CN113743790B - Method and system for improving rice quality of saline-alkali soil and storage medium - Google Patents
Method and system for improving rice quality of saline-alkali soil and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a system and a storage medium for improving the quality of saline-alkali soil rice, and relates to the field of saline-alkali soil improvement, wherein the method for improving the quality of the saline-alkali soil rice comprises the following steps: acquiring the physicochemical property of soil in a saline-alkali area, and analyzing the soil components according to the physicochemical property of the soil to generate soil component information; carrying out salinization evaluation on the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area; acquiring rice growth condition information and rice quality information in a saline-alkali area, and matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in a target area; establishing a soil improvement model based on a neural network, and generating an improvement scheme which is helpful for improving the rice quality according to the soil improvement direction through the soil improvement model.
Description
Technical Field
The invention relates to the field of saline-alkali soil improvement, in particular to a method and a system for improving the rice quality of saline-alkali soil and a readable storage medium.
Background
The rice is the first large grain crop in China, the planting mode mainly comprises two modes of rice transplanting and direct seeding, the requirements on the condition of the planting ground surface are strict, salinization is easily received, forced damage is caused, and yield is reduced. Therefore, the salinization of the soil is an important factor influencing the grain yield and quality in China, seriously restricts the development of agricultural production,
in order to improve the saline-alkali soil area and improve the rice quality in the saline-alkali soil area, a system needs to be developed to be matched with the saline-alkali soil area, and the system acquires soil component information; carrying out salinization evaluation on the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area; matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in a target area; and establishing a soil improvement model, and generating an improvement scheme which is helpful for improving the rice quality according to the soil improvement direction through the soil improvement model. In the implementation process of the system, how to analyze soil condition information according to rice growth condition information and rice quality information to generate soil nutrient deficiency information is an urgent problem which needs to be solved.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a method, a system and a storage medium for improving the quality of rice in saline-alkali soil.
The invention provides a method for improving the rice quality of saline-alkali soil, which comprises the following steps:
acquiring the physicochemical property of soil in a saline-alkali area, and analyzing the soil components according to the physicochemical property of the soil to generate soil component information;
carrying out salinization evaluation on the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area;
acquiring rice growth condition information and rice quality information in a saline-alkali area, and matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in a target area;
establishing a soil improvement model based on a neural network, and generating an improvement scheme which is helpful for improving the rice quality according to the soil improvement direction through the soil improvement model.
In this scheme, soil physicochemical property include soil organic matter content, soil salinity content, soil heavy metal pollutant content, soil structural information, soil temperature information, soil humidity information, the combination of one or more than two of soil pH value information, rice quality information include: one or the combination of more than two of the appearance quality, the taste quality, the nutrition quality and the sanitation quality of the rice.
In the scheme, salinization evaluation is carried out in the saline-alkali area according to the soil component information, and soil condition information in the saline-alkali area is generated, and the method specifically comprises the following steps:
acquiring a salinization evaluation standard of a saline-alkali soil area, generating an influence factor according to the evaluation standard and determining weight information;
extracting sub-information characteristics from the soil composition information according to the influence factors, and matching the sub-information characteristics with weight information to calculate evaluation information;
generating soil condition information in the saline-alkali area through the evaluation information;
in the scheme, the rice growth condition information and the rice quality information are matched with the soil condition information to generate the soil improvement direction in the saline-alkali area, and the method specifically comprises the following steps:
generating a rice growth time sequence according to the rice growth condition information and the nutrient information required by each growth stage;
carrying out segmentation extraction on nutrient characteristics required by each growth stage of the rice through rice growth time sequence segmentation, and matching with soil condition information in the saline-alkali area;
performing comparative analysis on soil condition information in the saline-alkali area through data indexes to generate nutrient deficiency information;
performing principal component analysis on the rice quality information, and evaluating soil condition information in a saline-alkali area; decomposing the rice quality information into a plurality of quality components, and calculating characteristic values and contribution rates of different quality components according to main component standards;
selecting main components, calculating a main component evaluation value, calculating a comprehensive evaluation value according to the main component evaluation value, and sorting the nutrient information missing from the soil according to the comprehensive evaluation value to generate a sorting result;
and polymerizing the nutrient information missing from the soil in the saline-alkali area according to the nutrient missing information and the sequencing result to generate the soil improvement direction in the saline-alkali area.
