A tobacco agronomic character collection and ideal plant type analysis and evaluation system and method

Abstract

The application provides a tobacco agronomic character collection and ideal plant type analysis and evaluation system and method, relates to the tobacco planting management technical field, and the system comprises: an internet of things equipment used for collecting tobacco agronomic character data; a mobile terminal used for pre-processing data to obtain tobacco agronomic character samples; a server end used for constructing a sample vector and a sample vector set according to the tobacco agronomic character samples, and calculating the Mahalanobis distance of the sample vector by using a robust Mahalanobis distance method according to a pre-constructed ideal plant type vector to obtain a first analysis and evaluation result; and the server end is also used for calculating the robust mean and standard deviation by using a minimum covariance determinant method according to the sample vector set, and calculating the robust value of the sample vector according to the robust mean, the standard deviation and the sample vector to obtain a second analysis and evaluation result. The application can improve the data collection efficiency, data management capability and difference analysis capability of tobacco agronomic characters.

Description

Technical Field

[0001] This application relates to the field of tobacco planting and management technology, and in particular to a system and method for collecting tobacco agronomic traits and analyzing and evaluating ideal plant type. Background Technology

[0002] As an important economic crop, tobacco's agronomic traits are key indicators for measuring tobacco leaf yield, quality, and variety selection. Accurate and efficient collection of these agronomic trait data, followed by scientific analysis and evaluation, not only provides important references for tobacco variety selection but also serves as an effective basis for assessing the cultivation and management level of planting bases.

[0003] The ideal plant type is a high-quality growth form set according to the breeding goals and production needs of tobacco. It has optimized agronomic trait indicators and can reflect the excellent characteristics and adaptability of tobacco varieties. In the planting base, by comparing with the ideal plant type, we can: (1) guide variety selection: by analyzing the differences in agronomic traits of different materials, select high-quality varieties that meet the breeding goals. (2) evaluate the cultivation management level: by combining years of data to track the trait changes of a certain variety, evaluate the cultivation management technology level of the planting base. (3) monitor variety stability: by comparing the trait differences between the ideal plant type and the sample over a long period of time, discover and analyze the performance fluctuations of a specific variety under cultivation management conditions.

[0004] However, the following major problems still exist in the collection, management, analysis and evaluation of tobacco agronomic traits data: (1) Low data collection efficiency: Traditional field measurements rely on manual operation, which is time-consuming and labor-intensive, making it difficult to meet the rapid collection needs under large-scale planting conditions; paper records are easily affected by subjectivity and environment, resulting in inaccurate data and cumbersome processing. (2) Lack of systematic data management: Existing methods mostly store data in a scattered record form, making data easy to lose or incomplete. (3) Single analysis method: Existing analysis methods often only target a single indicator, making it difficult to comprehensively measure the difference between the sample and the ideal plant type from multiple dimensions, and failing to effectively guide tobacco breeding and base management.

[0005] Therefore, how to improve the efficiency of data collection, data management, and differential analysis of tobacco agronomic traits is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] To address the aforementioned technical problems, this application provides a system for collecting tobacco agronomic traits and analyzing and evaluating ideal plant type, which can improve the efficiency of data collection, data management capabilities, and difference analysis capabilities for tobacco agronomic traits. This application also provides a method for collecting tobacco agronomic traits and analyzing and evaluating ideal plant type, achieving the same technical effects.

[0007] The first objective of this application is to provide a system for collecting tobacco agronomic traits and analyzing and evaluating ideal plant types.

[0008] The aforementioned objective of this application is achieved through the following technical solution: A system for collecting tobacco agronomic traits and analyzing and evaluating ideal plant type includes: an Internet of Things (IoT) device, a mobile terminal, and a server, wherein: The IoT device is used to collect tobacco agronomic trait data and transmit it to the mobile terminal; The mobile terminal is used to preprocess the tobacco agronomic trait data to obtain tobacco agronomic trait samples, and transmit the tobacco agronomic trait samples to the server. The server is used to construct sample vectors and sample vector sets based on the tobacco agronomic trait samples, and to calculate the Mahalanobis distance of the sample vectors using the robust Mahalanobis distance method based on the sample vectors, the sample vector sets and the pre-constructed ideal plant type vectors, and to obtain the first analysis and evaluation result based on the Mahalanobis distance of the sample vectors. The server is further configured to calculate the robust mean and standard deviation based on the sample vector set using the minimum covariance determinant method, calculate the robust value of the sample vector based on the robust mean, the standard deviation and the sample vector, and obtain the second analysis and evaluation result based on the robust value of the sample vector.

