CN113378793A - Crop yield prediction method and device - Google Patents
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Abstract
The invention discloses a crop yield prediction method and device, and relates to the technical field of computers. One embodiment of the method comprises: acquiring a plurality of first pictures of crops in the current period; acquiring attribute data and a target second picture corresponding to crops, wherein the attribute data comprise historical yield of the crops, and the target second picture is a historical contemporaneous crop picture corresponding to the current period; identifying target crops in the first picture according to the pre-trained image identification model and the plurality of first pictures, and determining a first quantity of the crops according to an identification result; and determining the predicted yield of the crop according to the first quantity, the second quantity of the crop in the target second picture and the historical yield. The embodiment realizes the advance prediction of the crop yield so as to deal with the market demand and the dynamic change of the crop yield in advance and better meet the market demand.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a crop yield prediction method and device.
Background
With the development of the technology, modern technical equipment is applied to agriculture more and more, the levels of agricultural water conservancy, mechanization and informatization are gradually improved, and the yield of crops is increased. However, since the yield of crops has a lag relative to the market demand of agricultural products, how to predict the yield of crops in advance to cope with the market demand and the dynamic change of the crop yield in advance is a problem to be solved.
Disclosure of Invention
In view of this, embodiments of the present invention provide a crop yield prediction method and apparatus, which can identify a first picture of a crop in a current cycle by using an image identification model, determine a first number of the crop, obtain attribute data including a historical yield and a target second picture, where the target second picture is a historical contemporaneous crop picture corresponding to the current cycle, and determine a predicted yield of the crop according to the first number, a second number of the crop in the target second picture and the historical yield of the crop, thereby implementing a pre-prediction of the crop yield to meet market demands in advance and dynamic changes of the crop yield better.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a crop yield determination method.
The crop yield determination method provided by the embodiment of the invention comprises the following steps:
acquiring a plurality of first pictures of crops in the current period;
acquiring attribute data and a target second picture corresponding to crops, wherein the attribute data comprise historical yield of the crops, and the target second picture is a historical contemporaneous crop picture corresponding to the current period;
identifying target crops in the first picture according to the pre-trained image identification model and the plurality of first pictures, and determining a first quantity of the crops according to an identification result;
and determining the predicted yield of the crop according to the first quantity, the second quantity of the crop in the target second picture and the historical yield.
Alternatively,
the image recognition model is obtained by utilizing a plurality of second pictures for training;
the target second picture is selected from a plurality of second pictures.
Alternatively,
the attribute data further includes: historical environment data and current environment data of crops in a current period;
determining a predicted yield of the crop based on the first quantity, the second quantity, and the historical yield, comprising:
respectively carrying out normalization processing on historical environment data and current environment data;
calculating a current yield offset parameter according to the historical environmental data and the current environmental data after normalization processing, wherein the current yield offset parameter is used for representing the change degree of the current environment and the historical environment;
a predicted yield of the crop is determined based on the first quantity, the second quantity, the historical yield, and the current yield offset parameter.
Alternatively,
the attribute data further includes: representing historical yield deviation parameters of the environmental change degree in the historical time period;
determining a predicted yield of the crop based on the first quantity, the second quantity, the historical yield, and the current yield offset parameter, comprising:
and correcting the predicted yield of the crops according to the historical yield deviation parameter and the current yield deviation parameter.
Alternatively,
the attribute data further includes: available inventory, historical sales and historical prices corresponding to the crop;
after determining the predicted yield of the crop, further comprising:
and determining the current pricing strategy of the crops according to the available inventory, the historical sales volume and the historical price corresponding to the crops.
Alternatively,
determining a current pricing strategy of the crops according to the available inventory, the historical sales volume and the historical price corresponding to the crops, wherein the current pricing strategy comprises the following steps:
calculating an inventory capacity parameter according to available inventory;
determining a price fluctuation parameter corresponding to the predicted yield according to the inventory capacity parameter, the historical sales and the predicted yield;
and determining the current pricing strategy of the crops according to the price fluctuation parameters and the historical price.
Alternatively,
obtaining a plurality of first pictures of crops in the current period, comprising:
dividing one or more planting areas of crops to obtain a plurality of shooting areas;
the crops are shot in each shooting area to obtain a plurality of first pictures.
