Crop disease intelligent early warning system and method

Abstract

The invention belongs to the technical field of crop disease early warning, and particularly relates to an intelligent early warning system and method for crop diseases, and the method comprises the steps: obtaining the information of crops in a current farmland, and building a farmland visual model; detecting the disease condition of the current farmland through farmland monitoring equipment, and generating a disease information feature set; the method comprises the following steps: collecting farmland related meteorological data, processing a disease information feature set, establishing a disease propagation analysis model, analyzing disease related factors, and establishing a farmland disease influence factor database; analyzing the disease development of the current farmland according to the farmland disease influence factor database, evaluating the disease risk of the grids without diseases in the farmland, feeding back the judgment result to the farmland visualization model, and warning related personnel. The accuracy of predicting the disease propagation trend and the disease occurrence position under the complex and changeable meteorological conditions is improved, crop loss caused by prevention and control time delay is avoided, and the disease prevention and control effect is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of crop disease early warning, and in particular relates to an intelligent crop disease early warning system and method. Background Art

[0002] With the development of modern agriculture, large-scale centralized planting is becoming more and more popular. Large farmlands have achieved real-time monitoring of farmland status through reasonable land planning, efficient mechanized operations, and the application of Internet of Things technology. However, centralized planting increases the risk of crop disease transmission.

[0003] In the existing technology, when warning of crop diseases, historical disease data is usually analyzed based on crop growth cycles and environmental factors to predict the development trend of the disease. However, traditional methods often rely on empirical judgment and simple statistical models. These methods may not be able to accurately predict the occurrence of diseases in complex environments, resulting in early warning information not being timely, thereby delaying the opportunity for prevention and control. At the same time, traditional methods may find it difficult to integrate multi-source data, unable to fully utilize the advantages of big data, realize real-time dynamic monitoring, and find it difficult to track the development trend of the disease. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent early warning system and method for crop diseases to solve the problems raised in the above background technology.

[0005] In order to achieve the purpose of the present invention, the present invention will be implemented by adopting the following technical solutions.

[0006] The intelligent early warning method for crop diseases comprises the following steps: Step S100: obtaining crop information in the current farmland and establishing a farmland visualization model, wherein the farmland visualization model includes three-dimensional farmland terrain data, crop information and disease warning information; Step S200: Detect the disease condition of the current farmland through farmland monitoring equipment to generate a disease information feature set; Step S300: Collect farmland-related meteorological data, process the disease information feature set, establish a disease propagation analysis model, analyze disease-related factors based on the disease propagation analysis model, and establish a database of farmland disease influencing factors; Step S400: Analyze the disease development of the current farmland according to the farmland disease influencing factor database, evaluate the disease risk of the grids in the farmland where no disease occurs, and feed back the judgment results to the farmland visualization model to warn relevant personnel; Accurately predict the disease spread trend and disease occurrence location under complex and changeable meteorological conditions, avoid crop losses caused by delayed prevention and control, help growers effectively prevent and control crop diseases, improve disease prevention and control effects, and reduce planting costs.

[0007] As a preferred solution of the present invention, the implementation process of step S100 includes the following steps: Step S101: Divide the current farmland into several grids according to the farmland images collected by the drone and number them, obtain the crop information in the current farmland through the sensors set in the farmland, and generate a crop information set, which is recorded as ,in, represents the crop feature set of the jth grid, J represents the total number of grids in the current farmland, , n represents the total number of crops in the grid; represents the location data of the i-th crop in the j-th grid, represents the growth characteristic data of the i-th crop in the j-th grid, wherein the growth characteristic data includes plant height, stem diameter, flower quantity and fruit quantity, represents the health characteristic data of the i-th crop in the j-th grid, wherein the health characteristic data includes the crop disease type and the disease occurrence time; Step S102: Establish a farmland visualization model based on virtual reality technology, and display the crop growth conditions of each grid of the farmland according to the crop information set.

[0008] As a preferred solution of the present invention, in step S200, the current farmland disease is detected by farmland monitoring equipment, and the corresponding position in the farmland visualization model is marked according to the location where the disease occurs; the database of the farmland visualization model is accessed to generate a disease information feature set, which is recorded as ,in, represents the disease information characteristics of the jth grid, , Represents the disease information characteristics of the i-th crop in the j-th grid; the disease information characteristics include disease type, disease occurrence time and crop performance characteristics.

