CN117561964B - Agricultural data management system and method based on artificial intelligence - Google Patents
Agricultural data management system and method based on artificial intelligence Download PDFInfo
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- CN117561964B CN117561964B CN202410051989.2A CN202410051989A CN117561964B CN 117561964 B CN117561964 B CN 117561964B CN 202410051989 A CN202410051989 A CN 202410051989A CN 117561964 B CN117561964 B CN 117561964B
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Abstract
The invention relates to the technical field of agricultural data management, in particular to an agricultural data management system and method based on artificial intelligence, comprising the steps of constructing an agricultural management cloud platform, analyzing the relation between historical irrigation records and crop diseases, and classifying the historical irrigation records to obtain a historical irrigation record set of farmlands; analyzing the physical and chemical properties of the soil of the farmland in the historical irrigation record, and affecting the growth state of crops to obtain soil affecting data of the farmland; extracting irrigation data of equipment control parameters of irrigation equipment from the historical irrigation records, and evaluating the influence degree of the equipment control parameters under different irrigation data on the growth state of crops in a farmland to obtain marked irrigation data of the farmland; and acquiring soil state data and crop state data in the farmland in the current period, intelligently adjusting equipment control parameters of irrigation equipment in the farmland in the current period, and intelligently managing the farmland.
Description
Technical Field
The invention relates to the technical field of agricultural data management, in particular to an artificial intelligence-based agricultural data management system and method.
Background
Artificial intelligence refers to an intelligent technology and system for simulating human beings, which can enable a computer system to perform tasks requiring human beings to perform, such as learning, reasoning, identifying images and other problems, and has the following advantages in agricultural data management by using artificial intelligence: 1. the data can be analyzed and predicted, and the artificial intelligence can identify the change trend of the data through the analysis of the agricultural production data, so that the crop production condition, the plant diseases and insect pests spreading trend and the like are predicted; 2. providing intelligent decision support, and based on analysis and prediction results of agricultural data, the artificial intelligence can provide intelligent decision support for farmers, thereby helping the farmers to improve agricultural production efficiency and economic benefit; 3. the automation of agricultural production, artificial intelligence can be applied to agricultural equipment, thereby realizing the automation of agricultural production, including automatic driving of tractors and the like.
In the agricultural production process, irrigation to crops has the effect of not replacing in guaranteeing the growth and development of crops, improving the agricultural production benefit and guaranteeing the grain safety, but at present, irrigation to crops is common, usually only a certain mode is adopted, and irrigation equipment can not be intelligently adjusted according to the states of the crops, so that unreasonable irrigation can occur frequently, and the agricultural production suffers unnecessary loss.
Disclosure of Invention
The invention aims to provide an artificial intelligence-based agricultural data management system and an artificial intelligence-based agricultural data management method, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an artificial intelligence-based agricultural data management method, the method comprising:
step S100: constructing an agricultural management cloud platform, acquiring historical disease records of crops in a farmland, acquiring historical irrigation records of irrigation equipment in the farmland, extracting disease information of the crops from the historical disease records, analyzing the relation between the historical irrigation records and the crop diseases, and classifying the historical irrigation records to obtain a historical irrigation record set of the farmland;
step S200: extracting soil data of the physical and chemical properties of soil of a farmland from the historical irrigation record, acquiring the growth state of crops in the farmland in the historical irrigation record, analyzing the influence degree of the physical and chemical properties of the soil of the farmland on the growth state of the crops in the historical irrigation record, and obtaining soil influence data of the farmland;
step S300: extracting irrigation data of equipment control parameters of irrigation equipment from historical irrigation records, and evaluating the influence degree of the equipment control parameters under different irrigation data on the growth state of crops in a farmland by combining soil physical and chemical properties with soil influence data of the crops to obtain marked irrigation data of the farmland;
Step S400: and acquiring soil state data and crop state data in the farmland in the current period, and intelligently adjusting equipment control parameters of irrigation equipment in the farmland in the current period by combining the marked irrigation data of the farmland to intelligently manage the farmland.
Further, step S100 includes:
step S101: constructing an agricultural management cloud platform, and extracting a time point when irrigation equipment starts to irrigate farmlands from a historical irrigation record of the irrigation equipment;
step S102: obtaining and collecting various disease types related to irrigation in crops to obtain an irrigation disease set of the crops, extracting disease information of the crops from historical disease records, analyzing the disease types of the crops, marking the historical disease records when the disease types exist in the irrigation disease set, and recording the historical disease records as irrigation historical disease records;
step S103: extracting a time point when the crop is detected with diseases from the irrigation historical disease records, and marking the historical irrigation records which are adjacent to the two historical irrigation records and are in front of the corresponding time points of the two historical irrigation records as the historical irrigation records related to the crop diseases in a period between the corresponding time points of the two adjacent historical irrigation records when the crop is detected with the diseases;
Step S104: and acquiring and collecting historical irrigation records marked as being related to crop diseases in each historical irrigation record to obtain a historical irrigation record set related to the crop diseases, and collecting historical irrigation records which are not marked as being related to the crop diseases to obtain a historical irrigation record set not related to the crop diseases.