[0009] Preferably, in the tobacco agronomic trait collection and ideal plant type analysis and evaluation system, the server is further used to construct the ideal plant type vector. Specifically, when constructing the ideal plant type vector, the server is used to: Based on all tobacco agronomic trait samples, the sample mean was calculated to obtain the mean sample. Based on the mean sample, construct the ideal plant type vector.

[0010] Preferably, in the tobacco agronomic trait collection and ideal plant type analysis and evaluation system, the server is further used to construct the ideal plant type vector. Specifically, when constructing the ideal plant type vector, the server is used to: Obtain the prediction model corresponding to each preset agronomic trait index in the sample vector, wherein the prediction model is specifically a Gaussian process regression model that takes the environmental feature vector as input and the predicted value of the preset agronomic trait index as output. Obtain real-time environmental feature vectors, input the real-time environmental feature vectors into the prediction model corresponding to each preset agronomic trait index, and obtain the real-time predicted value of each preset agronomic trait index. The ideal plant type vector is constructed based on the real-time predicted values ​​of each preset agronomic trait index.

[0011] Preferably, in the tobacco agronomic trait collection and ideal plant type analysis and evaluation system, the formula for calculating the Mahalanobis distance is: ; in, This represents the Mahalanobis distance. Represents the sample vector, This represents the ideal plant type vector. This indicates the transpose operation. This represents the inverse covariance matrix calculated from the set of sample vectors using the minimum covariance determinant.

[0012] Preferably, in the tobacco agronomic trait collection and ideal plant type analysis and evaluation system, the robustness value is calculated using the following formula: ; in, Indicates the robustness value, This represents the robust mean. This represents the standard deviation.

[0013] Preferably, in the tobacco agronomic trait collection and ideal plant type analysis and evaluation system, the server-side, when executing the step of obtaining the first analysis and evaluation result based on the Mahalanobis distance of the sample vectors, is specifically used for: The Mahalanobis distance between the sample vectors and the first preset threshold are compared to obtain the first analysis and evaluation result; The server-side, when executing the step of obtaining the second analysis and evaluation result based on the robustness value of the sample vector, is specifically used for: The robustness value of the sample vector and the second preset threshold are compared to obtain the second analysis and evaluation result.

[0014] Preferably, in the tobacco agronomic trait collection and ideal plant type analysis and evaluation system, the mobile terminal, when performing the preprocessing of the tobacco agronomic trait data to obtain tobacco agronomic trait samples, is specifically used for: Outlier removal is performed on the tobacco agronomic trait data, and missing values ​​are filled using interpolation. Then, a predetermined format conversion is performed to obtain tobacco agronomic trait samples.

[0015] Preferably, in the tobacco agronomic trait collection and ideal plant type analysis and evaluation system, the server is further used to perform statistical analysis based on the tobacco agronomic trait samples to obtain statistical indicators, wherein the statistical indicators include one or more of the mean, maximum value, minimum value, standard deviation and variance of each preset agronomic trait indicator.

[0016] Preferably, in the tobacco agronomic trait collection and ideal plant type analysis and evaluation system, the server is further configured to generate a table display based on the statistical indicators, the Mahalanobis distance of the sample vectors, and the robustness value of the sample vectors; The server is also used to generate radar charts and bar charts based on the sample vector and the ideal plant type vector. The server is also used to perform principal component analysis to extract the first principal component and the second principal component based on the tobacco agronomic trait sample, and to perform cluster analysis to obtain category labels, and to visualize the clustering results in a scatter plot with different colors.

[0017] The second objective of this application is to provide a method for collecting tobacco agronomic traits and analyzing and evaluating ideal plant types.