Alternatively,
according to image recognition model and many first pictures, discern the target crops in the first picture, include:
selecting one or more pictures to be identified from the first pictures, identifying the one or more pictures to be identified by adopting an image identification model, and determining a first number according to an identification result.
Alternatively,
after determining the predicted yield of the crop, further comprising:
and training the image recognition model by utilizing the first picture, and replacing the second picture with the first picture.
Alternatively,
the second number is obtained according to the recognition result of the image recognition model to the target second picture.
Alternatively,
when the target second picture is a plurality of pictures,
selecting a standard picture from the plurality of target second pictures, identifying the standard picture by using the image identification model to obtain the number of the target crops contained in the standard picture, and taking the number of the target crops contained in the standard picture as the second number.
Alternatively,
when the target second picture is a plurality of pictures,
and respectively identifying the plurality of target second pictures by using the image identification model to obtain the number of the target crops contained in each target second picture, and calculating the second number according to the number of the target crops contained in each target second picture.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a crop yield determination apparatus.
The crop yield determining device comprises a first obtaining module, a second obtaining module, an identification module and a yield prediction module; wherein:
the first acquisition module is used for acquiring a plurality of first pictures of crops in the current period;
the second acquisition module is used for acquiring attribute data and a target second picture corresponding to the crops, wherein the attribute data comprise the historical yield of the crops, and the target second picture is a historical contemporaneous crop picture corresponding to the current cycle;
the identification module is used for identifying the target crops in the first pictures according to the pre-trained image identification model and the plurality of first pictures and determining the first quantity of the crops according to the identification result;
and the yield prediction module is used for determining the predicted yield of the crops according to the first quantity, the second quantity of the crops in the target second picture and the historical yield.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided an electronic device for crop yield determination.
An electronic device for determining crop yield according to an embodiment of the present invention includes: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a crop yield determination method of an embodiment of the present invention.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided a computer-readable storage medium.
A computer-readable storage medium of an embodiment of the present invention has stored thereon a computer program that, when executed by a processor, implements a crop yield determination method of an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: the method can identify the first pictures of crops in the current period by using an image identification model, determine the first number of the crops, and acquire attribute data including historical yield and a target second picture, wherein the target second picture is the historical contemporary crop picture corresponding to the current period, and determine the predicted yield of the crops according to the first number, the second number of the crops in the target second picture and the historical yield of the crops, so that the crop yield can be predicted in advance, the market demand and the dynamic change of the crop yield can be responded in advance, and the market demand can be better met.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main steps of a crop yield determination method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a specific implementation of step S104;
FIG. 3 is a schematic diagram of the main steps of a method of determining a current pricing strategy for a crop;
FIG. 4 is a schematic diagram of the main steps of a method for calculating a price volatility parameter corresponding to a forecasted production;
FIG. 5 is a schematic diagram of the main modules of a crop yield determination apparatus according to an embodiment of the present invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments of the present invention and the technical features of the embodiments may be combined with each other without conflict.
Fig. 1 is a schematic diagram of the main steps of a crop yield determination method according to an embodiment of the present invention.
As shown in fig. 1, a crop yield determination method according to an embodiment of the present invention mainly includes the following steps:
step S101: acquiring a plurality of first pictures of crops in the current period;
step S102: acquiring attribute data and a target second picture corresponding to crops, wherein the attribute data comprise historical yield of the crops, and the target second picture is a historical contemporaneous crop picture corresponding to the current period;
step S103: identifying target crops in the first picture according to the pre-trained image identification model and the plurality of first pictures, and determining a first quantity of the crops according to an identification result;
step S104: and determining the predicted yield of the crop according to the first quantity, the second quantity of the crop in the target second picture and the historical yield.
In the embodiment of the invention, the crops can be shot once in each period of the growth of the crops so as to obtain the growth state of the crops in each period.
In the embodiment of the invention, as crops can be planted in one planting area or a plurality of planting areas, when the first picture is obtained, one or more planting areas of the crops can be divided to obtain a plurality of shooting areas; and shooting a first picture for the crops in each shooting area to obtain a plurality of first pictures. In a preferred embodiment of the present invention, when the plurality of photographing regions are divided, the photographing regions have the same area, there is no overlapping region between the photographing regions, and the photographing regions are grouped together to cover the entire planting region of the crop.