[0009] As a preferred solution of the present invention, the implementation process of step S300 includes the following steps: Step S301: Extract the center crop of the kth type of disease in the farmland according to the disease information feature set analysis: ; in, represents the location of the center crop of the k-th disease, where the center crop of the k-th disease is the transmission source of the k-th disease; Indicates the total number of crops with the disease information characteristic of the kth type of disease; Step S302: Analyze the central crop corresponding to the crop with disease according to the following formula: ; When the above equation holds true, the position of the central crop corresponding to the i-th crop in the j-th grid is determined as ; in, ; represents the similarity between the i-th crop in the j-th grid and the central crop of the k-th disease, B represents the total number of disease categories, The disease information characteristics of the central crop representing the k-th disease; Step S303: Collect farmland-related meteorological data through the sensor network deployed in the farmland, and record the farmland-related meteorological data in chronological order; generate a sample set for each central crop of various diseases and the diseased crop corresponding to the central crop, process the disease information feature set based on the recorded farmland-related meteorological data, and use the Pearson correlation coefficient to calculate the correlation between the farmland-related meteorological data and the occurrence of the disease, wherein the farmland-related meteorological data includes temperature, wind direction and precipitation; generate a disease propagation analysis model, wherein the disease propagation analysis model generates a correlation coefficient matrix between features according to the processing of the disease information feature set corresponding to each sample set, and the element in the a-th row and m-th column of the matrix is ​​denoted as L am , L am Represents the correlation coefficient between the a-th sample set and the m-th element in the farmland-related meteorological data; linearly expands the matrix to establish a database of farmland disease influencing factors, wherein the database of farmland disease influencing factors includes the correlation between the disease type and each element in the farmland-related meteorological data.

[0010] According to the above method, the crops with diseases in the farmland are integrated, the disease center is traced, the transmission source of various diseases is determined, and then the correlation between diseased crops and meteorological data is analyzed. The correlation coefficient matrix between the features reflects the influence of different meteorological conditions on the transmission of different types of diseases. The transmission influence includes positive influence and negative influence. The positive influence means that the meteorological elements help to accelerate the spread of the disease, and the negative influence means that the meteorological elements help to restrain the spread of the disease.

[0011] As a preferred solution of the present invention, the implementation process of step S400 includes the following steps: Step S401: Obtain farmland-related meteorological data within the next unit time through a third-party API; calculate the disease risk index of the grid without disease in the current farmland according to the following formula: ; Among them, BH j represents the disease risk index of the jth disease-free grid in the current farmland; represents the shortest distance between the jth disease-free grid and the crop with the kth disease in the farmland, represents the correlation coefficient between the kth disease and the mth element in the farmland-related meteorological data, obtained from the farmland disease influencing factor database; , M represents the total number of elements in the farmland-related meteorological data; Step S402: Calculate the disease risk index of each grid of the farmland, preset the disease risk index threshold, if the disease risk index of the jth grid without disease in the current farmland is greater than the disease risk index threshold, then it is determined that the jth grid of the farmland has a disease risk, and the judgment result is fed back to the farmland visualization model to warn relevant personnel.

[0012] The intelligent early warning system for crop diseases includes: a visualization model building module, a disease detection module, an influencing factor analysis module, and a disease early warning module; The visualization model building module is used to obtain crop information in the current farmland and build a farmland visualization model; The disease detection module is used to detect diseases in the current farmland through farmland monitoring equipment, and mark the corresponding positions in the farmland visualization model according to the locations where the diseases appear; The influencing factor analysis module is used to collect farmland-related meteorological data, process the disease information feature set, establish a disease propagation analysis model, analyze disease-related factors based on the disease propagation analysis model, and establish a farmland disease influencing factor database; The disease warning module is used to predict the current farmland disease development based on the farmland disease influencing factor database, evaluate the disease risk of each area of ​​the farmland, and feed the evaluation results back to the farmland visualization model to warn relevant personnel.

[0013] Furthermore, the visualization model building module includes a farmland data acquisition unit, a three-dimensional model building unit and a farmland data updating unit; The farmland data acquisition unit is used to divide the current farmland into a number of grids and number them according to the farmland images collected by the drone, obtain the crop information in the current farmland through the sensors set in the farmland, and generate a crop information set; The three-dimensional model building unit builds a farmland visualization model based on virtual reality technology, and displays the crop growth conditions of each grid of the farmland according to the crop information set; The farmland data updating unit is used to update the disease warning information in the farmland visualization model according to the detection results of the disease detection module.