[0018] The second objective of this application is achieved through the following technical solution: A method for collecting tobacco agronomic traits and analyzing and evaluating ideal plant type, based on the tobacco agronomic trait collection and ideal plant type analysis and evaluation system described above, wherein the system includes: an Internet of Things (IoT) device, a mobile terminal, and a server; and the method includes: The IoT device is used to collect tobacco agronomic trait data and transmit it to the mobile terminal. Using the mobile terminal, the tobacco agronomic trait data are preprocessed to obtain tobacco agronomic trait samples, and the tobacco agronomic trait samples are transmitted to the server. Using the server, a sample vector and a set of sample vectors are constructed based on the tobacco agronomic trait samples. Based on the sample vectors, the set of sample vectors, and the pre-constructed ideal plant type vector, the robust Mahalanobis distance method is used to calculate the Mahalanobis distance of the sample vectors. Based on the Mahalanobis distance of the sample vectors, the first analysis and evaluation result is obtained. Using the server, based on the sample vector set, the robust mean and standard deviation are calculated using the minimum covariance determinant method. Based on the robust mean, the standard deviation, and the sample vector, the robust value of the sample vector is calculated. Based on the robust value of the sample vector, the second analysis and evaluation result is obtained.

[0019] The above technical solution uses IoT devices to collect tobacco agronomic trait data and transmits it to mobile terminals, enabling rapid and accurate measurement and real-time data transmission of agronomic traits in the field, significantly improving work efficiency and reducing human error. The mobile terminals preprocess the tobacco agronomic trait data to obtain tobacco agronomic trait samples, which are then transmitted to the server, achieving automatic preprocessing of agronomic trait data and sending scattered sample data to a unified server for digital management. On the server side, based on the tobacco agronomic trait samples, sample vectors and sample vector sets are constructed. Then, based on the sample vectors, sample vector sets, and a pre-constructed ideal plant type vector, the robust Mahalanobis distance method is used to calculate the sample... The Mahalanobis distance of the sample vector is used to obtain the first analysis and evaluation result. By introducing robust Mahalanobis distance, a comprehensive processing of correlations, scale differences, and outliers among traits is achieved, making the multi-trait comprehensive difference assessment method more stable, more realistic, and more statistically significant. On the server side, based on the sample vector set, the robust mean and standard deviation are calculated using the minimum covariance determinant method. Based on the robust mean, standard deviation, and sample vector, the robust value of the sample vector is calculated. Based on the robust value of the sample vector, the second analysis and evaluation result is obtained. By introducing the robust value index, the accuracy of difference identification is significantly improved, the difference between the sample and the ideal plant type is accurately assessed, and a quantitative basis for variety improvement is provided.

[0020] In summary, the above technical solutions can improve the efficiency of data collection, data management, and differential analysis of tobacco agronomic traits. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is an interactive schematic diagram of a tobacco agronomic trait collection and ideal plant type analysis and evaluation system provided in the embodiments of this application; Figure 2 The radar chart provided in the embodiments of this application; Figure 3 This is a bar chart provided in the embodiments of this application; Figure 4 This is a scatter plot provided in the embodiments of this application; Figure 5 This is a flowchart illustrating a method for collecting tobacco agronomic traits and analyzing and evaluating ideal plant type, as provided in the embodiments of this application. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] In the embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. The system embodiments described below are merely illustrative. For example, the division of units and modules is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or modules can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, and can be electrical, mechanical, or other forms.

[0025] In addition, each functional unit in the various embodiments of this application can be integrated into a single processor, or each unit can be a separate device, or two or more units can be integrated into a single device; each functional unit in the various embodiments of this application can be implemented in hardware or in the form of hardware plus software functional units.

[0026] Those skilled in the art will understand that all or part of the steps of the following method embodiments can be implemented by program instructions and related hardware. The aforementioned program instructions can be stored in a computer-readable storage medium. When the program instructions are executed, they perform the steps of the following method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0027] It should be understood that the use of terms such as "system," "device," "unit," and / or "module" in this application is merely one method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0028] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "a plurality of" or "several" means two or more, unless otherwise explicitly specified.

[0029] If a flowchart is used in this application, it is used to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0030] It should also be noted that, in this document, terms such as “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes the aforementioned element.

[0031] The embodiments in this application are written in a progressive manner.

