CN112930817A - Intelligent crop planting method, device, system, terminal equipment and medium - Google Patents
Intelligent crop planting method, device, system, terminal equipment and medium Download PDFInfo
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Abstract
The embodiment of the invention discloses an intelligent crop planting method, device, system, terminal equipment and medium. The intelligent planting method of the crops comprises the following steps: acquiring actual growth indexes and actual growth environment parameters of crops; and judging whether the actual growth environment parameters meet a first preset condition and/or judging whether the actual growth indexes meet a second preset condition, and generating and displaying a growth environment parameter regulation and control strategy of crops when at least one judgment result is negative. According to the intelligent planting method, the device, the system, the terminal equipment and the medium for the crops, provided by the embodiment of the invention, the actual growth indexes and the actual growth environment parameters of the crops can be obtained, and the growth environment parameter regulation and control strategy for the crops is generated through a crop growth model according to the obtained actual growth indexes and the actual growth environment parameters of the crops, so that the intelligence and the accuracy of the intelligent planting technology are improved.
Description
Technical Field
The invention belongs to the technical field of Internet of things, and particularly relates to an intelligent crop planting method, device, system, terminal equipment and medium.
Background
The traditional planting industry adopts an artificial planting mode, the labor productivity is low, the influence of environmental factors is easy to cause lower allocation efficiency of agricultural resources, and the crop structure and the regional layout are not scientific and reasonable.
In order to improve the configuration efficiency of agricultural resources and reasonably arrange crop structures and regions, an intelligent crop planting technology is provided. The intelligent crop planting technology is realized based on the Internet of things technology and the automatic control technology. The intelligent planting technology automatically carries out the farming operation or generates the production guidance suggestion according to the actual growth environmental factors of the crops and/or the judgment result of whether the growth condition of the crops reaches the preset condition, so that the working personnel carries out the relevant farming operation according to the production guidance suggestion.
The existing preset conditions are usually set according to manual experience, the manual experience mainly aims at a general planting mode of crops, the conditions can not be adjusted according to local conditions, and the most appropriate conditions can not be set according to the planting environment and the growth characteristics of the crops, so how to further improve the intelligence of the intelligent planting technology is a technical problem to be solved by the current planting industry.
Disclosure of Invention
The embodiment of the invention provides an intelligent planting method, device and system for crops, which can acquire actual growth indexes and actual growth environment parameters of the crops, and generate a growth environment parameter regulation and control strategy for the crops through a crop growth model according to the acquired actual growth indexes and actual growth environment parameters of the crops, so that the intelligence and the accuracy of an intelligent planting technology are improved.
In a first aspect, an embodiment of the present invention provides an intelligent crop planting method, which is applied to a terminal device, and includes: acquiring actual growth indexes and actual growth environment parameters of crops; judging whether the actual growth environment parameters meet a first preset condition and/or judging whether the actual growth indexes meet a second preset condition; when at least one judgment result is negative, generating and displaying a crop growth environment parameter regulation strategy; the first preset condition and the second preset condition are set by a crop growth model according to a first mapping corresponding relation between the growth environment parameters of the crops and the expected growth indexes of the crops.
As some realizations of the first aspect, the actual growing environmental parameter of the crop comprises soil fertility.
As some realizations of the first aspect, the obtaining of the actual growth indicator of the crop specifically includes: acquiring an image of a crop; and identifying the actual growth index of the crop from the image so as to obtain the actual growth index of the crop.
As some realizations of the first aspect, after acquiring the actual growth indicator and the actual growth environment parameter of the crop, the method further includes: and inputting the actual growth indexes and the actual growth environment parameters of the crops into the crop growth model so as to update the crop growth model.
As some realizations of the first aspect, the crop growth model further includes a second mapping relationship between the crop growth indicator and the growth condition evaluation result.
As some realizations of the first aspect, after acquiring the actual growth indicator and the actual growth environment parameter of the crop, the method further includes: and displaying the actual growth indexes and/or the actual growth environmental parameters of the crops.
As some realizations of the first aspect, after obtaining the actual growth indicator of the crop, the method further includes: obtaining a growth condition evaluation result of the crops according to the actual growth indexes of the crops and the crop growth model; and displaying the growth condition evaluation result of the crops.
As some realizations of the first aspect, after obtaining the actual growth indicator of the crop, the method further includes: obtaining a profit analysis result of the crops according to the actual growth indexes of the crops and the crop growth model; and displaying the income analysis result of the crops.
In a second aspect, an embodiment of the present invention provides an intelligent crop planting device, including: the acquiring unit is used for acquiring actual growth indexes and actual growth environment parameters of crops; the judging unit is used for judging whether the actual growth environment parameters meet a first preset condition and/or judging whether the actual growth indexes meet a second preset condition; the first generation unit is used for generating a growth environment parameter regulation strategy of the crops when at least one judgment result is negative; the first display unit is used for displaying the growth environment parameter regulation strategy of the crops; the first preset condition and the second preset condition are set by a crop growth model according to a first mapping relation between the growth environment parameters of the crops and the expected growth indexes of the crops.
As some realizations of the second aspect, the actual growing environmental parameter of the crop comprises soil fertility.
As some realizations of the second aspect, the obtaining unit is specifically: the acquisition subunit is used for acquiring an image of the crop; and the identification subunit is used for identifying the actual growth index of the crop from the image so as to obtain the actual growth index of the crop.
