Fertilization indication method for diagnosing one-leaf falling of rice by using SPAD instrument

Abstract

The invention relates to the technical field of rice seedling cultivation, in particular to a fertilization indication method for diagnosing one-leaf falling of rice by using an SPAD instrument, and the method comprises the following steps: obtaining SPAD values, nitrogen application amounts and yields of various rice in each region in any growth period, and obtaining SPAD values of each sub-region; the method comprises the following steps: determining an optimal diagnosis period on the basis of correlation conditions between SPAD values and yields, determining an optimal fitting model by analyzing fitting conditions of all nitrogen application amounts and SPAD values of various rice in different fitting methods in the optimal diagnosis period, and analyzing differences between SPAD values of various areas of various rice in the optimal diagnosis period and SPAD values corresponding to the maximum yields. The fertilizer is applied to various rice. The invention aims to obtain the optimal fitting model and improve the precision of rice fertilization amount control.

Description

Technical Field

[0001] This application relates to the technical field of rice seedling cultivation, and specifically relates to a fertilization indication method for diagnosing the last leaf of rice using a SPAD instrument. Background Art

[0002] During the growth process of rice, nitrogen is a key element for promoting plant growth, increasing yield, and improving quality. However, currently, there are common phenomena of excessive or insufficient nitrogen fertilizer application in paddy fields, which not only fail to achieve the expected rice yield and quality but may also cause environmental pollution of nitrogen. Therefore, reasonable fertilization and accurate diagnosis of the nitrogen demand of rice have become one of the core tasks for achieving sustainable agricultural development. Traditional nitrogen diagnosis methods mainly rely on chemical detection and leaf color analysis. Among them, the SPAD instrument, as a commonly used non-destructive detection tool, is widely used in rice fertilization management due to its simple operation and low cost.

[0003] The existing technology realizes fertilization indication by analyzing the corresponding relationship between the SPAD value of rice, yield, and nitrogen application rate, and selecting a suitable fitting model for data fitting. However, the existing technology selects fitting models by evaluating the fitting accuracy of each fitting model using methods such as the coefficient of determination and mean square error. It does not consider that evaluation techniques such as the coefficient of determination and mean square error evaluate the overall error. If a certain rice area is affected by soil factors and irrigation conditions, resulting in large error fluctuations in the SPAD value, it will cause a large local fitting error for this data point, which will further affect the overall fitting error of the fitting model, leading to an inappropriate selection of the fitting model and reducing the accuracy of controlling the rice fertilization amount. Summary of the Invention

[0004] To solve the above technical problems, this application provides a fertilization indication method for diagnosing the last leaf of rice using a SPAD instrument to solve the existing problems.

[0005] A fertilization indication method for diagnosing the last leaf of rice using a SPAD instrument in this application adopts the following technical solutions:

[0006] An embodiment of this application provides a fertilization indication method for diagnosing the last leaf of rice using a SPAD instrument, and this method includes the following steps:

[0007] Divide the planting areas of various rice into multiple areas, apply different amounts of nitrogen fertilizer to different areas, divide each area into multiple sub-areas, obtain the SPAD values, nitrogen application rates, and yields of various rice in each area at any growth stage, and obtain the SPAD values of each sub-area;

[0008] Based on the correlation between SPAD values and yield, the best diagnostic period is screened out from all growth stages of various rice varieties, and all nitrogen application rates and SPAD values of various rice varieties at the best diagnostic period are analyzed to determine the optimal fitting model. Specifically:

[0009] Use different fitting methods to fit the binary groups composed of nitrogen application rates and SPAD values in all regions of various rice varieties. The distances from all binary groups to the fitting curve under any fitting method form an error sequence. The error sequence is divided into multiple subsequences, and the distribution of all elements in each subsequence is analyzed to determine the fluctuation value of various rice varieties under any fitting method;

[0010] Within each region of various rice varieties, the deviation of the SPAD value in each sub-region from the average SPAD value of all other sub-regions is used as the difference index. The difference indices of all sub-regions are clustered to obtain the significant set of each region. Based on the distribution of all difference indices within the significant set, error elements are screened out from the error sequence, and the differences between each error element and the distribution of all other elements in its corresponding subsequence are compared. Combining with the fluctuation value, the deviation value of various rice varieties under any fitting method is determined;

[0011] Analyze the change trend of the fitting curve of various rice varieties under any fitting method to determine the change index of various rice varieties under any fitting method, so as to determine the trend value of various rice varieties under any fitting method. Combining with the deviation value, the optimal fitting model is determined;

[0012] Analyze the difference between the SPAD value in each region of various rice varieties during the best diagnostic period and the SPAD value corresponding to the maximum yield, and combine with the optimal fitting model to apply fertilizer to various rice varieties.

