CN108053078A - A kind of production prediction method, server and computer readable storage medium - Google Patents
A kind of production prediction method, server and computer readable storage medium Download PDFInfo
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Abstract
The present embodiments relate to agriculture fields, disclose a kind of production prediction method, server and computer readable storage medium.In the present invention, client is obtained by crop sales volume and/or customer evaluation and likes crop, and the client of acquisition is liked into crop, as target crop；The quantity of the target crop in predeterminable area is obtained, and obtains the weight data of the single target crop in predeterminable area；According to the quantity of target crop and the weight data of single target crop, crop forecast production of the affiliated batch of target crop in predeterminable area is obtained.So that user would know that accurate client likes the forecast production of crop, then can planting scheme be adjusted according to forecast production, be conducive to improve user's income.
Description
Technical Field
The embodiment of the invention relates to the field of agriculture, in particular to a yield prediction method, a server and a computer-readable storage medium.
Background
In the prior art, in order to predict the crop yield of land planting, the yield of crops at the next year of harvest is usually analyzed by combining factors such as climate, soil, crop growth period, plant quality and the like with the yield data of past crops, and due to factors such as soil, weather and the like, the yield of all crops in a defined area is usually predicted when the crop yield is predicted. For the user, the yield of the crops is known in advance, and a subsequent working plan, such as arrangement of transportation, management and marketing and the like, can be made in time according to the predicted yield. At present, the yield of crops per mu per year is generally predicted according to the single fruit weight, the fruit bearing spike number of each plant and the number of plants per mu in China.
The inventor finds that at least the following problems exist in the prior art: in the prior art, due to the fact that prediction depends on uncertain factors such as climate and plant quality and the prediction period is basically long, the prediction result is not accurate and has large deviation, and the prediction cannot provide effective help for improving the planting method.
Disclosure of Invention
An object of an embodiment of the present invention is to provide a yield prediction method, a server, and a computer-readable storage medium, so that a user can obtain a more accurate predicted yield of a customer favorite crop, and then can adjust a planting plan according to the predicted yield, which is beneficial to improving user profits.
To solve the above technical problem, an embodiment of the present invention provides a yield prediction method, including:
obtaining the favorite crops of the client through the crop sales volume and/or the client evaluation, and taking the obtained favorite crops of the client as target crops;
acquiring the number of target crops in a preset area, and acquiring weight data of a single target crop in the preset area;
and obtaining the predicted crop yield of the batch to which the target crop belongs in the preset area according to the number of the target crops and the weight data of the single target crop.
An embodiment of the present invention further provides a server, including: at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a yield prediction method as described above.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the yield prediction method
Compared with the prior art, the method and the device have the advantages that the favorite crops of the customers are obtained through crop sales and/or customer evaluation, the obtained favorite crops of the customers are used as the target crops, so that the users can know the types of the favorite crops of the customers, the predicted yield of the crops of the batch to which the target crops belong in the preset area is obtained through obtaining the number of the target crops in the preset area and obtaining the weight data of the single target crop in the preset area according to the number of the target crops and the weight data of the single target crop, uncertain factors such as weather and plant quality are not needed, long-period yield prediction is not needed, the users can know the more accurate predicted yield of the favorite crops of the customers, then the planting plan can be adjusted according to the predicted yield, and the user income is improved.
In addition, acquiring the number of target crops in the preset area specifically comprises: acquiring characteristic information of a target crop; acquiring a picture of a preset area; and identifying the number of objects with the characteristic information in the picture, and taking the number of the objects as the number of the target crops in the preset area. The number of the target crops in the preset area is obtained by analyzing the number of the objects with the characteristic information of the target crops in the picture, so that the accurate number of the target crops in the preset area can be obtained without a large amount of manpower, and the manpower cost is reduced.
In addition, acquiring weight data of a single target crop in a preset area specifically comprises the following steps: acquiring weight data of a preset number of target crops in a preset area; obtaining average weight data of the single target crop in a preset area according to the weight data of the preset number of target crops; and taking the average weight data of the single target crop as the weight data of the single target crop. The weight data is obtained by measuring the weight of the preset number of target crops, so that the average weight data of a single target crop can be obtained, and the cost of human resources can be reduced while the accuracy of the data is ensured to the maximum extent by taking the average weight data of the single target crop as the weight data of the single target crop.