As some realizations of the second aspect, the apparatus further comprises: and the updating unit is used for inputting the actual growth indexes and the actual growth environment parameters of the crops into the crop growth model so as to update the crop growth model.
As some realizations of the second aspect, the apparatus further comprises: and the second display unit is used for displaying the actual growth indexes and the actual growth environmental parameters of the crops.
As some realizations of the second aspect, the apparatus further comprises: and the second generation unit is used for acquiring the growth condition evaluation result of the crop according to the actual growth index of the crop and the crop growth model.
As some realizations of the second aspect, the apparatus further comprises: and the third display unit is used for displaying the growth condition evaluation result of the crops.
As some realizations of the second aspect, the apparatus further comprises: and the third generation unit is used for acquiring a profit analysis result of the crops according to the actual growth indexes of the crops and the crop growth model.
As some realizations of the second aspect, the apparatus further comprises: and the fourth display unit is used for displaying the income analysis result of the crops.
In a third aspect, an embodiment of the present invention provides an intelligent crop planting system, including: the collecting device is used for collecting the actual growth indexes and the actual growth environment parameters of the crops and sending the collected actual growth indexes and the actual growth environment parameters of the crops to the processor; the processor is used for judging whether the actual growth environment parameters meet a first preset condition and/or judging whether the actual growth indexes meet a second preset condition; when at least one judgment result is negative, generating a growth environment parameter regulation strategy of the crops; the first preset condition and the second preset condition are set by a crop growth model according to a first mapping relation between a growth environment parameter of crops and an expected growth index of the crops; the display equipment is used for displaying the growth environment parameter regulation strategy of the crops.
As some realizations of the third aspect, the actual growing environmental parameter of the crop comprises soil fertility.
As some realizations of the third aspect, the processor is further configured to: and inputting the actual growth indexes and the actual growth environment parameters of the crops into the crop growth model so as to update the crop growth model.
As some realizations of the third aspect, the display device is further configured to display an actual growth indicator and/or an actual growth environment parameter of the crop.
As some realizations of the third aspect, the processor is further configured to obtain a growth status evaluation result of the crop according to the actual growth indicator of the crop and the crop growth model.
As some realizations of the third aspect, the display device is further used for displaying the growth condition evaluation result of the crop.
As some realizations of the third aspect, the processor is further configured to obtain the charging analysis result of the crop according to the actual growth indicator of the crop and the crop growth model.
As some realizations of the third aspect, the display device is further configured to display the results of the profit analysis of the crop.
In a fourth aspect, an embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored on the memory and operable on the processor, and when the computer program is executed by the processor, the steps of the method for intelligently planting crops according to the first aspect are implemented.
In a fifth aspect, embodiments of the present invention provide a computer storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing the steps of the method for intelligently growing crops according to the first aspect.
Compared with the prior art, the method has the following beneficial effects: in the embodiment of the invention, the intelligent crop planting system can collect the actual growth environment parameters and the actual growth indexes of crops, then compares the obtained growth environment parameters with the expected growth indexes of the crops, and the crop growth model sets the preset conditions which are most beneficial to the growth of the crops according to the corresponding relation between the two parameters. And comparing the obtained actual growth environment parameters of the crops and the actual growth indexes of the crops with preset conditions, and generating a growth environment parameter regulation and control strategy according to the comparison result. The setting of the preset conditions is completely automatically completed through a machine, the artificial experience is not limited, the conditions can be adjusted according to the local conditions, the adjustment is carried out according to the growth condition of crops, and the intellectualization and the accuracy of the intelligent planting technology are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a method for training a crop growth model according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for intelligently planting crops according to an embodiment of the present invention;
fig. 3 is a graphical interface diagram for uploading crop images according to an embodiment of the present invention;
fig. 4 is a graphical interface diagram of a growth environment parameter regulation strategy according to an embodiment of the present invention.
Fig. 5 is a schematic flow chart of an intelligent crop planting method according to a second embodiment of the present invention;
fig. 6 is a schematic flow chart of an intelligent crop planting method according to a third embodiment of the present invention;
fig. 7 is a schematic flow chart of an intelligent crop planting method according to a fourth embodiment of the present invention;
FIG. 8 is a graphical interface diagram of growth status evaluation provided by the fourth embodiment of the present invention;
fig. 9 is a schematic flow chart of an intelligent crop planting method according to a fifth embodiment of the present invention;
FIG. 10 is a graphical interface diagram of revenue analysis provided by example five of the present invention;
fig. 11 is a schematic device diagram of an intelligent crop planting method according to a sixth embodiment of the present invention;
fig. 12 is a system schematic diagram of an intelligent crop planting method according to a seventh embodiment of the present invention.
Fig. 13 is a schematic diagram of a hardware structure of a terminal device for implementing an embodiment of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The background technology part can know that the existing crop intelligent planting technology can not be suitable according to local conditions and can not set the most suitable conditions according to the planting environment and the growth characteristics of crops, so how to further improve the intelligence of the intelligent planting technology and the accuracy are technical problems to be solved by the current planting industry.
In order to solve the above problems, embodiments of the present invention provide an intelligent crop planting method, apparatus, system, terminal device and medium.
