the plant, the planting time and the land cost;
s6, after the effective scheme is converted, the converted effective scheme is transmitted to a scheme uploading unit, and the effective scheme is transmitted to a scheme previewing module for independent previewing;
s7, when the scheme is previewed, a success rate conversion unit in the scheme previewing module can convert the success feasibility rate of the scheme, if the success rate is lower than percent, the scheme is automatically conveyed into an error scheme collecting unit, the error scheme collecting unit conveys the error scheme into a scheme matching unit for next time, scheme matching is carried out again until the scheme with the success rate of percent is matched, if the success rate conversion unit converts the scheme into the power of more than percent, the scheme is regarded as an effective scheme, the effective scheme is conveyed into a scheme distribution unit, and the effective scheme is distributed into a management implementation unit through a scheme distribution unit, so that cultivators or farmers can conveniently carry out plant protection nursing;
s8, after plant nursing, the plant protection status can be reported through the treatment detection unit, when the treatment implementation unit is implemented, the image monitoring unit can be started to monitor the real-time treatment status of the plant in real time, the monitored picture is transmitted to the timing uploading unit, the timing uploading unit can shoot and upload the image in the monitoring picture in timing mode, the shot image is transmitted to the abnormity analysis unit to analyze the abnormity of the plant on the picture, the data is transmitted to the data uploading unit after analysis, the data is transmitted to the big database for storage through the data uploading unit, the data capacity in the big database is improved, the big database can conveniently and effectively analyze the abnormity status of various plants, and the use efficiency of the method is improved.
The invention provides a plant protection operation planning method based on artificial intelligence, which has the following beneficial effects:
1. according to the sauce canning and packaging equipment capable of compressing the filler, necessary data can be increased for the large database through the arrangement of the large data integration unit, the large data conversion unit and the large data classification unit, so that when any plant is in an abnormal condition, necessary adaptation can be performed on the abnormal condition through the data in the large database, the matching success rate of the data is improved, the use efficiency is indirectly improved, the data can be subjected to necessary classification, the running time during data matching is shortened, the waiting time of a user is reduced, and the utilization rate of a system is improved;
2. according to the sauce canning and packaging equipment capable of compressing the filler, abnormal plant photos can be compared with the same case according to data in a large database through the arrangement of the data matching unit, the scheme matching unit, the medicine application regulating and controlling unit and the scheme uploading unit, if the same case is matched, abnormal plants can be cured in the same mode according to a plant protection method of the same case, the plant cure success rate is improved, and the medicine application amount can be intelligently regulated and controlled according to the plant size, the plant growth environment and various factors in different cases according to the regulation and control of the medicine application regulating and controlling unit, so that the plant cure probability is further increased;
3. according to the sauce material canning and packaging equipment capable of compressing the filler, due to the arrangement of the camera, the image processing unit and the digital-to-analog conversion unit, a cultivation worker or a farmer can immediately take pictures of abnormal plants, the conditions of the abnormal plants are locked at the first time, the efficiency of later-stage system planning is improved, the cultivation worker or the farmer can upload the pictures of the abnormal plants at any time in hours in an acquisition mode of the image acquisition module, a solution is obtained as soon as possible, and the plant death probability is reduced as much as possible;
4. this can compress tightly sauce material canning equipment of filler, through the setting through data detection unit, data analysis unit and data preprocessing unit, can carry out necessary data processing to the plant picture of shooing, improve the treatment effect in system's later stage to can carry out the independent picture of scratching to the abnormal part of photo department and handle, enlarge abnormal part, reduce the system later stage because the picture is unclear and the misjudgement risk that leads to, improve the safety in utilization of system.
Drawings
FIG. 1 is a schematic overall flow chart of a plant protection operation planning method based on artificial intelligence according to the present invention;
FIG. 2 is a schematic diagram of the internal flow of a big database of the plant protection operation planning method based on artificial intelligence of the present invention;
FIG. 3 is a schematic view of an internal flow of an intelligent diagnosis module of the plant protection operation planning method based on artificial intelligence of the present invention;
FIG. 4 is a schematic view of an internal flow of an image acquisition module of the plant protection operation planning method based on artificial intelligence of the present invention;
fig. 5 is a schematic view of an internal flow of a data analysis module of the plant protection operation planning method based on artificial intelligence of the present invention.