In this scheme, the soil improvement model is established based on a neural network, and an improvement scheme which is helpful for improving the rice quality is generated through the soil improvement model according to the soil improvement direction, specifically:
establishing a soil improvement model based on a neural network, and performing initialization training;
introducing the soil condition information and the soil improvement direction into the soil improvement model to generate an initial improvement scheme;
combining the initial improvement scheme with geographical environment information and meteorological environment information of the region of the saline-alkali land area to generate a feasibility coefficient, and performing scheme feasibility evaluation;
judging whether the feasibility coefficient is larger than a preset feasibility coefficient threshold value or not;
if the rice quality is larger than the preset value, outputting the initial improvement scheme to generate an improvement scheme which is helpful for improving the rice quality, and displaying the improvement scheme according to a preset mode; if the initial improvement scheme is smaller than the preset improvement scheme, the initial improvement scheme is newly established.
In this scheme, still include:
collecting the rice growth condition information and the rice quality information of the improved soil, and performing feature extraction on the rice growth condition information and the rice quality information of the soil before and after improvement to respectively generate feature sets;
comparing and analyzing the characteristic set of the rice in the improved soil with the characteristic set of the rice in the soil before improvement;
generating a deviation rate in a preset calculation mode, and presetting a threshold value of the deviation rate;
marking the characteristics of which the deviation rate is greater than the deviation rate threshold value, extracting rice growth condition information and rice quality information corresponding to the characteristics, and calculating improvement ratio information;
judging whether the improvement proportion information reaches a preset improvement proportion information threshold value or not;
if the improvement proportion information is smaller than a preset improvement proportion information threshold value, generating feedback information according to the improvement proportion information and the rice characteristic set of the improved soil;
and generating a second soil improvement scheme according to the feedback information.
The second aspect of the invention also provides a system for improving the quality of rice in saline-alkali soil, which comprises: the device comprises a memory and a processor, wherein the memory comprises a method program for improving the quality of the rice in the saline-alkali soil, and the method program for improving the quality of the rice in the saline-alkali soil realizes the following steps when being executed by the processor:
acquiring the physicochemical property of soil in a saline-alkali area, and analyzing the soil components according to the physicochemical property of the soil to generate soil component information;
carrying out salinization evaluation on the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area;
acquiring rice growth condition information and rice quality information in a saline-alkali area, and matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in a target area;
establishing a soil improvement model based on a neural network, and generating an improvement scheme which is helpful for improving the rice quality according to the soil improvement direction through the soil improvement model.
In this scheme, soil physicochemical property include soil organic matter content, soil salinity content, soil heavy metal pollutant content, soil structural information, soil temperature information, soil humidity information, the combination of one or more than two of soil pH value information, rice quality information include: one or the combination of more than two of the appearance quality, the taste quality, the nutrition quality and the sanitation quality of the rice.
In the scheme, salinization evaluation is carried out in the saline-alkali area according to the soil component information, and soil condition information in the saline-alkali area is generated, and the method specifically comprises the following steps:
acquiring a salinization evaluation standard of a saline-alkali soil area, generating an influence factor according to the evaluation standard and determining weight information;
extracting sub-information characteristics from the soil composition information according to the influence factors, and matching the sub-information characteristics with weight information to calculate evaluation information;
generating soil condition information in the saline-alkali area through the evaluation information;
in the scheme, the rice growth condition information and the rice quality information are matched with the soil condition information to generate the soil improvement direction in the saline-alkali area, and the method specifically comprises the following steps:
generating a rice growth time sequence according to the rice growth condition information and the nutrient information required by each growth stage;
carrying out segmentation extraction on nutrient characteristics required by each growth stage of the rice through rice growth time sequence segmentation, and matching with soil condition information in the saline-alkali area;
performing comparative analysis on soil condition information in the saline-alkali area through data indexes to generate nutrient deficiency information;
performing principal component analysis on the rice quality information, and evaluating soil condition information in a saline-alkali area; decomposing the rice quality information into a plurality of quality components, and calculating characteristic values and contribution rates of different quality components according to main component standards;
selecting main components, calculating a main component evaluation value, calculating a comprehensive evaluation value according to the main component evaluation value, and sorting the nutrient information missing from the soil according to the comprehensive evaluation value to generate a sorting result;
and polymerizing the nutrient information missing from the soil in the saline-alkali area according to the nutrient missing information and the sequencing result to generate the soil improvement direction in the saline-alkali area.
