CN111985728A - Method for establishing organic sorghum yield prediction model - Google Patents
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Abstract
The invention particularly relates to a method for establishing an organic sorghum yield prediction model. The method for establishing the organic sorghum yield prediction model comprises the steps of dividing the growth cycle of organic sorghum into three periods of a planting initial period, a growth period and a maturation period, and respectively constructing a training data set; preprocessing and variable screening are carried out on the three training data sets, the most important characteristic variables are screened out, and the model is trained; adjusting model variables and evaluating the feasibility of the model until the requirements of a modeler and the reference coefficient are evaluated; and finally, storing the trained three models in different growth stages into a background big data cluster respectively, so that the later-stage user can call the models conveniently. The method for establishing the organic sorghum yield prediction model can only predict the organic sorghum yield in different growth stages, improves the prediction speed and the prediction accuracy, can also provide early warning prompts for users, timely reminds the users to take rescue measures and provides judgment reference and reference for the organic sorghum.
Description
Technical Field
The invention relates to the technical field of machine learning algorithms, in particular to a method for establishing an organic sorghum yield prediction model.
Background
Organic sorghum is one of the raw materials used for brewing wine frequently in wineries, and the yield of the organic sorghum is crucial to the operation of wineries. However, the existing organic sorghum prediction mainly depends on the expert to estimate the yield of different plots according to the area by virtue of experience, the artificial prediction result is not very accurate, and the error deviation is large.
Based on the above, the invention provides a method for establishing an organic sorghum yield prediction model.
Disclosure of Invention
In order to make up for the defects of the prior art, the invention provides a simple and efficient method for establishing an organic sorghum yield prediction model.
The invention is realized by the following technical scheme:
a method for establishing an organic sorghum yield prediction model is characterized by comprising the following steps:
first, a training data set is constructed
Dividing the growth cycle of the organic sorghum into three periods of a planting initial period, a growth period and a maturation period, taking the basic attributes of planting, natural environment data of the planting period and historical data of field management data as initial explanatory variables of the model, taking the historical yield record of a planting plot as response variables of the model, and respectively assembling three model training data sets according to the growth stage of the organic sorghum;
second, data preprocessing
Preprocessing three training data sets, including observing abnormal data points in the data sets and removing suspicious human error input information;
third step, variable screening
In order to improve the stability and the prediction precision of the model, the preprocessed data are subjected to variable screening, 15 most important characteristic variables are screened out, and the characteristic variables are subjected to a co-linearity problem test;
the fourth step, training the model
Setting the yield of the organic sorghum as a response variable, taking other characteristic variables influencing the yield of the organic sorghum as explanatory variables of a model, and taking training data of the organic sorghum in three different growth periods as a training data set to train the model;
fifth step, model diagnosis
Observing and adjusting model variables and evaluating feasibility of the model, and continuously and circularly adjusting until requirements of modelers and reference coefficient evaluation are achieved;
sixth, model application
And storing the trained models of the organic sorghum yield in the early stage of planting, the growth stage and the maturity stage into a background big data cluster respectively, so as to facilitate later-stage user calling.
In the first step, the training data set consists of three parts:
the basic planting attributes including the area of the land, the soil temperature, the soil humidity, the soil pH value and the content information of special chemical substances are obtained through manual recording and detection of a soil detector;
the natural environment data of the planting period, including recent temperature change, illumination time and the influence degree of special natural disasters, are acquired through a local meteorological observation station and a base station where a land parcel is located;
the field management data, the record of the amount of the fertilizer used by the land, the amount of the seeds used, the times of the diseases, the pests and the weeds and the times of field support of agricultural technicians are obtained by field collection.
In the second step, when the characteristic data of a certain column in the training data set has an empty condition, if the empty column accounts for less than 5% of the total columns, selecting to fill the average value of the corresponding column in the empty column, otherwise, deleting the characteristic data of the column or manually filling the empty column.
And in the third step, measuring the co-linear severity of variables in regression modeling by using a coefficient of variance concept in mathematical statistics, and removing variables with coefficient of variance exceeding 10 to obtain three training data of the organic sorghum with different growth periods as a training data set of the yield of the organic sorghum at different periods.
And in the fourth step, compiling the organic sorghum yield prediction model by adopting a Statsmodels professional regression modeling extension tool language package in the Python language, and training the models by taking the training data of the three organic sorghum with different growth periods as a training data set, thereby obtaining the three organic sorghum yield prediction models with different growth periods.
