A Method and System for Monitoring Plant Diseases and Insect Pests in Smart Agriculture

Abstract

The present invention discloses a method and system for monitoring agricultural pests and diseases in smart agriculture. The method includes data collection, data preprocessing, constructing a risk assessment model for pests and diseases, and monitoring agricultural pests and diseases. The present invention belongs to the field of agricultural monitoring, specifically referring to a method and system for monitoring agricultural pests and diseases in smart agriculture. By introducing an environmental noise suppression metric, this solution identifies and removes abnormal monitoring data, and introduces a time decay factor and the Euclidean distance between samples, enabling noise suppression to consider not only the similarity between samples but also the dynamic changes in time and space; improving the accuracy and real-time processing ability of agricultural pest and disease monitoring; optimizing the pest and disease risk assessment value by defining a dynamic adjustment function, dynamically adjusting the assessment value according to the prediction errors of different sample data to improve the prediction accuracy; designing a dynamic trend loss for pests and diseases and introducing retrospective residual optimization to continuously reduce errors in multiple predictions, thereby improving the effect of agricultural pest and disease monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural monitoring, and specifically refers to a method and system for monitoring pests and diseases in smart agriculture. Background Art

[0002] The method for monitoring agricultural pests and diseases is to collect and analyze data related to crop pests and diseases, timely identify, predict and evaluate the risks of pests and diseases that crops may face, so as to take appropriate control measures and reduce the impact of pests and diseases on agricultural production. However, the general method for monitoring agricultural pests and diseases has problems such as poor accuracy in agricultural pest and disease monitoring due to the influence of noise, interference from environmental changes, equipment errors and failures; the general method for monitoring agricultural pests and diseases ignores errors and uncertainties, and the risk assessment of future pests and diseases is inaccurate or too rough when facing high volatility and complex environments, resulting in poor monitoring effects of pests and diseases. Summary of the Invention

[0003] In view of the above situation, to overcome the defects of the prior art, the present invention provides a method and system for monitoring pests and diseases in smart agriculture. Aiming at the problem that the general method for monitoring agricultural pests and diseases has poor accuracy in agricultural pest and disease monitoring due to the influence of noise, interference from environmental changes, equipment errors and failures, this solution introduces an environmental noise suppression metric to identify and remove abnormal monitoring data, and introduces a time decay factor and the Euclidean distance between samples, so that noise suppression not only considers the similarity between samples, but also considers the dynamic changes in time and space; improves the accuracy and real-time processing ability of agricultural pest and disease monitoring; aiming at the problem that the general method for monitoring agricultural pests and diseases ignores errors and uncertainties, and the risk assessment of future pests and diseases is inaccurate or too rough when facing high volatility and complex environments, resulting in poor monitoring effects of pests and diseases, this solution optimizes the pest and disease risk assessment value by defining a dynamic adjustment function, dynamically adjusts the assessment value according to the prediction errors of different sample data, and improves the prediction accuracy; by designing a dynamic trend loss of pests and diseases and introducing a retrospective residual optimization, continuously reduces the error in multiple predictions, thereby improving the monitoring effect of agricultural pests and diseases.

[0004] The technical solution adopted by the present invention is as follows: A method for monitoring pests and diseases in smart agriculture provided by the present invention includes the following steps:

[0005] Step S1: Data collection;

[0006] Step S2: Data preprocessing;

[0007] Step S3: Construct a pest and disease risk assessment model;

[0008] Step S4: Monitoring of agricultural pests and diseases.

[0009] Further, in step S1, the data collection is to collect historical agricultural pest and disease monitoring data; the historical agricultural pest and disease monitoring data includes pest and disease type data, time, geospatial data, climate environment data, crop growth data, pesticide data, and pest and disease risk assessment results; the pest and disease risk assessment results include normal, mild danger, moderate danger, and severe danger; the pest and disease risk assessment result is used as a data label.

[0010] Further, in step S2, the data preprocessing is to perform data cleaning, data conversion, normalization processing, and data optimization on the collected data to construct a time series data set; the data cleaning is to process missing values and duplicate values; the data conversion is to convert the collected data into a vector form; the normalization processing is to normalize the data based on the maximum-minimum normalization method; the data optimization is to delete the data if the environmental noise suppression metric value of the data is lower than the metric threshold compared with 90% of the other data; the environmental noise suppression metric value between data is expressed as: ; where A and B are different sample data; is the normalization coefficient; N is the total number of samples; and are the shape parameter and scale parameter respectively; is the gamma function; is the time decay factor; is the sample sampling time; t is the current time; is the weight decay coefficient; d(·) is the Euclidean distance; is the current sample; is the average sample of the data set.