In the embodiment of the present invention, the area of each divided photographing region is SdHistorical yield of crops in addition to attribute data
Also includes the total area S of multiple planting areas of the cropszThus, the yield per shot area can be obtained
For example, taking the crop of Kyoho grapes as an example, the area of each divided shooting area is 0.25 mu, i.e. SdAssuming that the total area S of the multiple growing regions of the Kyoho grape is 0.25z200 mu, and the historical yield K is 13000 weight units per month, the yield of each shooting area can be calculated
In the embodiment of the invention, the image recognition model is obtained by utilizing a plurality of second pictures for training; the target second picture is selected from a plurality of second pictures.
In the embodiment of the invention, the second number is obtained according to the recognition result of the image recognition model on the target second picture.
In the embodiment of the present invention, when the number of the target second pictures is multiple, a standard picture may be selected from the multiple target second pictures, the standard picture is identified by using the image identification model, the number of the target crops included in the standard picture is obtained, and the number of the target crops included in the standard picture is used as the second number.
In the embodiment of the present invention, when there are a plurality of target second pictures, the image recognition model may also be used to respectively recognize the plurality of target second pictures to obtain the number of target crops contained in each target second picture, and the second number is calculated according to the number of target crops contained in each target second picture.
In the embodiment of the present invention, the image recognition model is a CNN (Convolutional Neural Network) model obtained by performing multiple iterative training optimizations based on multiple second pictures. At this time, the specific implementation process of step S103 may be to identify the target crops in the multiple first pictures one by using an image identification model, and then summarize the identification results to obtain the first quantity of the crops; or randomly selecting a certain number of sampling pictures from the plurality of first pictures, identifying the target crops in the sampling pictures by using an image identification model, and determining the first number of the crops according to the identification result.
In a preferred embodiment of the present invention, a certain number of first sampling pictures are randomly selected from the plurality of first pictures, and then the target crop in the first sampling pictures is identified by using an image identification model, so as to reduce the cost of computing resources in the identification process. Wherein, the first isThe number of pictures is D, while the number of first sampled pictures t does not generally exceed
And the number of the first sampling pictures of the target crops can be identified to be r, wherein r is larger than 0.8t under the normal condition, and if r is smaller than or equal to 0.8t, the first sampling pictures need to be selected again.
Specifically, the image recognition model may be used to perform rain removal optimization on the t first sampling pictures respectively and then match the t first sampling pictures with the standard image of the target crop, so as to determine the number N of the target crop in each first sampling picturerThen the first amount M of the croprThe calculation formula of (2) is as follows:
wherein N is a second number corresponding to the target second picture, NrFor the number of target crops identified in the first sample picture, KcFor the production of each shot area, MrIs the first quantity of crop. At this time, according to the first quantity, the second quantity of the crops in the target second picture and the historical yield, the calculation formula for determining the predicted yield of the crops can be as follows:
wherein M isrIs a first number of crops, D is the number of first pictures, t is the number of first sampling pictures, r is the number of first sampling pictures that can identify a target crop, and K is the predicted yield of the crop.
In the embodiment of the invention, after r is determined to be greater than 0.8t according to the plurality of first sampling pictures, a second sampling picture can be further randomly selected from the plurality of first sampling pictures, and the number of the target crops in the second sampling picture is identified by using the image identification model, so that the expenditure of computing resources in the identification process is further reduced. At this time, according to the first quantity, the second quantity of the crops in the target second picture and the historical yield, the calculation formula for determining the predicted yield of the crops can be as follows:
wherein r isiFor identifying the number of target crops in the second sampling picture,
to identify riTotal number of crops on individual target crops, KcFor the yield of each shooting area, N is the second number of crops in the target second picture, D is the number of the first pictures, t is the number of the first sampling pictures, and r is the number of the first sampling pictures capable of identifying the target crops.