By using the intelligent crop planting method, the intelligent crop planting device, the intelligent crop planting system, the terminal equipment and the medium, the intelligent crop planting system can acquire the actual growth environment parameters and the actual growth indexes of crops, then compare the acquired growth environment parameters with the expected growth indexes of the crops, and the crop growth model sets the preset conditions which are most beneficial to the growth of the crops according to the corresponding relation between the two parameters. And comparing the obtained actual growth environment parameters of the crops and the actual growth indexes of the crops with preset conditions, and generating a growth environment parameter regulation and control strategy according to the comparison result. The setting of the preset conditions is completely automatically completed through a machine, the artificial experience is not limited, the conditions can be adjusted according to the local conditions, the adjustment is carried out according to the growth condition of crops, and the intellectualization and the accuracy of the intelligent planting technology are improved.
First, a crop growth model and a training method thereof will be described.
Wherein the crop growth model can be an AI (Artificial Intelligence) model,
as one implementation, as shown in fig. 1, the training method of the crop growth model may include the following steps:
s101: and acquiring a training sample set, wherein the training sample set comprises growth environment parameters of crops, crop growth indexes corresponding to the growth environment parameters, growth condition evaluation results of the crops and income analysis results of the crops.
S102: learning a first mapping relation between a growth environment parameter of a crop and a crop growth index corresponding to the growth environment parameter, learning a second mapping relation between the crop growth index and a growth condition evaluation result of the crop corresponding to the crop growth index, and learning a third mapping relation between the crop growth index and a profit analysis result of the crop corresponding to the crop growth index.
As an example, the first mapping relationship is learned by associating rule learning algorithms, such as Apriori algorithm and Eclat algorithm, to find a rule of a relationship between a growth environment parameter of a crop and a crop growth index corresponding thereto.
The first mapping relation reflects the growth environment parameters of the crops and the corresponding relation between the growth indexes of the crops corresponding to the growth environment parameters, and provides data support for guiding the farming operation and improving the production efficiency.
S103: and determining model parameters of the crop growth model according to the first mapping relation, the second mapping relation and the third mapping relation, so as to obtain the crop growth model.
The method for training the crop growth model is provided by the application. The training method comprises the steps of learning a first mapping relation between a growth environment parameter of a crop and a crop growth index corresponding to the growth environment parameter; learning a second mapping relation between the obtained crop growth indexes and growth condition evaluation results of the corresponding crops; and learning a third mapping relation between the obtained crop growth indexes and the income analysis results of the corresponding crops to obtain a crop growth model. The crop growth model can generate a growth environment parameter regulation strategy according to the obtained actual growth environment parameters of the crops and the actual growth indexes of the crops. The setting of the preset conditions is completely and automatically completed, manual experience is not limited, the conditions can be adjusted according to local conditions, the adjustment is carried out according to the growth condition of crops, and the intellectualization and the accuracy of the intelligent planting technology are improved.
Firstly, a specific implementation manner of the intelligent crop planting method provided by the embodiment of the application is introduced.
Example one
Referring to fig. 2, fig. 2 is a schematic flow chart illustrating an intelligent crop planting method according to an embodiment of the present invention. As shown in fig. 2, the intelligent crop planting method includes:
s201, acquiring actual growth indexes and actual growth environment parameters of crops.
In the embodiment of the present invention, the actual growth index of the crop is data reflecting the growth condition of the crop, and as an example, the actual growth index of the crop may be at least one of plant height, fresh weight, root length, effective dry matter accumulation amount and maximum leaf surface width. As an example, the obtained actual growth indicator of the crop may be: the plant height is 7cm, the root length is 9cm, and the effective dry matter accumulation amount is 0.08 g.
In the embodiment of the invention, the actual growth environment parameters of the crops are data capable of reflecting the growth environment of the crops, and can be the actual growth environment parameters of the crops collected by the parameter collecting equipment. As an example, the actual growth environment parameter of the crop may include at least one of soil fertility, soil humiture, air humiture, and illumination intensity. As an example, the obtained actual growth environment parameters of the crop may be: the soil humidity is 75%.
Wherein, a soil sensor can be used for collecting soil fertility; collecting soil humidity by using a soil humidity sensor; collecting the soil temperature by using a soil temperature sensor; collecting illumination intensity by using an illumination intensity sensor; collecting air temperature by using an air temperature sensor; air humidity was collected using an air humidity sensor.
It should be noted that, in the embodiment of the present application, when the actual growth environmental parameter of the crop includes soil fertility, the production operation of the crop is effectively guided according to the accurate soil fertility data, the drawback that fertilization depends on artificial experience is avoided, and the accuracy of the planting technology is further improved.
As an implementation manner of the present application, obtaining an actual growth index of a crop may include the following steps:
a1: an image of the crop is acquired.
The user obtains the crop images through the camera arranged in the planting environment, and selects to upload the crop images on the terminal interface shown in fig. 3. The left end of the interface displays the crop images selected by the user to be uploaded, a confirmation button is arranged below the interface, and the selected crop images can be uploaded by clicking the confirmation button.
A2: and identifying the actual growth index of the crop from the image so as to obtain the actual growth index of the crop.
As an implementation mode, the obtained crop image can be subjected to image recognition through an image recognition technology, and the actual growth index of the crop is obtained through recognition.
S202, judging whether the actual growth environment parameters meet a first preset condition and/or judging whether the actual growth indexes meet a second preset condition.
In the embodiment of the application, the first preset condition and the second preset condition are set by the crop growth model according to a first mapping relation between the growth environment parameter of the crop and the expected growth index of the crop.