[0032] like Figure 1 As shown in the figure, this application provides a tobacco agronomic trait collection and ideal plant type analysis and evaluation system, including: an Internet of Things (IoT) device, a mobile terminal, and a server, wherein: Internet of Things (IoT) devices are used to collect tobacco agronomic trait data and transmit it to mobile terminals; Specifically, IoT devices can include Bluetooth calipers and Bluetooth measuring tapes. Using these devices, agronomical trait data of tobacco in the field can be collected quickly and accurately. Specific indicators include one or more of the following: topping height, number of leaves, internode distance, stem circumference, middle leaf length, middle leaf width, upper leaf length, and upper leaf width. The IoT devices can connect to mobile terminals via Bluetooth, using Bluetooth text data transmission to transmit tobacco agronomical trait data to the mobile terminal in real time, achieving automatic data recording and significantly improving work efficiency and reducing human error. The IoT devices and mobile terminals can also connect via Wi-Fi or other communication methods; this application is not limited to these. To ensure the accuracy and standardization of measurements, data collection is strictly carried out in accordance with the standard "Methods for Surveying and Measuring Tobacco Agronomic Traits" (YC / T 142-2010). During the measurement process, the surveyor can measure the agronomical trait indicators of tobacco item by item according to the prompts on the mobile terminal.

[0033] A mobile terminal is used to preprocess tobacco agronomic trait data to obtain tobacco agronomic trait samples, and then transmit the tobacco agronomic trait samples to the server. Specifically, the mobile terminal can use a web service page for data collection and management. Surveyors can select the project name, field, and sample on the web service page and enter tobacco agronomic trait data item by item. The mobile terminal supports item-by-item measurement and automatically jumps to the next indicator entry, reducing operation steps. To improve data quality, the mobile terminal also preprocesses the tobacco agronomic trait data sent by IoT devices to obtain tobacco agronomic trait samples. The scattered tobacco agronomic trait samples are then transmitted to the server via a wireless network for unified data management and analysis. In some embodiments, when the mobile terminal performs preprocessing of tobacco agronomic trait data to obtain tobacco agronomic trait samples, it specifically performs outlier removal processing on the tobacco agronomic trait data, fills in missing values ​​using interpolation, and then performs a predetermined format conversion to obtain tobacco agronomic trait samples. This improves data quality through outlier handling and missing value filling, and enhances the efficiency of subsequent server-side data processing by unifying the data format.

[0034] On the server side, it is used to construct sample vectors and sample vector sets based on tobacco agronomic trait samples. Based on the sample vectors, sample vector sets and pre-constructed ideal plant type vectors, the robust Mahalanobis distance method is used to calculate the Mahalanobis distance of the sample vectors. Based on the Mahalanobis distance of the sample vectors, the first analysis and evaluation results are obtained. Specifically, the server can store and manage all tobacco agronomic trait samples uploaded by mobile terminals. These samples can be stored in a central database on the server, project by project, and support regular backups to ensure data security and long-term validity. The server uses the Robust Mahalanobis Distance method for difference measurement. Specifically, the server can select a tobacco agronomic trait sample to be analyzed and evaluated from multiple stored samples, and construct a sample vector based on this sample, denoted as... Based on multiple stored tobacco agronomic trait samples, a sample vector set is constructed, which may include multiple sample vectors. A sample matrix, denoted as X, is constructed from the sample vector set. Then, the covariance matrix is ​​estimated from the sample matrix X using the Minimum Covariance Determinant (MCD) method. The MCD method uses only the smallest "volume" of data, removes extreme outliers, and obtains a covariance matrix that is extremely insensitive to outliers. Then, based on this covariance matrix... The inverse covariance matrix is ​​calculated. The MCD method is existing technology, and its specific calculation process will not be described in detail here. Then, a pre-constructed ideal plant type vector is obtained, denoted as... In this ideal plant type vector, the preset agronomic trait indicators are the same as those in the sample vector. The values ​​of the preset agronomic trait indicators in the ideal plant type vector can be manually preset according to actual needs or automatically generated by the server. For example, in some embodiments, the server is also used to construct the ideal plant type vector. When constructing the ideal plant type vector, the server specifically performs the following steps: calculates the sample mean based on all tobacco agronomic trait samples to obtain the mean sample; constructs the ideal plant type vector based on the mean sample. A reference value is set based on the sample mean for subsequent sample difference evaluation. After obtaining the sample vector... inverse covariance matrix and ideal plant type vector Then, the Mahalanobis distance of the sample vectors can be calculated. The specific calculation formula is as follows: ; in, Represents Mahalanobis distance, This indicates the transpose operation. This represents the inverse covariance matrix calculated from the sample vector set using the minimum covariance determinant. The smaller the value, the closer the tobacco agronomic trait sample to the ideal plant type is to the trait being analyzed and evaluated. The larger the value, the more significant the deviation between the tobacco agronomic trait sample to be analyzed and evaluated and the ideal plant type. The server can then use the Mahalanobis distance of the sample vectors. Based on the size and preset evaluation rules, the first analysis and evaluation results are obtained.