In this scheme, the soil improvement model is established based on a neural network, and an improvement scheme which is helpful for improving the rice quality is generated through the soil improvement model according to the soil improvement direction, specifically:
establishing a soil improvement model based on a neural network, and performing initialization training;
introducing the soil condition information and the soil improvement direction into the soil improvement model to generate an initial improvement scheme;
combining the initial improvement scheme with geographical environment information and meteorological environment information of the region of the saline-alkali land area to generate a feasibility coefficient, and performing scheme feasibility evaluation;
judging whether the feasibility coefficient is larger than a preset feasibility coefficient threshold value or not;
if the rice quality is larger than the preset value, outputting the initial improvement scheme to generate an improvement scheme which is helpful for improving the rice quality, and displaying the improvement scheme according to a preset mode; if the initial improvement scheme is smaller than the preset improvement scheme, the initial improvement scheme is newly established.
In this scheme, still include:
collecting the rice growth condition information and the rice quality information of the improved soil, and performing feature extraction on the rice growth condition information and the rice quality information of the soil before and after improvement to respectively generate feature sets;
comparing and analyzing the characteristic set of the rice in the improved soil with the characteristic set of the rice in the soil before improvement;
generating a deviation rate in a preset calculation mode, and presetting a threshold value of the deviation rate;
marking the characteristics of which the deviation rate is greater than the deviation rate threshold value, extracting rice growth condition information and rice quality information corresponding to the characteristics, and calculating improvement ratio information;
judging whether the improvement proportion information reaches a preset improvement proportion information threshold value or not;
if the improvement proportion information is smaller than a preset improvement proportion information threshold value, generating feedback information according to the improvement proportion information and the rice characteristic set of the improved soil;
and generating a second soil improvement scheme according to the feedback information.
The third aspect of the invention also provides a computer readable storage medium, which contains a program of the method for improving the quality of the saline-alkali soil rice, and when the program of the method for improving the quality of the saline-alkali soil rice is executed by a processor, the method for improving the quality of the saline-alkali soil rice is realized by any one of the steps of the method for improving the quality of the saline-alkali soil rice.
The invention discloses a method, a system and a storage medium for improving the quality of saline-alkali soil rice, and relates to the field of saline-alkali soil improvement, wherein the method for improving the quality of the saline-alkali soil rice comprises the following steps: acquiring the physicochemical property of soil in a saline-alkali area, and analyzing the soil components according to the physicochemical property of the soil to generate soil component information; carrying out salinization evaluation on the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area; acquiring rice growth condition information and rice quality information in a saline-alkali area, and matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in a target area; establishing a soil improvement model based on a neural network, and generating an improvement scheme which is helpful for improving the rice quality according to the soil improvement direction through the soil improvement model. In the implementation process, the invention analyzes the soil problem which needs to be improved most in the saline-alkali area by using the rice growth condition information and the rice quality information, generates a corresponding solution to the soil problem by using a soil improvement model, and displays the targeted improvement of the rice planting in the saline-alkali area.
Drawings
FIG. 1 shows a flow chart of a method of the invention for facilitating rice quality improvement in saline and alkaline land;
FIG. 2 is a flow chart of the method for producing soil improvement directions in saline-alkali areas according to the present invention;
FIG. 3 illustrates a flow chart of a method of the present invention for creating a soil improvement generating amendment;
FIG. 4 shows a block diagram of a system for facilitating rice quality enhancement in saline and alkaline land according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
FIG. 1 shows a flow chart of a method for improving the rice quality of saline-alkali soil according to the invention.
As shown in fig. 1, a first aspect of the present invention provides a method for improving rice quality in saline-alkali soil, including:
s102, acquiring soil physicochemical properties in saline-alkali areas, and performing soil component analysis according to the soil physicochemical properties to generate soil component information;
s104, performing salinization evaluation on the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area;
s106, obtaining the rice growth condition information and the rice quality information in the saline-alkali area, and matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in a target area;
and S108, establishing a soil improvement model based on the neural network, and generating an improvement scheme which is helpful for improving the rice quality according to the soil improvement direction through the soil improvement model.
It should be noted that the soil physicochemical properties include one or a combination of two or more of soil organic matter content, soil salinity content, soil heavy metal pollutant content, soil structural information, soil temperature information, soil humidity information, and soil acidity and alkalinity information, and the rice quality information includes: one or the combination of more than two of the appearance quality, the taste quality, the nutrition quality and the sanitation quality of the rice.
The method for evaluating salinization in the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area specifically comprises the following steps:
acquiring a salinization evaluation standard of a saline-alkali soil area, generating an influence factor according to the evaluation standard and determining weight information;
extracting sub-information characteristics from the soil composition information according to the influence factors, and matching the sub-information characteristics with weight information to calculate evaluation information;
generating soil condition information in the saline-alkali area through the evaluation information;
FIG. 2 shows a flow chart of the method for generating soil improvement direction in saline-alkali area according to the invention.