[0014] Furthermore, the influencing factor analysis module includes a disease characteristic analysis unit, a disease propagation analysis unit and a farmland disease influencing factor database establishment unit; The disease feature analysis unit is used to analyze the central crops of various diseases according to the disease information feature set; The disease spread analysis unit is used to analyze the disease spread rules according to the central crops of various diseases; The farmland disease influencing factor database establishing unit is used to establish a farmland disease influencing factor database according to the output of the disease propagation analysis unit. Beneficial Effects

[0015] (1) The present invention establishes a farmland visualization model to update the crop status in real time, making it easier for managers to understand and analyze farmland conditions; (2) The present invention collects and analyzes farmland-related meteorological data, establishes a disease propagation analysis model, establishes a database of farmland disease influencing factors, accumulates disease prevention and control knowledge, and provides support for long-term management; accurately predicts disease propagation trends and disease occurrence locations under complex and changeable meteorological conditions, avoids crop losses caused by delayed prevention and control time, helps growers effectively prevent and control crop diseases, takes preventive measures in advance, reduces the occurrence and spread of crop diseases, improves disease prevention and control effects, and reduces planting costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the process of the intelligent early warning system for crop diseases of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in combination with the embodiments of the present invention and the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] As an embodiment of the present invention, Figure 1 As shown, the present invention provides an intelligent early warning method for crop diseases, comprising the following steps: Step S100: obtaining crop information in the current farmland and establishing a farmland visualization model, wherein the farmland visualization model includes three-dimensional farmland terrain data, crop information and disease warning information; Step S101: Divide the current farmland into several grids according to the farmland images collected by the drone and number them, obtain the crop information in the current farmland through the sensors set in the farmland, and generate a crop information set, which is recorded as ,in, represents the crop feature set of the jth grid, J represents the total number of grids in the current farmland, , n represents the total number of crops in the grid; represents the location data of the i-th crop in the j-th grid, represents the growth characteristic data of the i-th crop in the j-th grid, wherein the growth characteristic data includes plant height, stem diameter, flower quantity and fruit quantity, represents the health characteristic data of the i-th crop in the j-th grid, wherein the health characteristic data includes the crop disease type and the disease occurrence time; Step S102: Establish a farmland visualization model based on virtual reality technology, and display the crop growth conditions of each grid of the farmland according to the crop information set.

[0019] Step S200: Detect the diseases of the current farmland through the farmland monitoring equipment, mark the corresponding positions in the farmland visualization model according to the locations where the diseases appear; access the database of the farmland visualization model to generate a disease information feature set, recorded as ,in, represents the disease information characteristics of the jth grid, , Represents the disease information characteristics of the i-th crop in the j-th grid; the disease information characteristics include disease type, disease occurrence time and crop performance characteristics.

[0020] Step S300: Collect farmland-related meteorological data, process the disease information feature set, establish a disease propagation analysis model, analyze disease-related factors based on the disease propagation analysis model, and establish a database of farmland disease influencing factors; Step S301: Extract the center crop of the kth type of disease in the farmland according to the disease information feature set analysis: ; in, represents the location of the center crop of the k-th disease, where the center crop of the k-th disease is the transmission source of the k-th disease; Indicates the total number of crops with the disease information characteristic of the kth type of disease; Step S302: Analyze the central crop corresponding to the crop with disease according to the following formula: ; When the above equation holds true, the position of the central crop corresponding to the i-th crop in the j-th grid is determined as ; in, ; represents the similarity between the i-th crop in the j-th grid and the central crop of the k-th disease, B represents the total number of disease categories, The disease information characteristics of the central crop representing the k-th disease; Step S303: Collect farmland-related meteorological data through the sensor network deployed in the farmland, and record the farmland-related meteorological data in chronological order; generate a sample set for each central crop of various diseases and the diseased crop corresponding to the central crop, process the disease information feature set based on the recorded farmland-related meteorological data, and use the Pearson correlation coefficient to calculate the correlation between the farmland-related meteorological data and the occurrence of the disease, wherein the farmland-related meteorological data includes temperature, wind direction and precipitation; generate a disease propagation analysis model, wherein the disease propagation analysis model generates a correlation coefficient matrix between features based on the processing of the disease information feature set corresponding to each sample set, which can be displayed using a heat map, and the element in the ath row and mth column of the matrix is ​​denoted as L am , L am Represents the correlation coefficient between the a-th sample set and the m-th element in the farmland-related meteorological data; linearly expands the matrix, inserts new values ​​into the matrix by interpolation method, so as to smoothly expand the size of the matrix so that the farmland disease influencing factor database can include daily meteorological values; establishes a farmland disease influencing factor database, which includes the correlation between the disease type and each element in the farmland-related meteorological data.