Further, step S200 includes:
step S201: acquiring a historical irrigation record, wherein the irrigation equipment records the values corresponding to the physicochemical properties of each soil in the soil of the farmland before the crops in the farmland begin to be irrigated, and records the values as a first soil value, acquiring the values corresponding to the physicochemical properties of each soil in the soil of the farmland after the crops in the farmland are irrigated by the irrigation equipment, and records the values as a second soil value, and collecting the values of the first soil and the second soil in the historical irrigation record, so as to obtain the soil data of each physicochemical property of each soil;
step S202: the specific process for acquiring the growth state of the crops in the farmland in the history irrigation record comprises the steps of acquiring the numerical values corresponding to various crop growth indexes of the crops in the farmland before the irrigation equipment starts to irrigate the crops in the farmland in the history irrigation record;
Step S203: when the types of crops in the farmland are the same in the two historical irrigation records, but all the crop growth indexes of the farmland are not completely the same, carrying out similarity determination on the crop growth states in the farmland in the two historical irrigation records, wherein the specific process of carrying out similarity determination on the crops in the farmland in the a-th historical irrigation record and the b-th historical irrigation record comprises the steps of calculating the difference value of all the crop growth indexes in the farmland in the a-th historical irrigation record and the b-th historical irrigation record, wherein the difference value of the crop growth indexes in the c-th historical irrigation record in the a-th historical irrigation record and the b-th historical irrigation record is calculated，/>In the a historical irrigation record, the corresponding value of the c crop growth index of the farmland,/-for>In the b historical irrigation record, the corresponding value of the c crop growth index of the farmland;
step S204: calculating the difference value between the a-th historical irrigation record and the b-th historical irrigation recordWherein->The difference value of the growth index of the ith crop in the a-th historical irrigation record and the b-th historical irrigation record is j, and the j is the total number of the growth indexes of the crops in the a-th historical irrigation record and the b-th historical irrigation record;
Step S205: when the difference value of the crop in the a-th historical irrigation record and the crop in the b-th historical irrigation record is smaller than a preset crop difference value threshold value, judging that the a-th historical irrigation record is similar to the growth state of the crop in the b-th historical irrigation record;
the obtaining of the preset crop difference value threshold is that the worker judges the crops of the same kind with similar growth states in a crop experiment group, and then calculates the average value of the crop difference values of the same kind with similar growth states in the experiment group, and the average value is used as the crop difference value threshold of the crops of the same kind;
step S206: acquiring a historical irrigation record set which does not contain historical disease records in an agricultural field, collecting the historical irrigation records of the same crop type, acquiring soil data of all soil physicochemical properties in all the historical irrigation records which are judged to be similar in growth state of the crop, averaging second soil values in the soil data of all the soil physicochemical properties, recording the second soil values as second marked soil values of all the soil physicochemical properties in all the historical irrigation records, and replacing the second soil values in the soil data of all the soil physicochemical properties by the second marked soil values of all the soil physicochemical properties in all the historical irrigation records to obtain marked soil data of all the soil physicochemical properties of all the historical irrigation records with similar growth state of the crop;
Step S207: in each historical irrigation record which is judged to be similar in growth state of crops, the corresponding values of each crop growth index are averaged and serve as marked crop values of each crop growth index of the crops, and each historical irrigation record is recorded as the affiliated historical irrigation record of the marked crop values of each crop growth index;
step S208: acquiring marked soil data of various soil physical and chemical properties of marked crop values of various crop growth indexes in various crop types, recording and collecting the marked soil data to obtain crop soil influence data of the crop types, and acquiring and collecting the soil influence data of the crops of various crop types in a farmland to obtain soil influence data of the farmland;
the above steps are used for judging the growth state similarity of crops according to each historical irrigation record, because the requirements of the crops on irrigation are different under different states, even if the crops of the same kind are under different states, the requirements of the crops on irrigation are different, but the analysis of the irrigation condition of the crops cannot only consider the irrigation quantity of the irrigation equipment on farmlands, and because the crops absorb water by soil, the soil data of the physical and chemical properties of the soil in each historical irrigation record are taken as consideration standards, so that the method is more reasonable and accurate.
Further, step S300 includes:
step S301: extracting irrigation data of various equipment control parameters of irrigation equipment when irrigation is performed on a farmland from the historical irrigation record, and acquiring various crop growth indexes and crop types of crops in the farmland from the historical irrigation record;
step S302: acquiring a historical irrigation record set containing historical disease records in an agricultural field, recording each historical irrigation record as a comparison historical irrigation record, and judging the growth state similarity of crops between each comparison historical irrigation record and each historical irrigation record based on the judging mode of the step S202 to acquire each comparison historical irrigation record and each historical irrigation record judged to be similar to the crops;
step S303: the method comprises the steps of performing soil state similarity judgment on a control historical irrigation record and a historical irrigation record which are judged to be similar in growth state of crops, wherein the specific judgment comprises the steps of obtaining soil influence data of the historical irrigation record, calculating the difference value of physical and chemical properties of each item of soil of the control historical irrigation record and the historical irrigation record, wherein the difference value of the physical and chemical properties of the f-th item of soil，/>Average value of second soil value and first soil value representing physicochemical property of f-th soil in control history irrigation record,/- >Average value of second marked soil value and first soil value representing physical and chemical properties of f-th soil in history irrigation record,/->Is->And->Average value of (2);
step S304: calculating the average value of the difference values of the physicochemical properties of each item of soil in the control historical irrigation record and the historical irrigation record, setting an average value threshold value of the difference values, and judging that the soil states of the control historical irrigation record and the historical irrigation record are similar when the average value of the difference values is smaller than the average value threshold value, and judging that the control historical irrigation record and the historical irrigation record are similar historical irrigation records;
the setting process of the average value threshold value comprises the steps of randomly sampling soil in a farmland by a professional to obtain an experimental group of the soil, analyzing physical and chemical properties of each soil of the soil, shooting a soil picture, judging by the professional to obtain each soil sample judged to be similar in soil state, calculating the difference value of each physical and chemical property among each soil sample judged to be similar in soil, taking an average value of the difference value, taking the average value of the difference value, and taking the average value of each soil sample judged to be similar in soil in the experimental group of the soil to obtain the average value threshold value of the difference value;
Step S305: when the comparison historical irrigation records which are similar historical irrigation records are identical to the irrigation data of the equipment control parameters in the historical irrigation records, marking the irrigation data of the equipment control parameters as characteristic irrigation data;
step S306: acquiring each historical irrigation record of each marked crop value in each crop growth index, acquiring and collecting irrigation data corresponding to each equipment control parameter in each historical irrigation record to obtain an irrigation data set corresponding to each equipment control parameter, and removing characteristic irrigation data from the irrigation data set corresponding to each equipment control parameter;
step S307: and acquiring and collecting irrigation data sets corresponding to various equipment control parameters of various crop types, wherein the marked crop values of various crop growth indexes belong to the equipment control parameters, so as to obtain marked irrigation data of farmlands.