[0013] Preferably, the method for determining the fluctuation value of various rice varieties under any fitting method is as follows:

[0014] For the error sequence of various rice varieties under any fitting method, calculate the mean value of all elements in each subsequence as the average error of each subsequence;

[0015] Calculate the variance and cumulative sum of the average errors of all subsequences in the error sequence respectively, and take the product of the variance and the cumulative sum as the fluctuation value of various rice varieties under any fitting method.

[0016] Preferably, the process for obtaining the significant set of each region is as follows:

[0017] Cluster the difference indices of all sub-regions in each region of various rice varieties to obtain multiple clustering clusters. Calculate the mean value of all difference indices in each clustering cluster, denoted as the difference mean of each clustering cluster. Among all clustering clusters, the clustering cluster with the largest difference mean is used as the significant set of each region.

[0018] Preferably, the process of screening out all error elements from the error sequence is as follows:

[0019] For various rice varieties under any fitting method, the mean difference of the significant sets in all regions is used as the input of the threshold segmentation algorithm to output the segmentation threshold. The region where the significant set corresponding to the mean difference greater than the segmentation threshold is located is used as the error region, and the elements corresponding to the binary tuples of all error regions in the error sequence are used as error elements.

[0020] Preferably, the method for determining the deviation value of various rice varieties under any fitting method is as follows:

[0021] For the error sequence of various rice varieties under any fitting method, calculate the difference between each error element and the mean of all the remaining elements in its subsequence, and use the differences of all error elements as the error index of various rice varieties under any fitting method.

[0022] The deviation value A of the u-th rice variety under the fitting method i u,i is expressed as: In the formula, B u,i represents the coefficient of determination of all binary tuples of the u-th rice variety under the fitting method i; C u,i represents the fluctuation value of the u-th rice variety under the fitting method i; D u,i represents the error index of the u-th rice variety under the fitting method i; T u represents the number of all clustering clusters of the u-th rice variety; exp() represents the exponential function with the natural constant as the base.

[0023] Preferably, the expression of the change index of various rice varieties under any fitting method is:

[0024] Extract the peaks and valleys on the fitting curve of various rice varieties under any fitting method, connect the adjacent peaks and valleys, calculate the slope of the connected straight line, and arrange the slopes of the connected straight lines between all adjacent peaks and valleys in ascending order according to the appearance time of the peaks to form the slope sequence of various rice varieties under any fitting method.

[0025] The change index R of the u-th rice variety under the fitting method i u,i is expressed as: In the formula, E u,i represents the ratio of the number of all negative elements to the number of all positive elements in the first-order difference sequence of the slope sequence of the u-th rice variety under the fitting method i; F u,i represents the ratio of the number of all positive elements to the number of all negative elements in the first-order difference sequence of the slope sequence of the u-th rice variety under the fitting method i; Z u represents the classification of the u-th rice variety, Z uWhen it is equal to 1, it indicates that the u-th type of rice is hybrid rice, Z u When it is equal to 0, it indicates that the u-th type of rice is conventional rice.

[0026] Preferably, the expression of the trend value of each type of rice under any fitting method is:

[0027] In the formula, M u,i represents the trend value of the u-th type of rice under the fitting method i; V u,i represents the number of all elements in the first-order difference sequence of the slope sequence of the u-th type of rice under the fitting method i; represents the difference between the q-th element and its adjacent previous element in the first-order difference sequence of the slope sequence of the u-th type of rice under the fitting method i; exp() represents the exponential function with the natural constant as the base; norm[] represents the normalization function; max() represents the maximum value function.

[0028] Preferably, the method for determining the optimal fitting model is:

[0029] The high-fitting coefficient R i of the fitting method i is expressed as: In the formula, represents the mean value of the trend values of all types of hybrid rice under the fitting method i; represents the mean value of the trend values of all types of conventional rice under the fitting method i; represents the mean value of the deviation values of all types of rice under the fitting method i; ∈ represents a preset constant greater than 0;

[0030] Among all fitting methods, the fitting method with the largest high-fitting coefficient is used as the optimal fitting model.

[0031] Preferably, the fertilization of each type of rice includes:

[0032] In all regions of each type of rice during the optimal diagnosis period, the SPAD corresponding to the maximum yield is recorded as the optimal value of each type of rice during the optimal diagnosis period. If the SPAD value of the j-th region of the u-th type of rice under the optimal diagnosis period is less than the optimal value of the u-th type of rice, then it is necessary to fertilize the u-th type of rice in the j-th region. The deviation between the SPAD value of the u-th type of rice in the j-th region and the optimal value is used as the input of the optimal fitting model, and the nitrogen fertilizer content to be supplemented is output; otherwise, it is not necessary to fertilize the u-th type of rice in the j-th region.