In the figure: 1. a large database; 101. a big data integration unit; 102. a big data conversion unit; 103. a big data classification unit; 2. an intelligent diagnosis module; 201. a data matching unit; 202. a scheme matching unit; 203. a medication regulation and control unit; 204. a scheme uploading unit; 3. an image acquisition module; 301. a camera; 302. an image processing unit; 303. a digital-to-analog conversion unit; 4. a data analysis module; 401. a data detection unit; 402. a data analysis unit; 403. a data preprocessing unit; 5. an intelligent conversion unit; 6. a scheme rehearsal module; 601. a success rate conversion unit; 602. an error scenario collection unit; 603. a scheme allocation unit; 7. a treatment implementation unit; 8. a treatment detection unit; 9. an image monitoring unit; 10. a timing uploading unit; 11. an abnormality analysis unit; 12. and a data uploading unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1-5, the present invention provides a technical solution: a plant protection operation planning method based on artificial intelligence comprises a big database 1 and an image monitoring unit 9, wherein the big database 1 is electrically connected with an intelligent diagnosis module 2 through a lead, the intelligent diagnosis module 2 is electrically connected with a scheme preview module 6 through a lead, the big database 1 comprises a big data integration unit 101, a big data conversion unit 102 and a big data classification unit 103, the big data integration unit 101 is electrically connected with the big data conversion unit 102 through a lead, the big data conversion unit 102 is electrically connected with the big data classification unit 103, necessary data can be increased in the big database 1, when any plant is in an abnormal condition, necessary adaptation can be carried out on the abnormal condition through the data in the big database 1, the matching success rate of the data is improved, the use efficiency is indirectly improved, and the big data integration unit 101, The big data conversion unit 102 and the big data classification unit 103 are electrically connected in parallel through a wire, and the big data integration unit 101, the big data conversion unit 102 and the big data classification unit 103 are electrically connected in series with the intelligent diagnosis module 2 through wires, so that data can be classified as necessary, the running time of data matching is shortened, the waiting time of a user is reduced, and the utilization rate of the system is improved;
the intelligent diagnosis module 2 comprises a data matching unit 201, a scheme matching unit 202, a medication regulation and control unit 203 and a scheme uploading unit 204, wherein the data matching unit 201 is electrically connected with the scheme matching unit 202 in an output mode through a lead, the scheme matching unit 202 is electrically connected with the medication regulation and control unit 203 in an output mode through a lead, the medication regulation and control unit 203 is electrically connected with the scheme uploading unit 204 in an output mode through a lead, abnormal plant photos can be compared according to data in a large database 1, if the abnormal plant photos are matched with the same cases, abnormal plants can be cured in the same mode according to a plant protection method of the same cases, the plant cure success rate is improved, the data matching unit 201, the scheme matching unit 202, the medication regulation and control unit 203 and the scheme uploading unit 204 are electrically connected in parallel through leads, and the data matching unit 201, the scheme matching unit 202, the medication regulation and control, The scheme matching unit 202, the medication regulation and control unit 203 and the scheme uploading unit 204 are electrically connected in series with the scheme previewing module 6 through leads, so that the dosage of the plants can be intelligently regulated and controlled according to the regulation and control of the medication regulation and control unit 203, the plant size, the plant growth environment and various factors in different cases, and the cure rate of the plants is further increased;
the plan preview module 6 comprises a success rate conversion unit 601, an error plan collection unit 602 and a plan distribution unit 603, the success rate conversion unit 601 is electrically connected with the error plan collection unit 602 through a lead, the success rate conversion unit 601 is electrically connected with the plan distribution unit 603 through a lead, the image acquisition module 3 is electrically connected with the data analysis module 4 through a lead, the image acquisition module 3 comprises a camera 301, an image processing unit 302 and a digital-to-analog conversion unit 303, the camera 301 is electrically connected with the image processing unit 302 through a lead, the image processing unit 302 is electrically connected with the digital-to-analog conversion unit 303 through a lead, and by setting the camera 301, the image processing unit 302 and the digital-to-analog conversion unit 303, a grower or a farmer can immediately take pictures of abnormal plants, the situation of abnormal plants is locked at the first time, the efficiency of later-stage system planning is improved, the camera 301, the image processing unit 302 and the digital-to-analog conversion unit 303 are electrically connected in parallel through a lead, the camera 301, the image processing unit 302 and the digital-to-analog conversion unit 303 are electrically connected in series with the data analysis module 4 through leads, and by means of the acquisition mode of the image acquisition module 3, a cultivation worker or a farmer can upload images of the abnormal plants at any time in 24 hours, so that a solution is obtained as soon as possible, and the plant death probability is reduced as much as possible;
