CN116894514A - Crop yield prediction method and system based on soil quality index - Google Patents
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Abstract
The invention relates to the technical field of yield prediction, and particularly provides a crop yield prediction method and system based on a soil quality index, wherein the method comprises the following steps: selecting a soil quality evaluation index, and establishing a standard scoring function between the soil quality evaluation index and the soil quality; screening the soil quality evaluation index, establishing a minimum data set evaluation index, and calculating a soil quality index by using the minimum data set evaluation index; acquiring a real-time growth state of crops, correcting the soil quality index according to the real-time growth state, and acquiring the corrected soil quality index; and carrying out yield prediction on crops to be predicted based on the corrected soil quality index. The invention can provide accurate soil quality assessment and crop yield prediction, provides effective decision support for agricultural production, optimizes farmland management and resource utilization, and is beneficial to improving the efficiency and sustainability of agricultural production.
Description
Technical Field
The invention relates to the technical field of yield prediction, in particular to a crop yield prediction method and system based on a soil quality index.
Background
Crop yield prediction is an important task in the agricultural field, which plays a key role in agricultural production, market planning and resource management. Accurate prediction of crop yield can help farmers, agricultural enterprises and governments to make scientific decisions, improve agricultural production benefits, ensure stability of food supply and realize sustainable development. However, current crop yield prediction methods present some problems and challenges.
The yield of the substance is comprehensively influenced by various factors, and the prior method is often difficult to accurately capture and comprehensively consider the influence of the complex factors, so that the accuracy of the prediction result is limited. Crop yield prediction requires a large amount of agricultural data, and acquiring and integrating such data is a tedious, time-consuming and labor-consuming task, and may be affected by problems such as incomplete data, poor data quality, and the like. The existing crop yield prediction method is often based on a statistical model or an empirical formula, and has certain limitations on accuracy and instantaneity. With the development of agricultural technology and the advancement of data acquisition technology, more accurate and real-time prediction methods are needed to meet the demands of agricultural production.
Therefore, it is necessary to design a crop yield prediction method and system based on soil quality index to solve the problems of the current crop yield prediction technology.
Disclosure of Invention
In view of the above, the invention provides a crop yield prediction method and a crop yield prediction system based on a soil quality index, which aim to solve the problems of complicated data collection, higher difficulty in processing a large amount of data, low prediction precision and no real-time performance in the current crop yield prediction technology.
In one aspect, the invention provides a crop yield prediction method based on a soil quality index, comprising:
selecting a soil quality evaluation index, and establishing a standard scoring function between the soil quality evaluation index and the soil quality;
screening the soil quality evaluation index, establishing a minimum data set evaluation index, and calculating a soil quality index by using the minimum data set evaluation index;
acquiring a real-time growth state of crops, correcting the soil quality index according to the real-time growth state, and acquiring the corrected soil quality index;
and carrying out yield prediction on crops to be predicted based on the corrected soil quality index.
Further, selecting a soil quality evaluation index, and establishing a standard scoring function between the soil quality evaluation index and the soil quality, wherein the method comprises the following steps:
the soil quality evaluation indexes comprise organic matters, total nitrogen, quick-acting phosphorus, quick-acting potassium, microorganism biomass nitrogen, microorganism biomass carbon, sucrase, phosphatase, urease, volume weight and pH value (acid-base value is also called pH value, and is a sign for chemically measuring the acid-base ratio of liquid);
Wherein, the standard scoring functions of organic matters, total nitrogen, quick-acting phosphorus, quick-acting potassium, microbial biomass nitrogen, microbial biomass carbon, sucrase, phosphatase and urease adopt a withdrawal type function;
the calculation formula is as follows:
where U is the upper value of the function, L is the lower value of the function, and X is the measured value.
Further, establishing a standard scoring function between the soil quality evaluation index and the soil quality, and further comprising:
the standard scoring function of the volume weight and the PH value adopts a trapezoidal function;
the calculation formula is as follows:
wherein U is the upper limit value of the function, L is the lower limit value of the function, O 1 And O 2 And X is a measured value, and is an optimal value of the function.
Further, after screening the soil quality evaluation index and before establishing the minimum data set evaluation index, the method includes:
respectively calculating the Norm value of each soil quality evaluation index, and establishing the minimum data set evaluation index according to the magnitude of the Norm value;
the Norm value is calculated as follows:
wherein N is ik Is the comprehensive load of the ith index on the first k PCs with the characteristic value larger than 1; u (U) ik Is the load value of the ith index on the kth PC; lambda (lambda) k Is the eigenvalue of the kth PC.
Further, when the minimum data set evaluation index is used for calculating the soil quality index, the method comprises the following steps:
the calculation formula is as follows:
wherein SQI is soil quality index, W i Is the weight of the ith evaluation index, S i Is the membership degree of the ith evaluation index, and n is the number of the evaluation indexes in each data set.
