CN115965161B - Crop yield prediction method based on artificial intelligence and historical data - Google Patents
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Abstract
The invention discloses a crop yield prediction method based on artificial intelligence and historical data, which relates to crop yield prediction and comprises the following steps: firstly, current state data of crops in a region to be predicted is obtained, and based on the current state data of the crops, the predicted yield is obtained through a yield prediction model
The method comprises the steps of carrying out a first treatment on the surface of the Then obtaining the environmental data of the area to be predicted, and based on the environmental data, matching in a large database to obtain the predicted yield
The method comprises the steps of carrying out a first treatment on the surface of the Finally, in predicting yield
And predicting yield
On the basis of (1) combining a preset weight X and a preset weight Y to obtain a predicted yield
The method comprises the steps of carrying out a first treatment on the surface of the According to the invention, in the crop yield prediction process, the current state data and the environment data of the crops are taken as consideration factors, and the current state data and the environment data are deeply correlated, so that the accuracy of crop yield prediction is greatly improved.
Description
Technical Field
The invention relates to crop yield prediction, in particular to a crop yield prediction method based on artificial intelligence and historical data.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The crop yield is directly related to the grain safety. The current crop yield prediction method mainly comprises two aspects, namely, crop yield prediction based on environmental change is performed, and the method accords with the overall growth rule of crop varieties, but ignores the actual growth state of crops, only performs preliminary estimation on crop yield, and has general prediction accuracy; on the other hand, the method is based on multi-temporal remote sensing images and is combined with a prediction model to predict, and the method considers the actual growth state of crops, but does not relate to environmental changes.
Although both methods can be used for predicting crop yield, the variety of factors considered is relatively single, resulting in poor prediction accuracy.
Disclosure of Invention
The invention aims at: aiming at the problems that the prediction accuracy is not high due to single variety of factors considered in the existing crop yield prediction method, the crop yield prediction method based on artificial intelligence and historical data is provided, environmental changes and the growth state of crops are closely related, crop yield is predicted, and the prediction accuracy of crop yield is greatly improved, so that the problems are solved.
The technical scheme of the invention is as follows:
a crop yield prediction method based on artificial intelligence and historical data, comprising:
step S1: acquiring current state data of crops in a region to be predicted, and performing baseObtaining predicted yield from current state data of crops through a yield prediction model
；
Step S2: acquiring environmental data of a region to be predicted, and matching in a large database based on the environmental data to obtain predicted yield
；
Step S3: in predicting yield
And predictive yield->
On the basis of (1) combining preset weightsXAnd weightYObtaining predicted yield->
。
Further, the establishment process of the yield prediction model is as follows:
step A: acquiring original state information of crops at each time point in a growth period from a big data platform
；
And (B) step (B): acquiring sample state information of crops in a region to be predicted at each time point in a growth period from sample crop historical growth data of the region to be predicted
；
Step C: based on sample state information of crops in a region to be predicted at each time point in a growth period
Original status information for the crop at various time points during the growth cycle +.>
Correction is carried out to obtain standard state information of crops in the region to be predicted at each time point in the growth period +.>
。
Further, the status information is a set consisting of stem diameter, stem height, leaf size, leaf color, whether or not the result, fruit size, fruit color, expressed as:
wherein:
Further, the correction includes:
if the deviation between the sample state information and the original state information is within 5%, the original state information is used as standard state information;
if the deviation between the sample state information and the original state information is 5% -10%, taking the average value of the sample state information and the original state information as standard state information;
and if the deviation between the sample state information and the original state information is more than 10%, taking the sample state information as standard state information.