According to the embodiment of the invention, the rice growth condition information and the rice quality information are matched with the soil condition information to generate the soil improvement direction in the saline-alkali area, and the method specifically comprises the following steps:
s202, generating a rice growth time sequence according to the rice growth condition information and the nutrient information required by each growth stage;
s204, carrying out segmentation and extraction on nutrient characteristics required by each growth stage of the rice through rice growth time sequence segmentation, and matching with soil condition information in the saline-alkali area;
s206, comparing and analyzing soil condition information in the saline-alkali area through data indexing to generate nutrient deficiency information;
s208, performing principal component analysis on the rice quality information, and evaluating soil condition information in the saline-alkali area; decomposing the rice quality information into a plurality of quality components, and calculating characteristic values and contribution rates of different quality components according to main component standards;
s210, selecting main components, calculating main component evaluation values, calculating comprehensive evaluation values according to the main component evaluation values, and sorting the nutrient missing information of the soil according to the comprehensive evaluation values to generate sorting results;
s212, according to the nutrient deficiency information and the sequencing result, polymerizing the nutrient information deficient in the soil in the saline-alkali area to generate the soil improvement direction in the saline-alkali area.
Fig. 3 shows a flow chart of a method of the present invention for establishing a soil improvement generating amendment regime.
According to the embodiment of the invention, a soil improvement model is established based on a neural network, and an improvement scheme which is beneficial to improving the rice quality is generated through the soil improvement model according to the soil improvement direction, specifically:
s302, establishing a soil improvement model based on a neural network, and performing initialization training;
s304, importing the soil condition information and the soil improvement direction into the soil improvement model to generate an initial improvement scheme;
s306, combining the initial improvement scheme with geographical environment information and meteorological environment information of the region of the saline-alkali soil region to generate a feasibility coefficient, and performing scheme feasibility evaluation;
s308, judging whether the feasibility coefficient is larger than a preset feasibility coefficient threshold value or not;
s310, if the rice quality is larger than the preset value, outputting the initial improvement scheme to generate an improvement scheme which is helpful for improving the rice quality, and displaying the improvement scheme according to a preset mode; if the initial improvement scheme is smaller than the preset improvement scheme, the initial improvement scheme is newly established.
It should be noted that, the soil improvement model is established based on the neural network, and the initialization training is performed, specifically: preprocessing acquired soil condition information and soil improvement direction data, grouping initial data to obtain a plurality of data sets, importing the data sets into a neural network model to generate an output result after first learning, analyzing and calculating the initial learning rate of each data set according to the obtained first output result, wherein the initial learning rate is in direct proportion to a dispersion coefficient, importing the output result after the first learning into the neural network model again, continuing learning for N times, keeping linear correlation of loss functions of the data sets in each learning process, outputting the output result after N times of learning of the neural network model, comparing and calculating the output results of the data sets to obtain a result deviation rate, judging whether the result deviation rate is smaller than a preset deviation rate threshold value, and if the result deviation rate is smaller than the preset deviation rate threshold value and the error rate is smaller than the preset threshold value, the neural network is proved to be trained.
The method also comprises the step of verifying the improvement scheme through the rice growth condition information and the rice quality information of the improved soil, and specifically comprises the following steps:
collecting the rice growth condition information and the rice quality information of the improved soil, and performing feature extraction on the rice growth condition information and the rice quality information of the soil before and after improvement to respectively generate feature sets;
comparing and analyzing the characteristic set of the rice in the improved soil with the characteristic set of the rice in the soil before improvement;
generating a deviation rate in a preset calculation mode, and presetting a threshold value of the deviation rate;
marking the characteristics of which the deviation rate is greater than the deviation rate threshold value, extracting rice growth condition information and rice quality information corresponding to the characteristics, and calculating improvement ratio information;
judging whether the improvement proportion information reaches a preset improvement proportion information threshold value or not;
if the improvement proportion information is smaller than a preset improvement proportion information threshold value, generating feedback information according to the improvement proportion information and the rice characteristic set of the improved soil;
and generating a second soil improvement scheme according to the feedback information.