Further, step S400 includes:
step S401: acquiring farmland crop state data in a current period, wherein the crop state data are corresponding values of various crop growth indexes of crops in a farmland and crop types;
step S402: extracting marked crop values of various crop growth indexes of the same crop species as the crops in the current period from soil influence data of a farmland, judging the growth state similarity of the crops based on the step S202, acquiring irrigation data corresponding to various equipment control parameters of the marked crop values of various crop growth indexes from marked irrigation data of the farmland, irrigating the farmland according to the irrigation data corresponding to the various equipment control parameters, and collecting various soil physicochemical properties of soil after farmland irrigation until absolute values of differences between the various soil physicochemical property corresponding values and second soil values in the marked soil data are smaller than a preset threshold, intelligently adjusting various equipment control parameters of irrigation equipment in the farmland in the current period, and intelligently managing the farmland;
The absolute value of the difference between the corresponding values of the physical and chemical properties of each item of soil and the second soil value in the marked soil data is smaller than a preset threshold, wherein the preset threshold is set by taking a soil sample from the irrigated soil of the irrigation equipment by a professional, analyzing the soil sample by a worker to obtain a soil sample judged to be similar by the worker, and respectively making differences between the corresponding values of the physical and chemical properties of the same item of soil in each soil sample judged to be similar to obtain the difference between the physical and chemical properties of the same item of soil in each soil sample judged to be similar to obtain the maximum value of the difference, and taking the maximum value of the difference as the threshold corresponding to the physical and chemical properties of the same item of soil.
In order to better realize the method, an agricultural data management system is also provided, and comprises a historical irrigation record set module, a soil influence data module, a marked irrigation data module and an intelligent management module;
the historical irrigation record set module is used for classifying historical irrigation records of irrigation equipment in the farmland to obtain a historical irrigation record set of the farmland;
the soil influence data module is used for analyzing the physical and chemical properties of the soil of the farmland in the historical irrigation records, influencing the growth state of crops and obtaining soil influence data of the farmland;
The marked irrigation data module is used for evaluating the influence degree of the equipment control parameters under different irrigation data on the growth state of crops in the farmland to obtain marked irrigation data of the farmland;
the intelligent management module is used for intelligently adjusting the control parameters of each device of irrigation equipment in the farmland in the current period and intelligently managing the farmland.
Further, the historical irrigation record set module comprises an irrigation historical disease record unit and a historical irrigation record set unit;
the irrigation history disease record unit is used for acquiring and collecting various disease types related to irrigation in crops, marking the history disease record and recording the history disease record as an irrigation history disease record;
and the historical irrigation record set unit is used for acquiring and collecting the historical irrigation records marked as being related to the crop diseases in each historical irrigation record to obtain a historical irrigation record set related to the crop diseases, and collecting the historical irrigation records not marked as being related to the crop diseases to obtain a historical irrigation record set not related to the crop diseases.
Further, the soil influence data module comprises a growth state similar unit and a soil influence data unit of crops;
The crop growth state similarity unit is used for judging the growth state similarity of the two crops recorded by historical irrigation;
and the soil influence data unit is used for acquiring and collecting the soil influence data of the crops of each crop type in the farmland to obtain the soil influence data of the farmland.
Further, the marked irrigation data module comprises a history irrigation record similar unit and a marked irrigation data unit;
the historical irrigation record similarity unit is used for performing similarity judgment on the comparison historical irrigation record and the historical irrigation record to obtain similar historical irrigation records;
the marked irrigation data unit is used for acquiring and collecting irrigation data sets corresponding to various equipment control parameters to which the marked crop values of various crop growth indexes belong in various crop types to obtain marked irrigation data of farmlands.
Further, the intelligent management module comprises an intelligent management unit;
the intelligent management unit is used for intelligently adjusting the control parameters of each device of the irrigation device in the farmland in the current period and intelligently managing the farmland.