Further, acquiring a real-time growth state of the crop, correcting the soil quality index according to the real-time growth state, and acquiring a corrected soil quality index, wherein the real-time growth state of the crop comprises plant height, leaf area index and chlorophyll content;
presetting a first preset height H1, a second preset height H2, a third preset height H3 and a fourth preset height H4, wherein H1 is more than H2 and less than H3 and less than H4;
presetting a first preset correction coefficient A1, a second preset correction coefficient A2, a third preset correction coefficient A3 and a fourth preset correction coefficient A4, wherein A1 is more than A2 and less than A3 and less than A4;
according to the relation between the plant height H0 and each preset height, selecting a correction coefficient to correct the soil quality index, and obtaining the corrected soil quality index, wherein the method comprises the following steps:
when H1 is less than or equal to H0 and less than H2, selecting the first preset correction coefficient A1 to correct the soil quality index SQI, and obtaining a corrected soil quality index SQI.A1;
When H2 is less than or equal to H0 and less than H3, selecting the second preset correction coefficient A2 to correct the soil quality index SQI, and obtaining a corrected soil quality index SQI.A2;
when H3 is less than or equal to H0 and less than H4, selecting the third preset correction coefficient A3 to correct the soil quality index SQI, and obtaining a corrected soil quality index SQI.A3;
when H4 is less than or equal to H0, the fourth preset correction coefficient A4 is selected to correct the soil quality index SQI, and the corrected soil quality index SQI×A4 is obtained.
Further, after selecting the i-th preset correction index Ai to correct the soil quality index SQI to obtain a corrected soil quality index sqi×ai, i=1, 2,3,4, and correcting the soil quality index according to the real-time growth state to obtain a corrected soil quality index, the method further includes:
presetting a first preset leaf area index Z1, a second preset leaf area index Z2, a third preset leaf area index Z3 and a fourth preset leaf area index Z4, wherein Z1 is more than Z2 and less than Z3 and less than Z4;
selecting a correction coefficient to carry out secondary correction on the corrected soil quality index Q I x Ai according to the magnitude relation between the leaf area index Z0 and each preset index, and obtaining the soil quality index after secondary correction, wherein the method comprises the following steps:
When Z1 is less than or equal to Z0 and less than Z2, selecting the first preset correction index A1 to carry out secondary correction on the corrected soil quality index Q I Ai, and obtaining a secondary corrected soil quality index Q I Ai A1;
when Z2 is less than or equal to Z0 and less than Z3, selecting the second preset correction index A2 to carry out secondary correction on the corrected soil quality index Q I Ai, and obtaining a secondary corrected soil quality index Q I Ai A2;
when Z3 is less than or equal to Z0 and less than Z4, selecting the third preset correction index A3 to carry out secondary correction on the corrected soil quality index Q I Ai, and obtaining a secondary corrected soil quality index Q I Ai A3;
and when Z4 is less than or equal to Z0, selecting the fourth preset correction index A4 to carry out secondary correction on the corrected soil quality index Q I Ai, and obtaining a soil quality index QI Ai A4 after secondary correction.
Further, after selecting the i-th preset correction index Ai to perform secondary correction on the corrected soil quality index Q I ×ai to obtain a secondary corrected soil quality index sqi×ai, i=1, 2,3,4, and correcting the soil quality index according to the real-time growth state to obtain a corrected soil quality index, the method further includes:
Presetting a first preset chlorophyll content Y1, a second preset chlorophyll content Y2, a third preset chlorophyll content Y3 and a fourth preset chlorophyll content Y4, wherein Y1 is more than Y2 and less than Y3 and less than Y4;
according to the relation between the chlorophyll content Y0 and each preset content, selecting a correction coefficient to perform three corrections on the soil quality index QI×Ai×Ai after the secondary correction, and obtaining the soil quality index after the three corrections, including:
when Y1 is less than or equal to Y0 and less than Y2, selecting the first preset correction index A1 to perform three corrections on the secondarily corrected soil quality index Q I Ai to obtain a three-corrected soil quality index Q I Ai A1;
when Y2 is less than or equal to Y0 and less than Y3, selecting the second preset correction index A2 to perform three corrections on the secondarily corrected soil quality index Q I Ai to obtain a three-corrected soil quality index Q I Ai A2;
when Y3 is less than or equal to Y0 and less than Y4, selecting the third preset correction index A3 to perform three corrections on the secondarily corrected soil quality index Q I Ai to obtain a three-corrected soil quality index Q I Ai A3;
when Y4 is less than or equal to Y0, selecting the fourth preset correction index A4 to perform three corrections on the secondarily corrected soil quality index Q I ×ai×ai, and obtaining the secondarily corrected soil quality index Q I ×ai×ai×a4.
Further, predicting the yield of the crop to be predicted based on the corrected soil quality index, including:
presetting a first preset yield C1, a second preset yield C2, a third preset yield C3 and a fourth preset yield C4, wherein C1 is more than C2 and less than C3 and less than C4;
presetting a first preset quality index SQI 1, a second preset quality index SQI2, a third preset quality index SQI3 and a fourth preset quality index SQI4, wherein SQI 1 is more than SQI2 and less than SQI3 and less than SQI4;
selecting a predicted yield according to the magnitude relation between the corrected soil quality index QIAiA AiA Ai and each preset quality index;
when the SQI 1 is less than or equal to QI, aiA, ai is less than SQI2, the predicted value of crop yield is the first preset yield C1;
when the SQI2 is less than or equal to QI, ai, A, ai and less than SQI3, the predicted value of crop yield is the second preset yield C2;
when the SQI3 is less than or equal to QI, ai, A, ai and less than SQI4, the predicted value of crop yield is the third preset yield C3;
when the SQI4 is less than or equal to QI, ai, A, ai, the predicted value of crop yield is the fourth preset yield C4.