[0035] In some embodiments, when the server performs the calculation of the Mahalanobis distance of the sample vectors to obtain a first analysis and evaluation result, it specifically performs the following: comparing the Mahalanobis distance of the sample vectors with the magnitude of a first preset threshold to obtain the first analysis and evaluation result. Specifically, the first preset threshold can be set based on actual needs; when the Mahalanobis distance of the sample vectors... When the value exceeds a first preset threshold, the tobacco agronomic trait sample to be analyzed and evaluated can be identified as an abnormal sample and used as the first analysis and evaluation result. When the Mahalanobis distance of the sample vectors... When the value is less than or equal to the first preset threshold, the tobacco agronomic trait sample to be analyzed and evaluated can be identified as a normal sample and used as the first analysis and evaluation result. This application is not limited to this.

[0036] Traditional Euclidean distance assumes that traits are independent and identically distributed, which cannot handle the correlation between traits or adapt to different scales. This application adopts robust Mahalanobis distance as a measure of total difference, which can comprehensively handle the correlation between traits, scale differences and outliers, making the multi-trait comprehensive difference assessment method more stable, more realistic and statistically significant.

[0037] On the server side, it is also used to calculate the robust mean and standard deviation based on the sample vector set using the minimum covariance determinant method. Based on the robust mean, standard deviation, and sample vector, it calculates the robust value of the sample vector and obtains the second analysis and evaluation result based on the robust value of the sample vector.

[0038] Specifically, the server can also calculate the robust mean and standard deviation based on the sample vector set using the minimum covariance determinant method, denoted as [examples would be inserted here]. and The specific calculation process of the MCD method is existing technology. After obtaining the sample vector... Robust mean and standard deviation Then, the robustness value of the sample vector can be calculated. The specific calculation formula is as follows: ; in, This represents the robustness value. The smaller the absolute value, the smaller the significant difference between the tobacco agronomic trait sample to be analyzed and evaluated and the ideal plant type. The larger the absolute value, the greater the significant difference between the tobacco agronomic trait sample to be analyzed and evaluated and the ideal plant type. The server can then use the robustness value of the sample vector. Based on the size and preset evaluation rules, the second analysis and evaluation results are obtained. By introducing the robust value index, the accuracy of difference identification is significantly improved, the difference between the sample and the ideal plant type is accurately assessed, and a quantitative basis is provided for variety improvement.

[0039] In some embodiments, on the server side, when obtaining a second analysis and evaluation result based on the robustness value of the sample vector, the specific steps are: comparing the robustness value of the sample vector with the magnitude of a second preset threshold to obtain a first analysis and evaluation result. Specifically, the second preset threshold can be set based on actual needs; for example, it can be set to 2.5 when the robustness value of the sample vector... When the absolute value of the sample vector exceeds the second preset threshold, it can be considered that there is a significant difference between the tobacco agronomic trait sample to be analyzed and evaluated and the ideal plant type. This difference is used as the second analysis and evaluation result. When the absolute value is less than or equal to the second preset threshold, it can be determined that the tobacco agronomic trait sample to be analyzed and evaluated has little difference from the significant trait of the ideal plant type, and this can be used as the second analysis and evaluation result. This application is not limited to this.

[0040] Currently, the following major problems still exist in the collection, management, analysis and evaluation of tobacco agronomic traits data: (1) Low data collection efficiency: Traditional field measurements rely on manual operation, which is time-consuming and labor-intensive, making it difficult to meet the rapid collection needs under large-scale planting conditions; paper records are easily affected by subjectivity and environment, resulting in inaccurate data and cumbersome data processing. (2) Lack of systematic data management: Existing methods mostly store data in a scattered record form, making data easy to lose or incomplete. (3) Single analytical methods: Existing analytical methods often only target a single indicator, making it difficult to comprehensively measure the difference between the sample and the ideal plant type from multiple dimensions, and thus failing to effectively guide tobacco breeding and base management.