Compared with the prior art, the invention has the following beneficial effects: according to the intelligent irrigation control system, intelligent adjustment of irrigation of crops in a farmland is realized, refined analysis of irrigation is realized, the growth states of the crops after the irrigation process of different crops are obtained through historical irrigation records in the farmland, so that the influence of all equipment control parameters of irrigation equipment on the growth states of the crops in the irrigation process is obtained, and in consideration of the fact that the influence of all equipment control parameters of the irrigation equipment on the growth states of the crops in the actual process needs to be observed for a certain time, the intelligent irrigation control system can realize real-time control of the irrigation conditions of the crops by obtaining data of all soil physicochemical properties of soil in the farmland after the irrigation, so that the irrigation process of the crops is more scientific and reasonable.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a method of the present invention for an artificial intelligence based agricultural data management system and method;
FIG. 2 is a schematic block diagram of an artificial intelligence based agricultural data management system and method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: an artificial intelligence-based agricultural data management method, the method comprising:
step S100: constructing an agricultural management cloud platform, acquiring historical disease records of crops in a farmland, acquiring historical irrigation records of irrigation equipment in the farmland, extracting disease information of the crops from the historical disease records, analyzing the relation between the historical irrigation records and the crop diseases, and classifying the historical irrigation records to obtain a historical irrigation record set of the farmland;
Wherein, step S100 includes:
step S101: constructing an agricultural management cloud platform, and extracting a time point when irrigation equipment starts to irrigate farmlands from a historical irrigation record of the irrigation equipment;
step S102: obtaining and collecting various disease types related to irrigation in crops to obtain an irrigation disease set of the crops, extracting disease information of the crops from historical disease records, analyzing the disease types of the crops, marking the historical disease records when the disease types exist in the irrigation disease set, and recording the historical disease records as irrigation historical disease records;
step S103: extracting a time point when the crop is detected with diseases from the irrigation historical disease records, and marking the historical irrigation records which are adjacent to the two historical irrigation records and are in front of the corresponding time points of the two historical irrigation records as the historical irrigation records related to the crop diseases in a period between the corresponding time points of the two adjacent historical irrigation records when the crop is detected with the diseases;
step S104: the method comprises the steps of obtaining and collecting historical irrigation records marked as being related to crop diseases in each historical irrigation record to obtain a historical irrigation record set related to the crop diseases, and collecting historical irrigation records not marked as being related to the crop diseases to obtain a historical irrigation record set not related to the crop diseases;
Step S200: extracting soil data of the physical and chemical properties of soil of a farmland from the historical irrigation record, acquiring the growth state of crops in the farmland in the historical irrigation record, analyzing the influence degree of the physical and chemical properties of the soil of the farmland on the growth state of the crops in the historical irrigation record, and obtaining soil influence data of the farmland;
for example, various soil physicochemical properties include soil moisture content, soil texture, soil ph, soil nutrient content, and the like;
wherein, step S200 includes:
step S201: acquiring a historical irrigation record, wherein the irrigation equipment records the values corresponding to the physicochemical properties of each soil in the soil of the farmland before the crops in the farmland begin to be irrigated, and records the values as a first soil value, acquiring the values corresponding to the physicochemical properties of each soil in the soil of the farmland after the crops in the farmland are irrigated by the irrigation equipment, and records the values as a second soil value, and collecting the values of the first soil and the second soil in the historical irrigation record, so as to obtain the soil data of each physicochemical property of each soil;
step S202: the specific process for acquiring the growth state of the crops in the farmland in the history irrigation record comprises the steps of acquiring the numerical values corresponding to various crop growth indexes of the crops in the farmland before the irrigation equipment starts to irrigate the crops in the farmland in the history irrigation record;
For example, crop growth indicators of crops include plant height of crops in a farmland, total number of crops in a farmland, and the like;
step S203: when the types of crops in the farmland are the same in the two historical irrigation records, but all the crop growth indexes of the farmland are not completely the same, carrying out similarity determination on the crop growth states in the farmland in the two historical irrigation records, wherein the specific process of carrying out similarity determination on the crops in the farmland in the a-th historical irrigation record and the b-th historical irrigation record comprises the steps of calculating the difference value of all the crop growth indexes in the farmland in the a-th historical irrigation record and the b-th historical irrigation record, wherein the difference value of the crop growth indexes in the c-th historical irrigation record in the a-th historical irrigation record and the b-th historical irrigation record is calculated，/>In the a historical irrigation record, the corresponding value of the c crop growth index of the farmland,/-for>In the b historical irrigation record, the corresponding value of the c crop growth index of the farmland;
step S204: calculating the difference value between the a-th historical irrigation record and the b-th historical irrigation recordWherein->The difference value of the growth index of the ith crop in the (a) th historical irrigation record and the (b) th historical irrigation record is j, and is the (a) th historical irrigation record and the (b) th historical record In irrigation record, total number of terms of crop growth index of crops;
for example, in the 1 st historical irrigation record, the 4 th crop growth index of the crop corresponds to a value ofIn the history irrigation record of 10 and 2, the corresponding value of the growth index of the 4 th crop of the crops is +.>At the end of the line of the,；
step S205: when the difference value of the crop in the a-th historical irrigation record and the crop in the b-th historical irrigation record is smaller than a preset crop difference value threshold value, judging that the a-th historical irrigation record is similar to the growth state of the crop in the b-th historical irrigation record;