Compared with the prior art, the invention has the beneficial effects that: by selecting the soil quality evaluation index, a standard scoring function between the soil quality evaluation index and the soil quality is established, and the quality condition of the soil is effectively evaluated. By screening the soil quality evaluation indexes, the minimum data set evaluation index is established, the most critical and representative indexes are extracted from a large amount of data, and the workload of data collection and processing is reduced. The minimum data set evaluation index is utilized to calculate the soil quality index, so that the potential and limitation of the soil can be accurately known by an agricultural manager, and a basis is provided for reasonable farmland management and resource utilization. By timely monitoring and adjusting the soil quality index, the actual influence of the soil on the crop growth can be reflected more accurately, and the accuracy and reliability of prediction are improved. By establishing a prediction model and applying the modified index, the yield level of the crop to be predicted can be accurately predicted. The method is favorable for making reasonable farmland management strategies in time, adjusting decisions in aspects of fertilization, irrigation, crop planting and the like, thereby improving crop yield to the maximum extent and realizing the maximization of benefits of agricultural production.
On the other hand, the invention also provides a crop yield prediction system based on the soil quality index, which comprises the following steps:
and the acquisition module is used for: the method comprises the steps of selecting a soil quality evaluation index and establishing a standard scoring function between the soil quality evaluation index and the soil quality;
the calculation module: the method comprises the steps of screening the soil quality evaluation indexes, establishing minimum data set evaluation indexes, and calculating soil quality indexes by using the minimum data set evaluation indexes;
and a correction module: the method comprises the steps of acquiring a real-time growth state of crops, correcting the soil quality index according to the real-time growth state, and acquiring the corrected soil quality index;
and a prediction module: and the method is used for predicting the yield of crops to be predicted based on the corrected soil quality index.
It can be appreciated that the above crop yield prediction method and system based on the soil quality index have the same beneficial effects, and will not be described herein.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a flowchart of a crop yield prediction method based on soil quality indicators according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a crop yield prediction system based on a soil quality index according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
Referring to fig. 1, the present embodiment provides a crop yield prediction method based on a soil quality index, including:
step S100: and selecting a soil quality evaluation index, and establishing a standard grading function between the soil quality evaluation index and the soil quality.
Step S200: screening the soil quality evaluation index, establishing a minimum data set evaluation index, and calculating the soil quality index by using the minimum data set evaluation index.
Step S300: and acquiring the real-time growth state of the crops, correcting the soil quality index according to the real-time growth state, and acquiring the corrected soil quality index.
Step S400: and carrying out yield prediction on crops to be predicted based on the corrected soil quality index.
Specifically, in step S100, key soil quality evaluation indexes are selected, and a standard scoring function with the soil quality is established, so that the quality condition of the soil can be objectively evaluated by scoring the indexes. In step S200, the soil quality evaluation index is screened, and a minimum data set evaluation index is established, so that complexity of data acquisition and processing is reduced, and prediction efficiency is improved. In step S300, the real-time growth state of the crop is obtained, and the current agricultural production condition can be reflected more accurately by correcting the real-time growth state and the soil quality index, so that the accuracy of prediction is improved. In step S400, yield prediction is performed on the crop to be predicted based on the corrected soil quality index.
It can be understood that the quality of the soil can be comprehensively and objectively estimated by selecting key soil quality evaluation indexes and establishing a scoring function, and a scientific basis is provided for crop yield prediction. In the process of screening the evaluation index of the minimum data set, the complexity of data acquisition and processing is simplified, and the time and resource cost are saved. This not only improves the efficiency of the prediction, but also makes the method easier to implement and popularize. The soil quality index is corrected by combining the real-time growth state, so that the prediction result is more accurate, the actual condition of agricultural production can be reflected in time, and a more targeted decision basis is provided. The accuracy and the practicability of prediction can be effectively improved, scientific support is provided for agricultural production and decision making, and sustainable development of agriculture is promoted.
In some embodiments of the present application, selecting a soil quality evaluation index in step S100, and establishing a standard scoring function between the soil quality evaluation index and the soil quality includes: soil quality evaluation indexes comprise organic matters, total nitrogen, quick-acting phosphorus, quick-acting potassium, microbial biomass nitrogen, microbial biomass carbon, sucrase, phosphatase, urease, volume weight and pH value.
Wherein, the standard scoring functions of organic matters, total nitrogen, quick-acting phosphorus, quick-acting potassium, microbial biomass nitrogen, microbial biomass carbon, sucrase, phosphatase and urease adopt a withdrawal type function.
The calculation formula is as follows:
where U is the upper value of the function, L is the lower value of the function, and X is the measured value.
In some embodiments of the present application, the standard scoring function that also includes volume weight and PH in step S100 is a trapezoidal function.
The calculation formula is as follows:
wherein U is the upper limit value of the function, and L is the lower limit value of the function，O 1 And O 2 And X is a measured value, and is an optimal value of the function.