Further, the step S1 includes:
step S11: acquiring three-dimensional image data of a region to be predicted;
step S12: extracting state information of crops from three-dimensional image data
；
Step S13: status information of crops
As input, judging and outputting the growth state of the crops through a yield prediction model;
step S14: based on the determined growth state of the crop, combining the historical crop yield data of the sample of the area to be predicted to obtain the predicted yield
。
Further, the step S13 includes:
step S131: status information of crops
Inputting a yield prediction model, and matching corresponding standard state information +.>
；
Step S132: according to the matched standard state information
Reversely determining the time point of the crops;
step S133: and determining the growth state of the crops according to the time point where the crops are reversely determined and the time point where the crops are actually positioned.
Further, the step S133 includes:
when the time point of the reverse determination crops is earlier than the actual time point of the crops, judging that the crops grow slowly;
when the time point of the reverse determination crops coincides with the actual time point of the crops, judging that the crops grow normally;
and when the time point of the reverse determination crops is later than the actual time point of the crops, the crops are judged to grow fast.
Further, the step S14 includes:
step S141: sequencing historical crop yield data in a region to be predicted according to the sequence from high to low;
wherein, the front 1/3 is the historical yield of the crops in the area to be predicted when the crops grow fast
；/>
The middle 1/3 is the historical yield of the crops in the area to be predicted when the crops grow normally
；
The later 1/3 is the historical yield of crops in the area to be predicted when the crops grow slowly
；
Step S142: for a pair of
、
、
Averaging by the following formula to obtain +.>
、
、
；
Step S143: the growth state of the crops determined in the step S13 is combined with
、
、
Matching is performed to obtain a predicted yield->
。
Further, the environmental data includes: temperature, illumination intensity, carbon dioxide concentration in air, light-receiving area of plants and soil fertilizer content;
the step S2 includes:
step S21: acquiring environmental data of a region to be predicted;
step S22: according to the environmental data, matching is carried out in a big data platform, and the crop yield corresponding to the matching result is used as the predicted yield
。
Further, the determination rule of the weight in the step S3 is as follows:
if the crop growth status determined in step S13 is that the crop growth is slow, the yield is predicted
Weights of (2)X=0.6, predicted yield +.>
Weights of (2)Y=0.4；
If the crop growth status determined in step S13 is that the crop growth is normal, the yield is predicted
Weights of (2)X=0.5, predicted yield +.>
Weights of (2)Y=0.5；
If the crop growth status determined in step S13 is that the crop growth is fast, the yield is predicted
Weights of (2)X=0.6, predicted yield +.>
Weights of (2)Y=0.5；
The predicted yield
Calculated by the following formula:
compared with the prior art, the invention has the beneficial effects that:
a crop yield prediction method based on artificial intelligence and historical data comprises the steps of firstly obtaining current state data of crops in a region to be predicted, and obtaining predicted yield through a yield prediction model based on the current state data of the crops
The method comprises the steps of carrying out a first treatment on the surface of the Then obtaining the environmental data of the area to be predicted, and based on the environmental data, matching in a large database to obtain the predicted yield
The method comprises the steps of carrying out a first treatment on the surface of the Finally in predicting yield->
And predictive yield->
On the basis of (1) combining preset weightsXAnd weightYObtaining predicted yield->
The method comprises the steps of carrying out a first treatment on the surface of the In the crop yield prediction process, the method takes the current state data and the environment data of the crops as consideration factors and deeply correlates the current state data and the environment data of the crops, so that the accuracy of crop yield prediction is greatly improved. />
Drawings
FIG. 1 is a flow chart of a crop yield prediction method based on artificial intelligence and historical data;
FIG. 2 is a flow chart of a method for building a yield prediction model.
Detailed Description
It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The features and capabilities of the present invention are described in further detail below in connection with examples.
Example 1
The current crop yield prediction method mainly comprises two aspects, namely, crop yield prediction based on environmental change is performed, and the method accords with the overall growth rule of crop varieties, but ignores the actual growth state of crops, only performs preliminary estimation on crop yield, and has general prediction accuracy; on the other hand, the method is based on multi-temporal remote sensing images and is combined with a prediction model to predict, and the method considers the actual growth state of crops, but does not relate to environmental changes. Although both methods can be used for predicting crop yield, the variety of factors considered is relatively single, resulting in poor prediction accuracy.