According to the embodiment of the invention, when an improved scheme which is beneficial to improving the rice quality is generated, the scheme is formulated according to the rice seed selection, management and the region of saline-alkali soil, and specifically the following steps are carried out:
selecting high-yield, high-quality, disease-resistant and saline-alkali-resistant rice varieties suitable for the saline-alkali soil region from a germplasm resource database according to the soil condition information of the saline-alkali soil region;
acquiring geographical position information in a saline-alkali area, acquiring latitude information according to the geographical position information, and acquiring an illumination duration time sequence according to the latitude information;
obtaining regional area information of the saline-alkali soil, establishing a neural network model, and importing the regional area information and the illumination duration time sequence into the neural network model;
estimating the quantity of seedlings through the neural network model, and generating an applicable rice plant distance and an applicable transplanting mode;
and generating a planting scheme according to the distance of the rice plants and the planting mode, and integrating the planting scheme into an improved scheme which is beneficial to improving the rice quality.
It should be noted that by adapting the optimal planting scheme to the saline-alkali soil region, the rice population structure is optimized, the habitat such as field ventilation, light transmission, temperature and humidity distribution is improved, the efficient utilization of rice light and temperature resources is realized, and the high and stable yield of rice is prevented from being promoted. Meanwhile, the improvement of the quality of rice can be realized in saline-alkali soil areas through crop rotation planting, the utilization rate of the soil is improved, the aim of reducing or even eliminating plant diseases and insect pests is achieved, and a suitable crop rotation planting method is selected, and specifically: generating soil biological characteristic information according to soil condition information of a saline-alkali soil area, preprocessing the soil biological characteristic information, preprocessing the preprocessed soil biological characteristic information, introducing the preprocessed soil biological characteristic information into a neural network model to generate crop rotation mode information, and presetting a crop rotation mode information threshold interval; and determining a rotation mode information threshold interval corresponding to the rotation mode information, and taking a rotation planting method corresponding to the threshold interval as the optimization of the saline-alkali land area.
FIG. 4 shows a block diagram of a system for facilitating rice quality enhancement in saline and alkaline land according to the present invention.
The second aspect of the present invention also provides a system 4 for improving the quality of rice in saline-alkali soil, comprising: a memory 41 and a processor 42, wherein the memory includes a program of the method for improving the quality of the rice in the saline-alkali soil, and the program of the method for improving the quality of the rice in the saline-alkali soil realizes the following steps when being executed by the processor:
acquiring the physicochemical property of soil in a saline-alkali area, and analyzing the soil components according to the physicochemical property of the soil to generate soil component information;
carrying out salinization evaluation on the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area;
acquiring rice growth condition information and rice quality information in a saline-alkali area, and matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in a target area;
establishing a soil improvement model based on a neural network, and generating an improvement scheme which is helpful for improving the rice quality according to the soil improvement direction through the soil improvement model.
It should be noted that the soil physicochemical properties include one or a combination of two or more of soil organic matter content, soil salinity content, soil heavy metal pollutant content, soil structural information, soil temperature information, soil humidity information, and soil acidity and alkalinity information, and the rice quality information includes: one or the combination of more than two of the appearance quality, the taste quality, the nutrition quality and the sanitation quality of the rice.
The method for evaluating salinization in the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area specifically comprises the following steps:
acquiring a salinization evaluation standard of a saline-alkali soil area, generating an influence factor according to the evaluation standard and determining weight information;
extracting sub-information characteristics from the soil composition information according to the influence factors, and matching the sub-information characteristics with weight information to calculate evaluation information;
generating soil condition information in the saline-alkali area through the evaluation information;
according to the embodiment of the invention, the rice growth condition information and the rice quality information are matched with the soil condition information to generate the soil improvement direction in the saline-alkali area, and the method specifically comprises the following steps:
generating a rice growth time sequence according to the rice growth condition information and the nutrient information required by each growth stage;
carrying out segmentation extraction on nutrient characteristics required by each growth stage of the rice through rice growth time sequence segmentation, and matching with soil condition information in the saline-alkali area;
performing comparative analysis on soil condition information in the saline-alkali area through data indexes to generate nutrient deficiency information;
performing principal component analysis on the rice quality information, and evaluating soil condition information in a saline-alkali area; decomposing the rice quality information into a plurality of quality components, and calculating characteristic values and contribution rates of different quality components according to main component standards;
selecting main components, calculating a main component evaluation value, calculating a comprehensive evaluation value according to the main component evaluation value, and sorting the nutrient information missing from the soil according to the comprehensive evaluation value to generate a sorting result;
and polymerizing the nutrient information missing from the soil in the saline-alkali area according to the nutrient missing information and the sequencing result to generate the soil improvement direction in the saline-alkali area.