As described above, the first preset condition and the second preset condition are set according to the crop growth model according to the first mapping relationship between the growth environment parameter of the crop and the expected growth index of the crop. For example, in a crop growth model, if the soil humidity corresponding to the expected growth index of the crop is 65% to 75%, it is determined whether the actual growth environment parameter meets a first preset condition, which specifically is: and judging whether the actual soil humidity of the crops is between 65% and 75%.
For example: and when the actual soil humidity is 60%, judging that the actual soil humidity does not meet the first preset condition. When the value of the actual soil humidity is within the interval range, such as the actual soil humidity is 73%, it is judged that the actual soil humidity satisfies the first preset condition.
In addition, in the crop growth model, if the expected crop growth index is 5cm, it is determined whether the actual growth index meets a second preset condition, which may specifically be: judging whether the plant height of the crops reaches 5cm,
for example: and when the actual root length is 3cm, judging that the actual root length does not meet a second preset condition. And when the actual root length is 5cm, judging that the actual root length meets a second preset condition.
S203, when at least one judgment result is negative, generating and displaying a crop growth environment parameter regulation strategy.
The step may specifically be: and when the actual growth environment parameter does not meet the first preset condition and the actual growth index meets the second preset condition, or when the actual growth environment parameter does not meet the first preset condition and the actual growth index does not meet the second preset condition, or when the actual growth environment parameter meets the first preset condition and the actual growth index does not meet the second preset condition, generating and displaying a growth environment parameter regulation and control strategy of the crops.
As an implementation, the crop growth environment parameter regulation and control strategy may be displayed in the form of an interface. FIG. 4 shows a control strategy interface for the growth environment parameters of a crop. Figure 4 shows a strategy for regulating soil fertility, and regulating light intensity through thinning. The method specifically comprises the following steps:
and (3) displaying topdressing types, a fertilization mode, the fertilizer element content of each mu of soil, the water content of each mu of soil and the soil humidity under the topdressing operation. Such as: the topdressing of the nitrogen fertilizer adopts a wetting mode to topdress, wherein 2.5Kg of urea, 2.5Kg of water and about 70 percent of soil humidity are adopted. "
Correspondingly displaying the growth condition, thinning mode and seedling spacing of seedlings under thinning operation, such as: the fifth true leaf grows out, and the seedlings are thinned artificially. The distance is about 10 km. ".
The above is a specific implementation manner of the intelligent crop planting method provided in the embodiment of the present application. In the specific implementation mode, the intelligent crop planting system can collect the actual growth environment parameters and the actual growth indexes of crops, then compares the obtained growth environment parameters with the expected growth indexes of the crops, and the crop growth model sets the preset conditions which are most favorable for the growth of the crops according to the first mapping relation between the two parameters. And comparing the obtained actual growth environment parameters of the crops and the actual growth indexes of the crops with preset conditions, and generating a growth environment parameter regulation and control strategy according to the comparison result. The setting of the preset conditions is completely automatically completed through a machine, the artificial experience is not limited, the conditions can be adjusted according to the local conditions, the adjustment is carried out according to the growth condition of crops, and the intellectualization and the accuracy of the intelligent planting technology are improved.
In order to further improve the accuracy of the crop growth model, the present application provides another example for updating the crop growth model, please refer to example two.
Example two
As shown in fig. 5, the intelligent crop planting method provided in the second embodiment of the present application includes the following steps:
s301, acquiring actual growth indexes and actual growth environment parameters of crops.
S302, judging whether the actual growth environment parameter meets a first preset condition and/or judging whether the actual growth index meets a second preset condition. The first preset condition and the second preset condition are set by a crop growth model according to a first mapping relation between the growth environment parameters of the crops and the expected growth indexes of the crops.
S303, when at least one judgment result is negative, generating and displaying a growth environment parameter regulation and control strategy of the crops.
S301 to S303 are the same as S201 to S203 in the first embodiment, and are not described in detail here for brevity.
S304, inputting the actual growth indexes and the actual growth environmental parameters of the crops into the crop growth model so as to update the crop growth model.
The step may specifically be: and inputting the actual growth indexes and the actual growth environment parameters of the crops acquired in the step S301 into a crop growth model, and optimizing the model parameters of the crop growth model according to the corresponding relation between the actual growth indexes and the actual growth environment parameters of the crops, so that the crop growth model is updated, and the crop growth model is more accurate.
The above is a specific implementation manner of the intelligent crop planting method provided in the second embodiment of the present application. In this specific implementation manner, in addition to the beneficial effects of the first embodiment, the crop growth model can be updated in real time by using the actual growth indexes and the actual growth environment parameters of the crops, so that the crop growth model is more accurate, and further, the crop growth model can provide a more accurate growth environment parameter regulation and control strategy for a user, thereby effectively guiding the farming operation and improving the production efficiency.
In addition, in order to enable the user to master the current growth condition of the crops, the application provides another embodiment to show the collected actual growth indexes and actual growth environment parameters of the crops, please refer to embodiment three.
EXAMPLE III
As shown in fig. 6, the intelligent crop planting method provided by the third embodiment of the present application includes the following steps:
s401, acquiring actual growth indexes and actual growth environment parameters of crops.
S402, judging whether the actual growth environment parameters meet a first preset condition and/or judging whether the actual growth indexes meet a second preset condition. The first preset condition and the second preset condition are set by a crop growth model according to a first mapping relation between the growth environment parameters of the crops and the expected growth indexes of the crops.