In addition, after the predicted yield of the crop of the batch to which the target crop belongs in the preset area is obtained, the method further comprises the following steps: obtaining the crop yield of the target crop batch in the preset area according to the tracing information; judging whether the crop yield is matched with the predicted crop yield or not; and if the judgment result is yes, judging that the tracing information is accurate. By comparing the predicted yield of the target crop with the crop yield obtained according to the tracing information, whether the crop yield is matched with the predicted yield or not is known, and if not, the tracing information is described to be wrong, so that the tracing information of the user crop is prevented from being stolen by other people, and the user rights and interests are guaranteed.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.
FIG. 1 is a flow chart of a yield prediction method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a yield prediction method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a server according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it will be appreciated by those of ordinary skill in the art that numerous technical details are set forth in order to provide a better understanding of the present application in various embodiments of the present invention. However, the technical solution claimed in the present application can be implemented without these technical details and various changes and modifications based on the following embodiments.
A first embodiment of the present invention relates to a yield prediction method. The core of the embodiment lies in that the favorite crops of the client are obtained through the crop sales volume and/or the client evaluation, and the obtained favorite crops of the client are used as target crops; acquiring the number of target crops in a preset area, and acquiring weight data of a single target crop in the preset area; according to the number of the target crops and the weight data of a single target crop, the predicted crop yield of the batch to which the target crop belongs in the preset area is obtained, so that a user can know the more accurate predicted yield of the favorite crops of the customer, then, a planting plan can be adjusted according to the predicted yield, and the user income is improved. The following describes the implementation details of the yield prediction method of the present embodiment in detail, and the following is only provided for the convenience of understanding and is not necessary for implementing the present embodiment.
The yield prediction method in the embodiment can be used to verify the accuracy of the tracing information of crops such as tomatoes, apples, oranges, etc., and the crops are taken as tomatoes as an example to be described below. The specific flow chart is shown in fig. 1, and comprises:
and step S101, acquiring the favorite crops of the customer through the crop sales volume and/or the customer evaluation, and taking the acquired favorite crops of the customer as target crops.
Specifically, the shop owner can record the crop sales volume of the shop and then upload the crop sales volume to the server, so that the server can know which crop is most popular with the client according to the crop sales volume recorded by the shop owner. If the customer purchases the crops on the network, the customer can also leave a purchase record, the server can collect the crop sales data of various markets, and the types of the crops most popular with the customer are obtained according to the crop sales data of the markets. The server can also know the most popular crop types of the customers according to the evaluation left by the customers after the market consumption, such as: after the customer purchases the tomatoes in an online market or a real market, the preference degree of the tomatoes can be scored on the market app through the mobile phone, the higher the score is, the more the customer likes the tomatoes, and the server can judge which crop is the favorite crop of the customer according to the scores of the tomatoes and the customer for other crops. After the types of the crops which are favored by the customers are acquired, the types of the crops which are favored by the customers are set as target crops.
Step S102, acquiring the number of target crops in a preset area, and acquiring weight data of a single target crop in the preset area.
Specifically, it is preferable that the target crop is a crop having a harvest cycle of about one week, that is, a crop having a short harvest cycle, and the yield is predicted more accurately, and the target crop number in the preset area is to be obtained when the yield of the target crop is predicted.
Example 1: the method comprises the steps of obtaining characteristic information of target crops, obtaining pictures of preset areas, identifying the number of objects with the characteristic information in the pictures, and taking the number of the objects as the number of the target crops in the preset areas.
Specifically, the target crop has its own characteristic information, which includes: the shape of the target crop, the size of the target crop, the color of the target crop, etc., i.e., the target crop can be identified from the plurality of objects by using the characteristic information. And the area of presetting the region for user self setting can be for the region of user's planting crop, and the usable smart machine of user, unmanned aerial vehicle like carrying the camera acquire target area image information. In the following, an unmanned aerial vehicle is described as an intelligent device, but the present invention is not limited to this.