In the fifth step, the variable adjustment and evaluation of the model comprises the following steps:
s1, observing the fitting degree between the constructed model and data by using a residual diagnostic graph, observing outliers and strongly-influenced sample points, and removing strongly-influenced outliers from a interest group in the process of training the model;
s2, evaluating whether the fitting power of the variables is reasonable or not by drawing a GAM (generalized addition model) graph in statistics on important variable indexes in the model and adding an alternative model scheme;
s3, characteristic variables with the T test P value larger than 0.2 in the statistical diagnosis result of the model are eliminated, and T test shows that no obvious linear relation exists between higher characteristic variables and yield;
and S4, calculating a correction decision coefficient (Adjusted R-Square) of the candidate model, an AIC (Akaike information criterion) Chi information criterion and a mean Square error sum obtained by the model in a test data set, and finally comparing and selecting the model with the highest interpretation strength and fitting degree to the original data.
In the sixth step, continuously monitoring basic planting attributes, natural environment data and field management data in a planting period in a sorghum growth stage, inputting monitoring data into characteristic variables of the model, and calling an organic sorghum yield prediction model in a corresponding growth stage according to the growth stage input by a user to periodically predict the yield of the organic sorghum;
and if the predicted value is smaller than the user demand or the experience value, sending out early warning information to remind the user of timely remedying the abnormal condition.
And in the sixth step, if the purchasing quantity is more than 15% of the predicted yield in the grain collecting stage, the farmer is considered to have the phenomenon of borrowing or being filled with the good products, and the model sends out a system prompt.
The invention has the beneficial effects that: the method for establishing the organic sorghum yield prediction model can only predict the organic sorghum yield in different growth stages, improves the prediction speed and the prediction accuracy, can also provide early warning prompts for users, timely reminds the users to take rescue measures and provides judgment reference and reference for the organic sorghum.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of a method for establishing an organic sorghum yield prediction model according to the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the embodiment of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for establishing the organic sorghum yield prediction model comprises the following steps:
first, a training data set is constructed
The model invention divides the growth cycle of the organic sorghum into three periods according to the growth characteristics of the organic sorghum and considering that the fertilizing amount, the rainfall amount and other external factors of the organic sorghum in different growth stages are different: the method comprises the following steps of planting in an initial stage, a growth stage and a maturation stage, taking basic attributes of planting, natural environment data of the planting stage and historical data of field management data as initial explanatory variables of a model, taking historical yield records of a planted plot as response variables of the model, and respectively assembling three model training data sets according to the growth stage of the organic sorghum;
second, data preprocessing
Preprocessing three training data sets, including observing abnormal data points in the data sets and removing suspicious human error input information;
third step, variable screening
In order to improve the stability and the prediction precision of the model, the preprocessed data are subjected to variable screening, 15 most important characteristic variables are screened out, and the characteristic variables are subjected to a co-linearity problem test;
the fourth step, training the model
Setting the yield of the organic sorghum as a response variable, taking other characteristic variables influencing the yield of the organic sorghum as explanatory variables of a model, and taking training data of the organic sorghum in three different growth periods as a training data set to train the model;
fifth step, model diagnosis
Observing and adjusting model variables and evaluating feasibility of the model, and continuously and circularly adjusting until requirements of modelers and reference coefficient evaluation are achieved;
sixth, model application
And storing the three prediction models of the yield of the trained organic sorghum in the early planting stage, the growth stage and the maturity stage into a background big data cluster respectively, so that the models are convenient for later-stage user calling.
In the first step, the training data set consists of three parts:
the basic planting attributes including the area of the land, the soil temperature, the soil humidity, the soil pH value and the content information of special chemical substances are obtained through manual recording and detection of a soil detector;
the natural environment data of the planting period, including recent temperature change, illumination time and the influence degree of special natural disasters, are acquired through a local meteorological observation station and a base station where a land parcel is located;
the field management data, the record of the amount of the fertilizer used by the land, the amount of the seeds used, the times of the diseases, the pests and the weeds and the times of field support of agricultural technicians are obtained by field collection.
In the second step, when the characteristic data of a certain column in the training data set has an empty condition, if the empty column accounts for less than 5% of the total columns, selecting to fill the average value of the corresponding column in the empty column, otherwise, deleting the characteristic data of the column or manually filling the empty column.
