CN114168806A - Intelligent agricultural information platform based on wireless sensor network - Google Patents
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Abstract
The invention discloses an intelligent agricultural information platform based on a wireless sensor network, which belongs to the technical field of agricultural planting and comprises a database, a data acquisition module, a client, an analysis module and a server; the data acquisition module is used for acquiring crop planting information, processing the acquired data, marking the processed data as planting guidance data and sending the planting guidance data to the database for storage; the client comprises a growth information uploading unit, a retrieval unit and a display unit; the retrieval unit is used for retrieving planting data in the crop planting process, acquiring crop classification and planting areas needing to be retrieved by a user, retrieving in the database according to the crop classification and the planting areas, and acquiring corresponding planting guidance data when the retrieval is successful; the acquired data is processed by establishing a normalization model, so that the processed data is more convenient for users to use and provides support for subsequent steps.
Description
Technical Field
The invention belongs to the technical field of agricultural planting, and particularly relates to an intelligent agricultural information platform based on a wireless sensor network.
Background
The crops refer to various plants cultivated in agriculture, and comprise two major types of grain crops and economic crops (oil crops, vegetable crops, flowers, grasses and trees); edible crops are one of the sources of basic food for humans; along with scientific and technological development, the planting of crops has also taken place very big change, is planted by traditional soil and slowly changes into the incubator and plants, adopts the mode of water planting to carry out crops and plants, and the required growing environment of allotment crops that can be more convenient for the resident also can carry out crops at home and plant in the city.
However, because the cultivation box is adopted for cultivation, the crop yield is greatly different due to different nutrient solution ratios, nutrient solution components, illumination intensity, illumination time, wind power and other factors, most of growers lack channels for knowing information about crop planting, and the current planting methods used by many growers are not scientific enough, low in yield, poor in quality and low in market competitiveness;
therefore, an intelligent agricultural information platform based on a wireless sensor network is urgently needed to be provided at present, and the intelligent agricultural information platform is used for solving the problem that at present, growers cannot know the most scientific water planting mode.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides an intelligent agricultural information platform based on a wireless sensor network.
The purpose of the invention can be realized by the following technical scheme:
the intelligent agricultural information platform based on the wireless sensor network comprises a database, a data acquisition module, a client, an analysis module and a server;
the data acquisition module is used for acquiring crop planting information, processing the acquired data, marking the processed data as planting guidance data and sending the planting guidance data to the database for storage;
the client comprises a growth information uploading unit, a retrieval unit and a display unit; the retrieval unit is used for retrieving planting data in the crop planting process, acquiring crop classification and planting areas needing to be retrieved by a user, retrieving in the database according to the crop classification and the planting areas, and acquiring corresponding planting guidance data when the retrieval is successful;
when the retrieval fails, sending the crop classification and planting area to a data acquisition module for data acquisition, and sending the acquired data to a database and a retrieval unit by the data acquisition module respectively;
the growth information uploading unit is used for uploading crop growth information at different planting stages by a user and sending the uploaded crop growth information to the analysis module; and the analysis module analyzes the crop growth information uploaded by the user.
Further, the working method of the data acquisition module comprises the following steps:
setting crop information acquisition types and data sources, acquiring data from the data sources according to the set crop information acquisition types, processing the acquired data, classifying the data after data processing, and marking classification labels as classified data;
establishing a normalization model, wherein the normalization model is a neural network model, inputting the classification data into the normalization model to obtain the normalization data, marking classification labels and area labels on the normalization data, marking the classification labels and the area labels as planting guidance data, and sending the planting guidance data to a database for storage.
Further, the planting guidance data includes planting stage data, classification, and planting area.
Further, the display unit is used for displaying the received information.
Furthermore, in the working method of the retrieval unit, when the data acquisition module does not acquire the data of the corresponding classification and planting area, a manual supplement signal is generated and sent to the server, the server sends the manual supplement signal to the administrator, and the administrator manually acquires the corresponding planting guidance data and uploads the planting guidance data to the database for storage.
Further, the working method of the analysis module comprises the following steps:
acquiring a crop image, establishing an extraction model, and inputting the crop image into the extraction model to obtain the size, the shape and the color level of crops; the method comprises the steps of sending crop classification, planting time and planting area uploaded by a user to a database for matching, obtaining planting guide data corresponding to a planting stage, obtaining the standard size, standard shape and standard color scale of the corresponding crop according to the planting guide data, and grading the standard shape and the crop shape of the crop;
construction of cosine similarity function
Wherein i and j are the interest degree vectors of the upload data i and the standard data j respectively, and the interest degree vectors comprise: crop size, shape scores and color scales;
determining an interestingness function of the crop data based on the crop size, shape score and color scale:
setting a threshold value X1 when wij<And X1, generating an alarm signal, sending the alarm signal to a display unit in the client, and prompting a user to plant according to the planting guidance data.