And S403, when at least one judgment result is negative, generating and displaying a growth environment parameter regulation strategy of the crops.
S401 to S403 are the same as S201 to S203 in the first embodiment, and for the sake of brevity, they will not be described in detail here.
And S404, displaying the actual growth indexes and/or the actual growth environmental parameters of the crops.
As an implementation of the present application, the actual growth indicator of the crop may be displayed in the form of an interface.
Fig. 3 shows the actual values of root length, leaf number, effective dry matter accumulation, fresh weight, root length and maximum leaf width of the crop and the expected values corresponding to each growth index.
The interface displays the actual growth index of the crops and the expected value corresponding to the growth index in a tabular form, so that the user can understand the actual growth index conveniently.
The third embodiment of the present application provides a specific implementation manner of an intelligent crop planting method. In this specific implementation manner, in addition to the beneficial effects of the first embodiment, the obtained actual growth index and/or actual growth environment parameter of the crop can be displayed, so that the user can know the growth condition of the crop more, and the related farming operation can be performed conveniently.
In addition, in order to show the growth condition of the current crop from multiple angles, the present application provides another embodiment to evaluate the growth condition of the crop and show the evaluation result, please refer to example four.
Example four
As shown in fig. 7, the intelligent crop planting method provided by the fourth embodiment of the present application includes the following steps:
s501, acquiring actual growth indexes and actual growth environment parameters of crops.
S502, judging whether the actual growth environment parameters meet a first preset condition and/or judging whether the actual growth indexes meet a second preset condition. The first preset condition and the second preset condition are set by a crop growth model according to a first mapping relation between the growth environment parameters of the crops and the expected growth indexes of the crops.
S503, when at least one judgment result is negative, generating and displaying a crop growth environment parameter regulation and control strategy.
S501 to S503 are the same as S201 to S203 in the first embodiment, and for the sake of brevity, will not be described in detail here.
And S504, obtaining the growth condition evaluation result of the crops according to the actual growth indexes of the crops and the crop growth model.
And S505, displaying the growth condition evaluation result of the crops.
As an implementation manner of the present application, the growth condition evaluation result of the crop can also be displayed in the form of an interface. Fig. 8 shows a result interface of the growth condition evaluation of the crop. Specifically shows the curve graphs of plant height, leaf number, effective dry matter accumulation, fresh weight, root length, maximum leaf area width and expected values corresponding to various growth indexes. And each curve graph correspondingly shows the changes of the actual values and the expected values corresponding to different growth indexes in the growth process of the crops by using two different curves. Above the graph, the actual and expected values of the growth indicator are also listed. Therefore, the growth condition of the crops is more clearly and intuitively reflected, and the understanding of the user is facilitated.
The above is a specific implementation manner of the intelligent crop planting method provided by the fourth embodiment of the present application. In this specific implementation manner, in addition to the beneficial effects of the first embodiment, the growth condition evaluation result of the crop can be obtained according to the actual growth index of the crop and the actual environmental parameter, and the obtained growth condition evaluation result is displayed. Therefore, the method is helpful for users to understand the growth condition of crops, develop farming operation and improve the production efficiency.
In addition, in order to enable the user to master the profit analysis of the current crop, the present application provides another embodiment to perform the profit analysis on the crop and display the profit analysis result, please refer to embodiment five.
EXAMPLE five
As shown in fig. 9, the intelligent crop planting method provided in the fifth embodiment of the present application includes the following steps:
s601, acquiring actual growth indexes and actual growth environment parameters of crops.
S602, judging whether the actual growth environment parameter meets a first preset condition and/or judging whether the actual growth index meets a second preset condition. The first preset condition and the second preset condition are set by a crop growth model according to a first mapping relation between the growth environment parameters of the crops and the expected growth indexes of the crops.
S603, when at least one judgment result is negative, generating and displaying a growth environment parameter regulation and control strategy of the crops.
S601 to S603 are the same as S201 to S203 in the first embodiment, and for the sake of brevity, they will not be described in detail here.
And S604, obtaining a profit analysis result of the crops according to the actual growth indexes of the crops and the crop growth model.
And S605, displaying the income analysis result of the crops.
As an implementation of the present application, the income analysis results of the crops can also be displayed in the form of an interface. FIG. 10 shows a revenue analysis results interface for a crop. And specifically showing the predicted revenue, investment analysis and revenue analysis.
Wherein the investment analysis comprises the estimation of personnel working hours, investment, personnel cost and investment cost. For example: the inputs may include: organic fertilizer, urea, compound fertilizer, potassium sulfate and Chinese cabbage seeds. Correspondingly, the cost of the input product is estimated to be 1247 yuan.
The revenue analysis includes, among other things, projected yield and projected sales. For example: the expected yield is 2500 kg and the expected sales is 12500 Yuan.
And the income analysis result is shown to the user, so that the user can know the input cost and expected income in the planting process more, and the user can distribute funds more reasonably to carry out agricultural production.
The above is a specific implementation manner of the intelligent crop planting method provided by the application. In this specific implementation manner, in addition to the beneficial effects of the first embodiment, a profit analysis result of the crop can be obtained according to the actual growth index and the actual environmental parameter of the crop, and the obtained profit analysis result is displayed. The method can enable the user to visually know the income analysis and the investment analysis of the crop growth, and is beneficial to reasonably planning the farm operation.