[0033] This application has at least the following beneficial effects:

[0034] By analyzing the fluctuations in the fitting effects between nitrogen application rates and the SPAD values of rice under different fitting methods, this application constructs a fluctuation value, which can preliminarily evaluate the fitting effects of fitting methods for large amounts of data. Further, by analyzing the differences in SPAD values between different sub-regions within the same area, a deviation value is constructed to exclude the influence of environmental factors on the fitting effect, improve the accuracy of the fitting results, and help to more clearly judge the fitting effects of different fitting methods. Further, by analyzing the changes in SPAD values with nitrogen application rates for different types of rice during the optimal diagnosis period, a trend consistency coefficient is constructed to reflect the true change trend of the data, which helps to select the optimal fitting model for each type of rice, and thus control the fertilization amount according to the optimal fitting model, improving the accuracy of controlling the fertilization amount of rice. This application evaluates the fitting effects of different fitting methods from multiple aspects such as the whole, local, and data trends, selects the optimal fitting model most suitable for different rice during the optimal diagnosis period, and improves the accuracy of controlling the fertilization amount of rice. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0036] Figure 1 It is a flowchart of the steps of a method for indicating fertilization of the first leaf from the top of rice using a SPAD instrument provided by an embodiment of this application;

[0037] Figure 2 It is a flowchart of the steps for obtaining the optimal fitting model provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] To further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a method for indicating fertilization of the first leaf from the top of rice using a SPAD instrument proposed according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0040] The following specifically describes the specific solution of a fertilization indication method for diagnosing the first leaf from the top of rice using a SPAD instrument in combination with the accompanying drawings.

[0041] A fertilization indication method for diagnosing the first leaf from the top of rice using a SPAD instrument provided by an embodiment of the present application. Specifically, the following fertilization indication method for diagnosing the first leaf from the top of rice using a SPAD instrument is provided. Please refer to Figure 1 , and the method includes the following steps:

[0042] S1: Divide the planting areas of various rice varieties into multiple regions, apply different amounts of nitrogen fertilizer to different regions, divide each region into multiple sub-regions, obtain the SPAD values, nitrogen application amounts, and yields of various rice varieties in each region at any growth stage, and obtain the SPAD values of each sub-region.

[0043] The entire growth process of rice includes the tillering stage, jointing stage, young panicle differentiation stage, booting stage, heading stage. In this embodiment, four rice varieties, namely the mainstream hybrid rice "Shenyou 26", "Shenyou 28" and the conventional rice "Nanjing 46", "Chongshang 2022", are used as the targets. Through eight nitrogen application rate treatments of 0 kg / hm 2 , 105 kg / hm 2 , 135 kg / hm 2 , 150 kg / hm 2 , 225 kg / hm 2 , 300 kg / hm 2 , 337.5 kg / hm 2 , 375 kg / hm 2 , nitrogen application treatments are carried out on the four rice varieties respectively.

[0044] In this embodiment, the planting areas of various rice varieties are evenly divided into 8 regions, different amounts of nitrogen fertilizer are applied to different regions, and each region is divided into 3 sub-regions. Six rice plants are selected in each sub-region, and the SPAD values of the first leaf from the top of these 6 rice plants at any growth stage during their growth process are measured using a SPAD instrument. Among them, the measurement position is 1 / 2 from the base of the leaf, and the veins and leaf edges are avoided during measurement. The average value of the SPAD values of these 6 rice plants is used as the SPAD value of the sub-region where these 6 rice plants are located, and the average value of the SPAD values of all sub-regions is used as the SPAD value of each region. At the same time, the nitrogen application amount and yield of various rice varieties in each region at any growth stage are obtained.

[0045] S2: Based on the correlation between the SPAD value and the yield, screen out the best diagnosis period from all growth stages of various rice varieties. By analyzing the fitting situation of all nitrogen application amounts and SPAD values of various rice varieties under different fitting methods during the best diagnosis period, determine the optimal fitting model.

[0046] After obtaining the relevant data of rice, a fitting model can be constructed to analyze the corresponding relationships among various data, and the rice yield can be predicted based on the fitting model and the existing data, so as to achieve the fertilization indication for rice. Therefore, how to select a model with high fitting accuracy is crucial.

[0047] S201: Fit the binary groups composed of nitrogen application rates and SPAD values in all regions of various rice varieties using different fitting methods to obtain the fitting curves under different fitting methods. Then, the distances from all binary groups to the corresponding fitting points on the fitting curve under any one fitting method are used to form an error sequence. The error sequence is divided into multiple subsequences, and the fluctuation values of various rice varieties under any one fitting method are determined by analyzing the average distribution and dispersion degree of all elements in each subsequence.

[0048] (1) Obtain the optimal diagnosis periods of various rice varieties.