Therefore, in order to solve the above problems, the present embodiment proposes a crop yield prediction method based on artificial intelligence and historical data, which takes current state data and environmental data of crops as consideration factors, and deeply correlates the current state data and the environmental data, thereby greatly improving accuracy of crop yield prediction.
Specifically, referring to fig. 1, a crop yield prediction method based on artificial intelligence and historical data specifically includes the following steps:
step S1: acquiring current state data of crops in a region to be predicted, and obtaining predicted yield through a yield prediction model based on the current state data of the crops
The method comprises the steps of carrying out a first treatment on the surface of the It should be noted that the region to be predicted is the region where the plant to be estimated to produce (i.e. crop) is located;
step S2: acquiring environmental data of a region to be predicted, and matching in a large database based on the environmental data to obtain predicted yield
；
Step S3: in predicting yield
And predictive yield->
On the basis of (1) combining preset weightsXAnd weightYObtaining predicted yield->
；
Referring to fig. 2, in this embodiment, specifically, the process of establishing the yield prediction model is as follows:
step A: acquiring original state information of crops at each time point in a growth period from a big data platform
The method comprises the steps of carrying out a first treatment on the surface of the It should be noted that the big data platform includes all the materials which can be obtained at present and are related to the growth rule and the growth data of crops; wherein the original status information of the crop at each time point in the growth cycle is +.>
Namely, the original state information of the crops at a specific time point in the growth period;
and (B) step (B): acquiring sample state information of crops in a region to be predicted at each time point in a growth period from sample crop historical growth data of the region to be predicted
；
Step C: based on sample state information of crops in a region to be predicted at each time point in a growth period
Original status information for the crop at various time points during the growth cycle +.>
Correction is carried out to obtain standard state information of crops in the region to be predicted at each time point in the growth period +.>
。
In this embodiment, specifically, the status information is a set including a stem diameter, a stem height, a leaf size, a leaf color, a result, a fruit size, and a fruit color, and is expressed as:
wherein:
in this embodiment, specifically, the correction includes:
if the deviation between the sample state information and the original state information is within 5%, the original state information is used as standard state information; the deviation refers to a deviation of a value;
if the deviation between the sample state information and the original state information is 5% -10%, taking the average value of the sample state information and the original state information as standard state information;
and if the deviation between the sample state information and the original state information is more than 10%, taking the sample state information as standard state information.
In this embodiment, specifically, the step S1 includes:
step S11: acquiring three-dimensional image data of a region to be predicted; preferably, the three-dimensional image data may be acquired in a variety of ways, for example: acquiring through a 3D camera; the present embodiment is not limited thereto;
step S12: extracting state information of crops from three-dimensional image data
The method comprises the steps of carrying out a first treatment on the surface of the It should be noted that, the state information is extracted from the three-dimensional image data, which belongs to a conventional technical means in the field of three-dimensional images, and those skilled in the art should know that the description is not repeated here;
step S13: status information of crops
As input, judging and outputting the growth state of the crops through a yield prediction model;
step S14: based on the determined growth state of the crop, combining the historical crop yield data of the sample of the area to be predicted to obtain the predicted yield
。
In this embodiment, specifically, the step S13 includes:
step S131: status information of crops
Inputting a yield prediction model, and matching corresponding standard state information +.>
；
Step S132: according to the matched standard state information
Reversely determining the time point of the crops;
step S133: and determining the growth state of the crops according to the time point where the crops are reversely determined and the time point where the crops are actually positioned.
In this embodiment, specifically, the step S133 includes:
when the time point of the reverse determination crops is earlier than the actual time point of the crops, judging that the crops grow slowly;
when the time point of the reverse determination crops coincides with the actual time point of the crops, judging that the crops grow normally;
and when the time point of the reverse determination crops is later than the actual time point of the crops, the crops are judged to grow fast.