step S206: acquiring a historical irrigation record set which does not contain historical disease records in an agricultural field, collecting the historical irrigation records of the same crop type, acquiring soil data of all soil physicochemical properties in all the historical irrigation records which are judged to be similar in growth state of the crop, averaging second soil values in the soil data of all the soil physicochemical properties, recording the second soil values as second marked soil values of all the soil physicochemical properties in all the historical irrigation records, and replacing the second soil values in the soil data of all the soil physicochemical properties by the second marked soil values of all the soil physicochemical properties in all the historical irrigation records to obtain marked soil data of all the soil physicochemical properties of all the historical irrigation records with similar growth state of the crop;
Step S207: in each historical irrigation record which is judged to be similar in growth state of crops, the corresponding values of each crop growth index are averaged and serve as marked crop values of each crop growth index of the crops, and each historical irrigation record is recorded as the affiliated historical irrigation record of the marked crop values of each crop growth index;
step S208: acquiring marked soil data of various soil physical and chemical properties of marked crop values of various crop growth indexes in various crop types, recording and collecting the marked soil data to obtain crop soil influence data of the crop types, and acquiring and collecting the soil influence data of the crops of various crop types in a farmland to obtain soil influence data of the farmland;
step S300: extracting irrigation data of equipment control parameters of irrigation equipment from historical irrigation records, and evaluating the influence degree of the equipment control parameters under different irrigation data on the growth state of crops in a farmland by combining soil physical and chemical properties with soil influence data of the crops to obtain marked irrigation data of the farmland;
wherein, step S300 includes:
step S301: extracting irrigation data of various equipment control parameters of irrigation equipment when irrigation is performed on a farmland from the historical irrigation record, and acquiring various crop growth indexes and crop types of crops in the farmland from the historical irrigation record;
Step S302: acquiring a historical irrigation record set containing historical disease records in an agricultural field, recording each historical irrigation record as a comparison historical irrigation record, and judging the growth state similarity of crops between each comparison historical irrigation record and each historical irrigation record based on the judging mode of the step S202 to acquire each comparison historical irrigation record and each historical irrigation record judged to be similar to the crops;
step S303: the method comprises the steps of performing soil state similarity judgment on a control historical irrigation record and a historical irrigation record which are judged to be similar in growth state of crops, wherein the specific judgment comprises the steps of obtaining soil influence data of the historical irrigation record, calculating the difference value of physical and chemical properties of each item of soil of the control historical irrigation record and the historical irrigation record, wherein the difference value of the physical and chemical properties of the f-th item of soil，/>Average value of second soil value and first soil value representing physicochemical property of f-th soil in control history irrigation record,/->Average value of second marked soil value and first soil value representing physical and chemical properties of f-th soil in history irrigation record,/->Is->And->Average value of (2);
for example, in the control history, the average of the second soil value and the first soil value of the physicochemical property of the 3 rd soil 50, in the history irrigation record, the average value of the second marked soil value and the first soil value of the physicochemical property of the soil of item 3 +.>30->Differential value of physicochemical Properties of soil of item 3 +.>=0.5；
Step S304: calculating the average value of the difference values of the physicochemical properties of each item of soil in the control historical irrigation record and the historical irrigation record, setting an average value threshold value of the difference values, and judging that the soil states of the control historical irrigation record and the historical irrigation record are similar when the average value of the difference values is smaller than the average value threshold value, and judging that the control historical irrigation record and the historical irrigation record are similar historical irrigation records;
step S305: when the comparison historical irrigation records which are similar historical irrigation records are identical to the irrigation data of the equipment control parameters in the historical irrigation records, marking the irrigation data of the equipment control parameters as characteristic irrigation data;
step S306: acquiring each historical irrigation record of each marked crop value in each crop growth index, acquiring and collecting irrigation data corresponding to each equipment control parameter in each historical irrigation record to obtain an irrigation data set corresponding to each equipment control parameter, and removing characteristic irrigation data from the irrigation data set corresponding to each equipment control parameter;
Step S307: acquiring and collecting irrigation data sets corresponding to various equipment control parameters of various crop types, wherein the marked crop values of various crop growth indexes belong to the equipment control parameters to obtain marked irrigation data of farmlands;
step S400: acquiring soil state data and crop state data in a farmland in a current period, intelligently adjusting equipment control parameters of irrigation equipment in the farmland in the current period by combining marked irrigation data of the farmland, and intelligently managing the farmland;
wherein, step S400 includes:
step S401: acquiring farmland crop state data in a current period, wherein the crop state data are corresponding values of various crop growth indexes of crops in a farmland and crop types;
step S402: extracting marked crop values of various crop growth indexes of the same crop species as the crops in the current period from soil influence data of a farmland, judging the growth state similarity of the crops based on the step S202, acquiring irrigation data corresponding to various equipment control parameters of the marked crop values of various crop growth indexes from marked irrigation data of the farmland, irrigating the farmland according to the irrigation data corresponding to the various equipment control parameters, and collecting various soil physicochemical properties of soil after farmland irrigation until absolute values of differences between the various soil physicochemical property corresponding values and second soil values in the marked soil data are smaller than a preset threshold, intelligently adjusting various equipment control parameters of irrigation equipment in the farmland in the current period, and intelligently managing the farmland;
In order to better realize the method, an agricultural data management system is also provided, and comprises a historical irrigation record set module, a soil influence data module, a marked irrigation data module and an intelligent management module;
the historical irrigation record set module is used for classifying historical irrigation records of irrigation equipment in the farmland to obtain a historical irrigation record set of the farmland;
the soil influence data module is used for analyzing the physical and chemical properties of the soil of the farmland in the historical irrigation records, influencing the growth state of crops and obtaining soil influence data of the farmland;