[0049] Since each rice variety has multiple growth stages, and the corresponding relationships between the SPAD values of rice at different growth stages and the rice yield are not consistent, it is necessary to select the growth stage with the strongest correlation between the SPAD value and the yield from multiple cultivation periods, and construct a model based on the relevant data of rice at this growth stage to ensure the reliability of the prediction model. The specific determination process of the optimal diagnosis period is as follows:

[0050] Form two-dimensional data points with the yields and SPAD values of various rice varieties in all regions at any growth stage, and use all the two-dimensional data points of all regions as the input of the quadratic function regression equation, and output the determination coefficient as the determination coefficient of various rice varieties at any growth stage;

[0051] Among all the growth stages of various rice varieties, the growth stage corresponding to the maximum determination coefficient is used as the optimal diagnosis period of various rice varieties.

[0052] Among them, the method of obtaining the determination coefficient using the quadratic function regression equation is a well-known technology, and its specific principle process will not be elaborated here.

[0053] (2) Analyze the fitting accuracies of various fitting methods.

[0054] Fit the binary groups composed of all nitrogen application rates and SPAD values of various rice varieties using different fitting methods to obtain the fitting curves under different fitting methods, where the abscissa is the nitrogen application rate and the ordinate is the SPAD value. In this embodiment, different fitting methods at least include: quadratic polynomial fitting method, logarithmic fitting method, power function fitting method, exponential function fitting method, least squares method, linear regression model.

[0055] Further, for various rice varieties under any fitting method, the distances from all the said binary pairs to the fitting curve under any fitting method are used to form an error sequence, and the error sequence is evenly divided into multiple subsequences. The mean value of all elements in each subsequence is calculated as the average error of each subsequence. If the mean value of all elements in a subsequence is larger, that is, the average error is larger, it indicates that the fitting effect of the current fitting method in the corresponding region under the corresponding nitrogen application rate is poorer; on the contrary, if the mean value of all elements in a subsequence is smaller, that is, the average error is smaller, it indicates that the fitting effect of the current fitting method in the corresponding region under the corresponding nitrogen application rate is better.

[0056] Further, the variances and cumulative sums of the average errors of all subsequences in the error sequence are calculated respectively, and the product of the variance and the cumulative sum is used as the fluctuation value of various rice varieties under any fitting method.

[0057] From the fluctuation values of various rice varieties under any fitting method, it can be understood that if the variance of the average errors of all subsequences is larger, it means that the fitting effect of the current fitting method fluctuates more under different nitrogen application rates, indicating that the overall fitting effect is poorer, and the larger the cumulative sum of the average errors of all subsequences, it means that the fitting effect of the current fitting method on the SPAD value and the nitrogen application rate under different nitrogen application rates is worse. That is, the larger the variance and the larger the cumulative sum, the larger the obtained fluctuation value, indicating that the fitting effect of the current fitting method on the SPAD value and the nitrogen application rate fluctuates more under different nitrogen application rate conditions, and the overall fitting effect is poorer;

[0058] On the contrary, if the variance of the average errors of all subsequences is smaller, it means that the fitting effect of the current fitting method is more stable under different nitrogen application rates, indicating that the overall fitting effect is better, and the smaller the cumulative sum of the average errors of all subsequences, it means that the fitting effect of the current fitting method on the SPAD value and the nitrogen application rate under different nitrogen application rates is better. That is, the smaller the variance and the smaller the cumulative sum, the smaller the obtained fluctuation value, indicating that the fitting effect of the current fitting method on the SPAD value and the nitrogen application rate is more stable under different nitrogen application rate conditions, and the overall fitting effect is better.

[0059] S202: In each region of various rice varieties, the deviation of the SPAD value in each sub-region from the average SPAD value of all the other sub-regions is used as the difference index. The difference indices of all sub-regions are clustered to obtain the significant set of each region. Based on the average distribution of all difference indices within the significant set of each region, all error elements are screened out from the error sequence. The differences between each error element and the average distribution of all the other elements in its corresponding subsequence are compared, and in combination with the said fluctuation value, the deviation value of various rice varieties under any fitting method is determined.

[0060] Factors such as soil environment, irrigation conditions, and light intensity can all cause changes in the SPAD of rice. Therefore, further analysis is required. If the nitrogen application rate is the same in a rice-growing area, the measured SPAD values of the rice should also be relatively consistent. Therefore, the greater the difference between the SPAD value of a certain area and the SPAD values of areas with the same nitrogen application rate, the greater the degree to which the SPAD value of this area is affected by environmental factors, and the greater the original error of the SPAD value of this area.