Furthermore, the crop image comprises a crop comparison ruler, and when the crop image does not comprise the crop comparison ruler, the user is required to upload the crop image again.
Further, α1、α2、α3For adjustingPitch coefficient, and α1、α2、α3Has a value range of [0, 1 ]]。
Compared with the prior art, the invention has the beneficial effects that: the method has the advantages that a platform for quickly consulting crop planting methods is provided for users, the users are helped to plant the crops better, the yield and the market competitiveness of the crops are improved by adopting a proper planting method, the problem that the users cannot know the most scientific planting mode is solved, meanwhile, the crops in different planting stages are examined, the users are helped to find problems in time, the remediation is carried out in time, and the economic loss of the users is reduced; the acquired data is processed by establishing a normalization model, so that the processed data is more convenient for users to use and provides support for subsequent steps.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the intelligent agricultural information platform based on the wireless sensor network includes a database, a data acquisition module, a client, an analysis module and a server;
the data acquisition module is used for acquiring crop planting information, and the specific method comprises the following steps:
the method comprises the following steps of setting crop information collection types, namely the crop planting information needing to be collected, such as types of crops of Chinese chives, shallots, garlic, spinach, flowers and the like, setting a data source, wherein the data source is discussed and set by an expert group, namely the source of data collected by a data collection module is generally a website with public confidence related to agricultural planting information, collecting data from the data source according to the set crop information collection types, and the collected data is information related to planting of crops in different stages, such as planting information related to garlic, and in the planting stage: the method comprises the steps of determining the depth of a water culture nutrient solution, the components and proportion of the nutrient solution, the planting distance, the illumination intensity, illumination time, wind power size and other information in an incubator, corresponding image data, preparing, planting, maintaining and harvesting of other stages, and processing the collected data in which regions are suitable because the planting environments of different regions may not be the same, wherein the collected data are processed by data processing including data deduplication and deletion, wherein the deduplication is repeated data, the deletion is incomplete data, and the specific deduplication and deletion methods are conventional technical means in the field, so that the specific method description is not needed in the patent, the data after data processing are classified, classification labels are marked as classification data, and the classification labels are crop data of which classes the corresponding data belong to, for example, the data marked with the leek label is planting information related to the leek;
establishing a normalization model, wherein the normalization model is a neural network model, the normalization model is obtained by training the neural network model, the output result is the crop planting information which is uniformly classified and marked as normalization data, garlic is taken as an example, the planting area, the preparation stage, the planting stage, the maintenance stage, the harvesting stage and the like are adopted, the example stage is not fixed, the specific stage can be adjusted by a manager according to the actual situation, and the specific uniform format is discussed and set by an expert group; inputting the classification data into a normalization model to obtain normalization data, and marking a classification label and a region label for the normalization data, wherein the region label is the region for which the corresponding normalization data is used; marking the plant guidance data as the plant guidance data, and sending the plant guidance data to a database for storage; the planting guidance data includes planting stage data, classification and planting area.