In particular, soil quality is an intrinsic property of the soil itself that is determined by seeking a balance and overall performance between different functions of the soil, which cannot be directly obtained by sensory or instrumental analysis, but must be expressed speculatively or synthetically quantified according to known soil external properties. In evaluating the soil quality, it is necessary to select those soil quality indexes which best represent the nature of the soil quality and represent the relationships between various soil properties and soil functions. Therefore, selecting a proper evaluation index is a precondition for obtaining a better reflection of the actual soil quality.
In particular, the standard scoring function is actually a relationship between the evaluation index and the crop growth effect curve. The threshold value of the standard scoring function is determined according to the suitability or the restriction of the crop growth, and the curve is converted into a broken line, so that the evaluation index is converted into a dimensionless value between 0.1 and 1. Organic matter, total nitrogen, quick-acting phosphorus, quick-acting potassium, microbial biomass carbon, microbial biomass nitrogen, sucrase, phosphatase and urease can all adopt a ring-up function to calculate membership value. The unit weight and pH are calculated using a trapezoidal function. For each index, after selecting a suitable standard scoring function, it is necessary to determine thresholds such as an upper limit U, a lower limit L, an optimal value L, and the like of the standard scoring function. And finally substituting the measured values of the soil quality indexes into a standard scoring function to calculate and obtain the score.
It can be understood that by selecting proper evaluation indexes and establishing standard scoring functions, the quality condition of soil can be comprehensively evaluated, and complex soil properties can be converted into a unified scoring system, so that comparison and analysis are facilitated. The upper model function and the trapezoidal function are adopted as scoring functions, and the scoring threshold value can be determined according to the suitability or the restriction of the crop growth, so that the influence of soil on the crop growth can be reflected more accurately. The calculation method is simple and clear, is easy to implement and operate, and is suitable for large-scale soil quality evaluation and crop yield prediction.
In some embodiments of the present application, after the screening of the soil quality evaluation index in step S200 and before the establishment of the minimum data set evaluation index, the method further includes: and respectively calculating the Norm values of the soil quality evaluation indexes, and establishing a minimum data set evaluation index according to the Norm values.
The Norm value is calculated as follows:
wherein N is ik Is the integrated load of the ith index on the first k PCs with eigenvalues greater than 1. U (U) ik Is the load value of the ith index on the kth PC. Lambda (lambda) k Is the eigenvalue of the kth PC.
Specifically, the soil quality is directly analyzed by adopting the evaluation index on a large spatial scale, and the data acquisition cost is high. The effect of dimension reduction is realized through principal component analysis, so that the analysis dimension is reduced, and the information of the evaluation index can be reflected as much as possible.
Specifically, principal component analysis is performed on the initially selected index, a principal component PC with a characteristic value greater than 1 is extracted, indexes with load absolute values greater than or equal to 0.5 on the same PC are divided into a group, and if the load absolute value of one index on both PCs is greater than or equal to 0.5, the indexes are combined into a group with low correlation with other indexes. If the absolute value of the load of the index on each PC is smaller than 0.5, the index is divided into a group with the highest absolute value of the load. And respectively calculating the Norm values of the indexes in each group, selecting the indexes of which the Norm values are within 10% of the maximum Norm value of each group, analyzing the correlation among the selected indexes in each group, and if the correlation coefficient value is more than or equal to 0.5, selecting the index with the highest Norm value to enter the minimum data set to evaluate the indexes. Otherwise, if the correlation coefficient value is smaller than 0.5, both enter the minimum data set evaluation index. The Norm value is the length of the vector normal mode of the index in the multidimensional space consisting of components, and the longer the length is, the larger the comprehensive load value of the index in all PCs is, and the stronger the capability of interpreting comprehensive information is.
It will be appreciated that in principal component analysis, the covariance matrix or correlation coefficient matrix between the original variables is first calculated. Then, a group of eigenvalues and corresponding eigenvectors are obtained by performing eigenvalue decomposition or singular value decomposition on the matrix. The eigenvectors represent a linear relationship between the original variable and the principal component, while the eigenvalues represent the degree of variance interpretation in the data. According to the magnitude of the characteristic values, the main components with larger characteristic values are selected as main information extraction. These principal components are linear combinations of the original variables with uncorrelated and lower correlation. Each principal component corresponds to a characteristic value, and the larger the characteristic value is, the more data variances the principal component can interpret. The principal component PC plays a role in data analysis in reducing the dimension and data compression. They are new variables obtained by linear transformation of the original variables, have less correlation, and can better represent the main characteristics of the data. In soil quality evaluation, principal component analysis can be used for integrating a plurality of soil quality evaluation indexes to extract principal components which can better represent soil quality, thereby simplifying the evaluation process and improving the evaluation accuracy.
It will be appreciated that the principal component PC is a new variable obtained by principal component analysis for linearly combining the original soil quality evaluation indexes to reduce the dimensionality and correlation of the data and extract the main information. Each principal component corresponds to a characteristic value that represents the variance of the data that the principal component can interpret. The Norm value is an index for evaluating the relative importance or degree of normalization of each soil quality evaluation index. The calculation formula of the Norm value is based on the principal component analysis result, including the comprehensive load of the principal component PC on the first few principal components with the eigenvalues greater than 1. The main components PC and Norm values play different roles in the soil quality assessment method. The main component PC simplifies soil quality evaluation by reducing and extracting main information, and the Norm value is used for evaluating the importance and normalization of indexes so as to comprehensively evaluate and compare the contribution degrees of different indexes.