In this embodiment, specifically, the step S14 includes:
step S141: sequencing historical crop yield data in a region to be predicted according to the sequence from high to low; it should be noted that, the historical crop yield data in the area to be predicted refers to the yield of the same variety of crops planted in the area to be predicted in the past year, and preferably, the historical crop yield data of the first 6 years or the first 12 years of the area to be measured is selected for sorting;
wherein, the front 1/3 is the historical yield of the crops in the area to be predicted when the crops grow fast
；
The middle 1/3 is the historical yield of the crops in the area to be predicted when the crops grow normally
；
The later 1/3 is the historical yield of crops in the area to be predicted when the crops grow slowly
；
Step S142: for a pair of
、
、
Averaging by the following formula to obtain +.>
、
、
；
Step S143: the growth state of the crops determined in the step S13 is combined with
、
、
Matching is performed to obtain a predicted yield->
The method comprises the steps of carrying out a first treatment on the surface of the If the growth of crops is judged to be slow, the yield is predicted>
Get->
Is a value of (2); if the crop growth is judged to be normal, the yield is predicted>
Get->
Is a value of (2); if the crop growth is judged to be fast, the yield is predicted>
Get->
Is a value of (2).
In this embodiment, the specific environmental data includes: temperature, illumination intensity, carbon dioxide concentration in air, light-receiving area of plants and soil fertilizer content; it should be noted that, according to the actual variety of crops, the environmental data includes but is not limited to the above, and may be adaptively increased;
the step S2 includes:
step S21: acquiring environmental data of a region to be predicted;
step S22: according to the environmental data, matching is carried out in a big data platform, and the crop yield corresponding to the matching result is used as the predicted yield
The method comprises the steps of carrying out a first treatment on the surface of the That is, the environmental data which is consistent with the environmental data of the area to be predicted is found in the big data platform (when the environmental data is not completely consistent, similar environmental data is selected), the crop yield of the area to be predicted is predicted according to the yield data corresponding to the environmental data in the big data platform, wherein the conversion of the cultivation area is related, in the prediction process, the yield data in a unit area is calculated according to the yield data corresponding to the environmental data in the big data platform, and then the crop yield of the area to be predicted is calculated according to the yield data in the unit area>
。
In this embodiment, specifically, the determination rule of the weight in step S3 is as follows:
if the pesticide determined in the step S13The crop growth state is that the crop growth is slow, the yield is predicted
Weights of (2)X=0.6, predicted yield +.>
Weights of (2)Y=0.4；
If the crop growth status determined in step S13 is that the crop growth is normal, the yield is predicted
Weights of (2)X=0.5, predicted yield +.>
Weights of (2)Y=0.5；
If the crop growth status determined in step S13 is that the crop growth is fast, the yield is predicted
Weights of (2)X=0.6, predicted yield +.>
Weights of (2)Y=0.5；
The predicted yield
Calculated by the following formula:
the foregoing examples merely represent specific embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that, for those skilled in the art, several variations and modifications can be made without departing from the technical solution of the present application, which fall within the protection scope of the present application.
This background section is provided to generally present the context of the present invention and the work of the presently named inventors, to the extent it is described in this background section, as well as the description of the present section as not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present invention.