According to the embodiment of the invention, a soil improvement model is established based on a neural network, and an improvement scheme which is beneficial to improving the rice quality is generated through the soil improvement model according to the soil improvement direction, specifically:
establishing a soil improvement model based on a neural network, and performing initialization training;
introducing the soil condition information and the soil improvement direction into the soil improvement model to generate an initial improvement scheme;
combining the initial improvement scheme with geographical environment information and meteorological environment information of the region of the saline-alkali land area to generate a feasibility coefficient, and performing scheme feasibility evaluation;
judging whether the feasibility coefficient is larger than a preset feasibility coefficient threshold value or not;
if the rice quality is larger than the preset value, outputting the initial improvement scheme to generate an improvement scheme which is helpful for improving the rice quality, and displaying the improvement scheme according to a preset mode; if the initial improvement scheme is smaller than the preset improvement scheme, the initial improvement scheme is newly established.
It should be noted that, the soil improvement model is established based on the neural network, and the initialization training is performed, specifically: preprocessing acquired soil condition information and soil improvement direction data, grouping initial data to obtain a plurality of data sets, importing the data sets into a neural network model to generate an output result after first learning, analyzing and calculating the initial learning rate of each data set according to the obtained first output result, wherein the initial learning rate is in direct proportion to a dispersion coefficient, importing the output result after the first learning into the neural network model again, continuing learning for N times, keeping linear correlation of loss functions of the data sets in each learning process, outputting the output result after N times of learning of the neural network model, comparing and calculating the output results of the data sets to obtain a result deviation rate, judging whether the result deviation rate is smaller than a preset deviation rate threshold value, and if the result deviation rate is smaller than the preset deviation rate threshold value and the error rate is smaller than the preset threshold value, the neural network is proved to be trained.
The method also comprises the step of verifying the improvement scheme through the rice growth condition information and the rice quality information of the improved soil, and specifically comprises the following steps:
collecting the rice growth condition information and the rice quality information of the improved soil, and performing feature extraction on the rice growth condition information and the rice quality information of the soil before and after improvement to respectively generate feature sets;
comparing and analyzing the characteristic set of the rice in the improved soil with the characteristic set of the rice in the soil before improvement;
generating a deviation rate in a preset calculation mode, and presetting a threshold value of the deviation rate;
marking the characteristics of which the deviation rate is greater than the deviation rate threshold value, extracting rice growth condition information and rice quality information corresponding to the characteristics, and calculating improvement ratio information;
judging whether the improvement proportion information reaches a preset improvement proportion information threshold value or not;
if the improvement proportion information is smaller than a preset improvement proportion information threshold value, generating feedback information according to the improvement proportion information and the rice characteristic set of the improved soil;
and generating a second soil improvement scheme according to the feedback information.
According to the embodiment of the invention, when an improved scheme which is beneficial to improving the rice quality is generated, the scheme is formulated according to the rice seed selection, management and the region of saline-alkali soil, and specifically the following steps are carried out:
selecting high-yield, high-quality, disease-resistant and saline-alkali-resistant rice varieties suitable for the saline-alkali soil region from a germplasm resource database according to the soil condition information of the saline-alkali soil region;
acquiring geographical position information in a saline-alkali area, acquiring latitude information according to the geographical position information, and acquiring an illumination duration time sequence according to the latitude information;
obtaining regional area information of the saline-alkali soil, establishing a neural network model, and importing the regional area information and the illumination duration time sequence into the neural network model;
estimating the quantity of seedlings through the neural network model, and generating an applicable rice plant distance and an applicable transplanting mode;
and generating a planting scheme according to the distance of the rice plants and the planting mode, and integrating the planting scheme into an improved scheme which is beneficial to improving the rice quality.
It should be noted that by adapting the optimal planting scheme to the saline-alkali soil region, the rice population structure is optimized, the habitat such as field ventilation, light transmission, temperature and humidity distribution is improved, the efficient utilization of rice light and temperature resources is realized, and the high and stable yield of rice is prevented from being promoted. Meanwhile, the improvement of the quality of rice can be realized in saline-alkali soil areas through crop rotation planting, the utilization rate of the soil is improved, the aim of reducing or even eliminating plant diseases and insect pests is achieved, and a suitable crop rotation planting method is selected, and specifically: generating soil biological characteristic information according to soil condition information of a saline-alkali soil area, preprocessing the soil biological characteristic information, preprocessing the preprocessed soil biological characteristic information, introducing the preprocessed soil biological characteristic information into a neural network model to generate crop rotation mode information, and presetting a crop rotation mode information threshold interval; and determining a rotation mode information threshold interval corresponding to the rotation mode information, and taking a rotation planting method corresponding to the threshold interval as the optimization of the saline-alkali land area.