When the user utilizes unmanned aerial vehicle to acquire regional image information, after the scope of the preset region of input, unmanned aerial vehicle can carry out automatic sampling, then send the picture of the preset region of sampling to the server through wiFi on the unmanned aerial vehicle or bluetooth equipment, after the server obtained the picture that unmanned aerial vehicle transmitted, application image recognition technology, the object quantity that has characteristic information in the discernment image, because the object that has characteristic information can be regarded as the target crop, therefore the image in the region is preset in the accessible discernment, acquire the quantity of target crop in the preset region. The accurate target crop number in the preset area can be obtained without a large amount of manpower, the manpower cost is reduced, and the intellectualization of data acquisition is realized.
Example 2: the method comprises the steps of obtaining the number of target crops on plants with preset number, obtaining the number of plants in a preset area, and obtaining the number of the target crops in the preset area according to the number of the target crops on the plants with the preset number and the number of the plants in the preset area.
Specifically, the target crops on a certain number of plants can be collected first, and the number of the target crops on an average plant can be calculated by calculating the number of the collected target crops. Then, the number of plants in the preset area is obtained through manual calculation or a method of identifying the preset area picture after the preset area picture is obtained through intelligent equipment, and the number of target crops in the preset area can be obtained through calculation according to the number of plants in the preset area and the number of target crops on the average plant.
Taking tomatoes as an example, the tomatoes are divided into large fruits, medium fruits and small fruits according to the fruit diameter of the tomatoes, the large fruits are used as target crops, 36 tomatoes are selected in a preset area, and the number of the large fruits of each tomato to be harvested is recorded respectively. It should be noted that in the present embodiment, the number of samples is not limited to 36, and the user can determine the number of samples according to actual needs, and the invention should not be limited thereto. And (3) calculating the total number of big fruits on 36 tomatoes, dividing the total number of the big fruits by 36 to obtain the average number of the big fruits on one tomato (represented by N1), and multiplying N1 by the number N of the plants in the preset area to obtain the number of the target crops in the preset area.
The weight data of the single target crop in the preset area can be obtained by the following method, but the method is not limited in practical implementation.
The method comprises the steps of obtaining weight data of a preset number of target crops in a preset area, obtaining average weight data of a single target crop in the preset area according to the weight data of the preset number of target crops, and using the average weight data of the single target crop as the weight data of the single target crop.
The user firstly obtains a certain amount of target crops in a preset area and calculates the total weight data of the target crops, the average weight of a single target crop can be obtained by dividing the obtained weight data by the number of the selected target crops, the average weight data of the single target crop can be used as the weight data of the single target crop, and the accuracy of the data can be guaranteed to the maximum extent, and meanwhile, the labor cost is reduced.
Taking a tomato as an example, the tomato fruit has big and small sizes, and when the target crop is the big fruit of the tomato, the tomato can be divided into big fruit, medium fruit and small fruit according to the size of the fruit diameter of the tomato. Taking 30-100 samples of the big fruits, weighing the weight data of the samples, and then calculating the average weight data of the single big fruits as the weight data of the single big fruits.
And S103, obtaining the predicted crop yield of the batch to which the target crop belongs in the preset area according to the number of the target crops and the weight data of the single target crop.
Specifically, after the number of the target crops and the weight data of a single target crop are obtained, the total weight of the target crops in the preset area is obtained by multiplying the number of the target crops and the weight data of the single target crop, and since the target crops are already on the plant, uncertain factors such as soil, climate and the like are not needed for the yield prediction result, the yield prediction obtained according to the obtained number of the target crops in the preset area and the weight data of the single target crop is accurate.
The total weight of the obtained target crops in the preset area can be used as the predicted yield of the crops in the preset area of the batch to which the target crops belong. The predicted yield of the batch of the target crops, namely the total weight of the batch of the target crops when the target crops are shipped, enables a user to know the more accurate predicted yield of the favorite crops of the customer, then adjusts a planting plan according to the predicted yield, improves the income of the user, masters the predicted yield of the favorite crops of the customer, timely adjusts the supply-demand relationship, increases the benefit of the user, determines the optimal yield of the favorite crops of the customer in a period by comparing the predicted yields of the favorite crops of the customer in multiple batches in the same area, determines the optimal yield of the favorite crops of the customer in the optimal period, and improves the quality of the favorite crops of the customer.
Taking the example of example 2 in step S102 as an example, the number of big fruits of the tomato in the preset area is N1 × N, the single fruit weight data of the big fruits is W, i.e. the predicted yield l (kg) of the crop in the preset area of the batch to which the big fruits of the tomato belong is N1 × W/1000 kg.