Taking the aforementioned Kyoho grape as an example, the total area S of the multiple planting regionszThe area S of each shooting area is divided into 200 mudFor 0.25 mu, 800 shooting areas can be divided and numbered, and a first picture is taken for each shooting area, so that 800 first pictures are obtained, namely D is 800. 20 first sample pictures are randomly extracted from 800 first pictures, namely t is 20. The 20 first sampling pictures are subjected to rain treatment and then input into an image recognition model, and 19 target crops are recognized: and 1 Kyoho grape can not be matched with the standard image of the target crop, namely r is 19 and is more than 0.8 multiplied by 20. Therefore, 1 second sampling picture can be randomly extracted from the 20 first sampling pictures, the second sampling picture is identified by using the image identification model, and 8 target crops, namely r, are identified from the second sampling pictureiI.e. there are 8 bunches of Kyoho grapes. And the number of Kyoho grapes on each cluster is: 25, 29, 27, 31, 32, 31, 28, 36, so the total number of crops
At this time, the first number and the second number of the crops in the target second picture are calculatedAnd historical yield, determining the predicted yield of the crop as
In this embodiment of the present invention, the attribute data may further include: historical environment data and current environment data of crops in a current period; the historical environment data is the same as the environment data included in the current environment data in category, and the category of the environment data includes, but is not limited to, any one or more of the following: temperature, water source data, humidity, and light intensity. At this time, the specific implementation process of step S104 is shown in fig. 2, and mainly includes the following steps:
step S201: respectively carrying out normalization processing on historical environment data and current environment data;
step S202: calculating a current yield offset parameter according to the historical environmental data and the current environmental data after normalization processing, wherein the current yield offset parameter is used for representing the change degree of the current environment and the historical environment;
step S203: a predicted yield of the crop is determined based on the first quantity, the second quantity, the historical yield, and the current yield offset parameter.
In the embodiment of the invention, the normalized historical environment data and the current environment data are both represented in the form of feature vectors. For example, if the historical environmental data and the current environmental data in the attribute data include three types of environmental data, the normalized current environmental data is represented as<μ1,ν1,ο1>Wherein, mu1Is a normalized value of the first environmental data in the current environmental data, and v is the same as the normalized value1And o1Respectively are normalized values of the second and the third environmental data in the current environmental data. The current yield offset parameter is calculated by
Wherein d ispOffset parameters for current productionThe number is used for representing the change degree of the current environment compared with the historical environment; mu.sbIs a normalized value of the first environmental data in the historical environmental data, and v is the same as the normalized valuebAnd obThe normalized values of the second and the third environmental data in the historical environmental data are respectively. After the current yield offset parameter is calculated, the predicted yield of the crop is determined according to the first quantity, the second quantity, the historical yield and the current yield offset parameter.
For example, the types of the environment data included in the historical environment data and the current environment data are: the temperature, humidity and illumination intensity of the sensor in the current period can be obtained by averaging 25, 50 and 300 temperature data, humidity data and illumination intensity data collected by the sensor in the current period, and the temperature data, humidity data and illumination intensity data in the historical environmental data are 26, 44 and 350 respectively. Then respectively carrying out normalization processing on the historical environment data and the current environment data to obtain normalized current environment data<0.067,0.133,0.800>Normalized historical environmental data is<0.065,0.105,0.830>Calculating a current yield offset parameter as
In this embodiment of the present invention, the attribute data may further include: representing historical yield deviation parameters of the environmental change degree in the historical time period; the predicted yield of the crop can be corrected at this time according to the historical yield offset parameter and the current yield offset parameter.
Specifically, the historical production offset parameter includes a maximum production offset parameter d over the historical time periodgAnd a minimum yield excursion parameter dd. Taking a minimum value d from the historical yield offset parameter and the current yield offset parametermin＝min{dg,dd,dpIf dmin＝dgIf the correction parameter ε is 1+ dg(ii) a If d ismin＝ddIf the correction parameter is 1-dd(ii) a If d ismin＝dpIf the correction parameter e is 1. IntoAnd correcting the predicted yield of the crops according to a correction parameter epsilon, wherein the correction formula is
K is the predicted yield before correction,
the corrected predicted yield is used to improve the prediction accuracy.
D is calculated by the abovepFor example, 0.04 for the crop, the maximum yield excursion parameter d for this cropg0.06, minimum yield offset parameter dd0.08, minimum value dmin＝0.04＝dpIf the corrected parameter epsilon is 1, the predicted yield of the crop is corrected in the current period
The same as the predicted yield before correction.