[0061] Therefore, by analyzing the differences in the SPAD values of various rice varieties between different sub-areas within the same area, the influence of environmental factors on the fitting method is excluded. The specific process is as follows:

[0062] Within each area of various rice varieties, the deviation between the SPAD value in each sub-area and the average SPAD value of all other sub-areas is used as the difference index. The difference indices of all sub-areas within each area are clustered to obtain multiple clustering clusters. The mean value of all difference indices in each clustering cluster is calculated and denoted as the difference mean of each clustering cluster. Among all the clustering clusters, the clustering cluster with the largest difference mean is used as the significant set for each area; among them, the larger the difference index, the greater the difference in the SPAD values of rice between different sub-areas in areas with the same nitrogen application rate; conversely, the smaller the difference index, the smaller the difference in the SPAD values of rice between different sub-areas in areas with the same nitrogen application rate.

[0063] It should be noted that there are many commonly used clustering algorithms. In this embodiment, the k-means clustering algorithm is used to cluster the difference indices. In the actual application process, as other implementation methods, implementers can also use other clustering methods such as the DPC density peak clustering algorithm. There are no special restrictions on the selection of clustering methods in this embodiment.

[0064] Among them, the k-means clustering algorithm is a well-known technology, and its clustering principle will not be elaborated here.

[0065] Furthermore, for various rice varieties under any fitting method, the difference means of the significant sets of all areas are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. The area where the significant set corresponding to the difference mean greater than the segmentation threshold is located is used as the error area, and the elements corresponding to the binary groups of all error areas in the error sequence are used as error elements.

[0066] For the error sequence of various rice varieties under any fitting method, calculate the difference between each error element and the mean value of all other elements in its sub-sequence, and use the differences of all error elements as the error index of various rice varieties under any fitting method;

[0067] Further, based on the error index, the fluctuation value, and the determination coefficient of various rice under any fitting method, the deviation value of various rice under any fitting method is determined, specifically:

[0068] The deviation value A of the u-th type of rice under the fitting method i u,i has the following expression: In the formula, B u,i represents the determination coefficient of all binary groups of the u-th type of rice under the fitting method i; C u,i represents the fluctuation value of the u-th type of rice under the fitting method i; D u,i represents the error index of the u-th type of rice under the fitting method i; T u represents the number of all clustering clusters of the u-th type of rice; exp() represents the exponential function with the natural constant as the base.

[0069] Among them, the process of obtaining the determination coefficient according to the fitting method is a well-known technology, and its specific process will not be elaborated here.

[0070] From the deviation values of various rice under any fitting method, it can be understood that if T u is 1, that is, when the number of clustering clusters is 1, it means that the SPAD values of the sub-regions under the same nitrogen application rate are relatively consistent, and there are no significant errors in the original data. At this time, the deviation value can be determined by the determination coefficient and the fluctuation value. The smaller the fluctuation value and the larger the determination coefficient, the smaller the obtained deviation value, indicating that there are no large differences in the SPAD values of the rice in the region where the sub-region is located under the same nitrogen application rate; if T u is greater than 1, that is, when the number of clustering clusters is greater than 1, it means that the SPAD values of the rice in the region where the sub-region is located are affected by the environment and there are initial errors. Therefore, the deviation value is determined by the determination coefficient, the fluctuation value, and the error index. At this time, the larger the fluctuation value, the smaller the determination coefficient, and the larger the error index, the larger the obtained deviation value, which reflects that there are errors in the initial SPAD values of the region where the sub-region is located under the same nitrogen application rate. Therefore, it is necessary to further evaluate the fitting effect of the rice region with errors.

[0071] S203: Divide the fitting curves of various rice under any fitting method into multiple segments of curves, determine the change index of various rice under any fitting method by analyzing the change trend of each segment of the curve, so as to determine the trend value of various rice under any fitting method, and combine the deviation value to determine the optimal fitting model.

[0072] By analyzing the data variation characteristics between the SPAD value of rice and the nitrogen application rate at the optimal diagnosis stage, it can be seen that for hybrid rice at the booting stage, as the nitrogen application rate increases, the SPAD value of the first leaf from the top of the hybrid rice shows a trend of first increasing, then leveling off, and finally gradually decreasing; while for conventional rice at the young panicle differentiation stage, as the nitrogen application rate increases, the SPAD value of the first leaf from the top of the conventional rice shows an increasing trend.

[0073] Therefore, by analyzing the fitting situation between the SPAD value and the nitrogen application rate of different types of rice within their respective optimal diagnosis periods, the highly fitting coefficients of each fitting method are determined, specifically:

[0074] Extract the peaks and valleys on the fitting curves of various types of rice under any fitting method, connect the adjacent peaks and valleys, calculate the slope of the connected straight lines, and arrange the slopes of the connected straight lines between all adjacent peaks and valleys in ascending order according to the time when the peaks appear to form the slope sequence of various types of rice under any fitting method;

[0075] It should be noted that there are many common peak and valley extraction algorithms. In this embodiment, the automatic multi-scale peak search algorithm is used to obtain the peaks and valleys on the fitting curve. In the actual application process, as other implementation manners, the implementer can also use other methods. There is no special limitation on the selection of the peak and valley extraction algorithm in this embodiment.