[0011] Further, in step S3, the construction of the pest and disease risk assessment model specifically includes the following steps:

[0012] Step S31: Preliminary construction; Based on LSTM, predict the pest and disease risk assessment value, and optimize the pest and disease risk assessment value through defining a dynamic adjustment function, and then construct the pest and disease risk assessment model; Define the dynamic adjustment function which is expressed as: ; ; The updated formula of the pest and disease risk assessment optimized by the dynamic adjustment function is: ; where, is the sample data; is the prediction error of the LSTM for the sample data; x is the predicted pest and disease risk value of the LSTM; and are the pest and disease risk assessment values after and before optimization respectively; is the adaptive weight function; is the noise threshold; is the uncertainty of the i-th sample; is the weight decay coefficient; is the scale factor;

[0013] Step S32: Design the dynamic trend loss of pests and diseases; the dynamic trend loss ML of pests and diseases is expressed as: ; where N is the total number of samples; and are the actual pest and disease risk value and the predicted actual pest and disease risk value of the i-th sample respectively; and are the actual pest and disease risk value and the predicted actual pest and disease risk value of the (i - 1)-th sample respectively; is the smoothing term; is the trend mutation penalty factor; is the risk change amount;

[0014] Step S33: Review the residual optimization; the review residual function is expressed as: ; where is the prediction error of the i-th sample; is the threshold for controlling the penalty residual; and are used to adjust the non-linear shape; is the residual constraint factor; m is the number of samples for review; j is the sample index; is the prediction error of the (i - j)-th sample; Q is used to control the rate of exponential decay;

[0015] Step S34: Construct the final loss function; the loss function TL of the pest and disease risk assessment model is expressed as: ; where is the loss weight;

[0016] Step S35: Model determination; the time series dataset corresponding to the historical agricultural pest and disease monitoring data is pre-divided into a test set and a training set in advance; the pest and disease risk assessment model updates the parameters using the gradient descent algorithm; when the loss of the pest and disease risk assessment model converges for the training set, the training of the pest and disease risk assessment model is completed; a prediction threshold is set in advance, and when the prediction accuracy rate of the trained pest and disease risk assessment model for the test set is higher than the prediction threshold, the pest and disease risk assessment model is established; otherwise, re-divide the dataset, adjust the initial parameters, and retrain.

[0017] Furthermore, in step S4, the agricultural pest and disease monitoring is based on the established pest and disease risk assessment model, real-time collects agricultural pest and disease monitoring data, and realizes agricultural pest and disease monitoring based on the pest and disease risk assessment results predicted by the model.

[0018] A smart agriculture pest and disease monitoring system provided by the present invention includes a data acquisition module, a data preprocessing module, a pest and disease risk assessment model construction module, and an agricultural pest and disease monitoring module;

[0019] The data acquisition module collects historical agricultural pest and disease monitoring data and sends the data to the data preprocessing module;

[0020] The data preprocessing module performs data cleaning, data conversion, standardization processing, and data optimization on the collected data to construct a time series data set; and sends the data to the pest and disease risk assessment model construction module;

[0021] The pest and disease risk assessment model construction module constructs a pest and disease risk assessment model by defining a dynamic adjustment function, designing a pest and disease dynamic trend loss, and introducing a retrospective residual optimization; and sends the data to the agricultural pest and disease monitoring module;

[0022] The agricultural pest and disease monitoring module realizes agricultural pest and disease monitoring based on the established pest and disease risk assessment model for the real-time collected agricultural pest and disease monitoring data.

[0023] The beneficial effects achieved by the present invention using the above solution are as follows:

[0024] (1) Aiming at the problem that the general agricultural pest and disease monitoring method is affected by noise, interfered by environmental changes, equipment errors and failures, resulting in poor accuracy of agricultural pest and disease monitoring, this solution improves the accuracy and real-time processing ability of agricultural pest and disease monitoring by introducing an environmental noise suppression metric to identify and remove abnormal monitoring data, and introducing a time decay factor and the Euclidean distance between samples, so that noise suppression considers not only the similarity between samples, but also the dynamic changes in time and space.