the data analysis module 4 comprises a data detection unit 401, a data analysis unit 402 and a data preprocessing unit 403, the data detection unit 401 is electrically connected with the data analysis unit 402 through a wire, the data analysis unit 402 is electrically connected with the data preprocessing unit 403 through a wire, necessary data processing can be performed on the shot plant picture, the processing effect of the later stage of the system is improved, the abnormal part of the picture can be independently processed by scratching, the data detection unit 401, the data analysis unit 402 and the data preprocessing unit 403 are electrically connected in parallel through wires, the data detection unit 401, the data analysis unit 402 and the data preprocessing unit 403 are electrically connected with the intelligent diagnosis module 2 in series through wires, the abnormal part is amplified, the misjudgment risk caused by unclear pictures at the later stage of the system is reduced, and the use safety of the system is improved, the big database 1 is electrically output-connected with the intelligent conversion unit 5 through a wire, the scheme distribution unit 603 is electrically output-connected with the administration implementation unit 7 through a wire, the administration implementation unit 7 is electrically output-connected with the administration detection unit 8 through a wire, the image monitoring unit 9 is electrically output-connected with the administration implementation unit 7 through a wire, the image monitoring unit 9 is electrically output-connected with the timed uploading unit 10 through a wire, the timed uploading unit 10 is electrically output-connected with the abnormality analysis unit 11 through a wire, and the abnormality analysis unit 11 is electrically output-connected with the data uploading unit 12 through a wire.
In conclusion, when the sauce material canning and packaging equipment capable of compressing fillers is used, firstly, the equipment is butted with a big database 1 through the internet, a user downloads plant protection research schemes required to be used in the later period, various schemes are stored in a big data integration unit 101, the schemes are transmitted to the inside of a big data conversion unit 102 through a lead to be subjected to centralized conversion, the schemes are transmitted to the inside of a big data classification unit 103 through the lead to be subjected to unified classification, later-period inquiry is facilitated, a grower or farmer can shoot abnormal plants by using a camera 301 of a handheld terminal if the plants are found to be abnormal, shot pictures are transmitted to the inside of an image processing unit 302, after independent image matting processing is carried out on abnormal areas of the plants in the slices, the processed pictures are transmitted to a digital-to-analog conversion unit 303 to carry out data conversion on the pictures, and the later-period system can be identified conveniently, the data picture enters the data analysis module 4, the data detection unit 401 in the data analysis module 4 uniformly detects the data, the detected data is transmitted to the data analysis unit 402, the data analysis unit 402 analyzes the internal data, useful information is transmitted to the data preprocessing unit 403 for necessary preprocessing after analysis, the data is converted into a document with a single format for later archiving, the document is transmitted to the intelligent diagnosis module 2 through a wire after being preprocessed, the data matching unit 201 in the intelligent diagnosis module 2 can match the abnormal phenomenon in the file through the data in the big database 1, the data is transmitted to the scheme matching unit 202 after matching, the scheme matching unit 202 transmits the data to the inside of the big data classification unit 103 for matching of an effective management scheme, after the matching of the effective schemes is completed, the effective schemes are transmitted into the big data conversion unit 102 through a lead, the data are converted and then transmitted into the intelligent conversion unit 5, the intelligent conversion unit 5 can convert the effective schemes and the data schemes to be solved in a direct proportion, the converted data are transmitted into the medicine application regulation and control unit 203, the medicine application regulation and control unit 203 can intelligently convert the measurement and range of the medicine according to the difference of the size, planting time and land cost of the plants in the same proportion, the converted effective schemes are transmitted into the scheme uploading unit 204, the effective schemes are transmitted into the scheme previewing module 6 for independent previewing, when the schemes are previewed, the success rate conversion unit 601 in the scheme previewing module 6 can convert the success feasibility rate of the schemes, if the success rate is lower than 80%, the schemes are automatically transmitted into the error scheme collecting unit 602, the error scheme collecting unit 602 transmits the error scheme to the scheme matching unit 202 for further scheme matching until a scheme with a success rate of 80% is matched, if the success rate conversion unit 601 converts the scheme to a power of more than 80%, the scheme is regarded as an effective scheme, the effective scheme is transmitted to the scheme distributing unit 603, the effective scheme is distributed to the treatment implementing unit 7 through the scheme distributing unit 603, so that a grower or a farmer can conveniently perform plant protection and nursing, after the plant is nursed, the protection status can be reported through the treatment detecting unit 8, and