The third aspect of the invention also provides a computer readable storage medium, which contains a program of the method for improving the quality of the saline-alkali soil rice, and when the program of the method for improving the quality of the saline-alkali soil rice is executed by a processor, the method for improving the quality of the saline-alkali soil rice is realized by any one of the steps of the method for improving the quality of the saline-alkali soil rice.
The invention discloses a method, a system and a storage medium for improving the quality of saline-alkali soil rice, and relates to the field of saline-alkali soil improvement, wherein the method for improving the quality of the saline-alkali soil rice comprises the following steps: acquiring the physicochemical property of soil in a saline-alkali area, and analyzing the soil components according to the physicochemical property of the soil to generate soil component information; carrying out salinization evaluation on the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area; acquiring rice growth condition information and rice quality information in a saline-alkali area, and matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in a target area; establishing a soil improvement model based on a neural network, and generating an improvement scheme which is helpful for improving the rice quality according to the soil improvement direction through the soil improvement model. In the implementation process, the invention analyzes the soil problem which needs to be improved most in the saline-alkali area by using the rice growth condition information and the rice quality information, generates a corresponding solution to the soil problem by using a soil improvement model, and displays the targeted improvement of the rice planting in the saline-alkali area.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (8)
1. A method for improving the quality of rice in saline-alkali soil is characterized by comprising the following steps:
acquiring the physicochemical property of soil in a saline-alkali area, and analyzing the soil components according to the physicochemical property of the soil to generate soil component information;
carrying out salinization evaluation on the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area;
acquiring rice growth condition information and rice quality information in a saline-alkali area, and matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in a target area;
establishing a soil improvement model based on a neural network, and generating an improvement scheme which is beneficial to improving the rice quality according to the soil improvement direction through the soil improvement model;
matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in the saline-alkali area, which specifically comprises the following steps:
generating a rice growth time sequence according to the rice growth condition information and the nutrient information required by each growth stage;
carrying out segmentation extraction on nutrient characteristics required by each growth stage of the rice through rice growth time sequence segmentation, and matching with soil condition information in the saline-alkali area;
performing comparative analysis on soil condition information in the saline-alkali area through data indexes to generate nutrient deficiency information;
performing principal component analysis on the rice quality information, and evaluating soil condition information in a saline-alkali area; decomposing the rice quality information into a plurality of quality components, and calculating characteristic values and contribution rates of different quality components according to main component standards;
selecting main components, calculating a main component evaluation value, calculating a comprehensive evaluation value according to the main component evaluation value, and sorting the nutrient information missing from the soil according to the comprehensive evaluation value to generate a sorting result;
and polymerizing the nutrient information missing from the soil in the saline-alkali area according to the nutrient missing information and the sequencing result to generate the soil improvement direction in the saline-alkali area.
2. The method according to claim 1, wherein the soil physicochemical properties comprise one or a combination of more than two of soil organic matter content, soil salt content, soil heavy metal pollutant content, soil structural information, soil temperature information, soil humidity information and soil pH information, and the rice quality information comprises: one or the combination of more than two of the appearance quality, the taste quality, the nutrition quality and the sanitation quality of the rice.
3. The method for improving the quality of rice in saline-alkali soil according to claim 1, wherein the salinization evaluation is performed on the saline-alkali soil region according to the soil component information to generate soil condition information in the saline-alkali soil region, and the method specifically comprises the following steps:
acquiring a salinization evaluation standard of a saline-alkali soil area, generating an influence factor according to the evaluation standard and determining weight information;
extracting sub-information characteristics from the soil composition information according to the influence factors, and matching the sub-information characteristics with weight information to calculate evaluation information;
and generating soil condition information in the saline-alkali area through the evaluation information.
4. The method for improving the rice quality of the saline-alkali soil according to claim 1, wherein a soil improvement model is established based on the neural network, and an improvement scheme for improving the rice quality is generated through the soil improvement model according to the soil improvement direction, specifically:
establishing a soil improvement model based on a neural network, and performing initialization training;
introducing the soil condition information and the soil improvement direction into the soil improvement model to generate an initial improvement scheme;
combining the initial improvement scheme with geographical environment information and meteorological environment information of the region of the saline-alkali land area to generate a feasibility coefficient, and performing scheme feasibility evaluation;
judging whether the feasibility coefficient is larger than a preset feasibility coefficient threshold value or not;
if the rice quality is larger than the preset value, outputting the initial improvement scheme to generate an improvement scheme which is helpful for improving the rice quality, and displaying the improvement scheme according to a preset mode; if the initial improvement scheme is smaller than the preset improvement scheme, the initial improvement scheme is newly established.