the marked irrigation data module is used for evaluating the influence degree of the equipment control parameters under different irrigation data on the growth state of crops in the farmland to obtain marked irrigation data of the farmland;
the intelligent management module is used for intelligently adjusting various equipment control parameters of irrigation equipment in the farmland in the current period and intelligently managing the farmland;
the historical irrigation record set module comprises an irrigation historical disease record unit and a historical irrigation record set unit;
the irrigation history disease record unit is used for acquiring and collecting various disease types related to irrigation in crops, marking the history disease record and recording the history disease record as an irrigation history disease record;
The historical irrigation record set unit is used for acquiring and collecting historical irrigation records marked as being related to crop diseases in each historical irrigation record to obtain a historical irrigation record set related to the crop diseases, and collecting historical irrigation records which are not marked as being related to the crop diseases to obtain a historical irrigation record set not related to the crop diseases;
the soil influence data module comprises a growth state similar unit and a soil influence data unit of crops;
the crop growth state similarity unit is used for judging the growth state similarity of the two crops recorded by historical irrigation;
the soil influence data unit is used for acquiring and collecting soil influence data of crops of various crop types in the farmland to obtain soil influence data of the farmland;
the marked irrigation data module comprises a history irrigation record similar unit and a marked irrigation data unit;
the historical irrigation record similarity unit is used for performing similarity judgment on the comparison historical irrigation record and the historical irrigation record to obtain similar historical irrigation records;
the marked irrigation data unit is used for acquiring and collecting irrigation data sets corresponding to various equipment control parameters to which the marked crop values of various crop growth indexes belong in various crop types to obtain marked irrigation data of farmlands;
The intelligent management module comprises an intelligent management unit;
the intelligent management unit is used for intelligently adjusting the control parameters of each device of the irrigation device in the farmland in the current period and intelligently managing the farmland.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An artificial intelligence-based agricultural data management method, the method comprising:
step S100: constructing an agricultural management cloud platform, acquiring historical disease records of crops in a farmland, acquiring historical irrigation records of irrigation equipment in the farmland, extracting disease information of the crops from the historical disease records, analyzing the relation between the historical irrigation records and the crop diseases, and classifying the historical irrigation records to obtain a historical irrigation record set of the farmland;
the step S100 includes:
step S101: constructing an agricultural management cloud platform, and extracting a time point when irrigation equipment starts to irrigate farmlands from a historical irrigation record of the irrigation equipment;
step S102: obtaining and collecting various disease types related to irrigation in crops to obtain an irrigation disease set of the crops, extracting disease information of the crops from historical disease records, analyzing the disease types of the crops, marking the historical disease records when the disease types exist in the irrigation disease set, and recording the historical disease records as irrigation historical disease records;
step S103: extracting a time point when the crop is detected with diseases from the irrigation historical disease records, and marking the historical irrigation records which are adjacent to the two historical irrigation records and are in front of the corresponding time points of the two historical irrigation records as the historical irrigation records related to the crop diseases in a period between the corresponding time points of the two adjacent historical irrigation records when the crop is detected with the diseases;
Step S104: the method comprises the steps of obtaining and collecting historical irrigation records marked as being related to crop diseases in each historical irrigation record to obtain a historical irrigation record set related to the crop diseases, and collecting historical irrigation records not marked as being related to the crop diseases to obtain a historical irrigation record set not related to the crop diseases;
step S200: extracting soil data of the physical and chemical properties of soil of a farmland from the historical irrigation record, acquiring the growth state of crops in the farmland in the historical irrigation record, analyzing the influence degree of the physical and chemical properties of the soil of the farmland on the growth state of the crops in the historical irrigation record, and obtaining soil influence data of the farmland;
the step S200 includes:
step S201: acquiring a historical irrigation record, wherein the irrigation equipment records the values corresponding to the physicochemical properties of each item of soil in the soil of a farmland before the crops in the farmland begin to be irrigated, and records the values as a first soil value, and acquires the values corresponding to the physicochemical properties of each item of soil in the soil of the farmland after the crops in the farmland are irrigated by the irrigation equipment, and records the values as a second soil value, and collecting the values of the first soil and the second soil values of the physicochemical properties of each item of soil in the historical irrigation record, so as to obtain the soil data of the physicochemical properties of each item of soil;
Step S202: the specific process for acquiring the growth state of the crops in the farmland in the history irrigation record comprises the steps of acquiring the numerical values corresponding to various crop growth indexes of the crops in the farmland before the irrigation equipment starts to irrigate the crops in the farmland in the history irrigation record;
step S203: when the types of crops in the farmland are the same in the two historical irrigation records, but all the crop growth indexes of the farmland are not completely the same, performing similarity determination on the crop growth states in the farmland in the two historical irrigation records, wherein the specific process of performing similarity determination on the crops in the farmland in the a-th historical irrigation record and the b-th historical irrigation record comprises the steps of calculating the difference value of all the crop growth indexes in the farmland in the a-th historical irrigation record and the b-th historical irrigation record, wherein the c-th crop in the a-th historical irrigation record and the b-th historical irrigation record growsDifference value of long index，/>In the a historical irrigation record, the corresponding value of the c crop growth index of the farmland,/-for>In the b historical irrigation record, the corresponding value of the c crop growth index of the farmland;
step S204: calculating the difference value between the a-th historical irrigation record and the b-th historical irrigation record Wherein->The difference value of the growth index of the ith crop in the a-th historical irrigation record and the b-th historical irrigation record is j, and the j is the total number of the growth indexes of the crops in the a-th historical irrigation record and the b-th historical irrigation record;