In order to compare the predicted yield of the target crops in different areas, the area areas of the different areas can be obtained, the area areas of the different areas and the predicted yield of the target crops are filled in a table, and the area with the highest predicted yield of the crops in the batch to which the target crops belong in unit area is obtained by comparison. These data have irreplaceable effects on how the user improves the planting plan. When the lot to which the target crop subjected to yield prediction belongs is shipped, whether or not the method for predicting yield still needs to be improved can be determined by comparing the crop yield of the target crop actually shipped with the predicted yield of the crop of the lot to which the target crop belongs.
Compared with the prior art, the first embodiment of the invention obtains the favorite crops of the customer through the crop sales volume and/or the customer evaluation, obtains the number of the favorite crops of the customer in the preset area, obtains the weight data of the favorite crops of the customer in the preset area, and finally obtains the predicted crop yield of the batch to which the favorite crops of the customer belong in the preset area, so that the user can know the more accurate predicted yield of the favorite crops of the customer, and then can adjust the planting plan according to the predicted yield, thereby being beneficial to improving the user income.
A second embodiment of the present invention relates to a yield prediction method. The embodiment is further improved on the basis of the first embodiment, and the specific improvement is as follows: in this embodiment, after obtaining the predicted crop yield of the batch to which the target crop belongs in the preset region, the method further includes: obtaining the crop yield of the target crop batch in the preset area according to the tracing information; judging whether the crop yield is matched with the predicted crop yield or not; and if the judgment result is yes, judging that the tracing information is accurate. The flow chart is shown in fig. 2, and comprises:
step S201, the favorite crops of the customer are obtained through the crop sales volume and/or the customer evaluation, and the obtained favorite crops of the customer are used as target crops. Since step 201 in this embodiment is substantially the same as step 101 in the first embodiment, it is not described herein again.
Step S202, the number of target crops in a preset area is obtained, and weight data of a single target crop in the preset area is obtained. Since step 202 in this embodiment is substantially the same as step 102 in the first embodiment, it is not repeated here.
Step S203, obtaining the predicted crop yield of the batch to which the target crop belongs in the preset area according to the number of the target crops and the weight data of the single target crop. Since step 203 in this embodiment is substantially the same as step 103 in the first embodiment, it is not described herein again.
And step S204, acquiring the crop yield of the target crop batch in the preset area according to the tracing information.
Specifically, the traceability information may be information such as a traceability code of the crop, a circulation of the target crop, and the like. Because links required for tracing and the detailed degree of each link information can be defined according to different crop types and the actual needs of local tracing through the tracing information, the target crop yield of batches in the preset area can be counted, and the counted target yield is the actual crop yield. That is, the crop yield of the batch in the preset area obtained by the retroactive information is used as a measure for determining whether the predicted yield of the crop of the batch to which the target crop belongs in the preset area is accurate.
For example, from the crop yield of a batch in a preset area, 500 kg of tomatoes are sold, 100 kg of tomatoes are issued to employees, and 600 kg of crop yield of the batch can be obtained through the traceability information.
It should be noted that the predicted yield of the batch to which the crop belongs in the preset area obtained in step 103 is the predicted yield of the batch to which the target crop belongs when the target crop is not delivered, and the yield of the batch of the crop in the preset area obtained in step 204 is the yield of the target crop after the target crop is delivered.
And S205, judging whether the crop yield is matched with the predicted crop yield, if so, entering S206, and if not, entering S207.
Specifically, whether the crop yield is matched with the predicted crop yield can be judged by judging whether the absolute value of the difference value between the crop yield and the predicted crop yield is within a preset threshold. For example, if the preset threshold is 50 kg, the predicted crop yield of the batch to which the crop belongs in the preset area is 1000kg through the step 102, the crop yield of the batch in the preset area is 900 kg through the trace back information in the step 204, and the absolute value of the difference between the crop yield and the predicted crop yield is 100 kg and is greater than the preset threshold (50 kg), so that it can be determined that the crop yield and the predicted crop yield are not matched, and the process proceeds to the step 207. Accordingly, if the predicted crop yield of the batch to which the crop belongs in the preset area is 1000kg through step 102, and the crop yield of the batch in the preset area is 980 kg through the retroactive information in step 103, the absolute value of the difference between the crop yield and the predicted crop yield is 20 kg, which is smaller than the preset threshold (50 kg), it can be determined that the crop yield and the predicted crop yield match, and the process proceeds to step 206.