5. The method for helping rice quality improvement of saline-alkali soil according to claim 1, further comprising the following steps:
collecting the rice growth condition information and the rice quality information of the improved soil, and performing feature extraction on the rice growth condition information and the rice quality information of the soil before and after improvement to respectively generate feature sets;
comparing and analyzing the characteristic set of the rice in the improved soil with the characteristic set of the rice in the soil before improvement;
generating a deviation rate in a preset calculation mode, and presetting a threshold value of the deviation rate;
marking the characteristics of which the deviation rate is greater than the deviation rate threshold value, extracting rice growth condition information and rice quality information corresponding to the characteristics, and calculating improvement ratio information;
judging whether the improvement proportion information reaches a preset improvement proportion information threshold value or not;
if the improvement proportion information is smaller than a preset improvement proportion information threshold value, generating feedback information according to the improvement proportion information and the rice characteristic set of the improved soil;
and generating a second soil improvement scheme according to the feedback information.
6. A system for improving the quality of rice in saline-alkali soil, which is characterized by comprising: the device comprises a memory and a processor, wherein the memory comprises a method program for improving the quality of the rice in the saline-alkali soil, and the method program for improving the quality of the rice in the saline-alkali soil realizes the following steps when being executed by the processor:
acquiring the physicochemical property of soil in a saline-alkali area, and analyzing the soil components according to the physicochemical property of the soil to generate soil component information;
carrying out salinization evaluation on the saline-alkali area according to the soil component information to generate soil condition information in the saline-alkali area;
acquiring rice growth condition information and rice quality information in a saline-alkali area, and matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in a target area;
establishing a soil improvement model based on a neural network, and generating an improvement scheme which is beneficial to improving the rice quality according to the soil improvement direction through the soil improvement model;
matching the rice growth condition information and the rice quality information with the soil condition information to generate a soil improvement direction in the saline-alkali area, which specifically comprises the following steps:
generating a rice growth time sequence according to the rice growth condition information and the nutrient information required by each growth stage;
carrying out segmentation extraction on nutrient characteristics required by each growth stage of the rice through rice growth time sequence segmentation, and matching with soil condition information in the saline-alkali area;
performing comparative analysis on soil condition information in the saline-alkali area through data indexes to generate nutrient deficiency information;
performing principal component analysis on the rice quality information, and evaluating soil condition information in a saline-alkali area; decomposing the rice quality information into a plurality of quality components, and calculating characteristic values and contribution rates of different quality components according to main component standards;
selecting main components, calculating a main component evaluation value, calculating a comprehensive evaluation value according to the main component evaluation value, and sorting the nutrient information missing from the soil according to the comprehensive evaluation value to generate a sorting result;
and polymerizing the nutrient information missing from the soil in the saline-alkali area according to the nutrient missing information and the sequencing result to generate the soil improvement direction in the saline-alkali area.
7. The system for improving rice quality in saline-alkali soil according to claim 6, wherein a soil improvement model is established based on the neural network, and an improvement scheme for improving rice quality is generated through the soil improvement model according to the soil improvement direction, specifically:
establishing a soil improvement model based on a neural network, and performing initialization training;
introducing the soil condition information and the soil improvement direction into the soil improvement model to generate an initial improvement scheme;
combining the initial improvement scheme with geographical environment information and meteorological environment information of the region of the saline-alkali land area to generate a feasibility coefficient, and performing scheme feasibility evaluation;
judging whether the feasibility coefficient is larger than a preset feasibility coefficient threshold value or not;
if the rice quality is larger than the preset value, outputting the initial improvement scheme to generate an improvement scheme which is helpful for improving the rice quality, and displaying the improvement scheme according to a preset mode; if the initial improvement scheme is smaller than the preset improvement scheme, the initial improvement scheme is newly established.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a program of a method for contributing to rice quality improvement in saline-alkali soil, and when the program of the method for contributing to rice quality improvement in saline-alkali soil is executed by a processor, the steps of the method for contributing to rice quality improvement in saline-alkali soil according to any one of claims 1 to 5 are implemented.
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