[0041] The above embodiments utilize IoT devices to collect tobacco agronomic trait data and transmit it to mobile terminals, enabling rapid and accurate measurement and real-time data transmission of agronomic traits in the field, significantly improving work efficiency and reducing human error. The mobile terminals preprocess the tobacco agronomic trait data to obtain tobacco agronomic trait samples, which are then transmitted to the server, achieving automatic preprocessing of agronomic trait data and sending scattered sample data to a unified server for digital management. On the server side, based on the tobacco agronomic trait samples, sample vectors and sample vector sets are constructed. Using the robust Mahalanobis distance method, the sample vectors, sample vector sets, and pre-constructed ideal plant type vectors are used to calculate the sample... The Mahalanobis distance of the vectors is used to obtain the first analytical evaluation result. By introducing robust Mahalanobis distance, a comprehensive handling of correlations, scale differences, and outliers among traits is achieved, making the multi-trait comprehensive difference assessment method more stable, realistic, and statistically significant. On the server side, based on the sample vector set, the robust mean and standard deviation are calculated using the minimum covariance determinant method. Based on the robust mean, standard deviation, and sample vectors, the robust value of the sample vectors is calculated, and the second analytical evaluation result is obtained based on the robust value of the sample vectors. By introducing the robust value index, the accuracy of difference identification is significantly improved, and the difference between the sample and the ideal plant type is accurately assessed, providing a quantitative basis for variety improvement. In summary, the above embodiments can improve the data collection efficiency, data management capabilities, and difference analysis capabilities of tobacco agronomic traits.

[0042] In other embodiments of this application, when the server performs the construction of the ideal plant type vector, it is specifically used for: Obtain the prediction model corresponding to each preset agronomic trait index in the sample vector. Specifically, the prediction model is a Gaussian process regression model that takes the environmental feature vector as input and the predicted value of the preset agronomic trait index as output. Obtain real-time environmental feature vectors, input the real-time environmental feature vectors into the prediction model corresponding to each preset agronomic trait index, and obtain the real-time predicted value of each preset agronomic trait index. An ideal plant type vector is constructed based on the real-time predicted values ​​of each preset agronomic trait index.

[0043] Specifically, each preset agronomic trait index in the sample vector can be flexibly set based on actual needs. For example, preset agronomic trait indexes include one or more of the following: topped plant height, number of leaves, internode distance, stem circumference, middle leaf length, middle leaf width, upper leaf length, and upper leaf width. For each preset agronomic trait index, a corresponding prediction model can be pre-constructed. This prediction model is specifically a Gaussian Process Regression (GPR) model that takes an environmental feature vector as input and outputs the predicted value of the preset agronomic trait index. The environmental feature vector can include multiple indicators such as temperature, rainfall, accumulated temperature, available potassium in the soil, and organic matter content. Each prediction model can be independently trained based on historical observation data of the corresponding preset agronomic trait index. Then, the real-time environmental feature vector is obtained and input into the prediction model corresponding to each preset agronomic trait index to obtain the real-time predicted value of each preset agronomic trait index. This serves as a basic reference value for constructing an ideal plant type vector. Used for subsequent sample difference assessment.

[0044] In some embodiments, the expression for a Gaussian process regression model is: ; ; in, Represents a continuous function to be predicted. It follows a Gaussian process distribution. Represents the environmental feature vector. Represents the mean function, This refers to kernel functions, such as the commonly used radial basis function (RBF). This represents the length scale parameter. This represents the relative weights controlling small-scale and large-scale features.

[0045] For each preset agronomic trait index The server side uses existing historical observation data. Train independent Gaussian process models, inputting real-time environmental feature vectors. The index value for predicting the ideal plant type, that is, the real-time predicted value of each preset agronomic trait index. .

[0046] In this embodiment, an environment-dependent prediction model is introduced, abandoning the traditional fixed vector ideal plant type. This makes the ideal plant type vector no longer a fixed constant, but a "dynamic vector" that changes with the environment. It can fit the nonlinear and complex relationship between traits and the environment, automatically adapt to different production areas and different years, improve the accuracy of subsequent difference analysis, and significantly enhance the scientific nature and intelligence of the system.

