CN113207511A - Pesticide application method and system based on pesticide resistance monitoring and readable storage medium - Google Patents
Pesticide application method and system based on pesticide resistance monitoring and readable storage medium Download PDFInfo
- Publication number
- CN113207511A CN113207511A CN202110347359.6A CN202110347359A CN113207511A CN 113207511 A CN113207511 A CN 113207511A CN 202110347359 A CN202110347359 A CN 202110347359A CN 113207511 A CN113207511 A CN 113207511A
- Authority
- CN
- China
- Prior art keywords
- pesticide
- information
- crop
- index
- crops
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G13/00—Protecting plants
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
- A01M7/0089—Regulating or controlling systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Environmental Sciences (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Insects & Arthropods (AREA)
- Pest Control & Pesticides (AREA)
- Wood Science & Technology (AREA)
- Zoology (AREA)
- Artificial Intelligence (AREA)
- Toxicology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a pesticide application method, a pesticide application system and a readable storage medium based on pesticide resistance monitoring, which are used for acquiring crop image information in a target area; establishing a neural network model for pest and disease identification, and training the neural network model; introducing crop image information in the target area into the neural network model, carrying out pesticide proportioning and spraying by combining a big data system according to an output result of the neural network model, collecting pest-damaged crop quantity information in the target area after a preset time after pesticide application, and comparing actual pest-damaged crop quantity information with preset pest-damaged crop quantity information after pesticide application to obtain a deviation ratio; judging whether the deviation rate is greater than a preset deviation rate or not; and if so, generating pest pesticide resistance information, and adjusting the pesticide proportioning concentration according to the pest pesticide resistance information.
Description
Technical Field
The present invention relates to a pesticide application method, and more particularly, to a pesticide application method, system and readable storage medium based on pesticide resistance monitoring.
Background
Diseases and pests are direct factors influencing crop yield and are one of main agricultural disasters of countries in the world. Large-scale plant diseases and insect pests cause huge losses to agricultural production and national economy. According to the statistics of the food and agriculture organization of the united nations, the loss of the world food yield caused by diseases and insect pests accounts for more than 20 percent of the total food yield, and if the dosage of pesticides is not carefully controlled in the process of controlling the diseases and insect pests, the phenomena of environmental damage and pollution or poor control effect and the like are easily caused.
In order to enable pesticide application to be more intelligent and efficient, a smart pesticide application system needs to be developed for implementation, the pesticide application system acquires crop image information in a target area, introduces the crop image information into a neural network model, identifies plant diseases and insect pests through the neural network model and combines big data to determine the pesticide ratio concentration, applies the pesticide image information, collects pest-affected crop information in the target area after a preset time, analyzes and calculates the crop disease condition index and the pesticide control effect index according to the pest-affected crop information to generate pesticide resistance information, adjusts the pesticide ratio concentration through the generated pesticide resistance information to achieve the purpose of efficiently controlling the plant diseases and insect pests, and how to analyze and calculate the crop disease condition index and the pesticide control effect index according to the pest-affected crop information in the implementation process of the smart pesticide application system, how to adjust the proportioning concentration of the pesticide through pesticide resistance information is an urgent problem to be solved.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a pesticide application method, a pesticide application system and a readable storage medium based on pesticide resistance monitoring.
The invention provides a pesticide application method based on pesticide resistance monitoring in a first aspect, which comprises the following steps:
acquiring crop image information in a target area;
establishing a neural network model, and performing initialization learning;
identifying plant diseases and insect pests by the neural network model and determining the proportion concentration of the pesticide by combining big data, and applying;
collecting pest-damaged crop information in a target area after a preset time;
calculating and generating a crop disease index and a pesticide control effect index according to the pest-damaged crop information;
and analyzing and generating pesticide resistance information through the pesticide control effect index, and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
In the scheme, the crop disease index and the pesticide control effect index are calculated and generated according to the pest crop information, and the method specifically comprises the following steps:
acquiring crop image information in a target area after pesticide application;
preprocessing the crop image information and then introducing the preprocessed crop image information into the neural network model;
the neural network model identifies crops suffering from diseases and insect pests, and counts the number of the crops suffering from the diseases and insect pests;
comparing the actual pest-suffered crop quantity information with preset quantity information after pesticide application to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate or not;
and if so, calculating and generating a crop disease index and a pesticide control effect index according to the image information of the crops suffering from the insect pests.