when the treatment implementing unit 7 is implemented, the image monitoring unit 9 can be started to perform real-time monitoring on the real-time treatment status of the plant, the monitored picture is transmitted to the timing uploading unit 10, and the timing uploading unit 10 can perform shooting and uploading on the image in the monitored picture at regular time, the shot image is conveyed to the abnormality analysis unit 11, the abnormality of the plants on the image is analyzed, the data is conveyed to the data uploading unit 12 after the analysis, the data is conveyed to the big database 1 through the data uploading unit 12 to be stored, the data capacity in the big database 1 is improved, the big database 1 can effectively analyze the abnormal conditions of various plants, and the use efficiency of the method is improved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. A plant protection operation planning method based on artificial intelligence comprises a big database (1) and an image monitoring unit (9), and is characterized in that: the large database (1) is electrically connected with an intelligent diagnosis module (2) through a lead, the intelligent diagnosis module (2) is electrically connected with a scheme preview module (6) through a lead, the scheme preview module (6) comprises a success rate conversion unit (601), an error scheme collection unit (602) and a scheme distribution unit (603), the success rate conversion unit (601) is electrically connected with the error scheme collection unit (602) through a lead, the success rate conversion unit (601) is electrically connected with the scheme distribution unit (603) through a lead, the image acquisition module (3) is electrically connected with a data analysis module (4) through a lead, the large database (1) is electrically connected with an intelligent conversion unit (5) through a lead, and the scheme distribution unit (603) is electrically connected with a management implementation unit (7) through a lead, the treatment implementation unit (7) is electrically output and connected with the treatment detection unit (8) through a lead, the image monitoring unit (9) is electrically output and connected with the treatment implementation unit (7) through a lead, the image monitoring unit (9) is electrically output and connected with the timing uploading unit (10) through a lead, the timing uploading unit (10) is electrically output and connected with the abnormality analysis unit (11) through a lead, and the abnormality analysis unit (11) is electrically output and connected with the data uploading unit (12) through a lead.
2. The artificial intelligence based plant protection operation planning method according to claim 1, wherein: the big database (1) comprises a big data integration unit (101), a big data conversion unit (102) and a big data classification unit (103), wherein the big data integration unit (101) is electrically output and connected with the big data conversion unit (102) through a conducting wire, and the big data conversion unit (102) is electrically output and connected with the big data classification unit (103).
3. The artificial intelligence based plant protection operation planning method according to claim 2, wherein: the big data integration unit (101), the big data conversion unit (102) and the big data classification unit (103) are electrically connected in parallel through wires, and the big data integration unit (101), the big data conversion unit (102) and the big data classification unit (103) are electrically connected in series with the intelligent diagnosis module (2) through wires.
4. The artificial intelligence based plant protection operation planning method according to claim 1, wherein: the intelligent diagnosis module (2) comprises a data matching unit (201), a scheme matching unit (202), a medicine use regulation and control unit (203) and a scheme uploading unit (204), wherein the data matching unit (201) is electrically output and connected with the scheme matching unit (202) through a lead, the scheme matching unit (202) is electrically output and connected with the medicine use regulation and control unit (203) through a lead, and the medicine use regulation and control unit (203) is electrically output and connected with the scheme uploading unit (204) through a lead.
5. The artificial intelligence based plant protection operation planning method according to claim 4, wherein: the data matching unit (201), the scheme matching unit (202), the medication regulation and control unit (203) and the scheme uploading unit (204) are electrically connected in parallel through leads, and the data matching unit (201), the scheme matching unit (202), the medication regulation and control unit (203) and the scheme uploading unit (204) are electrically connected in series with the scheme previewing module (6) through leads.
6. The artificial intelligence based plant protection operation planning method according to claim 1, wherein: the image acquisition module (3) comprises a camera (301), an image processing unit (302) and a digital-to-analog conversion unit (303), wherein the camera (301) is electrically output and connected with the image processing unit (302) through a lead, and the image processing unit (302) is electrically output and connected with the digital-to-analog conversion unit (303) through a lead.
7. The artificial intelligence based plant protection operation planning method according to claim 6, wherein: the camera (301), the image processing unit (302) and the digital-to-analog conversion unit (303) are electrically connected in parallel through conducting wires, and the camera (301), the image processing unit (302) and the digital-to-analog conversion unit (303) are electrically connected in series with the data analysis module (4) through conducting wires.