In some embodiments of the present application, when the minimum data set evaluation index is used to calculate the soil quality index in step S200, the method includes:
the calculation formula is as follows:
wherein SQI is soil quality index, W i Is the weight of the ith evaluation index, S i Is the membership degree of the ith evaluation index, and n is the number of the evaluation indexes in each data set.
Specifically, the soil quality index integrates physical, chemical and biological indexes of farmland soil, and the higher the soil quality index is, the better the soil quality is. The weight value refers to the contribution of each evaluation index to the soil quality, and the larger the weight value is, the greater the importance of the index to the soil quality is.
It can be understood that by comprehensively considering soil indexes in different aspects, the quality condition of the soil can be more comprehensively known, and the method is not limited to evaluation of single indexes, and provides more accurate soil quality information. By setting the weight value, the influence degree of each index on the soil quality can be accurately measured, and the problem that the indexes are treated equally or some indexes are emphasized on one side is avoided. Through the consideration of membership, the weights of different indexes in the evaluation can be flexibly adjusted according to specific data set conditions, and the soil environment and research requirements are met.
In some embodiments of the present application, the real-time growth state of the crop is obtained in step S300, the soil quality index is corrected according to the real-time growth state, and the corrected soil quality index is obtained, wherein the real-time growth state of the crop includes the plant height, the leaf area index and the chlorophyll content. The first preset height H1, the second preset height H2, the third preset height H3 and the fourth preset height H4 are preset, and H1 is more than H2 and less than H3 and less than H4. The method comprises the steps of presetting a first preset correction coefficient A1, a second preset correction coefficient A2, a third preset correction coefficient A3 and a fourth preset correction coefficient A4, wherein A1 is more than A2 and less than A3 and less than A4.
Specifically, according to the relation between the plant height H0 and each preset height, a correction coefficient is selected to correct the soil quality index, and the corrected soil quality index is obtained, which comprises the following steps: when H1 is less than or equal to H0 and less than H2, a first preset correction coefficient A1 is selected to correct the soil quality index SQI, and the corrected soil quality index SQI is obtained. When H2 is less than or equal to H0 and less than H3, a second preset correction coefficient A2 is selected to correct the soil quality index SQ I, and the corrected soil quality index SQ I is obtained. When H3 is less than or equal to H0 and less than H4, a third preset correction coefficient A3 is selected to correct the soil quality index SQI, and the corrected soil quality index SQI is obtained. When H4 is less than or equal to H0, a fourth preset correction coefficient A4 is selected to correct the soil quality index SQI, and the corrected soil quality index SQI is obtained.
It can be understood that by setting the preset height and the preset correction coefficient, the soil quality index is corrected by selecting an appropriate correction coefficient according to the magnitude relation between the plant height and the preset height. The corrected soil quality index reflects the influence degree of the actual growth condition of the current crop on the soil quality.
In some embodiments of the present application, in step S300, after selecting the I-th preset correction index Ai to correct the soil quality index SQ I and obtaining the corrected soil quality index sqi×ai, i=1, 2,3,4, correcting the soil quality index according to the real-time growth state, and obtaining the corrected soil quality index, further including: the first preset leaf area index Z1, the second preset leaf area index Z2, the third preset leaf area index Z3 and the fourth preset leaf area index Z4 are preset, and Z1 is more than Z2 and less than Z3 and less than Z4.
Specifically, according to the magnitude relation between the leaf area index Z0 and each preset index, selecting a correction coefficient to perform secondary correction on the corrected soil quality index qi×ai, and obtaining the soil quality index after secondary correction, including: when Z1 is less than or equal to Z0 and less than Z2, selecting a first preset correction index A1 to carry out secondary correction on the corrected soil quality index Q I Ai, and obtaining a soil quality index Q I Ai A1 after secondary correction. When Z2 is less than or equal to Z0 and less than Z3, selecting a second preset correction index A2 to carry out secondary correction on the corrected soil quality index QI, and obtaining the soil quality index QI, ai and A2 after secondary correction. When Z3 is less than or equal to Z0 and less than Z4, selecting a third preset correction index A3 to carry out secondary correction on the corrected soil quality index Q I Ai, and obtaining a soil quality index Q I Ai A3 after secondary correction. When Z4 is less than or equal to Z0, selecting a fourth preset correction index A4 to carry out secondary correction on the corrected soil quality index QI, and obtaining the soil quality index QI, ai and A4 after secondary correction.