And step S206, judging that the tracing information is accurate.
Specifically, if the crop yield of the target crop is matched with the predicted crop yield, the description that the tracing information is really generated according to the circulation route of the target crop generating the predicted crop yield and the description that the tracing information is not generated according to the circulation routes of other batches of target crops or other crops is provided, so that the client can know the authenticity of the tracing information by inquiring the predicted yield of the target crop, the condition that a bad merchant steals or fakes the tracing information is prevented, and property and safety loss is brought to the client and the user who purchase the target crop.
Step S207, it is determined that the trace back information is inaccurate.
That is, if the crop yield of the target crop does not match the predicted crop yield, it can be described from the side that the traceability information has a problem, for example, the traceability information has a problem of being stolen or forged by a merchant. The authenticity of the tracing information can be judged in time, and the property safety of the customer and the user who buy the target crop is further ensured.
The steps of the above methods are divided for clarity, and the implementation may be combined into one step or split some steps, and the steps are divided into multiple steps, so long as the same logical relationship is included, which are all within the protection scope of the present patent; it is within the scope of the patent to add insignificant modifications to the algorithms or processes or to introduce insignificant design changes to the core design without changing the algorithms or processes.
A third embodiment of the invention relates to a server, as shown in fig. 3, comprising at least one processor 31; and the number of the first and second groups,
a memory 32 communicatively coupled to the at least one processor 31; wherein,
the memory 32 stores instructions executable by the at least one processor 31, the instructions being executable by the at least one processor 31 to enable the at least one processor 31 to perform the yield prediction method according to the first and second embodiments of the present invention.
Where the memory 32 and the processor 31 are coupled in a bus, the bus may comprise any number of interconnected buses and bridges, the buses coupling together one or more of the various circuits of the processor 31 and the memory 32. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor 31 is transmitted over a wireless medium via an antenna, which further receives the data and transmits the data to the processor 31.
The processor 31 is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And the memory 32 may be used to store data used by the processor 31 in performing operations.
A fourth embodiment of the present invention relates to a computer-readable storage medium storing a computer program. The computer program realizes the above-described method embodiments when executed by a processor.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples for carrying out the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
Claims (9)
1. A method of yield prediction, comprising:
acquiring a client favorite crop through crop sales and/or client evaluation, and taking the acquired client favorite crop as a target crop;
acquiring the number of the target crops in a preset area, and acquiring weight data of a single target crop in the preset area;
and obtaining the predicted crop yield of the batch to which the target crop belongs in the preset area according to the number of the target crops and the weight data of the single target crop.
2. The yield prediction method according to claim 1, wherein the obtaining of the number of the target crops in a preset area specifically comprises:
acquiring characteristic information of the target crop;
acquiring a picture of the preset area;
and identifying the number of objects with the characteristic information in the picture, and taking the number of the objects as the number of the target crops in the preset area.
3. The yield prediction method of claim 2, wherein the characterization information comprises: the size of the target crop, the color of the target crop.
4. The yield prediction method according to claim 1, wherein the obtaining of the weight data of the single target crop in the preset area specifically comprises:
acquiring weight data of a preset number of target crops in the preset area;
obtaining the average weight data of the single target crop in the preset area according to the weight data of the preset number of the target crops;
and taking the average weight data of the single target crop as the weight data of the single target crop.
5. The yield prediction method according to claim 1, wherein after obtaining the predicted yield of the crop in the preset area of the batch to which the target crop belongs, the method further comprises:
obtaining the crop yield of the batch of the target crop in the preset area according to the tracing information;
judging whether the crop yield is matched with the predicted crop yield;
and if the judgment result is yes, judging that the tracing information is accurate.
6. The yield prediction method of claim 5, wherein the retrospective information comprises: and the tracing code and the circulation information of the target crop.
7. The yield prediction method according to claim 5, wherein the determining whether the crop yield matches the predicted crop yield specifically comprises:
and judging whether the absolute value of the difference value between the crop yield and the predicted crop yield is within a preset threshold.
8. A server, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the production prediction method of any of claims 1 to 7.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the production prediction method of any one of claims 1 to 7.
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