Claims (5)
1. A crop yield prediction method based on artificial intelligence and historical data, comprising:
step S1: acquiring current state data of crops in a region to be predicted, and obtaining predicted yield through a yield prediction model based on the current state data of the crops
；
Step S2: acquiring environmental data of a region to be predicted, and matching in a large database based on the environmental data to obtain predicted yield
；
Step S3: in predicting yield
And predictive yield->
On the basis of (1) combining preset weightsXAnd weightYObtaining predicted yield->
；
The establishment process of the yield prediction model is as follows:
step A: acquiring original state information of crops at each time point in a growth period from a big data platform
；
And (B) step (B): sample crop calendar from area to be predictedIn the history growth data, sample state information of crops at each time point in a growth period in a region to be predicted is obtained
；
Step C: based on sample state information of crops in a region to be predicted at each time point in a growth period
Original status information for the crop at various time points during the growth cycle +.>
Correction is carried out to obtain standard state information of crops in the region to be predicted at each time point in the growth period +.>
；
The step S1 includes:
step S11: acquiring three-dimensional image data of a region to be predicted;
step S12: extracting state information of crops from three-dimensional image data
；
Step S13: status information of crops
As input, judging and outputting the growth state of the crops through a yield prediction model;
step S14: based on the determined growth state of the crop, combining the historical crop yield data of the sample of the area to be predicted to obtain the predicted yield
；
The step S13 includes:
step S131: status information of crops
Inputting a yield prediction model, and matching corresponding standard state information
；
Step S132: according to the matched standard state information
Reversely determining the time point of the crops;
step S133: determining the growth state of the crops according to the time point where the crops are positioned and the time point where the crops are actually positioned;
the step S133 includes:
when the time point of the reverse determination crops is earlier than the actual time point of the crops, judging that the crops grow slowly;
when the time point of the reverse determination crops coincides with the actual time point of the crops, judging that the crops grow normally;
when the time point of the reverse determination crops is later than the actual time point of the crops, the crops are judged to grow fast;
the step S14 includes:
step S141: sequencing historical crop yield data in a region to be predicted according to the sequence from high to low;
wherein, the front 1/3 is the historical yield of the crops in the area to be predicted when the crops grow fast
；
The middle 1/3 is the historical yield of the crops in the area to be predicted when the crops grow normally
；
The later 1/3 is the historical yield of crops in the area to be predicted when the crops grow slowly
；
Step S142: for a pair of
、
、
Averaging by the following formula to obtain +.>
、
、
；
Step S143: the growth state of the crops determined in the step S13 is combined with
、
、
Matching to obtain predictionYield->
。
2. The crop yield prediction method based on artificial intelligence and historical data according to claim 1, wherein the status information is a set consisting of stem diameter, stem height, leaf size, leaf color, whether or not result, fruit size, fruit color, expressed as:
wherein:
3. A crop yield prediction method based on artificial intelligence and historical data as claimed in claim 1, wherein the modifying comprises:
if the deviation between the sample state information and the original state information is within 5%, the original state information is used as standard state information;
if the deviation between the sample state information and the original state information is 5% -10%, taking the average value of the sample state information and the original state information as standard state information;
and if the deviation between the sample state information and the original state information is more than 10%, taking the sample state information as standard state information.
4. A crop yield prediction method based on artificial intelligence and historical data as claimed in claim 1, wherein the environmental data comprises: temperature, illumination intensity, carbon dioxide concentration in air, light-receiving area of plants and soil fertilizer content;
the step S2 includes:
step S21: acquiring environmental data of a region to be predicted;
step S22: according to the environmental data, matching is carried out in a big data platform, and the crop yield corresponding to the matching result is used as the predicted yield
。
5. The method for predicting crop yield based on artificial intelligence and historical data as recited in claim 4, wherein the determining rule of the weights in the step S3 is as follows:
if the crop growth status determined in step S13 is that the crop growth is slow, the yield is predicted
Weights of (2)X=0.6, predicted yield +.>
Weights of (2)Y=0.4；
If the crop growth status determined in step S13 is that the crop growth is normal, the yield is predicted
Weights of (2)X=0.5, predicted yield +.>
Weights of (2)Y=0.5；
If the crop growth status determined in step S13 is that the crop growth is fast, the yield is predicted
Weights of (2)X=0.6, predicted yield +.>
Weights of (2)Y=0.5；
The predicted yield
Calculated by the following formula:
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