In the scheme, the pesticide resistance information is generated by analyzing the crop disease index and the pesticide control effect index, and the pesticide proportioning concentration is adjusted according to the pesticide resistance information, which specifically comprises the following steps:
acquiring image information of crops suffering from insect pests;
preprocessing the image information of the crops suffering from the insect pests frame by frame, and extracting the area occupied by the leaf or fruit scab area;
calculating according to the area occupied by the scab area to obtain the disease index of the crops;
calculating to obtain a pesticide control effect index according to the crop disease index, and analyzing the pesticide control effect index to obtain pesticide resistance information;
and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
In the scheme, the crop disease condition index is obtained by calculation according to the area occupied by the scab area, specifically, classification is carried out according to the area occupied by the scab area of the pest-damaged crop, and the classified crop is matched with indexes of different levels, wherein the crop disease condition index calculation formula specifically comprises the following steps:
wherein beta represents the disease index of the crops, b represents the number of the crops suffering from the insect pests at each level, lambda represents the indexes of each level after the crops suffering from the insect pests are classified, and n represents the total number of the crops in the target area.
In the scheme, the index of the pesticide control effect is calculated according to the index of the disease condition of the crops, and the calculation formula of the index of the pesticide control effect is specifically as follows:
wherein f represents the index of the control effect of the pesticide, betaqIndicating the index of disease of the crop prior to pesticide application, betapIndicating the index of disease condition of the crops after pesticide application.
In this scheme, still include:
pesticide resistance information is generated through the pesticide control effect index analysis;
comparing the pesticide resistance information with preset resistance information to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate or not;
and if the index is larger than the preset value, combining the pesticide resistance information with the crop disease index to generate concentration adjustment information, and adjusting the pesticide proportioning concentration through the concentration adjustment information.
The second aspect of the present invention also provides a pesticide application system based on pesticide resistance monitoring, the system comprising: a memory, a processor, the memory including a pesticide application method program based on pesticide resistance monitoring, the method program based on pesticide resistance monitoring application when executed by the processor implementing the steps of:
acquiring crop image information in a target area;
establishing a neural network model, and performing initialization learning;
identifying plant diseases and insect pests by the neural network model and determining the proportion concentration of the pesticide by combining big data, and applying;
collecting pest-damaged crop information in a target area after a preset time;
calculating and generating a crop disease index and a pesticide control effect index according to the pest-damaged crop information;
and analyzing and generating pesticide resistance information through the pesticide control effect index, and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
In the scheme, the crop disease index and the pesticide control effect index are calculated and generated according to the pest crop information, and the method specifically comprises the following steps:
acquiring crop image information in a target area after pesticide application;
preprocessing the crop image information and then introducing the preprocessed crop image information into the neural network model;
the neural network model identifies crops suffering from diseases and insect pests, and counts the number of the crops suffering from the diseases and insect pests;
comparing the actual pest-suffered crop quantity information with preset quantity information after pesticide application to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate or not;
and if so, calculating and generating a crop disease index and a pesticide control effect index according to the image information of the crops suffering from the insect pests.
In the scheme, the pesticide resistance information is generated by analyzing the crop disease index and the pesticide control effect index, and the pesticide proportioning concentration is adjusted according to the pesticide resistance information, which specifically comprises the following steps:
acquiring image information of crops suffering from insect pests;
preprocessing the image information of the crops suffering from the insect pests frame by frame, and extracting the area occupied by the leaf or fruit scab area;
calculating according to the area occupied by the scab area to obtain the disease index of the crops;
calculating to obtain a pesticide control effect index according to the crop disease index, and analyzing the pesticide control effect index to obtain pesticide resistance information;
and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
In the scheme, the crop disease condition index is obtained by calculation according to the area occupied by the scab area, specifically, classification is carried out according to the area occupied by the scab area of the pest-damaged crop, and the classified crop is matched with indexes of different levels, wherein the crop disease condition index calculation formula specifically comprises the following steps:
wherein beta represents the disease index of the crops, b represents the number of the crops suffering from the insect pests at each level, lambda represents the indexes of each level after the crops suffering from the insect pests are classified, and n represents the total number of the crops in the target area.