And in the third step, measuring the co-linear severity of variables in regression modeling by using a coefficient of variance concept in mathematical statistics, and removing variables with coefficient of variance exceeding 10 to obtain three training data of the organic sorghum with different growth periods as a training data set of the yield of the organic sorghum at different periods.
And in the fourth step, compiling the organic sorghum yield prediction model by adopting a Statsmodels professional regression modeling extension tool language package in the Python language, and training the models by taking the training data of the three organic sorghum with different growth periods as a training data set, thereby obtaining the three organic sorghum yield prediction models with different growth periods.
In the fifth step, the variable adjustment and evaluation of the model comprises the following steps:
s1, observing the fitting degree between the constructed model and data by using a residual diagnostic graph, observing outliers and strongly-influenced sample points, and removing strongly-influenced outliers from a interest group in the process of training the model;
s2, evaluating whether the fitting power of the variables is reasonable or not by drawing a GAM (generalized addition model) graph in statistics on important variable indexes in the model and adding an alternative model scheme;
s3, eliminating characteristic variables with higher T test (the P value is larger than 0.2) in the statistical diagnosis result of the model, wherein the T test shows that no obvious linear relation exists between the characteristic variables with higher T test and the yield;
and S4, calculating a correction decision coefficient (Adjusted R-Square) of the candidate model, an AIC (Akaike information criterion) Chi information criterion and a mean Square error sum obtained by the model in a test data set, and finally comparing and selecting the model with the highest interpretation strength and fitting degree to the original data.
And (4) deducing the dosage requirement of the next year on the organic sorghum according to the brewing plan of the next year by the user, thereby making a planting plan. In order to ensure that the demand of the using amount is met, continuously monitoring basic planting attributes, natural environment data in a planting period and field management data in a sorghum growth stage, inputting monitoring values of variables in a page according to characteristic variables in a model, and adding a growth stage of the organic sorghum under the current calculated plot; after reading necessary parameters, the background task calls an organic sorghum yield prediction model in a corresponding growth stage to perform periodic prediction on the yield of the organic sorghum;
and if the predicted value is smaller than the user demand or the experience value, sending out early warning information to remind the user of timely remedying the abnormal condition.
And in the sixth step, if the purchasing quantity is more than 15% of the predicted yield in the grain collecting stage, the farmer is considered to have the phenomenon of borrowing or being filled with the good products, and the model sends out a system prompt.
Compared with the prior art, the method for establishing the organic sorghum yield prediction model has the following characteristics:
firstly, three training data sets are respectively constructed in consideration of the growth stage of the organic sorghum, and models are respectively constructed for the yield of the organic sorghum in three different periods, so that the models can be used for learning the influence degree of the organic sorghum in different periods on various indexes in a targeted manner.
And secondly, a result comparison function is added, namely when the user predicts the future yield of the organic sorghum through the constructed model, if the result is lower than the user demand or experience value, an early warning prompt is provided for the user, and the user can be reminded to take rescue measures for the corresponding plot yield shortage in time.
And thirdly, providing a set of rules for studying and judging the yield of the organic sorghum for customers through a machine learning and statistical field regression modeling method, namely determining main factors influencing the yield of the organic sorghum. The main factors have the characteristics of observability, adjustability and simulation after strict screening, and provide judgment basis and reference for subsequent research on organic sorghum and other crops.
The above-described embodiment is only one specific embodiment of the present invention, and general changes and substitutions by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention.
Claims (8)
1. A method for establishing an organic sorghum yield prediction model is characterized by comprising the following steps:
first, a training data set is constructed
Dividing the growth cycle of the organic sorghum into three periods of a planting initial period, a growth period and a maturation period, taking the basic attributes of planting, natural environment data of the planting period and historical data of field management data as initial explanatory variables of the model, taking the historical yield record of a planting plot as response variables of the model, and respectively assembling three model training data sets according to the growth stage of the organic sorghum;
second, data preprocessing
Preprocessing three training data sets, including observing abnormal data points in the data sets and removing suspicious human error input information;
third step, variable screening
In order to improve the stability and the prediction precision of the model, the preprocessed data are subjected to variable screening, 15 most important characteristic variables are screened out, and the characteristic variables are subjected to a co-linearity problem test;
the fourth step, training the model
Setting the yield of the organic sorghum as a response variable, taking other characteristic variables influencing the yield of the organic sorghum as explanatory variables of a model, and taking training data of the organic sorghum in three different growth periods as a training data set to train the model;
fifth step, model diagnosis
Observing and adjusting model variables and evaluating feasibility of the model, and continuously and circularly adjusting until requirements of modelers and reference coefficient evaluation are achieved;
sixth, model application
And storing the trained models of the organic sorghum yield in the early stage of planting, the growth stage and the maturity stage into a background big data cluster respectively, so as to facilitate later-stage user calling.