The client comprises a growth information uploading unit, a retrieval unit and a display unit;
the display unit is used for displaying the received information;
the retrieval unit is used for retrieving planting data in the crop planting process, and the specific method comprises the following steps:
acquiring crop classification and planting areas required to be searched by a user, searching in a database according to the acquired crop classification and planting areas, and acquiring corresponding planting guidance data when the search is successful;
when the retrieval fails, sending the crop classification and planting area to a data acquisition module for data acquisition, and sending the acquired data to a database and a retrieval unit by the data acquisition module respectively;
when the data acquisition module does not acquire the data of the corresponding classification and planting area, generating a manual supplement signal, sending the manual supplement signal to the server, sending the manual supplement signal to an administrator by the server, manually acquiring corresponding planting guidance data by the administrator, and uploading the corresponding planting guidance data to the database for storage;
the method for manually acquiring the corresponding planting guidance data by the administrator generally carries out discussion setting for an organization expert group;
the growth information uploading unit is used for uploading crop growth information in different planting stages by a user, the crop growth information comprises information such as classification, planting area, planting time and crop images, and the uploaded crop growth information is sent to the analysis module;
the analysis module is used for analyzing the crop growth information uploaded by the user, and the specific method comprises the following steps:
the method comprises the steps that a crop image is obtained, wherein the crop image comprises a crop comparison ruler, the crop comparison ruler is a straight ruler and is used as a reference object in the image, and when the crop comparison ruler does not exist in the crop image, a user is required to upload the crop image again; establishing an extraction model, inputting the crop image into the extraction model, and obtaining the size, the shape and the color level of the crop, wherein the size, the shape and the color level of the crop can be regarded as the average value of the size, the shape and the color level of the crop in the crop image;
acquiring crop classification, planting time and planting area uploaded by a user, sending the acquired crop classification, planting time and planting area to a database for matching to obtain planting guidance data of a corresponding planting stage, and acquiring a standard size, a standard shape and a standard color level of a corresponding crop according to the planting guidance data; grading the standard shape of the crop and the shape of the crop, and grading according to the difference between the standard shape of the crop and the shape of the crop on the basis of the standard shape of the crop;
construction of cosine similarity function
Wherein i and j are the interest degree vectors of the uploaded data i and the standard data j respectively, the smaller the included angle between i and j is, the higher the similarity is, and the interest degree vectors include: crop size, shape scores and color scales;
illustratively, the format of the interestingness vector is a triplet (x1, x2, x3), and the interestingness function for the crop data is determined from the crop size, shape score, and color rank:
wherein alpha is1、α2、α3To adjust the coefficient, α1、α2、α3Has a value range of [0, 1 ]]F, t, r are crop size score, shape score and color scale score in the crop data, respectively; setting an initial value and a value read from a database for the crop size score, the shape score and the color scale score respectively; the crop size score and the color scale score are both scored relative to the standard size and the standard color scale;
fijcrop size score, f, representing upload data i versus standard data jminScoring the smallest crop size recorded in the database, fmaxScoring the largest crop size recorded in the database; t is tijFor the uploaded data i the crop shape score, t, against the standard data jmaxScoring the maximum crop shape recorded in the database, tminScoring the smallest crop shape recorded in the database; r isijFor uploading i crop colour scale score, r, against standard data jmaxFor the maximum crop colour scale score recorded in the database, rminScoring the smallest crop colour scale recorded in the database;
adjustment factor alpha1、α2、α3Can be within a specified range, i.e., [0, 1 ]]Generating random numbers, and then optimizing coefficients through a genetic algorithm; substituting the set initial value into the objective function wijThe iterative computation can be carried out by using a genetic algorithm toolbox carried in matlab software through the genetic algorithm optimization coefficient and the initial value;
fitness function evaluation interest function w for constructing genetic algorithmijThe higher the fitness value is, the better the individual is, so as to obtain the optimal solution of the objective function, and the fitness function is as follows:
beta is a constant, different values are taken to correspond to different fitness functions, and 1, 2, 3 and the like can be taken;
when b is min { w ═ min { (ij}，Fit(wij) When the value is equal to 0.5, a is wijTo min { wijThe distance of the distance between the two points, the initial values of a and b can be set manually, and the values of a and b are usually corrected continuously by the next generation of the cross mutation evolution of the genetic algorithm, so as to obtain wijIs brought into the interestingness objective function wijThe initial adjusting coefficients alpha 1, alpha 2 and alpha 3 are optimized, and the optimal solution of the adjusting coefficients can be obtained;
according to the obtained regulating coefficient alpha1、α2、α3The optimal solution of (1) is brought into:
determining an interestingness function wijInterest function wijThe larger the size is, the more the crop growth information uploaded by the user conforms to the planting guidance data;
setting a threshold value X1 when wij<When the planting instruction is X1, generating an alarm signal, sending the alarm signal to a display unit in the client, prompting a user to plant according to the planting instruction data, setting a manual contact channel, and directly contacting the user with a worker through the manual contact channel to set a solution with pertinence;
the method for establishing the extraction model comprises the following steps: acquiring a historical crop image, and setting a corresponding crop size and a corresponding color level for the historical crop image; establishing an artificial intelligence model, wherein the artificial intelligence model is a neural network model, and dividing historical crop images and corresponding crop sizes and color levels into a training set, a test set and a check set according to a set proportion; training, testing and verifying the artificial intelligent model through a training set, a testing set and a verifying set; and marking the trained artificial intelligence model as an extraction model.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and there may be other divisions when the actual implementation is performed; the modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of the embodiment.