In the scheme, the index of the pesticide control effect is calculated according to the index of the disease condition of the crops, and the calculation formula of the index of the pesticide control effect is specifically as follows:
wherein f represents the index of the control effect of the pesticide, betaqIndicating the index of disease of the crop prior to pesticide application, betapIndicating the index of disease condition of the crops after pesticide application.
In this scheme, still include:
pesticide resistance information is generated through the pesticide control effect index analysis;
comparing the pesticide resistance information with preset resistance information to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate or not;
and if the index is larger than the preset value, combining the pesticide resistance information with the crop disease index to generate concentration adjustment information, and adjusting the pesticide proportioning concentration through the concentration adjustment information.
The third aspect of the present invention also provides a computer readable storage medium, which includes a program of a method for pesticide application based on pesticide resistance monitoring, and when the program of the method for pesticide application based on pesticide resistance monitoring is executed by a processor, the steps of the method for pesticide application based on pesticide resistance monitoring as described in any one of the above are realized.
The invention discloses a pesticide application method, a pesticide application system and a readable storage medium based on pesticide resistance monitoring, which are used for acquiring crop image information in a target area; establishing a neural network model for pest and disease identification, and training the neural network model; introducing crop image information in the target area into the neural network model, carrying out pesticide proportioning and spraying by combining a big data system according to an output result of the neural network model, collecting pest-damaged crop quantity information in the target area after a preset time after pesticide application, and comparing actual pest-damaged crop quantity information with preset pest-damaged crop quantity information after pesticide application to obtain a deviation ratio; judging whether the deviation rate is greater than a preset deviation rate or not; if the pest ratio is larger than the set value, the disease index of the crops and the index of the pesticide control effect are obtained through analysis and calculation, pest pesticide resistance information is generated, and the pesticide proportioning concentration is adjusted according to the pest pesticide resistance information, so that scientific control of the crop pests is realized.
Drawings
FIG. 1 shows a flow chart of a method of pesticide application based on pesticide resistance monitoring according to the present invention;
FIG. 2 illustrates a flow chart of a method of analyzing pesticide resistance based on pest-affected crop information in accordance with the present invention;
FIG. 3 is a flow chart of a method for adjusting the mixture ratio concentration of pesticides according to pesticide resistance information;
fig. 4 shows a block diagram of a pesticide application system based on pesticide resistance monitoring according to the present invention.
Detailed description of the invention
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of a pesticide application method based on pesticide resistance monitoring according to the present invention.
As shown in fig. 1, a first aspect of the present invention provides a pesticide application method based on pesticide resistance monitoring, comprising:
s102, acquiring crop image information in a target area;
s104, establishing a neural network model for initial learning;
s106, identifying the pest and disease damage combined big data through the neural network model, determining the proportion concentration of the pesticide, and applying;
s108, collecting pest crop information in the target area after a preset time;
s110, calculating and generating a crop disease index and a pesticide control effect index according to the pest-damaged crop information;
and S112, analyzing and generating pesticide resistance information through the pesticide control effect index, and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
It should be noted that the pesticide ratio concentration is determined by identifying the plant diseases and insect pests through the neural network model and combining big data, and the pesticide ratio concentration is applied, specifically: the method comprises the steps of preprocessing acquired crop frame image information in a target area, wherein the preprocessing comprises image filtering, image segmentation, feature extraction and the like, the processed crop frame image information is led into a trained neural network model, the neural network model identifies the types of diseases and insect pests of crops through analysis and judgment, and the pesticide proportioning concentration is determined according to the output result of the neural network model by combining a big data knowledge system, wherein the big data knowledge system consists of a disease and insect pest database, a disease and insect pest historical control scheme and a pesticide application database.