In this embodiment of the present invention, the attribute data may further include: available inventory, historical sales and historical prices corresponding to the crop; at this time, after the predicted yield of the crop is determined, the current pricing strategy of the crop can be determined according to the available inventory, the historical sales volume and the historical price corresponding to the crop. Specifically, a method for determining a current pricing strategy for a crop is shown in fig. 3, and the method mainly comprises the following steps:
step S301: calculating an inventory capacity parameter according to available inventory;
step S302: determining a price fluctuation parameter corresponding to the predicted yield according to the inventory capacity parameter, the historical sales and the predicted yield;
step S303: and determining the current pricing strategy of the crops according to the price fluctuation parameters and the historical price.
In the embodiment of the present invention, in step S301, the formula for calculating the inventory capability parameter according to the available inventory is
WhereinAlpha is an inventory capacity parameter used for representing inventory capacity, and the stronger the inventory capacity is, the larger the available inventory is, the larger the value of the inventory capacity parameter is. For example, if the total inventory of a warehouse is 100 units/volume and the current available inventory is 10 units/volume, the inventory capacity parameter
In the embodiment of the present invention, in step S302, a price fluctuation parameter corresponding to the predicted yield may be calculated according to the inventory capacity parameter, the historical sales amount, and the predicted yield before correction; and calculating a price fluctuation parameter corresponding to the predicted yield according to the inventory capacity parameter, the historical sales and the corrected predicted yield. In a preferred embodiment of the present invention, a price fluctuation parameter corresponding to the predicted production is calculated according to the inventory capacity parameter, the historical sales volume and the corrected predicted production volume, specifically, as shown in fig. 4, fig. 4 is a method for calculating a price fluctuation parameter corresponding to the predicted production volume, the method mainly includes the following steps:
step S401: and calculating a predicted sales threshold according to the inventory capacity parameter, the historical sales and the corrected predicted yield.
The calculation formula of the predicted sales threshold is as follows:
Kymax＝Knext+α·Knext
Kymin＝Knext-α·Knext
wherein, KymaxTo maximum predicted sales threshold, KyminIs a minimum predicted sales threshold, alpha is an inventory capacity parameter, KnextIn order to be able to sell the amount of historical sales,
is the corrected predicted yield.
Step S402: and determining a price fluctuation parameter corresponding to the predicted yield according to the corrected predicted yield and the current sales threshold.
In the embodiment of the invention, aiming at different corrected predictionsThe yield is measured, and 5 price fluctuation parameters are corresponded to represent 5 price fluctuation risk levels: when in use
Then, determining that the price fluctuation parameter is 1, and indicating that the predicted yield can normally meet the predicted sales volume without price fluctuation risk; when in use
Then, determining that the price fluctuation parameter is 2, which indicates that the predicted yield is slightly lower than the predicted sales volume and the risk of lower price rise is possible; when in use
Then, determining the price fluctuation parameter as 3, which indicates that the predicted yield is slightly higher than the predicted sales volume and possibly has lower price drop risk; when in use
Then, determining that the price fluctuation parameter is 4, which indicates that the predicted yield is lower than the predicted sales volume and higher price rising risk is possible; when in use
Then, the price fluctuation parameter is determined to be 5, and there may be a higher risk of price drop.
In the embodiment of the present invention, the specific implementation manner of step S303 is: when the price fluctuation parameter is 1, the historical price of the crop can be used as the current pricing strategy of the crop because the crop has no risk of price fluctuation; when the price fluctuation parameter is 2, because the crop may have a lower price rising risk, a strategy mainly for improving the production end can be used, for example, adjusting the temperature and the humidity to be more suitable for the growth range of the crop, shortening the growth cycle of the crop and the like, so as to increase the yield of the crop, and because the price rising risk is lower, the sales end can still use the historical price of the crop as the current pricing strategy; when the price fluctuation parameter is 3, the crop can still be used as the crop has lower price drop riskThe yield of the crops is reduced by using strategies mainly improving the production end, such as adjusting the temperature and the humidity to be in a range which is not suitable for the growth of the crops, prolonging the growth cycle of the crops and the like, and the selling end can still use the historical price of the crops as the current pricing strategy because the price drop risk is low; when the price fluctuation parameter is 4, since there may be a high risk of price increase for the crop, the current pricing strategy determined by the seller should be: the price is increased on the basis of the historical price of the crops, and the reference price is
Wherein P iscFor the purpose of reference to the price,
to historical prices, KyminIs the minimum predicted sales threshold value and,
for the corrected predicted yield; when the price fluctuation parameter is 5, since the crop may have a higher risk of price drop, the current pricing strategy determined by the sales end should be: the price reduction promotion is carried out on the basis of the historical price of the crops, and the reference price is
Wherein P iscFor the purpose of reference to the price,
to historical prices, KymaxIs the maximum predicted sales threshold for the customer,
is the corrected predicted yield.