[0076] Among them, the automatic multi-scale peak search algorithm is a well-known technology, and its specific principle will not be elaborated here.

[0077] Furthermore, based on the changing trend of all elements in the slope sequence, the change index is determined. The specific process is as follows:

[0078] The change index R u,i of the u-th type of rice under the fitting method i has the following expression: u,i In the formula, E u,i represents the ratio of the number of all negative elements to the number of all positive elements in the first-order difference sequence of the slope sequence of the u-th type of rice under the fitting method i; F u represents the ratio of the number of all positive elements to the number of all negative elements in the first-order difference sequence of the slope sequence of the u-th type of rice under the fitting method i; Z u represents the classification of the u-th type of rice. When Z u = 1, it means the u-th type of rice is hybrid rice, and when Z

[0079] It can be understood from the change indices of various rice varieties under any fitting method that the optimal diagnosis period for hybrid rice is the booting stage. During the optimal diagnosis period of hybrid rice, as the nitrogen application rate increases, the SPAD value of the topmost leaf of hybrid rice first increases, then levels off, and finally gradually decreases. Therefore, when the fitting result is good, the number of negative elements in the first-order difference sequence of the slope sequence is more than the number of positive elements. If the number of negative elements is less than the number of positive elements, it indicates that the fitting effect of the current fitting method for the SPAD value and the nitrogen application rate is poor. For conventional rice during the young panicle differentiation stage, as the nitrogen application rate increases, the SPAD value of the topmost leaf of conventional rice shows an increasing trend. Therefore, when the fitting result is good, the number of positive elements in the first-order difference sequence of the slope sequence is more than the number of negative elements. If the number of positive elements is less than the number of positive elements, it indicates that the fitting effect of the current fitting method for the SPAD value and the nitrogen application rate is poor.

[0080] According to the change indices of various rice varieties under any fitting method, the trend values of various rice varieties under any fitting method are further determined. The specific process is as follows:

[0081] The trend value M of the u-th rice variety under the fitting method i u,i is expressed as:

[0082] In the formula, V u,i represents the number of all elements in the first-order difference sequence of the slope sequence of the u-th rice variety under the fitting method i; represents the difference between the q-th element and its adjacent previous element in the first-order difference sequence of the slope sequence of the u-th rice variety under the fitting method i; exp() represents the exponential function with the natural constant as the base; norm[] represents the normalization function; max() represents the maximum value function.

[0083] It can be understood from the trend values of various rice varieties under any fitting method that if the change index under the current fitting method is larger, and the difference between adjacent elements in the first-order difference sequence of the slope sequence is smaller, and the number of elements in the first-order difference sequence of the slope sequence is smaller, then the trend value is larger, indicating that the fitting effect of the current fitting method for the SPAD value and the nitrogen application rate is better; conversely, if the change index under the current fitting method is smaller, and the difference between adjacent elements in the first-order difference sequence of the slope sequence is larger, and the number of elements in the first-order difference sequence of the slope sequence is larger, then the trend value is smaller, indicating that the fitting effect of the current fitting method for the SPAD value and the nitrogen application rate is worse.

[0084] Furthermore, by comprehensively considering the trend value and the deviation value, the high-fitting coefficient of various fitting methods is determined. Specifically:

[0085] The high-fitting coefficient R of the fitting method i iThe expression is as follows: In the formula, represents the mean of the trend values of all hybrid rice varieties under the fitting method i; represents the mean of the trend values of all conventional rice varieties under the fitting method i; represents the mean of the deviation values of all rice varieties under the fitting method i; ∈ represents a preset constant greater than 0, which is used to prevent the denominator from being 0. In this embodiment, the value of ∈ is 0.01. On the premise of ensuring that the denominator is not 0 and does not overly affect the calculation result, the implementer can also set it according to the specific situation by himself / herself, and this embodiment does not make special restrictions.

[0086] It can be understood from the high fitting coefficients of various fitting methods that if the trend value is larger and the deviation value is smaller when using the current fitting method, the obtained high fitting coefficient is larger, indicating that the current fitting method has a better fitting effect on the SPAD value and nitrogen application rate of rice; on the contrary, if the trend value is smaller and the deviation value is larger when using the current fitting method, the obtained high fitting coefficient is smaller, indicating that the current fitting method has a worse fitting effect on the SPAD value and nitrogen application rate of rice.

[0087] Finally, among all fitting methods, the fitting method with the largest high fitting coefficient is used as the optimal fitting model.

[0088] Preferably, the flow chart of the steps for obtaining the optimal fitting model provided in this embodiment is as Figure 2 shown.