[0025] (2) Aiming at the problem that the general agricultural pest and disease monitoring method ignores errors and uncertainties, and the risk assessment of future pests and diseases is inaccurate or too rough in the face of high volatility and complex environments, resulting in poor pest and disease monitoring effects, this solution optimizes the pest and disease risk assessment value by defining a dynamic adjustment function, dynamically adjusts the assessment value according to the prediction errors of different sample data, and improves the prediction accuracy; by designing a pest and disease dynamic trend loss and introducing a retrospective residual optimization, the error is continuously reduced in multiple predictions, thereby improving the agricultural pest and disease monitoring effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flowchart of a smart agriculture pest and disease monitoring method provided by the present invention;

[0027] Figure 2 It is a schematic diagram of a smart agriculture pest and disease monitoring system provided by the present invention;

[0028] Figure 3 It is a schematic flowchart of step S3.

[0029] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. Specific embodiments

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0031] In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0032] Embodiment 1, refer to Figure 1 , A method for monitoring agricultural pests and diseases in smart agriculture provided by the present invention, the method includes the following steps:

[0033] Step S1: Data collection; collect historical agricultural pest and disease monitoring data;

[0034] Step S2: Data preprocessing; perform data cleaning, data conversion, standardization processing and data optimization on the collected data to construct a time series data set;

[0035] Step S3: Construct a pest and disease risk assessment model; by defining a dynamic adjustment function, designing the loss of the dynamic trend of pests and diseases and introducing the retrospective residual optimization, and then realizing the construction of the pest and disease risk assessment model;

[0036] Step S4: Agricultural pest and disease monitoring; realize agricultural pest and disease monitoring based on the established pest and disease risk assessment model for the real-time collected agricultural pest and disease monitoring data.

[0037] Embodiment 2, refer to Figure 1, this embodiment is based on the above embodiment. In step S1, the historical agricultural pest and disease monitoring data includes pest and disease type data, time, geospatial data, climate environment data, crop growth data, pesticide data, and pest and disease risk assessment results; the pest and disease risk assessment results include normal, slightly dangerous, moderately dangerous, and severely dangerous; the pest and disease risk assessment results are used as data labels; the pest and disease type data includes pest and disease types, pest and disease occurrence times, and growth stages of pests and diseases; the crop growth data includes crop types, crop growth stages, and crop health status; the climate environment data includes temperature, humidity, precipitation, wind speed, and wind direction; the geospatial data includes farmland geographical locations, field types, and crop planting area types; the pesticide data includes pesticide types, pesticide usage amounts, pesticide usage times, and application methods.

[0038] Embodiment 3, refer to Figure 1 , this embodiment is based on the above embodiment. In step S2, data cleaning is to handle missing values and duplicate values; data conversion is to convert the collected data into vector form; normalization processing is to normalize the data based on the maximum-minimum normalization method; data optimization is that sensors are affected by environmental changes, equipment errors, and failures, resulting in the collected data containing non-Gaussian noise. Therefore, an environmental noise suppression metric is constructed; to reduce the impact of noise, especially to process abnormal data from sensors and the external environment; if the environmental noise suppression metric values of the data are lower than the metric threshold for 90% of the other data, the data is deleted; the environmental noise suppression metric values between data are expressed as: ; where A and B are different sample data; is the normalization coefficient; N is the total number of samples; and are the shape parameter and scale parameter respectively; is the gamma function; is the time decay factor; is the sample sampling time; t is the current time; is the weight decay coefficient; d(·) is the Euclidean distance; is the current sample; is the average sample of the dataset.

[0039] By performing the above operations, for the problem that the general agricultural pest and disease monitoring method has poor accuracy in agricultural pest and disease monitoring due to the influence of noise and interference from environmental changes, equipment errors, and failures, this solution introduces an environmental noise suppression metric to identify and remove abnormal monitoring data, and introduces a time decay factor and the Euclidean distance between samples, so that noise suppression not only considers the similarity between samples, but also considers the dynamic changes in time and space; improving the accuracy and real-time processing ability of agricultural pest and disease monitoring.

[0040] Example 4, refer to Figure 1 and Figure 3 , this example is based on the above example. In step S3, constructing the pest and disease risk assessment model specifically includes the following steps:

[0041] Step S31: Preliminary construction; Based on LSTM to predict the pest and disease risk assessment value, and optimize the pest and disease risk assessment value through defining a dynamic adjustment function, and then construct the pest and disease risk assessment model; To calculate the pest and disease risk assessment of pests and diseases more effectively and avoid repeatedly calculating the inverse matrix of the gain matrix in a large-scale monitoring system, define the dynamic adjustment function , expressed as: ; ; The updated formula of the pest and disease risk assessment optimized by the dynamic adjustment function is: ; Where, is the sample data; is the prediction error of the LSTM for the sample data; x is the predicted pest and disease risk value of the LSTM; and are the pest and disease risk assessment values after and before optimization respectively; is the adaptive weight function; is the noise threshold; is the uncertainty of the i-th sample; is the weight decay coefficient; is the scale factor;