The above is a specific implementation manner of the intelligent crop planting method provided by the embodiment of the application. Based on the concrete implementation mode of the method, correspondingly, the application also provides a concrete implementation mode of the intelligent crop planting device. Please see example six.
EXAMPLE six
Fig. 11 shows a schematic structural diagram of an intelligent crop planting device 600 according to a sixth embodiment of the present invention. As shown in fig. 11, the apparatus includes:
an obtaining unit 1101, configured to obtain an actual growth index and an actual growth environment parameter of a crop;
a determining unit 1102, configured to determine whether the actual growth environment parameter meets a first preset condition, and/or determine whether the actual growth indicator meets a second preset condition;
a first generating unit 1103, configured to generate a growing environment parameter control policy for the crop when at least one of the determination results is negative.
And the first display unit 1104 is used for displaying the growth environment parameter regulation strategy of the crops.
As an implementation manner of the present application, the actual growth environment parameter acquired by the acquiring unit 1001 includes at least one of soil fertility, soil temperature and humidity, air temperature and humidity, and illumination intensity.
The actual growth index obtained by the obtaining unit 1101 includes at least one of plant height, fresh weight, root length, effective dry matter accumulation amount, and maximum leaf surface width.
As an implementation manner of the present application, the obtaining unit 1101 may further include:
the acquisition subunit is used for acquiring an image of the crop;
and the identification subunit is used for identifying the actual growth index of the crop from the image so as to obtain the actual growth index of the crop.
As an implementation of this application, the intelligent planting device of crops can also include:
and the updating unit is used for inputting the actual growth indexes and the actual growth environment parameters of the crops into the crop growth model so as to update the crop growth model.
As an implementation of this application, the intelligent planting device of crops can also include:
the second display unit 1114 is used for displaying the actual growth indexes and the actual growth environmental parameters of the crops.
As an implementation of this application, the intelligent planting device of crops can also include:
and a second generating unit 1113, configured to obtain a growth condition evaluation result of the crop according to the actual growth indicator of the crop and the crop growth model.
As an implementation of this application, the intelligent planting device of crops can also include:
a third display unit 1124 for displaying the growth status evaluation result of the crop.
As an implementation of this application, the intelligent planting device of crops can also include:
a third generating unit 1123, configured to obtain a revenue analysis result of the crop according to the actual growth indicator of the crop and the crop growth model.
As an implementation of this application, the intelligent planting device of crops can also include:
a fourth display unit 1134, configured to display the income analysis result of the crop.
The above intelligent planting device for crops provided by the application. This intelligent planting device of crops can accept the actual growth index and the actual growth environmental parameter of the crops that obtain the unit and upload through terminal equipment, judge through the judging unit whether actual growth index with the actual growth environmental parameter satisfies the preset condition, by the growth environmental parameter regulation and control strategy of first generating unit according to the judgement result generation crops of judging unit to show growth environmental parameter regulation and control strategy on terminal equipment through the display unit. The setting of the preset conditions is completely automatically completed through a machine, the artificial experience is not limited, the conditions can be adjusted according to the local conditions, the adjustment is carried out according to the growth condition of crops, and the intellectualization and the accuracy of the intelligent planting technology are improved.
The above is the concrete implementation of the crops intelligence planting device that this application embodiment provided. Based on the concrete implementation of the device, correspondingly, this application still provides the concrete implementation of crops intelligence planting system. Please see example seven.
EXAMPLE seven
Fig. 12 shows a schematic structural diagram of an intelligent crop planting system 1200 according to a seventh embodiment of the present invention. As shown in fig. 12, the system includes: acquisition device 1210, processor 1220 and presentation device 1230.
The collecting device 1210 is used for collecting an actual growth index and an actual growth environment parameter of the crop and sending the collected actual growth index and the actual growth environment parameter of the crop to the processor.
As an implementation of this application, collection equipment can include camera, soil sensor, soil temperature and humidity sensor, at least one in air temperature and humidity sensor, the illumination intensity sensor.
The processor 1220 is configured to receive the actual growth indicator and the actual growth environment parameter of the crop uploaded by the collecting device, and input the actual growth indicator and the actual growth environment parameter of the crop into the crop growth model. The crop growth model compares the obtained actual growth indexes and actual growth environment parameters of the crops with preset conditions, and generates a growth environment parameter regulation and control strategy according to the comparison result.
As an embodiment of the present application, the processor may be further configured to obtain a growth status evaluation result of the crop according to the obtained actual growth indicator of the crop through the crop growth model.
As an embodiment of the present application, the processor may be further configured to obtain a profit analysis result of the crop according to the obtained actual growth indicator of the crop through the crop growth model. The display device 1230 is used for displaying the generated growth environment parameter regulation strategy.
As an implementation of the present application, the display device includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, and the like.
As an implementation manner of the application, the display equipment can also display the growth condition evaluation result of the crops.
As an implementation of the present application, the display device may also display the revenue analysis results of the crops.
Fig. 13 is a schematic diagram of a hardware structure of a terminal device for implementing various embodiments of the present invention.
The terminal device 1300 includes but is not limited to: a radio frequency unit 1301, a network module 1302, an audio output unit 1303, an input unit 1304, a sensor 1305, a display unit 1306, a user input unit 1307, an interface unit 1308, a memory 1309, a processor 1310, a power supply 1311, and the like. Those skilled in the art will appreciate that the terminal device configuration shown in fig. 13 does not constitute a limitation of the terminal device, and that the terminal device may include more or fewer components than shown, or combine certain components, or a different arrangement of components. In the embodiment of the present invention, the terminal device includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted terminal, a wearable device, a pedometer, and the like.