[0089] S3: By analyzing the difference between the SPAD values of various rice varieties in each region during the best diagnosis period and the SPAD value corresponding to the maximum yield, and combining with the optimal fitting model, fertilize various rice varieties.

[0090] Among all regions of various rice varieties during the best diagnosis period, the SPAD corresponding to the maximum yield is denoted as the optimal value of various rice varieties during the best diagnosis period. Compare the SPAD values of various regions of various rice varieties during the best diagnosis period with the optimal value to determine whether fertilization is required in the corresponding region. Specifically:

[0091] If the SPAD value of the j-th region of the u-th rice variety during the best diagnosis period is less than the preset critical value, it means that the u-th rice variety in the j-th region is nitrogen-deficient and a large amount of nitrogen fertilizer needs to be supplemented; if the SPAD value of the j-th region of the u-th rice variety during the best diagnosis period is greater than or equal to the critical value and less than the optimal value at the same time, it means that the nitrogen in the u-th rice variety in the j-th region is relatively sufficient and a small amount of nitrogen fertilizer can be supplemented; if the SPAD value of the j-th region of the u-th rice variety during the best diagnosis period is greater than or equal to the optimal value, it means that the nitrogen in the u-th rice variety in the i-th region is excessive and no nitrogen fertilizer needs to be supplemented.

[0092] For the rice-growing areas that need to be supplemented with nitrogen fertilizer, the deviation between the SPAD value corresponding to the rice in the area and the optimal value is used as the input of the optimal fitting model, and the nitrogen fertilizer content to be supplemented is output.

[0093] It should be noted that the value of the preset critical value is set artificially, and the value of the preset critical value is less than the optimal value. In this embodiment, the value of the preset critical value is 0.9 times the optimal value. In actual application, as other implementation manners, the implementer can also combine and take a value within 0.9 - 0.95, and the specific situation can be set by itself, and this embodiment does not make special restrictions.

[0094] It should be noted that: the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0096] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; modifying the technical solutions recorded in the foregoing embodiments, or equivalently replacing some of the technical features, does not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of each embodiment of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for indicating fertilization using a SPAD instrument to diagnose rice leaf failure, characterized in that: The method comprises the following steps: The planting areas of various rice varieties are divided into multiple areas, different amounts of nitrogen fertilizers are applied to different areas, each area is divided into multiple sub-areas, and the SPAD value, nitrogen application amount and yield of various rice varieties in each area at any growth period are obtained, and the SPAD value of each sub-area is obtained; Based on the correlation between SPAD value and yield, the best diagnosis period was selected from all growth periods of various rice varieties. All nitrogen application rates and SPAD values ​​of various rice varieties under the best diagnosis period were analyzed to determine the optimal fitting model, which is as follows: Different fitting methods were used to fit the binary groups consisting of nitrogen application amount and SPAD value in all regions of various rice varieties. The distances of all binary groups to the fitting curves under any fitting method were used to form an error sequence. The error sequence was divided into multiple subsequences. The distribution of all elements in each subsequence was analyzed to determine the fluctuation value of various rice varieties under any fitting method. In each region of various rice varieties, the deviation of the SPAD value in each sub-region from the average SPAD value of all other sub-regions is used as the difference index. The difference indexes of all sub-regions are clustered to obtain the significant set of each region. Based on the distribution of all difference indexes in the significant set, the error elements are screened out from the error sequence. The difference between each error element and the distribution of all other elements in its sub-sequence is compared. Combined with the fluctuation value, the deviation value of various rice varieties under any fitting method is determined. Analyze the changing trend of the fitting curves of various rice varieties under any fitting method, determine the changing index of various rice varieties under any fitting method, determine the trend value of various rice varieties under any fitting method, and determine the optimal fitting model in combination with the deviation value; The differences between the SPAD values ​​of various rice regions during the optimal diagnosis period and the SPAD values ​​corresponding to the maximum yield were analyzed, and fertilization was performed on various rice varieties in combination with the optimal fitting model.

2. A method for indicating fertilization of rice leaves using a SPAD instrument as claimed in claim 1, characterized in that: The best diagnosis period is selected from all growth periods of various rice varieties, including: The yield and SPAD value of each region of various rice in any growth period are combined into two-dimensional data points, and the two-dimensional data points of all regions are used as inputs of a quadratic function regression equation, and a determination coefficient is output as the determination coefficient of various rice in any growth period; Among all growth periods of various rice varieties, the growth period corresponding to the maximum determination coefficient is taken as the optimal diagnosis period for various rice varieties.