In some embodiments of the present application, after selecting the i-th preset correction index Ai to perform secondary correction on the corrected soil quality index qi×ai to obtain the secondary corrected soil quality index sqi×ai, i=1, 2,3,4, correcting the soil quality index according to the real-time growth state, and obtaining the corrected soil quality index, further including: presetting a first preset chlorophyll content Y1, a second preset chlorophyll content Y2, a third preset chlorophyll content Y3 and a fourth preset chlorophyll content Y4, wherein Y1 is more than Y2 and less than Y3 and less than Y4;
Specifically, according to the relation between chlorophyll content Y0 and each preset content, a correction coefficient is selected to perform three corrections to the soil quality index qi×ai after the secondary correction, so as to obtain the soil quality index after the three corrections, including: when Y1 is less than or equal to Y0 and less than Y2, selecting a first preset correction index A1 to carry out three corrections on the soil quality index QI, ai after the secondary correction, and obtaining the soil quality index QI, ai, A1 after the three corrections. When Y2 is less than or equal to Y0 and less than Y3, selecting a second preset correction index A2 to perform three corrections on the soil quality index QI, ai after the secondary correction, and obtaining the soil quality index QI, ai, A2 after the three corrections. When Y3 is less than or equal to Y0 and less than Y4, selecting a third preset correction index A3 to perform three corrections on the soil quality index QI, ai after the secondary correction, and obtaining the soil quality index QI, ai, A3 after the three corrections. When Y4 is less than or equal to Y0, selecting a fourth preset correction index A4 to perform three corrections on the soil quality index QI, ai after the secondary correction, and obtaining the soil quality index QI, ai, A4 after the three corrections.
It will be appreciated that the growth height of a plant is an important parameter in the growth process of a crop, reflecting the growth status and biomass accumulation of the plant. The better soil quality generally provides proper nutrient supply and good root system environment, is beneficial to the root system growth and nutrient absorption of plants, thereby promoting the plant growth and increasing the plant height. Thus, soils with higher soil quality indices are generally highly associated with better plants. Leaf area index refers to the sum of plant leaf areas per unit of surface area, reflecting the extent of coverage of plant leaves and the intensity of photosynthesis. The quality of soil can influence the development of plant root systems and nutrient absorption capacity, and further influence the growth of plants and the growth of leaves. The better soil quality is beneficial to the development of plant root systems and the improvement of nutrient absorption, thereby promoting the growth of plant leaves and the increase of leaf areas. Thus, soils with higher soil quality indices are generally associated with larger leaf area indices. Chlorophyll is a key pigment for photosynthesis of plants, and reflects photosynthetic efficiency and nutrient utilization of plant leaves. The quality of soil can directly influence the nutrient supply and root system development of plants, and further influence the photosynthesis and chlorophyll synthesis of the plants. Better soil quality generally provides sufficient nutrients and good moisture retention, facilitating plant growth and photosynthesis, thereby promoting chlorophyll synthesis and chlorophyll content increase. Thus, soils with higher soil quality indices are generally associated with higher chlorophyll content. By monitoring and evaluating the changes of the indexes, the quality of the soil can be indirectly reflected, and important information about soil management and crop growth can be provided so as to optimize the soil environment and improve the yield and quality of crops.
In some embodiments of the present application, the predicting of the yield of the crop to be predicted based on the corrected soil quality index in step S400 includes: the first preset yield C1, the second preset yield C2, the third preset yield C3 and the fourth preset yield C4 are preset, and C1 is more than C2 and less than C3 and less than C4. The method comprises the steps of presetting a first preset quality index SQI 1, a second preset quality index SQI2, a third preset quality index SQI3 and a fourth preset quality index SQI4, wherein SQI 1 is smaller than SQI2 and smaller than SQI3 and smaller than SQI4.
Specifically, the predicted yield is selected according to the magnitude relation between the corrected soil quality index QIAiAiA Ai and each preset quality index. When the SQI 1 is less than or equal to QI, aiA, ai is less than SQI2, the predicted value of crop yield is a first preset yield C1. When SQI2 is less than or equal to QIAiAiA Ai < SQI3, the predicted crop yield is the second preset yield C2. When the SQI3 is less than or equal to QI, aiA, ai is less than SQI4, the predicted value of crop yield is a third preset yield C3. When the SQI4 is less than or equal to QI, ai, A, ai, the predicted value of crop yield is a fourth preset yield C4.
It can be understood that the accuracy of prediction is improved by comprehensively considering the correction value of the soil quality index and taking the influence factors of the soil quality into the prediction model. By setting different preset yields and preset quality indexes, the method can flexibly adapt to the requirements of different crops and soil environments, and realizes personalized yield prediction. By comparing the soil quality index with a preset quality index, guidance on soil quality improvement can be provided, and the method becomes a decision basis for optimizing the soil environment and improving the crop yield.
In the embodiment, the soil quality evaluation index is selected, so that a standard scoring function between the soil quality evaluation index and the soil quality is established, and the soil quality condition is effectively evaluated. By screening the soil quality evaluation indexes, the minimum data set evaluation index is established, the most critical and representative indexes are extracted from a large amount of data, and the workload of data collection and processing is reduced. The minimum data set evaluation index is utilized to calculate the soil quality index, so that the potential and limitation of the soil can be accurately known by an agricultural manager, and a basis is provided for reasonable farmland management and resource utilization. By timely monitoring and adjusting the soil quality index, the actual influence of the soil on the crop growth can be reflected more accurately, and the accuracy and reliability of prediction are improved. By establishing a prediction model and applying the modified index, the yield level of the crop to be predicted can be accurately predicted. The method is favorable for making reasonable farmland management strategies in time, adjusting decisions in aspects of fertilization, irrigation, crop planting and the like, thereby improving crop yield to the maximum extent and realizing the maximization of benefits of agricultural production.