The corrected predicted yield in the current cycle is still calculated as described above
The crop of (2) is taken as an example, and the crop can be obtained from attribute dataHistorical sales K ofnext13500, historical price
And the stock ability parameter alpha of the warehouse for storing the crop is 0.1, and the calculated predicted sales threshold value is as follows:
Kymax＝Knext+α·Knext＝13500+1350＝14850
Kymin＝Knext-α·Knext＝13500-1350＝12150
thus, it can be seen that
The corresponding price fluctuation parameter is 1, which indicates that the predicted yield can normally meet the predicted sales volume, and no price fluctuation risk exists, so that the current pricing strategy of the crops can be determined as follows: the historical price of the crop is used as the current pricing strategy, the selling price is not required to be adjusted, and the price is still kept 10.5 yuan.
In an embodiment of the present invention, after determining the predicted yield of the crop, the method further includes: the image recognition model is trained by utilizing the first picture, and the second picture is replaced by the first picture, so that the high timeliness of training data is guaranteed, and the probability of errors of the recognition result of the image recognition model is reduced.
According to the crop yield determination method, the first picture of the crop in the current period can be identified by using the image identification model, the first quantity of the crop is determined, the attribute data including the historical yield and the target second picture are acquired, wherein the target second picture is the historical crop picture in the same period corresponding to the current period, and the predicted yield of the crop is determined according to the first quantity, the second quantity of the crop in the target second picture and the historical yield of the crop, so that the crop yield can be predicted in advance, the market demand and the dynamic change of the crop yield can be met in advance, and the market demand can be better met.
Fig. 5 is a schematic diagram of main modules of a crop yield determination apparatus according to an embodiment of the present invention.
As shown in fig. 5, a crop yield determination apparatus 500 according to an embodiment of the present invention includes a first obtaining module 501, a second obtaining module 502, an identifying module 503, and a yield predicting module 504; wherein:
a first obtaining module 501, configured to obtain multiple first pictures of crops in a current period;
a second obtaining module 502, configured to obtain attribute data corresponding to the crop and a target second picture, where the attribute data includes a historical yield of the crop, and the target second picture is a historical contemporaneous crop picture corresponding to the current cycle;
the identification module 503 is configured to identify a target crop in a first picture according to the pre-trained image identification model and the plurality of first pictures, and determine a first quantity of the crop according to an identification result;
and a yield prediction module 504, configured to determine a predicted yield of the crop according to the first quantity, the second quantity of the crop in the target second picture, and the historical yield.
In the embodiment of the invention, the image recognition model is obtained by utilizing a plurality of second pictures for training; the target second picture is selected from a plurality of second pictures.
In this embodiment of the present invention, the attribute data further includes: historical environment data and current environment data of crops in a current period; the yield prediction module 504 is further configured to: respectively carrying out normalization processing on historical environment data and current environment data; calculating a current yield offset parameter according to the historical environmental data and the current environmental data after normalization processing, wherein the current yield offset parameter is used for representing the change degree of the current environment and the historical environment; a predicted yield of the crop is determined based on the first quantity, the second quantity, the historical yield, and the current yield offset parameter.
In this embodiment of the present invention, the attribute data further includes: representing historical yield deviation parameters of the environmental change degree in the historical time period; the yield prediction module 504 is further configured to: and correcting the predicted yield of the crops according to the historical yield deviation parameter and the current yield deviation parameter.
In this embodiment of the present invention, the attribute data further includes: available inventory, historical sales and historical prices corresponding to the crop; after determining the predicted yield of the crop, the yield prediction module 504 is further to: and determining the current pricing strategy of the crops according to the available inventory, the historical sales volume and the historical price corresponding to the crops.