[0021] According to the above method, the crops with diseases in the farmland are integrated, the disease center is traced, the transmission source of various diseases is determined, and then the correlation between diseased crops and meteorological data is analyzed. The correlation coefficient matrix between the features reflects the influence of different meteorological conditions on the transmission of different types of diseases. The transmission influence includes positive influence and negative influence. The positive influence means that the meteorological elements help to accelerate the spread of the disease, and the negative influence means that the meteorological elements help to restrain the spread of the disease.

[0022] Step S400: Analyze the disease development of the current farmland according to the farmland disease influencing factor database, evaluate the disease risk of the grids in the farmland where no disease occurs, and feed back the judgment results to the farmland visualization model to warn relevant personnel; Step S401: Obtain farmland-related meteorological data within the next unit time through a third-party API; calculate the disease risk index of the grid without disease in the current farmland according to the following formula: ; Among them, BH j represents the disease risk index of the jth disease-free grid in the current farmland; represents the shortest distance between the jth disease-free grid and the crop with the kth disease in the farmland, represents the correlation coefficient between the kth disease and the mth element in the farmland-related meteorological data, obtained from the farmland disease influencing factor database; , M represents the total number of elements in the farmland-related meteorological data; Step S402: Calculate the disease risk index of each grid of the farmland, preset the disease risk index threshold, if the disease risk index of the jth grid without disease in the current farmland is greater than the disease risk index threshold, then it is determined that the jth grid of the farmland has a disease risk, and the judgment result is fed back to the farmland visualization model to warn relevant personnel.

[0023] The technical solution of the present invention is described in detail above in conjunction with the embodiments / drawings, but the present invention is not limited to the above technical solution. For ordinary technicians in this technical field, after knowing the contents recorded in the present invention, they can make several equivalent changes and substitutions without departing from the principle of the present invention. These equivalent changes and substitutions should also be regarded as belonging to the protection scope of the present invention.

Claims

1. An intelligent early warning method for crop diseases, characterized in that: The following steps are involved: Step S100: obtaining crop information in the current farmland and establishing a farmland visualization model, wherein the farmland visualization model includes three-dimensional farmland terrain data, crop information and disease warning information; Step S200: Detect the disease condition of the current farmland through farmland monitoring equipment to generate a disease information feature set; Step S300: Collect farmland-related meteorological data, process the disease information feature set, establish a disease propagation analysis model, analyze disease-related factors based on the disease propagation analysis model, and establish a database of farmland disease influencing factors; Step S400: Analyze the disease development of the current farmland according to the farmland disease influencing factor database, evaluate the disease risk of the grids in the farmland where no disease occurs, and feed back the judgment results to the farmland visualization model to warn relevant personnel.

2. The intelligent early warning method for crop diseases according to claim 1, characterized in that: The implementation process of step S100 includes the following steps: Step S101: Divide the current farmland into several grids according to the farmland images collected by the drone and number them, obtain the crop information in the current farmland through the sensors set in the farmland, and generate a crop information set, which is recorded as ,in, represents the crop feature set of the jth grid, J represents the total number of grids in the current farmland, , n represents the total number of crops in the grid; represents the location data of the i-th crop in the j-th grid, represents the growth characteristic data of the i-th crop in the j-th grid, Represents the health characteristic data of the i-th crop in the j-th grid; Step S102: Establish a farmland visualization model based on virtual reality technology, and display the crop growth conditions of each grid of the farmland according to the crop information set.

3. The intelligent early warning method for crop diseases according to claim 1, characterized in that: In step S200, the current farmland diseases are detected by farmland monitoring equipment, and the corresponding positions in the farmland visualization model are marked according to the locations where the diseases occur; the database of the farmland visualization model is accessed to generate a disease information feature set, which is recorded as ,in, represents the disease information characteristics of the jth grid, , Represents the disease information characteristics of the i-th crop in the j-th grid.