In another preferred mode based on the above embodiment, referring to fig. 2, the present embodiment provides a crop yield prediction system based on a soil quality index, including:
And the acquisition module is used for: the method comprises the steps of selecting a soil quality evaluation index and establishing a standard scoring function between the soil quality evaluation index and the soil quality;
the calculation module: the method comprises the steps of screening soil quality evaluation indexes, establishing minimum data set evaluation indexes, and calculating soil quality indexes by using the minimum data set evaluation indexes;
and a correction module: the method comprises the steps of acquiring a real-time growth state of crops, correcting a soil quality index according to the real-time growth state, and acquiring the corrected soil quality index;
and a prediction module: the method is used for predicting the yield of crops to be predicted based on the corrected soil quality index.
It can be appreciated that the above crop yield prediction method and system based on the soil quality index have the same beneficial effects, and will not be described herein.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. A crop yield prediction method based on a soil quality index, comprising:
selecting a soil quality evaluation index, and establishing a standard scoring function between the soil quality evaluation index and the soil quality;
Screening the soil quality evaluation index, establishing a minimum data set evaluation index, and calculating a soil quality index by using the minimum data set evaluation index;
acquiring a real-time growth state of crops, correcting the soil quality index according to the real-time growth state, and acquiring the corrected soil quality index;
and carrying out yield prediction on crops to be predicted based on the corrected soil quality index.
2. The method for predicting crop yield based on soil quality indicators as claimed in claim 1, wherein selecting the soil quality evaluation indicator and establishing a standard scoring function between the soil quality evaluation indicator and the soil quality comprises:
the soil quality evaluation indexes comprise organic matters, total nitrogen, quick-acting phosphorus, quick-acting potassium, microbial biomass nitrogen, microbial biomass carbon, sucrase, phosphatase, urease, volume weight and pH value;
wherein, the standard scoring functions of organic matters, total nitrogen, quick-acting phosphorus, quick-acting potassium, microbial biomass nitrogen, microbial biomass carbon, sucrase, phosphatase and urease adopt a withdrawal type function;
the calculation formula is as follows:
where U is the upper value of the function, L is the lower value of the function, and X is the measured value.
3. The method for predicting crop yield based on a soil quality indicator of claim 2, wherein establishing a standard scoring function between the soil quality evaluation indicator and soil quality further comprises:
the standard scoring function of the volume weight and the PH value adopts a trapezoidal function;
the calculation formula is as follows:
wherein U is the upper limit value of the function, L is the lower limit value of the function, O 1 And O 2 And X is a measured value, and is an optimal value of the function.
4. The method for predicting crop yield based on soil quality indicators as set forth in claim 1, wherein after said screening of said soil quality evaluation indicators and before said establishing of said minimum dataset evaluation indicators, further comprises:
respectively calculating the Norm value of each soil quality evaluation index, and establishing the minimum data set evaluation index according to the magnitude of the Norm value;
the Norm value is calculated as follows:
wherein N is ik Is the comprehensive load of the ith index on the first k PCs with the characteristic value larger than 1; u (U) ik Is the load value of the ith index on the kth PC; lambda (lambda) k Is the eigenvalue of the kth PC.
5. The method for predicting crop yield based on soil quality index as claimed in claim 1, wherein the calculating of soil quality index using the minimum data set evaluation index comprises:
The calculation formula is as follows:
wherein SQI is soil quality index, W i Is the weight of the ith evaluation index, S i Is the membership degree of the ith evaluation index, and n is the number of the evaluation indexes in each data set.
6. The method for predicting crop yield based on a soil quality index according to claim 1, wherein a real-time growth state of a crop is obtained, the soil quality index is corrected according to the real-time growth state, and a corrected soil quality index is obtained, wherein the real-time growth state of the crop comprises a plant height, a leaf area index and a chlorophyll content;
presetting a first preset height H1, a second preset height H2, a third preset height H3 and a fourth preset height H4, wherein H1 is more than H2 and less than H3 and less than H4;
presetting a first preset correction coefficient A1, a second preset correction coefficient A2, a third preset correction coefficient A3 and a fourth preset correction coefficient A4, wherein A1 is more than A2 and less than A3 and less than A4;
according to the relation between the plant height H0 and each preset height, selecting a correction coefficient to correct the soil quality index, and obtaining the corrected soil quality index, wherein the method comprises the following steps:
when H1 is less than or equal to H0 and less than H2, selecting the first preset correction coefficient A1 to correct the soil quality index SQI, and obtaining a corrected soil quality index SQI x A1;
When H2 is less than or equal to H0 and less than H3, selecting the second preset correction coefficient A2 to correct the soil quality index SQI, and obtaining a corrected soil quality index SQI.A2;
when H3 is less than or equal to H0 and less than H4, selecting the third preset correction coefficient A3 to correct the soil quality index SQI, and obtaining a corrected soil quality index SQI.A3;
when H4 is less than or equal to H0, the fourth preset correction coefficient A4 is selected to correct the soil quality index SQI, and the corrected soil quality index SQI×A4 is obtained.