2. The method for modeling the yield of organic sorghum according to claim 1, wherein: in the first step, the training data set consists of three parts:
the basic planting attributes including the area of the land, the soil temperature, the soil humidity, the soil pH value and the content information of special chemical substances are obtained through manual recording and detection of a soil detector;
the natural environment data of the planting period, including recent temperature change, illumination time and the influence degree of special natural disasters, are acquired through a local meteorological observation station and a base station where a land parcel is located;
the field management data, the record of the amount of the fertilizer used by the land, the amount of the seeds used, the times of the diseases, the pests and the weeds and the times of field support of agricultural technicians are obtained by field collection.
3. A method for modeling organic sorghum production prediction according to claim 1 or 2, characterized in that: in the second step, when the characteristic data of a certain column in the training data set has an empty condition, if the empty column accounts for less than 5% of the total columns, selecting to fill the average value of the corresponding column in the empty column, otherwise, deleting the characteristic data of the column or manually filling the empty column.
4. A method for modeling organic sorghum production prediction according to claim 1 or 2, characterized in that: and in the third step, measuring the co-linear severity of variables in regression modeling by using a coefficient of variance concept in mathematical statistics, and removing variables with coefficient of variance exceeding 10 to obtain three training data of the organic sorghum with different growth periods as a training data set of the yield of the organic sorghum at different periods.
5. The method for modeling the yield of organic sorghum according to claim 4, wherein: and in the fourth step, compiling the organic sorghum yield prediction model by adopting a Statsmodels professional regression modeling extension tool language package in the Python language, and training the models by taking the training data of the three organic sorghum with different growth periods as a training data set, thereby obtaining the three organic sorghum yield prediction models with different growth periods.
6. The method for modeling the yield of organic sorghum according to claim 1 or 4, wherein: in the fifth step, the variable adjustment and evaluation of the model comprises the following steps:
s1, observing the fitting degree between the constructed model and data by using a residual diagnostic graph, observing outliers and strongly-influenced sample points, and removing strongly-influenced outliers from a interest group in the process of training the model;
s2, evaluating whether the fitting power of the variables is reasonable or not by drawing a GAM (gamma-gamma) graph in statistics on important variable indexes in the model and adding an alternative model scheme;
s3, characteristic variables with the T test P value larger than 0.2 in the statistical diagnosis result of the model are eliminated, and T test shows that no obvious linear relation exists between higher characteristic variables and yield;
and S4, calculating a correction decision coefficient of the candidate model, an AIC Chi information criterion and a mean square error sum obtained by the model in a test data set, and finally comparing and selecting the model with the highest explaining strength and fitting degree to the original data.
7. The method for modeling the yield of organic sorghum according to claim 2 or 6, wherein: in the sixth step, continuously monitoring basic planting attributes, natural environment data and field management data in a planting period in a sorghum growth stage, inputting monitoring data into characteristic variables of the model, and calling an organic sorghum yield prediction model in a corresponding growth stage according to the growth stage input by a user to periodically predict the yield of the organic sorghum;
and if the predicted value is smaller than the user demand or the experience value, sending out early warning information to remind the user of timely remedying the abnormal condition.
8. The method for modeling the yield of organic sorghum according to claim 7, wherein: and in the sixth step, if the purchasing quantity is more than 15% of the predicted yield in the grain collecting stage, the farmer is considered to have the phenomenon of borrowing or being filled with the good products, and the model sends out a system prompt.
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| CN113705657A (en) * | 2021-08-24 | 2021-11-26 | 华北电力大学 | Stepwise clustering statistical downscaling method for eliminating multiple collinearity based on difference method |
| CN116579521A (en) * | 2023-05-12 | 2023-08-11 | 中山大学 | Yield prediction time window determining method, device, equipment and readable storage medium |
| CN117217372A (en) * | 2023-09-08 | 2023-12-12 | 湖北泰跃卫星技术发展股份有限公司 | Method for predicting agricultural product yield and quantifying influence of agricultural activities on yield |
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