The processor 1310 is configured to receive the actual growth index and the actual growth environment parameter of the crop, which are uploaded by the acquisition device, and input the actual growth index and the actual growth environment parameter of the crop into the crop growth model. The crop growth model compares the obtained actual growth indexes and actual growth environment parameters of the crops with preset conditions, and generates a growth environment parameter regulation and control strategy according to the comparison result.
In the embodiment of the invention, after the terminal equipment acquires the actual growth indexes and the actual growth environment parameters of the crops, the acquired actual growth environment parameters and the actual growth indexes of the crops are compared with the preset conditions, and the growth environment parameter regulation and control strategy is generated according to the comparison result. The setting of the preset conditions is completely automatically completed through a machine, the artificial experience is not limited, the conditions can be adjusted according to the local conditions, the adjustment is carried out according to the growth condition of crops, and the intellectualization and the accuracy of the intelligent planting technology are improved.
It should be understood that, in the embodiment of the present invention, the radio frequency unit 1301 may be configured to receive and transmit signals during a message transmission or call process, and specifically, receive downlink data from a base station and then process the received downlink data to the processor 1310; in addition, the uplink data is transmitted to the base station. Typically, the radio frequency unit 1201 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 1301 can also communicate with a network and other devices through a wireless communication system.
The terminal device provides wireless broadband internet access to the user through the network module 1302, such as helping the user send and receive e-mails, browse web pages, and access streaming media.
The audio output unit 1303 can convert audio data received by the radio frequency unit 1301 or the network module 1302 or stored in the memory 1309 into an audio signal and output as sound. Also, the audio output unit 1303 can also provide audio output related to a specific function performed by the terminal apparatus 1300 (e.g., a call signal reception sound, a message reception sound, and the like). The audio output unit 503 includes a speaker, a buzzer, a receiver, and the like.
The input unit 1304 is used to receive audio or video signals. The input Unit 1304 may include a Graphics Processing Unit (GPU) 13041 and a microphone 13042, and the Graphics processor 13041 processes image data of still pictures or video obtained by an image capturing apparatus (such as a camera) in a video capture mode or an image capture mode. The processed image frames may be displayed on the display unit 1306. The image frames processed by the graphic processor 13041 may be stored in the memory 1309 (or other storage medium) or transmitted via the radio frequency unit 1301 or the network module 1302. The microphone 13042 can receive sounds and can process such sounds into audio data. The processed audio data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 1301 in case of a phone call mode.
The display unit 1306 is used to display information input by a user or information provided to the user. The display unit 1306 may include a display panel 13061, and the display panel 13061 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.
The user input unit 1307 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the terminal device. Specifically, the user input unit 1307 includes a touch panel 13071 and other input devices 13072. Touch panel 13071, also referred to as a touch screen, may collect touch operations by a user on or near it (e.g., operations by a user on touch panel 13071 or near touch panel 13071 using a finger, stylus, or any other suitable object or attachment). The touch panel 13071 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 1310, and receives and executes commands sent from the processor 1310. In addition, the touch panel 13071 can be implemented by various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 13071, the user input unit 507 may include other input devices 13072. In particular, the other input devices 13072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described herein again.
Further, the touch panel 13071 can be overlaid on the display panel 13061, and when the touch panel 13071 detects a touch operation on or near the touch panel, the touch operation can be transmitted to the processor 1310 to determine the type of the touch event, and then the processor 1310 can provide a corresponding visual output on the display panel 13061 according to the type of the touch event. Although in fig. 13, the touch panel 13071 and the display panel 13061 are two independent components to implement the input and output functions of the terminal device, in some embodiments, the touch panel 13071 and the display panel 13061 may be integrated to implement the input and output functions of the terminal device, and are not limited herein.
The interface unit 1308 is an interface for connecting an external device to the terminal apparatus 1300. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. Interface unit 1308 can be used to receive input from an external device (e.g., data information, power, etc.) and transmit the received input to one or more elements within terminal apparatus 1300 or can be used to transmit data between terminal apparatus 1300 and an external device.
The memory 1309 may be used to store software programs as well as various data. The memory 1309 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 1309 can include high-speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 1310 is a control center of the terminal device, connects various parts of the entire terminal device by using various interfaces and lines, and performs various functions of the terminal device and processes data by running or executing software programs and/or modules stored in the memory 1309 and calling data stored in the memory 1309, thereby performing overall monitoring of the terminal device. Processor 1310 may include one or more processing units; preferably, the processor 1310 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 1310.
The terminal device 1300 may further include a power supply 1311 (e.g., a battery) for supplying power to the various components, and preferably, the power supply 1311 may be logically connected to the processor 1310 via a power management system, so that functions of managing charging, discharging, and power consumption are performed via the power management system.
In addition, the terminal device 1300 includes some functional modules that are not shown, and are not described herein again.
Preferably, an embodiment of the present invention further provides a terminal device, which includes a processor 1310, a memory 1309, and a computer program that is stored in the memory 1309 and can be run on the processor 1310, where the computer program, when executed by the processor 1310, implements each process of the task management method embodiment, and can achieve the same technical effect, and details are not described here to avoid repetition.