step S205: when the difference value of the crop in the a-th historical irrigation record and the crop in the b-th historical irrigation record is smaller than a preset crop difference value threshold value, judging that the a-th historical irrigation record is similar to the growth state of the crop in the b-th historical irrigation record;
step S206: acquiring a historical irrigation record set which does not contain historical disease records in an agricultural field, collecting the historical irrigation records of the same crop type, acquiring soil data of all soil physicochemical properties in all the historical irrigation records which are judged to be similar in growth state of the crop, averaging second soil values in the soil data of all the soil physicochemical properties, recording the second soil values as second marked soil values of all the soil physicochemical properties in all the historical irrigation records, replacing the second soil values in the soil data of all the soil physicochemical properties with the second marked soil values of all the soil physicochemical properties in all the historical irrigation records, and obtaining marked soil data of all the soil physicochemical properties of all the historical irrigation records which are similar in growth state of the crop;
Step S207: in each historical irrigation record which is judged to be similar in growth state of crops, the corresponding values of all the crop growth indexes are averaged and serve as marked crop values of all the crop growth indexes of the crops, and each historical irrigation record is recorded as the historical irrigation record to which the marked crop values of all the crop growth indexes belong;
step S208: acquiring the marked soil data of each soil physicochemical property of the marked crop values of each crop growth index in each crop species, recording and collecting the marked soil data to obtain crop soil influence data of the crop species, and acquiring and collecting the soil influence data of the crops of each crop species in a farmland to obtain the soil influence data of the farmland;
step S300: extracting irrigation data of equipment control parameters of irrigation equipment from historical irrigation records, and evaluating the influence degree of the equipment control parameters under different irrigation data on the growth state of crops in a farmland by combining soil physical and chemical properties with soil influence data of the crops to obtain marked irrigation data of the farmland;
step S400: and acquiring soil state data and crop state data in the farmland in the current period, and intelligently adjusting equipment control parameters of irrigation equipment in the farmland in the current period by combining the marked irrigation data of the farmland to intelligently manage the farmland.
2. The method for managing agricultural data based on artificial intelligence according to claim 1, wherein said step S300 comprises:
step S301: extracting irrigation data of various equipment control parameters of irrigation equipment when irrigation is performed on a farmland from the historical irrigation record, and acquiring various crop growth indexes and crop types of crops in the farmland from the historical irrigation record;
step S302: acquiring a historical irrigation record set containing historical disease records in an agricultural field, recording each historical irrigation record as a comparison historical irrigation record, and judging the growth state similarity of crops between each comparison historical irrigation record and the historical irrigation record based on the judging mode of the step S202 to acquire each comparison historical irrigation record and each historical irrigation record judged to be similar to the crops;
step S303: the method comprises the steps of performing soil state similarity judgment on a control historical irrigation record and a historical irrigation record which are judged to be similar in growth state of crops, wherein the specific judgment comprises the steps of obtaining soil influence data of the historical irrigation record, and calculating the difference value of each soil physicochemical property of the control historical irrigation record and the historical irrigation record, wherein the difference value of the f-th soil physicochemical property is calculated ，/>Average value of second soil value and first soil value representing physicochemical property of f-th soil in control history irrigation record,/->Average value of second marked soil value and first soil value representing physical and chemical properties of f-th soil in history irrigation record,/->Is->And->Average value of (2);
step S304: calculating the average value of the difference values of the physicochemical properties of each item of soil in the control historical irrigation record and the historical irrigation record, setting an average value threshold value of the difference values, and judging that the control historical irrigation record and the historical irrigation record are similar in soil state when the average value of the difference values is smaller than the average value threshold value, and judging that the control historical irrigation record and the historical irrigation record are similar historical irrigation records;
step S305: when the comparison historical irrigation records which are similar historical irrigation records are identical to the irrigation data of a certain equipment control parameter in the historical irrigation records, marking the irrigation data of the certain equipment control parameter as characteristic irrigation data;
step S306: acquiring each historical irrigation record of each marked crop value in each crop growth index, acquiring and collecting irrigation data corresponding to each equipment control parameter in each historical irrigation record to obtain an irrigation data set corresponding to each equipment control parameter, and removing characteristic irrigation data from the irrigation data set corresponding to each equipment control parameter;
Step S307: and acquiring and collecting irrigation data sets corresponding to various equipment control parameters of various crop types, wherein the marked crop values of various crop growth indexes belong to the equipment control parameters, so as to obtain marked irrigation data of farmlands.
3. The method for managing agricultural data based on artificial intelligence according to claim 2, wherein said step S400 comprises:
step S401: acquiring farmland crop state data in a current period, wherein the crop state data are corresponding values of various crop growth indexes of crops in a farmland and crop types;
step S402: extracting marked crop values of various crop growth indexes of the same crop species as the crop in the current period from soil influence data of a farmland, judging the growth state similarity of the crop based on the step S202, acquiring irrigation data corresponding to various equipment control parameters of the marked crop values of various crop growth indexes from marked irrigation data of the farmland, irrigating the farmland according to the irrigation data corresponding to the various equipment control parameters, and collecting various soil physicochemical properties of the soil after farmland irrigation until absolute values of differences between the various soil physicochemical property corresponding values and second soil values in the marked soil data are smaller than a preset threshold, and intelligently adjusting various equipment control parameters of irrigation equipment in the farmland in the current period to carry out intelligent management on the farmland.