[0042] Step S32: Design the dynamic trend loss of pests and diseases; If the predicted pest risk in the future of the model prediction area increases and the actual situation is indeed so, it is regarded as a correct prediction; If the model predicts an increase in risk, but actually the risk decreases, a penalty is imposed; The dynamic trend loss ML of pests and diseases is expressed as: ; Where, N is the total number of samples; and are the actual pest and disease risk values and the predicted actual pest and disease risk values of the i-th sample respectively; and are the actual pest and disease risk values and the predicted actual pest and disease risk values of the (i - 1)-th sample respectively; is the smoothing term; is the trend mutation penalty factor; is the risk change amount; Q is used to control the rate of exponential decay;

[0043] Step S33: Retrospective residual optimization; In order to further improve the monitoring accuracy, design a residual function for non-linear optimization, and review the residual function , expressed as: ; Where, is the prediction error of the i-th sample; is a threshold value used to control the penalty residual; and is used to adjust the non - linear shape; is the residual constraint factor; m is the number of samples reviewed; j is the sample index; is the prediction error of the (i - j) - th sample;

[0044] Step S34: Construct the final loss function; the loss function TL of the pest and disease risk assessment model is expressed as: ; where is the loss weight;

[0045] Step S35: Model determination; the time - series data set corresponding to the historical agricultural pest and disease monitoring data is pre - divided into a test set and a training set; the pest and disease risk assessment model updates the parameters using the gradient descent algorithm; when the loss of the pest and disease risk assessment model converges for the training set, the training of the pest and disease risk assessment model is completed; a prediction threshold is preset, and when the prediction accuracy rate of the trained pest and disease risk assessment model for the test set is higher than the prediction threshold, the pest and disease risk assessment model is established; otherwise, re - divide the data set, adjust the initial parameters and retrain.

[0046] By performing the above operations, aiming at the problem that the general agricultural pest and disease monitoring method ignores errors and uncertainties, and the risk assessment of future pests and diseases is inaccurate or too rough in the face of high volatility and complex environments, resulting in poor pest and disease monitoring effects. In this solution, the pest and disease risk assessment value is optimized by defining a dynamic adjustment function, and the assessment value is dynamically adjusted according to the prediction errors of different sample data, improving the prediction accuracy; by designing the dynamic trend loss of pests and diseases and introducing the optimization of retrospective residuals, the error is continuously reduced in multiple predictions, thereby improving the agricultural pest and disease monitoring effect.

[0047] Example Five, refer to Figure 1 , based on the above example, in step S4, agricultural pest and disease monitoring is based on the established pest and disease risk assessment model, real - time collecting agricultural pest and disease monitoring data, and realizing agricultural pest and disease monitoring based on the pest and disease risk assessment results predicted by the model; when the pest and disease risk assessment results predicted by the model are medium - risk or high - risk, early warning processing is carried out.

[0048] Example Six, refer to Figure 2 , based on the above example, a smart agricultural pest and disease monitoring system provided by the present invention includes a data acquisition module, a data pre - processing module, a pest and disease risk assessment model construction module, and an agricultural pest and disease monitoring module;

[0049] The data acquisition module collects historical agricultural pest and disease monitoring data and sends the data to the data pre - processing module;

[0050] The data preprocessing module performs data cleaning, data conversion, standardization processing, and data optimization on the collected data; and sends the data to the pest and disease risk assessment model construction module;

[0051] The pest and disease risk assessment model construction module constructs a pest and disease risk assessment model by defining a dynamic adjustment function, designing a pest and disease dynamic trend loss, and introducing a retrospective residual optimization; and sends the data to the agricultural pest and disease monitoring module;

[0052] The agricultural pest and disease monitoring module realizes agricultural pest and disease monitoring based on the established pest and disease risk assessment model for the real-time collected agricultural pest and disease monitoring data.

[0053] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0054] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

[0055] The above describes the present invention and its implementation manners, and this description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention, and the actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and design similar structural manners and embodiments without creative work without departing from the purpose of the present invention, they shall fall within the protection scope of the present invention.