7. The method for predicting crop yield based on a soil quality index according to claim 6, wherein after selecting an i-th preset correction index Ai to correct the soil quality index SQI to obtain a corrected soil quality index SQI x Ai, i=1, 2,3,4, the correcting the soil quality index according to the real-time growth state to obtain a corrected soil quality index, further comprising:
presetting a first preset leaf area index Z1, a second preset leaf area index Z2, a third preset leaf area index Z3 and a fourth preset leaf area index Z4, wherein Z1 is more than Z2 and less than Z3 and less than Z4;
selecting a correction coefficient to carry out secondary correction on the corrected soil quality index QI×Ai according to the magnitude relation between the leaf area index Z0 and each preset index, and obtaining the soil quality index after secondary correction, wherein the method comprises the following steps:
When Z1 is less than or equal to Z0 and less than Z2, selecting the first preset correction index A1 to carry out secondary correction on the corrected soil quality index QI, and obtaining a soil quality index QI, ai and A1 after secondary correction;
when Z2 is less than or equal to Z0 and less than Z3, selecting the second preset correction index A2 to carry out secondary correction on the corrected soil quality index QI, and obtaining a soil quality index QI, ai and A2 after secondary correction;
when Z3 is less than or equal to Z0 and less than Z4, selecting the third preset correction index A3 to carry out secondary correction on the corrected soil quality index QI, and obtaining a soil quality index QI, ai and A3 after secondary correction;
and when Z4 is less than or equal to Z0, selecting the fourth preset correction index A4 to carry out secondary correction on the corrected soil quality index QI, and obtaining the soil quality index QI, ai and A4 after secondary correction.
8. The method for predicting crop yield based on a soil quality index according to claim 7, wherein after selecting an i-th preset correction index Ai to perform secondary correction on the corrected soil quality index QI x Ai to obtain a secondary corrected soil quality index SQI x Ai, i=1, 2,3,4, the correcting the soil quality index according to the real-time growth state to obtain a corrected soil quality index, further comprising:
Presetting a first preset chlorophyll content Y1, a second preset chlorophyll content Y2, a third preset chlorophyll content Y3 and a fourth preset chlorophyll content Y4, wherein Y1 is more than Y2 and less than Y3 and less than Y4;
according to the relation between the chlorophyll content Y0 and each preset content, selecting a correction coefficient to perform three corrections on the soil quality index QI×Ai×Ai after the secondary correction, and obtaining the soil quality index after the three corrections, including:
when Y1 is less than or equal to Y0 and less than Y2, selecting the first preset correction index A1 to perform three corrections on the soil quality index QI, ai and Ai after the secondary correction, and obtaining the soil quality index QI, ai, A1 after the three corrections;
when Y2 is less than or equal to Y0 and less than Y3, selecting the second preset correction index A2 to perform three corrections on the soil quality index QI, ai and Ai after the secondary correction, and obtaining the soil quality index QI, ai, A2 after the three corrections;
when Y3 is less than or equal to Y0 and less than Y4, selecting the third preset correction index A3 to perform three corrections on the soil quality index QI, ai and Ai after the secondary correction, and obtaining the soil quality index QI, ai, A3 after the three corrections;
when Y4 is less than or equal to Y0, selecting the fourth preset correction index A4 to perform three corrections on the soil quality index QI Ai after the secondary correction, and obtaining the soil quality index QI Ai A4 after the three corrections.
9. The method for predicting crop yield based on a soil quality index as claimed in claim 8, wherein predicting crop yield based on the corrected soil quality index comprises:
presetting a first preset yield C1, a second preset yield C2, a third preset yield C3 and a fourth preset yield C4, wherein C1 is more than C2 and less than C3 and less than C4;
presetting a first preset quality index SQI 1, a second preset quality index SQI2, a third preset quality index SQI 3 and a fourth preset quality index SQI4, wherein SQI 1 is more than SQI2 and less than SQI 3 and less than SQI4;
selecting a predicted yield according to the magnitude relation between the corrected soil quality index QIAiA AiA Ai and each preset quality index;
when the SQI 1 is less than or equal to QI, aiA, ai is less than SQI2, the predicted value of crop yield is the first preset yield C1;
when the SQI2 is less than or equal to QI, ai, A, ai and less than SQI 3, the predicted value of crop yield is the second preset yield C2;
when the SQI 3 is less than or equal to QI, ai, A, ai and less than SQI4, the predicted value of crop yield is the third preset yield C3;
when the SQI4 is less than or equal to QI, ai, A, ai, the predicted value of crop yield is the fourth preset yield C4.
10. A crop yield prediction system based on a soil quality index, comprising:
And the acquisition module is used for: the method comprises the steps of selecting a soil quality evaluation index and establishing a standard scoring function between the soil quality evaluation index and the soil quality;
the calculation module: the method comprises the steps of screening the soil quality evaluation indexes, establishing minimum data set evaluation indexes, and calculating soil quality indexes by using the minimum data set evaluation indexes;
and a correction module: the method comprises the steps of acquiring a real-time growth state of crops, correcting the soil quality index according to the real-time growth state, and acquiring the corrected soil quality index;
and a prediction module: and the method is used for predicting the yield of crops to be predicted based on the corrected soil quality index.
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