It will also be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above examples are only intended to illustrate the technical process of the present invention and not to limit the same, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical process of the present invention without departing from the spirit and scope of the technical process of the present invention.
Claims (8)
1. The intelligent agricultural information platform based on the wireless sensor network is characterized by comprising a database, a data acquisition module, a client, an analysis module and a server;
the data acquisition module is used for acquiring crop planting information, processing the acquired data, marking the processed data as planting guidance data and sending the planting guidance data to the database for storage;
the client comprises a growth information uploading unit, a retrieval unit and a display unit; the retrieval unit is used for retrieving planting data in the crop planting process, acquiring crop classification and planting areas needing to be retrieved by a user, retrieving in the database according to the crop classification and the planting areas, and acquiring corresponding planting guidance data when the retrieval is successful;
when the retrieval fails, sending the crop classification and planting area to a data acquisition module for data acquisition, and sending the acquired data to a database and a retrieval unit by the data acquisition module respectively;
the growth information uploading unit is used for uploading crop growth information at different planting stages by a user and sending the uploaded crop growth information to the analysis module; and the analysis module analyzes the crop growth information uploaded by the user.
2. The intelligent agricultural information platform based on wireless sensor network according to claim 1, wherein the working method of the data acquisition module comprises:
setting crop information acquisition types and data sources, acquiring data from the data sources according to the set crop information acquisition types, processing the acquired data, classifying the data after data processing, and marking classification labels as classified data;
establishing a normalization model, wherein the normalization model is a neural network model, inputting the classification data into the normalization model to obtain the normalization data, marking classification labels and area labels on the normalization data, marking the classification labels and the area labels as planting guidance data, and sending the planting guidance data to a database for storage.
3. The intelligent wireless sensor network-based agricultural information platform of claim 2, wherein the planting guidance data includes planting stage data, classification and planting area.
4. The intelligent wireless sensor network-based agricultural information platform according to claim 1, wherein the display unit is configured to display the received information.
5. The intelligent agricultural information platform based on wireless sensor network of claim 1, wherein the search unit is configured to generate a manual supplement signal when the data acquisition module does not acquire data corresponding to the classification and planting area, send the manual supplement signal to the server, send the manual supplement signal to the administrator by the server, and manually acquire corresponding planting guidance data by the administrator and upload the data to the database for storage.
6. The intelligent agricultural information platform based on wireless sensor network of claim 1, wherein the working method of the analysis module comprises:
acquiring a crop image, establishing an extraction model, and inputting the crop image into the extraction model to obtain the size, the shape and the color level of crops; the method comprises the steps of sending crop classification, planting time and planting area uploaded by a user to a database for matching, obtaining planting guide data corresponding to a planting stage, obtaining the standard size, standard shape and standard color scale of the corresponding crop according to the planting guide data, and grading the standard shape and the crop shape of the crop;
construction of cosine similarity function
Wherein i and j are the interest degree vectors of the upload data i and the standard data j respectively, and the interest degree vectors comprise: crop size, shape scores and color scales;
determining an interestingness function of the crop data based on the crop size, shape score and color scale:
a threshold value X1 is set, and,when w isij<And X1, generating an alarm signal, sending the alarm signal to a display unit in the client, and prompting a user to plant according to the planting guidance data.
7. The intelligent agricultural information platform based on wireless sensor network of claim 6, wherein the crop image comprises a crop comparison ruler, and when there is no crop comparison ruler in the crop image, the user is required to upload the crop image again.
8. The intelligent agricultural information platform based on wireless sensor network of claim 6, wherein α is1、α2、α3To adjust the coefficient, and a1、α2、α3Has a value range of [0, 1 ]]。
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114724086A (en) * | 2022-04-21 | 2022-07-08 | 安徽大旭智能科技有限公司 | Water and fertilizer integrated irrigation management system |
| CN115152613A (en) * | 2022-08-16 | 2022-10-11 | 合肥创农生物科技有限公司 | Gas dynamic balance control system based on planting cabinet |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114724086A (en) * | 2022-04-21 | 2022-07-08 | 安徽大旭智能科技有限公司 | Water and fertilizer integrated irrigation management system |
| CN115152613A (en) * | 2022-08-16 | 2022-10-11 | 合肥创农生物科技有限公司 | Gas dynamic balance control system based on planting cabinet |
| CN115152613B (en) * | 2022-08-16 | 2023-06-06 | 合肥创农生物科技有限公司 | Gas dynamic balance control system based on planting cabinet |
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