The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program realizes each process of the intelligent planting method embodiment, and can achieve the same technical effect, and in order to avoid repetition, the details are not repeated here. The computer-readable storage medium may be a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (14)
1. An intelligent crop planting method is characterized by comprising the following steps:
acquiring actual growth indexes and actual growth environment parameters of crops;
judging whether the actual growth environment parameters meet a first preset condition and/or judging whether the actual growth indexes meet a second preset condition;
when at least one judgment result is negative, generating and displaying a crop growth environment parameter regulation strategy; the first preset condition and the second preset condition are set by a crop growth model according to a first mapping relation between the growth environment parameters of the crops and the expected growth indexes of the crops.
2. The method of claim 1, wherein the actual environmental parameter for the growth of the crop comprises soil fertility.
3. The method according to claim 1, wherein the obtaining of the actual growth indicator of the crop is specifically:
acquiring an image of a crop;
and identifying the actual growth index of the crop from the image so as to obtain the actual growth index of the crop.
4. The method of claim 1, wherein after obtaining the actual growth indicator and the actual growth environment parameter of the crop, further comprising:
and inputting the actual growth indexes and the actual growth environment parameters of the crops into the crop growth model so as to update the crop growth model.
5. The method of claim 1, wherein after obtaining the actual growth indicator and the actual growth environment parameter of the crop, further comprising:
and displaying the actual growth indexes and/or the actual growth environmental parameters of the crops.
6. The method according to claim 1, wherein the crop growth model further comprises a second mapping relationship between the crop growth indicator and the growth status evaluation result;
after the actual growth index of the crop is obtained, the method further comprises the following steps:
obtaining a growth condition evaluation result of the crops according to the actual growth indexes of the crops and the second mapping relation;
and displaying the growth condition evaluation result of the crops.
7. The method of claim 1, wherein the crop growth model further comprises a third mapping of crop growth indicators to yield analysis results;
after the actual growth index of the crop is obtained, the method further comprises the following steps:
and obtaining a crop income analysis result according to the actual growth index of the crop and the third mapping relation.
8. The method according to any one of claims 1-7, further comprising:
training the crop growth model;
the training of the crop growth model specifically comprises the following steps:
acquiring a training sample set, wherein the training sample set comprises growth environment parameters of crops and crop growth indexes corresponding to the growth environment parameters;
learning a first mapping relation between the growth environment parameters of the crops and the crop growth indexes corresponding to the growth environment parameters;
and determining the model parameters of the crop growth model according to the first mapping relation, thereby obtaining the crop growth model.
9. The method of claim 8, wherein the training sample set further comprises a growth status assessment result of the crop corresponding to the crop growth indicator;
the training the crop growth model further comprises:
learning a second mapping relation between the crop growth indexes and the growth condition evaluation results of the crops corresponding to the crop growth indexes;
determining model parameters of the crop growth model according to the first mapping relation, so as to obtain the crop growth model, specifically:
and determining model parameters of the crop growth model according to the first mapping relation and the second mapping relation, so as to obtain the crop growth model.
10. The method of claim 9, wherein the training sample set further comprises a revenue analysis result for the crop corresponding to the crop growth indicator;
the training the crop growth model further comprises:
learning a third mapping relation between the crop growth index and a revenue analysis result of the crop corresponding to the crop growth index;
determining model parameters of the crop growth model according to the first mapping relation and the second mapping relation, so as to obtain the crop growth model, specifically:
and determining model parameters of the crop growth model according to the first mapping relation, the second mapping relation and the third mapping relation, so as to obtain the crop growth model.
11. The utility model provides an intelligent planting device of crops which characterized in that includes:
the acquiring unit is used for acquiring actual growth indexes and actual growth environment parameters of crops;
the judging unit is used for judging whether the actual growth environment parameters meet a first preset condition and/or judging whether the actual growth indexes meet a second preset condition;
the first generation unit is used for generating a growth environment parameter regulation strategy of the crops when at least one judgment result is negative;
the first display unit is used for displaying the growth environment parameter regulation strategy of the crops;
the first preset condition and the second preset condition are set by a crop growth model according to a first mapping relation between the growth environment parameters of the crops and the expected growth indexes of the crops.
12. An intelligent crop planting system is characterized by comprising a collecting device, a processor and a display device;
the collecting equipment is used for collecting the actual growth indexes and the actual growth environment parameters of the crops and sending the collected actual growth indexes and the actual growth environment parameters of the crops to the processor;
the processor is used for judging whether the actual growth environment parameters meet a first preset condition and/or judging whether the actual growth indexes meet a second preset condition; when at least one judgment result is negative, generating a growth environment parameter regulation strategy of the crops; the first preset condition and the second preset condition are set by a crop growth model according to a first mapping relation between a growth environment parameter of crops and an expected growth index of the crops;
the display equipment is used for displaying the growth environment parameter regulation strategy of the crops.
13. A terminal device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method for smart planting of crops as claimed in any one of claims 1 to 10.
14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for smart planting of crops as claimed in any one of claims 1 to 10.
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| CN105446309A (en) * | 2015-12-31 | 2016-03-30 | 西北工业大学 | Agricultural intelligent analysis monitoring system based on IOT (Internet of Things) |
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| CN109270909A (en) * | 2018-10-30 | 2019-01-25 | 四川洪诚电气科技有限公司 | Crops supervisory systems and method based on Internet of Things |
| CN110347127A (en) * | 2019-06-26 | 2019-10-18 | 北京农业智能装备技术研究中心 | Crop planting mandatory system and method based on cloud service |
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