3. The method for diagnosing rice leaf failure by using a SPAD instrument as claimed in claim 1, characterized in that: The method for determining the fluctuation values ​​of various rice varieties under any fitting method is as follows: For the error sequences of various rice varieties under any fitting method, the mean of all elements in each subsequence was calculated as the average error of each subsequence; The variance and cumulative sum of the average errors of all subsequences in the error sequence are calculated respectively, and the product of the variance and the cumulative sum is used as the fluctuation value of various rice under any fitting method.

4. The method for indicating fertilization of rice leaves using a SPAD instrument as claimed in claim 1, characterized in that: The acquisition process of the salient set of each region is as follows: The difference indexes of all sub-regions in each region of various rice varieties are clustered to obtain multiple clusters, and the means of all difference indexes in each cluster are calculated, which are recorded as the difference means of each cluster. Among all clusters, the cluster with the largest difference mean is taken as the significant set of each region.

5. The method for indicating fertilization of rice leaves using a SPAD instrument as claimed in claim 1, characterized in that: The process of screening out all error elements from the error sequence is as follows: For various rice varieties under any fitting method, the difference mean of the significant sets of all regions is used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. The region where the significant set corresponds to the difference mean greater than the segmentation threshold is used as the error region, and the elements corresponding to the binary groups of all error regions in the error sequence are used as error elements.

6. A method for indicating fertilization of rice leaves using a SPAD instrument as claimed in claim 4, characterized in that: The method for determining the deviation values ​​of various rice varieties under any fitting method is as follows: For the error sequence of various rice varieties under any fitting method, the difference between each error element and the mean of all other elements in the subsequence to which it belongs is calculated, and the difference of all error elements is used as the error index of various rice varieties under any fitting method; Deviation value A of the uth rice variety under fitting method i u,i The expression is: In the formula, B u,i represents the coefficient of determination of all pairs of the u-th rice under fitting method i; C u,i represents the fluctuation value of the u-th rice under fitting method i; D u,i represents the error index of the u-th rice under fitting method i; T u represents the number of all clusters of the u-th rice variety; exp() represents an exponential function with a natural constant as the base.

7. The method for indicating fertilization of rice leaves using a SPAD instrument as claimed in claim 1, characterized in that: The expression of the variation index of various rice varieties under any fitting method is: Extract the peaks and valleys on the fitting curves of various rice varieties under any fitting method, connect adjacent peaks and valleys, calculate the slope of the connected straight line, and arrange the slopes of the connected straight lines between all adjacent peaks and valleys in ascending order according to the peak appearance time to form a slope sequence of various rice varieties under any fitting method; The variation index R of the uth rice variety under fitting method i u,i The expression is: In the formula, E u,i represents the ratio of the number of all negative elements to the number of all positive elements in the first-order difference sequence of the slope sequence of the u-th rice under fitting method i; F u,i represents the ratio of the number of all positive elements to the number of all negative elements in the first-order difference sequence of the slope sequence of the u-th rice under fitting method i; Z u represents the classification of the u-th rice, Z u =1 indicates that the uth rice is hybrid rice, Z u =0 means that the u-th rice is conventional rice.

8. The method for indicating fertilization of rice leaves using a SPAD instrument as claimed in claim 7, characterized in that: The expression of the trend value of various rice under any fitting method is: Where M u,i represents the trend value of the u-th rice under fitting method i; V u,i represents the number of all elements in the first-order difference sequence of the slope sequence of the u-th rice under fitting method i; It represents the difference between the qth element and its adjacent previous element in the first-order difference sequence of the slope sequence of the uth rice variety under fitting method i; exp() represents an exponential function with a natural constant as the base; norm[] represents a normalization function; and max() represents a maximum value function.

9. The method for indicating fertilization of rice leaves using a SPAD instrument as claimed in claim 7, characterized in that: The method for determining the optimal fitting model is: The height fitting coefficient R of fitting method i i The expression is: In the formula, represents the mean of the trend values ​​of all hybrid rice species under fitting method i; represents the mean of the trend values ​​of all conventional rice varieties under fitting method i; represents the mean of the deviation values ​​of all rice varieties under fitting method i; ∈ represents a constant greater than 0; Among all the fitting methods, the fitting method with the largest height fitting coefficient is taken as the optimal fitting model.

10. The method for indicating fertilization of rice leaves using a SPAD instrument as claimed in claim 1, characterized in that: The fertilizing of various rice plants comprises: In all regions of various rice within the optimal diagnosis period, the SPAD corresponding to the maximum yield is recorded as the optimal value of various rice within the optimal diagnosis period. If the SPAD value of the j-th region of the u-th rice under the optimal diagnosis period is less than the optimal value of the u-th rice, it is necessary to fertilize the u-th rice in the j-th region, and the deviation between the SPAD value of the u-th rice in the j-th region and the optimal value is used as the input of the optimal fit model, and the output is the nitrogen fertilizer content that needs to be supplemented; otherwise, it is not necessary to fertilize the u-th rice in the j-th region.

Images