In an embodiment of the present invention, the yield prediction module 504 is further configured to: calculating an inventory capacity parameter according to available inventory; determining a price fluctuation parameter corresponding to the predicted yield according to the inventory capacity parameter, the historical sales and the predicted yield; and determining the current pricing strategy of the crops according to the price fluctuation parameters and the historical price.
In this embodiment of the present invention, the first obtaining module 501 is further configured to: dividing one or more planting areas of crops to obtain a plurality of shooting areas; the crops are shot in each shooting area to obtain a plurality of first pictures.
In this embodiment of the present invention, the identifying module 503 is further configured to: selecting one or more pictures to be identified from the first pictures, identifying the one or more pictures to be identified by adopting an image identification model, and determining a first number according to an identification result.
In an embodiment of the present invention, after determining the predicted yield of the crop, the identifying module 503 is further configured to: and training the image recognition model by utilizing the first picture, and replacing the second picture with the first picture.
In the embodiment of the invention, the second number is obtained according to the recognition result of the image recognition model on the target second picture.
In this embodiment of the present invention, when the target second picture is multiple, the identifying module 503 is further configured to: selecting a standard picture from the plurality of target second pictures, identifying the standard picture by using the image identification model to obtain the number of the target crops contained in the standard picture, and taking the number of the target crops contained in the standard picture as the second number.
In this embodiment of the present invention, when the target second picture is multiple, the identifying module 503 is further configured to: and respectively identifying the plurality of target second pictures by using the image identification model to obtain the number of the target crops contained in each target second picture, and calculating the second number according to the number of the target crops contained in each target second picture.
According to the crop yield determination device disclosed by the embodiment of the invention, the device can identify the first picture of the crop in the current period by using the image identification model, determine the first number of the crop, acquire the attribute data including the historical yield and the target second picture, wherein the target second picture is the picture of the crop in the historical period corresponding to the current period, and determine the predicted yield of the crop according to the first number, the second number of the crop in the target second picture and the historical yield of the crop, so that the crop yield can be predicted in advance, the market demand and the dynamic change of the crop yield can be met in advance, and the market demand can be better met.
Fig. 6 illustrates an exemplary system architecture 600 of a crop yield determination method or a crop yield determination apparatus to which embodiments of the present invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and an electronic device 605. Network 604 is used to provide a medium for communication links between terminal devices 601, 602, 603 and electronic device 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 601, 602, 603 to interact with the electronic device 605 via the network 604 to receive or send messages or the like.
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The electronic device 605 may be a server that provides various services, and may perform processing such as analysis on the received first picture, attribute data, and the like, and feed back a processing result (e.g., predicted yield and the like) to the terminal device.
It should be noted that the crop yield determination method provided by the embodiment of the present invention is generally executed by the electronic device 605, and accordingly, a crop yield determination apparatus is generally disposed in the electronic device 605.
It should be understood that the number of terminal devices, networks, and electronic devices in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and electronic devices, as desired for implementation.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a first acquisition module, a second acquisition module, an identification module, and a yield prediction module. The names of the modules do not limit the module itself in some cases, for example, the first acquiring module may be further described as a "module for acquiring first pictures of crops in the current period".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring a plurality of first pictures of crops in the current period; acquiring attribute data and a target second picture corresponding to crops, wherein the attribute data comprise historical yield of the crops, and the target second picture is a historical contemporaneous crop picture corresponding to the current period; identifying target crops in the first picture according to the pre-trained image identification model and the plurality of first pictures, and determining a first quantity of the crops according to an identification result; and determining the predicted yield of the crop according to the first quantity, the second quantity of the crop in the target second picture and the historical yield.
According to the technical scheme of the embodiment of the invention, the first picture of the crops in the current period can be identified by using the image identification model, the first quantity of the crops is determined, the attribute data including the historical yield and the target second picture are obtained, wherein the target second picture is the picture of the crops in the same historical period corresponding to the current period, and the predicted yield of the crops is determined according to the first quantity, the second quantity of the crops in the target second picture and the historical yield of the crops, so that the crop yield can be predicted in advance, the market demand and the dynamic change of the crop yield can be met in advance, and the market demand can be better met.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (14)
1. A crop yield determination method, comprising:
acquiring a plurality of first pictures of crops in the current period;
acquiring attribute data and a target second picture corresponding to the crops, wherein the attribute data comprise historical yield of the crops, and the target second picture is a historical contemporaneous crop picture corresponding to the current cycle;
identifying target crops in the first pictures according to a pre-trained image identification model and the first pictures, and determining a first quantity of the crops according to an identification result;
and determining the predicted yield of the crop according to the first quantity, the second quantity of the crop in the target second picture and the historical yield.