[0047] In other embodiments of this application, the server is further configured to perform statistical analysis based on tobacco agronomic trait samples to obtain statistical indicators. These statistical indicators include one or more of the mean, maximum, minimum, standard deviation, and variance of each preset agronomic trait indicator. By calculating these statistical indicators, the overall growth trend and data distribution of the samples can be quickly analyzed.

[0048] In other embodiments of this application, the server-side is further configured to generate a table display based on statistical indicators, the Mahalanobis distance of the sample vectors, and the robustness value of the sample vectors. Specifically, the server-side can generate a table containing statistical indicators, the Mahalanobis distance of the sample vectors, and the robustness value of the sample vectors, supporting sorting by column name, downloading, and table display.

[0049] In other embodiments of this application, the server-side is further configured to generate radar charts and bar charts based on the sample vectors and the ideal plant type vector. Specifically, the radar chart is used to display the overall performance of each sample across all agronomic traits, and the bar chart is used to identify specific differences between the sample and the ideal plant type, combined with... Figure 2 and Figure 3 As shown, radar charts and bar charts are used to display the differences between the sample (number BN1115) and the ideal plant type in various traits, marking significant traits and intuitively analyzing key traits.

[0050] In other embodiments of this application, the server is further configured to perform principal component analysis to extract the first principal component and the second principal component based on the tobacco agronomic trait sample, and to perform cluster analysis to obtain category labels, and to visualize the clustering results in a scatter plot with different colors.

[0051] Specifically, the server performs principal component analysis (PCA) on tobacco agronomic trait samples to extract the first principal component (PC1) and the second principal component (PC2). Then, it obtains the scores for each sample on PC1 and PC2, performs cluster analysis on the PCA scores, selects a suitable clustering algorithm (such as K-means based on Euclidean distance), and clusters the two-dimensional PCA scores. The clustering results are visualized by plotting a scatter plot, with the horizontal axis representing the first principal component (PC1) and the vertical axis representing the second principal component (PC2). Each point represents a tobacco agronomic trait sample. Based on the category labels obtained from clustering, samples in different clusters are marked with different colors, such as... Figure 4 As shown in the figure. In this embodiment, PCA analysis is combined to perform dimensionality reduction on agronomic trait data and extract the most representative principal components, which can intuitively reflect the overall distribution and differences of the samples, providing scientific and efficient technical support for tobacco variety breeding and planting management.

[0052] like Figure 5 As shown, in another embodiment of this application, a method for collecting tobacco agronomic traits and analyzing and evaluating ideal plant type is also provided. This method is implemented based on the aforementioned tobacco agronomic trait collection and ideal plant type analysis and evaluation system. The tobacco agronomic trait collection and ideal plant type analysis and evaluation system includes: an Internet of Things (IoT) device, a mobile terminal, and a server. The method includes: S101. Use Internet of Things (IoT) devices to collect tobacco agronomic trait data and transmit them to mobile terminals; S102. Using a mobile terminal, preprocess the tobacco agronomic trait data to obtain tobacco agronomic trait samples, and transmit the tobacco agronomic trait samples to the server. S103. Using the server, construct sample vectors and sample vector sets based on tobacco agronomic trait samples. Then, using the robust Mahalanobis distance method, calculate the Mahalanobis distance of the sample vectors based on the sample vectors, sample vector sets, and pre-constructed ideal plant type vectors. Based on the Mahalanobis distance of the sample vectors, obtain the first analysis and evaluation results. S104. Using the server, based on the sample vector set, the robust mean and standard deviation are calculated using the minimum covariance determinant method. Based on the robust mean, standard deviation, and sample vectors, the robust value of the sample vectors is calculated. Based on the robust value of the sample vectors, the second analysis and evaluation results are obtained.