4. The intelligent early warning method for crop diseases according to claim 1, characterized in that: The implementation process of step S300 includes the following steps: Step S301: Extract the center crop of the kth type of disease in the farmland according to the disease information feature set analysis: ； in, represents the location of the center crop of the kth disease, Indicates the total number of crops with the disease information characteristic of the kth type of disease; Step S302: Analyze the central crop corresponding to the crop with disease according to the following formula: ； When the above equation holds true, the position of the central crop corresponding to the i-th crop in the j-th grid is determined as ; in, ; represents the similarity between the i-th crop in the j-th grid and the central crop of the k-th disease, B represents the total number of disease categories, The disease information characteristics of the central crop representing the k-th disease; Step S303: Collect farmland-related meteorological data through the sensor network deployed in the farmland, and record the farmland-related meteorological data in chronological order; generate a sample set for each central crop of various diseases and the diseased crop corresponding to the central crop, process the disease information feature set based on the recorded farmland-related meteorological data, use the Pearson correlation coefficient to calculate the correlation between the farmland-related meteorological data and the occurrence of the disease, and generate a disease propagation analysis model. The disease propagation analysis model generates a correlation coefficient matrix between features based on the processing of the disease information feature set corresponding to each sample set, and the element in the a-th row and m-th column of the matrix is ​​denoted as L am , L am Represents the correlation coefficient between the a-th sample set and the m-th element in the farmland-related meteorological data; linearly expands the matrix to establish a database of farmland disease influencing factors, wherein the database of farmland disease influencing factors includes the correlation between the disease type and each element in the farmland-related meteorological data.

5. The intelligent early warning method for crop diseases according to claim 1, characterized in that: The implementation process of step S400 includes the following steps: Step S401: Obtain farmland-related meteorological data within the next unit of time through a third-party API; The disease risk index of the grid without disease in the current farmland is calculated according to the following formula: ； Among them, BH j represents the disease risk index of the jth disease-free grid in the current farmland; represents the shortest distance between the jth disease-free grid and the crop with the kth disease in the farmland, represents the correlation coefficient between the kth disease and the mth element in the farmland-related meteorological data, obtained from the farmland disease influencing factor database; , M represents the total number of elements in the farmland-related meteorological data; Step S402: Calculate the disease risk index of each grid of the farmland, preset the disease risk index threshold, if the disease risk index of the jth grid without disease in the current farmland is greater than the disease risk index threshold, then it is determined that the jth grid of the farmland has a disease risk, and the judgment result is fed back to the farmland visualization model to warn relevant personnel.

6. An intelligent early warning system for crop diseases, executing the intelligent early warning method for crop diseases according to any one of claims 1 to 5, characterized in that: include: Visual model building module, disease detection module, influencing factor analysis module and disease early warning module; The visualization model building module is used to obtain crop information in the current farmland and build a farmland visualization model; The disease detection module is used to detect diseases in the current farmland through farmland monitoring equipment, and mark the corresponding positions in the farmland visualization model according to the locations where the diseases appear; The influencing factor analysis module is used to collect farmland-related meteorological data, process the disease information feature set, establish a disease propagation analysis model, analyze disease-related factors based on the disease propagation analysis model, and establish a farmland disease influencing factor database; The disease warning module is used to predict the current farmland disease development based on the farmland disease influencing factor database, evaluate the disease risk of each area of ​​the farmland, and feed the evaluation results back to the farmland visualization model to warn relevant personnel.

7. The intelligent early warning system for crop diseases according to claim 6 is characterized in that: The visualization model building module includes a farmland data acquisition unit, a three-dimensional model building unit and a farmland data updating unit; The farmland data acquisition unit is used to divide the current farmland into a number of grids and number them according to the farmland images collected by the drone, obtain the crop information in the current farmland through the sensors set in the farmland, and generate a crop information set; The three-dimensional model building unit builds a farmland visualization model based on virtual reality technology, and displays the crop growth conditions of each grid of the farmland according to the crop information set; The farmland data updating unit is used to update the disease warning information in the farmland visualization model according to the detection results of the disease detection module.

8. The intelligent early warning system for crop diseases according to claim 6 is characterized by: The influencing factor analysis module includes a disease characteristic analysis unit, a disease propagation analysis unit and a farmland disease influencing factor database establishment unit; The disease feature analysis unit is used to analyze the central crops of various diseases according to the disease information feature set; The disease spread analysis unit is used to analyze the disease spread rules according to the central crops of various diseases; The farmland disease influencing factor database establishing unit is used to establish a farmland disease influencing factor database according to the output of the disease propagation analysis unit.

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