2. The method of claim 1,
the image recognition model is obtained by utilizing a plurality of second pictures for training;
the target second picture is selected from the plurality of second pictures.
3. The method of claim 1, wherein the attribute data further comprises: historical environmental data, current environmental data of the crop in the current period;
said determining a predicted yield of said crop based on said first quantity, said second quantity, and said historical yield comprises:
respectively carrying out normalization processing on the historical environment data and the current environment data;
calculating a current yield offset parameter according to the historical environmental data and the current environmental data after normalization processing, wherein the current yield offset parameter is used for representing the change degree of the current environment and the historical environment;
determining a predicted yield of the crop based on the first quantity, the second quantity, the historical yield, and the current yield offset parameter.
4. The method of claim 3,
the attribute data further includes: representing historical yield deviation parameters of the environmental change degree in the historical time period;
said determining a predicted yield of the crop based on the first quantity, the second quantity, the historical yield, and the current yield offset parameter comprises:
and correcting the predicted yield of the crop according to the historical yield deviation parameter and the current yield deviation parameter.
5. The method of claim 1, wherein the attribute data further comprises: available inventory, historical sales and historical prices corresponding to the crop;
after said determining the predicted yield of the crop, further comprising:
and determining the current pricing strategy of the crops according to the available inventory, the historical sales volume and the historical price corresponding to the crops.
6. The method of claim 5, wherein determining the current pricing strategy for the crop based on the available inventory, historical sales, and historical prices for the crop comprises:
calculating an inventory capacity parameter according to the available inventory;
determining a price fluctuation parameter corresponding to the predicted yield according to the inventory capacity parameter, the historical sales volume and the predicted yield;
and determining the current pricing strategy of the crops according to the price fluctuation parameters and the historical prices.
7. The method of claim 1, wherein said obtaining a plurality of first pictures of the crop in the current cycle comprises:
dividing one or more planting areas of the crops to obtain a plurality of shooting areas;
and shooting the crops in each shooting area to obtain the plurality of first pictures.
8. The method according to claim 1, wherein the identifying the target crop in the first picture according to the image recognition model and the plurality of first pictures comprises:
selecting one or more pictures to be identified from the first pictures, identifying the one or more pictures to be identified by adopting an image identification model, and determining the first number according to an identification result.
9. The method of claim 1, further comprising, after said determining the predicted yield of the crop:
and training the image recognition model by using the first picture, and replacing the second picture with the first picture.
10. The method of claim 1,
the second number is obtained according to the recognition result of the image recognition model to the target second picture.
11. The method according to claim 10, wherein when the target second picture is multiple,
selecting a standard picture from the plurality of second target pictures, identifying the standard picture by using the image identification model to obtain the number of the target crops contained in the standard picture, and taking the number of the target crops contained in the standard picture as the second number;
and/or the presence of a gas in the gas,
and respectively identifying the plurality of target second pictures by using the image identification model to obtain the number of the target crops contained in each target second picture, and calculating the second number according to the number of the target crops contained in each target second picture.
12. The crop yield determination device is characterized by comprising a first acquisition module, a second acquisition module, an identification module and a yield prediction module; wherein:
the first acquisition module is used for acquiring a plurality of first pictures of crops in the current period;
the second obtaining module is configured to obtain attribute data and a target second picture corresponding to the crop, where the attribute data includes a historical yield of the crop, and the target second picture is a crop picture of a historical period corresponding to the current period;
the identification module is used for identifying the target crops in the first pictures according to a pre-trained image identification model and the plurality of first pictures, and determining the first quantity of the crops according to the identification result;
and the yield prediction module is used for determining the predicted yield of the crops according to the first quantity, the second quantity of the crops in the target second picture and the historical yield.
13. An electronic crop yield determination device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-11.
14. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-11.
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