8. The artificial intelligence based plant protection operation planning method according to claim 1, wherein: the data analysis module (4) comprises a data detection unit (401), a data analysis unit (402) and a data preprocessing unit (403), wherein the data detection unit (401) is electrically connected with the data analysis unit (402) through a lead in an output mode, and the data analysis unit (402) is electrically connected with the data preprocessing unit (403) through a lead in an output mode.
9. The artificial intelligence based plant protection operation planning method according to claim 8, wherein: the data detection unit (401), the data analysis unit (402) and the data preprocessing unit (403) are electrically connected in parallel through leads, and the data detection unit (401), the data analysis unit (402) and the data preprocessing unit (403) are electrically connected in series with the intelligent diagnosis module (2) through leads.
10. The artificial intelligence based plant protection operation planning method according to claim 1, comprising the following steps:
s1, the large database (1) is connected with the internet, a user downloads plant protection research schemes required to be used in the later period, various schemes are stored in the large data integration unit (101), the schemes are transmitted to the inside of the large data conversion unit (102) through a lead to be converted in a centralized mode, and the schemes are transmitted to the inside of the large data classification unit (103) through the lead to be classified in a unified mode, so that later-period query is facilitated;
s2, cultivating personnel or farmers can use the camera (301) of the handheld terminal to shoot abnormal plants if the plants are found to be abnormal, the shot pictures are transmitted to the inside of the image processing unit (302), the processed pictures are transmitted to the digital-to-analog conversion unit (303) after the abnormal areas of the plants in the slices are subjected to independent matting processing, and the pictures are subjected to data conversion so as to be convenient for later-stage system identification;
s3, enabling the digitized picture to enter a data analysis module (4), after uniformly detecting data by a data detection unit 401 in the data analysis module (4), transmitting the detected data to the inside of a data analysis unit (402), analyzing the internal data by the data analysis unit (402), and after analyzing, sending useful information to a data preprocessing unit (403) for necessary preprocessing, so that the data is converted into a document with an individual format, and is convenient for later archiving;
s4, after data are preprocessed, the preprocessed data are transmitted to the inside of an intelligent diagnosis module (2) through a lead, a data matching unit (201) in the intelligent diagnosis module (2) can match the abnormal phenomena in the file through the data in a big database (1), after the matching is completed, the matched data are transmitted to a scheme matching unit (202), the scheme matching unit (202) transmits the data to the inside of a big data classification unit (103) to perform matching operation of an effective treatment scheme, and after the matching of the effective scheme is completed, the matched data are transmitted to a big data conversion unit 102 through the lead;
s5, converting the data, transmitting the converted data to an intelligent conversion unit (5), converting the effective scheme and the data scheme to be solved in a direct proportion mode by the intelligent conversion unit (5), and transmitting the converted data to the medicine use regulation and control unit (203), wherein the medicine use regulation and control unit (203) can intelligently convert the measurement and the range of medicine use in the same proportion mode according to the difference of the size, the planting time and the land cost of the plants;
s6, after the effective scheme is converted, the converted effective scheme is transmitted to a scheme uploading unit (204), and the effective scheme is transmitted to a scheme previewing module (6) for independent previewing;
s7, when a scheme is previewed, a success rate conversion unit (601) in the scheme previewing module (6) can convert the success feasibility rate of the scheme, if the success rate is lower than 80%, the scheme is automatically conveyed into an error scheme collecting unit (602), the error scheme collecting unit (602) conveys the error scheme to the inside of a scheme matching unit (202) for second time, scheme matching is carried out again until the scheme with the success rate of 80% is matched, if the success rate conversion unit (601) converts the scheme to the power of more than 80%, the scheme is regarded as an effective scheme, the effective scheme is conveyed into a scheme distribution unit (603), and the effective scheme is distributed into a management implementation unit (7) through the scheme distribution unit (603), so that a grower or a farmer can conveniently carry out plant protection nursing;
s8, after the plant is nursed, the plant protection status can be reported through the treatment detection unit (8), and when the administration implementing unit (7) implements, the image monitoring unit (9) can be started, the real-time treatment condition of the plants is monitored in real time, simultaneously, the monitored pictures are transmitted to a timing uploading unit (10), the timing uploading unit (10) can shoot and upload images in the monitoring pictures at regular time, the shot images are transmitted to an abnormity analyzing unit (11), analyzing the abnormal appearance of the plants on the picture, transmitting the data to a data uploading unit (12) after analysis, the data are transmitted to the large database (1) through the data uploading unit (12) to be stored, so that the data capacity in the large database (1) is improved, the large database (1) can conveniently and effectively analyze abnormal conditions of various plants, and the use efficiency of the method is improved.
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