[0053] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A system for collecting tobacco agronomic traits and analyzing and evaluating ideal plant type, characterized in that, include: IoT devices, mobile terminals, and server-side components, including: The IoT device is used to collect tobacco agronomic trait data and transmit it to the mobile terminal; The mobile terminal is used to preprocess the tobacco agronomic trait data to obtain tobacco agronomic trait samples, and transmit the tobacco agronomic trait samples to the server. The server is used to construct sample vectors and sample vector sets based on the tobacco agronomic trait samples, and to calculate the Mahalanobis distance of the sample vectors using the robust Mahalanobis distance method based on the sample vectors, the sample vector sets and the pre-constructed ideal plant type vectors, and to obtain the first analysis and evaluation result based on the Mahalanobis distance of the sample vectors. The server is further configured to calculate the robust mean and standard deviation based on the sample vector set using the minimum covariance determinant method, calculate the robust value of the sample vector based on the robust mean, the standard deviation and the sample vector, and obtain the second analysis and evaluation result based on the robust value of the sample vector. The server-side is also used to construct the ideal plant type vector. Specifically, when constructing the ideal plant type vector, the server-side is used for: Obtain the prediction model corresponding to each preset agronomic trait index in the sample vector, wherein the prediction model is specifically a Gaussian process regression model that takes the environmental feature vector as input and the predicted value of the preset agronomic trait index as output. Obtain real-time environmental feature vectors, input the real-time environmental feature vectors into the prediction model corresponding to each preset agronomic trait index, and obtain the real-time predicted value of each preset agronomic trait index. The ideal plant type vector is constructed based on the real-time predicted values ​​of each preset agronomic trait index.

2. The system as described in claim 1, characterized in that, The formula for calculating the Mahalanobis distance is: ； in, This represents the Mahalanobis distance. Represents the sample vector. This represents the ideal plant type vector. This indicates the transpose operation. This represents the inverse covariance matrix calculated from the set of sample vectors using the minimum covariance determinant.

3. The system as described in claim 2, characterized in that, The formula for calculating the robustness value is as follows: ； in, Indicates the robustness value, This represents the robust mean. This represents the standard deviation.

4. The system as described in claim 1, characterized in that, The server-side, when executing the step of obtaining the first analysis and evaluation result based on the Mahalanobis distance of the sample vectors, is specifically used for: The Mahalanobis distance between the sample vectors and the first preset threshold are compared to obtain the first analysis and evaluation result; The server-side, when executing the step of obtaining the second analysis and evaluation result based on the robustness value of the sample vector, is specifically used for: The robustness value of the sample vector and the second preset threshold are compared to obtain the second analysis and evaluation result.

5. The system as described in claim 1, characterized in that, When the mobile terminal performs preprocessing on the tobacco agronomic trait data to obtain tobacco agronomic trait samples, it is specifically used for: Outlier removal is performed on the tobacco agronomic trait data, and missing values ​​are filled using interpolation. Then, a predetermined format conversion is performed to obtain tobacco agronomic trait samples.

6. The system as described in claim 1, characterized in that, The server is also used to perform statistical analysis based on the tobacco agronomic trait samples to obtain statistical indicators, wherein the statistical indicators include one or more of the mean, maximum value, minimum value, standard deviation and variance of each preset agronomic trait indicator.

7. The system as described in claim 6, characterized in that, The server-side is also used to generate a table display based on the statistical indicators, the Mahalanobis distance of the sample vectors, and the robustness value of the sample vectors; The server is also used to generate radar charts and bar charts based on the sample vector and the ideal plant type vector. The server is also used to perform principal component analysis to extract the first principal component and the second principal component based on the tobacco agronomic trait sample, and to perform cluster analysis to obtain category labels, and to visualize the clustering results in a scatter plot with different colors.

8. A method for collecting tobacco agronomic traits and analyzing and evaluating ideal plant type, characterized in that, The tobacco agronomic trait collection and ideal plant type analysis and evaluation system based on any one of claims 1-7 is implemented, the system comprising: an Internet of Things (IoT) device, a mobile terminal, and a server, the method comprising: The IoT device is used to collect tobacco agronomic trait data and transmit it to the mobile terminal. Using the mobile terminal, the tobacco agronomic trait data are preprocessed to obtain tobacco agronomic trait samples, and the tobacco agronomic trait samples are transmitted to the server. Using the server, a sample vector and a set of sample vectors are constructed based on the tobacco agronomic trait samples. Based on the sample vectors, the set of sample vectors, and the pre-constructed ideal plant type vector, the robust Mahalanobis distance method is used to calculate the Mahalanobis distance of the sample vectors. Based on the Mahalanobis distance of the sample vectors, the first analysis and evaluation result is obtained. Using the server, based on the sample vector set, the robust mean and standard deviation are calculated using the minimum covariance determinant method. Based on the robust mean, the standard deviation, and the sample vector, the robust value of the sample vector is calculated. Based on the robust value of the sample vector, the second analysis and evaluation result is obtained.

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