Claims

1. A smart agricultural pest monitoring method, characterized by: The method comprises the following steps: Step S1: Data collection: Collect historical agricultural pest and disease monitoring data; Step S2: Data preprocessing: performing data cleaning, data conversion, standardization and data optimization on the collected data to construct a time series data set; Step S3: constructing a pest and disease risk assessment model; Step S4: agricultural pest monitoring: realizing agricultural pest monitoring based on the established pest risk assessment model and the agricultural pest monitoring data collected in real time; In step S2, the data optimization is to delete the data if the environmental noise suppression metric value of the data and the other 90% of the data is lower than the metric threshold; the environmental noise suppression metric value between the data is It is expressed as: ; Where A and B are different sample data; is the normalization coefficient; N is the total number of samples; and They are shape parameter and scale parameter respectively; is the gamma function; is the time decay factor; is the sample sampling time; t is the current time; is the weight decay coefficient; d(·) is the Euclidean distance; is the current sample; is the average sample of the data set; In step S3, the construction of the pest risk assessment model includes the following steps: Step S31: Preliminary construction: predict the pest risk assessment value based on LSTM, and optimize the pest risk assessment value by defining a dynamic adjustment function, thereby constructing a pest risk assessment model; define a dynamic adjustment function , expressed as: ; ; The updated formula for pest and disease risk assessment after dynamic adjustment function optimization is: ;in, is the sample data; is the prediction error of LSTM for sample data; x is the predicted pest risk value of LSTM; and are the pest and disease risk assessment values ​​before and after optimization, respectively; is the adaptive weight function; is the noise threshold; is the uncertainty of the i-th sample; is the weight decay coefficient; is the scale factor; Step S32: Design the dynamic trend loss of pests and diseases; the dynamic trend loss ML of pests and diseases is expressed as: ; Where N is the total sample size; and are the actual pest risk value and the predicted actual pest risk value of the i-th sample respectively; and are the actual pest risk value and the predicted actual pest risk value of the i-1th sample respectively; is a smoothing term; is the trend mutation penalty factor; is the risk change; Step S33: Review residual optimization; review residual function It is expressed as: ;in, is the prediction error of the i-th sample; is the threshold used to control the penalty residual; and Used to adjust nonlinear shapes; is the residual constraint factor; m is the number of samples reviewed; Q is used to control the rate of exponential decay; j is the sample index; is the prediction error of the ijth sample; Step S34: construct the final loss function; the loss function TL of the pest risk assessment model is expressed as: ;in, is the loss weight; ML is the dynamic trend loss of pests and diseases; is the retrospective residual function; Step S35: model determination; the time series data set corresponding to the historical agricultural pest and disease monitoring data is divided into a test set and a training set in advance; the pest and disease risk assessment model uses a gradient descent algorithm to update parameters; when the pest and disease risk assessment model converges with the training set loss, the pest and disease risk assessment model training is completed; a prediction threshold is set in advance, and when the prediction accuracy of the trained pest and disease risk assessment model for the test set is higher than the prediction threshold, the pest and disease risk assessment model is established; otherwise, the data set is re-divided, the initial parameters are adjusted, and re-training is performed.

2. The method for monitoring pests and diseases in smart agriculture according to claim 1, characterized in that: In step S1, the historical agricultural pest and disease monitoring data includes pest and disease type data, time, geographic space data, climate and environment data, crop growth data, pesticide data and pest and disease risk assessment results; the pest and disease risk assessment results include normal, mild danger, moderate danger and severe danger; the pest and disease risk assessment results are used as data labels.

3. A smart agricultural pest monitoring method according to claim 2, characterized in that: In step S4, the agricultural pest and disease monitoring is based on the established pest and disease risk assessment model, and the agricultural pest and disease monitoring data is collected in real time, and the agricultural pest and disease monitoring is realized based on the pest and disease risk assessment results predicted by the model.

4. The method for monitoring pests and diseases in smart agriculture according to claim 3, characterized in that: In step S2, the data cleaning is to process missing values ​​and duplicate values; the data conversion is to convert the collected data into a vector form; and the standardization processing is to standardize the data based on the maximum and minimum normalization method.

5. A smart agricultural pest monitoring system, used to implement a smart agricultural pest monitoring method as described in any one of claims 1 to 4, characterized in that: It includes data collection module, data preprocessing module, pest and disease risk assessment model building module and agricultural pest and disease monitoring module; The data acquisition module collects historical agricultural pest monitoring data and sends the data to the data preprocessing module; The data preprocessing module performs data cleaning, data conversion, standardization and data optimization on the collected data to construct a time series data set; And send the data to the pest risk assessment model building module; The pest risk assessment model building module defines a dynamic adjustment function, designs the pest dynamic trend loss and introduces retrospective residual optimization, thereby building a pest risk assessment model; and sends the data to the agricultural pest monitoring module; The agricultural pest and disease monitoring module realizes agricultural pest and disease monitoring based on the agricultural pest and disease monitoring data collected in real time based on the established pest and disease risk assessment model.

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