4. An agricultural data management system applying the artificial intelligence-based agricultural data management method according to any one of claims 1 to 3, wherein the agricultural data management system comprises a historical irrigation record set module, a soil influence data module, a marker irrigation data module and an intelligent management module;
the historical irrigation record set module is used for classifying historical irrigation records of irrigation equipment in the farmland to obtain a historical irrigation record set of the farmland;
the soil influence data module is used for analyzing the physical and chemical properties of the soil of the farmland in the historical irrigation records, influencing the growth state of crops and obtaining soil influence data of the farmland;
the marked irrigation data module is used for evaluating the influence degree of the equipment control parameters under different irrigation data on the growth state of crops in the farmland to obtain marked irrigation data of the farmland;
the intelligent management module is used for intelligently adjusting various equipment control parameters of irrigation equipment in the farmland in the current period and intelligently managing the farmland.
5. The agricultural data management system of claim 4, wherein the historical irrigation record set module comprises an irrigation historical disease record unit, a historical irrigation record set unit;
The irrigation history disease record unit is used for acquiring and collecting various disease types related to irrigation in crops, marking the history disease record and recording the history disease record as an irrigation history disease record;
the history irrigation record set unit is used for acquiring and collecting history irrigation records marked as being related to crop diseases in each history irrigation record to obtain a history irrigation record set related to the crop diseases, and collecting history irrigation records which are not marked as being related to the crop diseases to obtain a history irrigation record set not related to the crop diseases.
6. The agricultural data management system of claim 4, wherein the soil impact data module comprises a crop growth status similarity unit, a soil impact data unit;
the crop growth state similarity unit is used for judging the growth state similarity of the two crops recorded by historical irrigation;
and the soil influence data unit is used for acquiring and collecting soil influence data of crops of various crop types in the farmland to obtain soil influence data of the farmland.
7. The agricultural data management system of claim 4, wherein the tag irrigation data module comprises a historic irrigation record similarity unit, a tag irrigation data unit;
The history irrigation record similar unit is used for performing similarity judgment on the comparison history irrigation record and the history irrigation record to obtain history irrigation records which are similar to each other;
the marked irrigation data unit is used for acquiring and collecting irrigation data sets corresponding to various equipment control parameters to which the marked crop values of various crop growth indexes belong in various crop types to obtain marked irrigation data of farmlands.
8. The agricultural data management system of claim 4, wherein the intelligent management module comprises an intelligent management unit;
the intelligent management unit is used for intelligently adjusting various equipment control parameters of irrigation equipment in the farmland in the current period and intelligently managing the farmland.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108094370A (en) * | 2017-12-15 | 2018-06-01 | 河北省科学院应用数学研究所 | Control method of irrigation and device |
| EA202090498A1 (en) * | 2018-05-08 | 2020-07-06 | Рубикон Ресёрч Пти Лтд | METHOD AND SYSTEM OF WATER DISTRIBUTION AND DETERMINATION OF SOIL MOISTURE |
| CN114600751A (en) * | 2022-03-23 | 2022-06-10 | 西安建筑科技大学 | Irrigation system and method based on computer vision monitoring |
| CN116138145A (en) * | 2022-11-08 | 2023-05-23 | 台州科技职业学院 | Intelligent agricultural management system |
| CN116508635A (en) * | 2023-06-20 | 2023-08-01 | 河北工程大学 | Agricultural thing networking intelligent water-saving irrigation system |
| CN116530286A (en) * | 2023-04-12 | 2023-08-04 | 上海华维可控农业科技集团股份有限公司 | Water and fertilizer integrated irrigation management system and method for ecological high-standard farmland |
| CN116784216A (en) * | 2023-01-04 | 2023-09-22 | 沈磊 | Agricultural planting is with long-range intelligent management system based on internet of things |
| CN117114374A (en) * | 2023-10-25 | 2023-11-24 | 四川省商投信息技术有限责任公司 | Intelligent agricultural irrigation management system based on weather prediction |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9880537B2 (en) * | 2015-08-05 | 2018-01-30 | Clearag, Inc. | Customized land surface modeling for irrigation decision support in a crop and agronomic advisory service in precision agriculture |
| WO2021062247A1 (en) * | 2019-09-25 | 2021-04-01 | Blue River Technology Inc. | Treating plants using feature values and ground planes extracted from a single image |
-
2024
- 2024-01-15 CN CN202410051989.2A patent/CN117561964B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108094370A (en) * | 2017-12-15 | 2018-06-01 | 河北省科学院应用数学研究所 | Control method of irrigation and device |
| EA202090498A1 (en) * | 2018-05-08 | 2020-07-06 | Рубикон Ресёрч Пти Лтд | METHOD AND SYSTEM OF WATER DISTRIBUTION AND DETERMINATION OF SOIL MOISTURE |
| CN114600751A (en) * | 2022-03-23 | 2022-06-10 | 西安建筑科技大学 | Irrigation system and method based on computer vision monitoring |
| CN116138145A (en) * | 2022-11-08 | 2023-05-23 | 台州科技职业学院 | Intelligent agricultural management system |
| CN116784216A (en) * | 2023-01-04 | 2023-09-22 | 沈磊 | Agricultural planting is with long-range intelligent management system based on internet of things |
| CN116530286A (en) * | 2023-04-12 | 2023-08-04 | 上海华维可控农业科技集团股份有限公司 | Water and fertilizer integrated irrigation management system and method for ecological high-standard farmland |
| CN116508635A (en) * | 2023-06-20 | 2023-08-01 | 河北工程大学 | Agricultural thing networking intelligent water-saving irrigation system |
| CN117114374A (en) * | 2023-10-25 | 2023-11-24 | 四川省商投信息技术有限责任公司 | Intelligent agricultural irrigation management system based on weather prediction |
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