It should be noted that, the establishing of the neural network model and the initial learning are specifically: using crop pest related data or importing the crop pest related data into a pest database to obtain original data, grouping the obtained data into training sets through preprocessing, importing different groups of training sets into a neural network model for full learning, calculating initial learning rates of different groups of training sets, adjusting the neural network model according to the initial learning rates, generating different groups of output results after multiple times of learning, comparing the output results of the different groups of training sets to obtain an output result deviation rate, judging whether the output result deviation rate is smaller than a preset output result deviation rate threshold value, and if so, finishing the training of the neural network model.
FIG. 2 illustrates a flow chart of a method of analyzing pesticide resistance based on pest-affected crop information in accordance with the present invention;
according to the embodiment of the invention, the crop disease index and the pesticide control effect index are calculated and generated according to the pest crop information, and the method specifically comprises the following steps:
s202, acquiring crop image information in a target area after pesticide application;
s204, preprocessing the crop image information and then introducing the preprocessed crop image information into the neural network model;
s206, identifying crops suffering from diseases and insect pests by the neural network model, and counting the number of the crops suffering from the diseases and insect pests;
s208, comparing the actual pest-damaged crop quantity information after pesticide application with preset quantity information to obtain a deviation rate;
s210, judging whether the deviation rate is greater than a preset deviation rate;
and S212, if the number of the pests is larger than the preset value, calculating and generating a crop disease index and a pesticide control effect index according to the image information of the pests.
The method includes the steps of obtaining crop image information in a target area after pesticide application, preprocessing the image information, filtering out a background image through an edge-based monitoring algorithm, extracting leaf and fruit frame image data information of crops through an image segmentation algorithm, identifying and judging the extracted frame image data information through a neural network model, identifying the crops as the crops suffering from insect pests when the leaves or the fruits suffer from the insect pests, and grading according to the occupied area of the insect pest areas of the crops suffering from the insect pests.
FIG. 3 shows a flow chart of a method for adjusting the mixture ratio concentration of the pesticide according to the pesticide resistance information.
According to the embodiment of the invention, the pesticide resistance information is generated by analyzing the crop disease index and the pesticide control effect index, and the pesticide proportioning concentration is adjusted according to the pesticide resistance information, specifically:
s302, acquiring image information of crops suffering from insect pests;
s304, preprocessing the image information of the crops suffering from the insect pests frame by frame, and extracting the area occupied by the diseased leaf or fruit spot area;
s306, calculating according to the area occupied by the scab area to obtain a crop disease index;
s308, calculating to obtain a pesticide control effect index according to the crop disease index, and analyzing the pesticide control effect index to obtain pesticide resistance information;
and S310, adjusting the pesticide proportioning concentration according to the pesticide resistance information.
It should be noted that the disease index of the crops is obtained by calculation according to the area occupied by the scab area, specifically, the crops are classified according to the area occupied by the scab area of the crops suffering from insect pests, and the classified crops are matched with the indexes of each level, wherein the calculation formula of the disease index of the crops is specifically as follows:
wherein beta represents the disease index of the crops, b represents the number of the crops suffering from the insect pests at each level, lambda represents the indexes of each level after the crops suffering from the insect pests are classified, and n represents the total number of the crops in the target area.
It should be noted that the index of the control effect of the pesticide is calculated according to the index of the disease condition of the crop, and the formula for calculating the index of the control effect of the pesticide is specifically as follows:
wherein f represents the index of the control effect of the pesticide, betaqIndicating the condition of the crop before the pesticide is appliedIndex, betapIndicating the index of disease condition of the crops after pesticide application.
According to the embodiment of the invention, the method further comprises the following steps:
pesticide resistance information is generated through the pesticide control effect index analysis;
comparing the pesticide resistance information with preset resistance information to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate or not;
and if the index is larger than the preset value, combining the pesticide resistance information with the crop disease index to generate concentration adjustment information, and adjusting the pesticide proportioning concentration through the concentration adjustment information.
It should be noted that, the pesticide control effect indexes obtained through multiple calculations are analyzed, the pesticide control effect indexes obtained through multiple calculations are screened and sorted to obtain effective collection values, the effective collection values are subjected to mean value processing, pesticide resistance information is generated through correlation calculation according to the obtained pesticide control effect index mean values, the pesticide resistance information is combined with the disease index of crops to generate concentration adjustment information, the pesticide proportioning concentration is adjusted through the concentration adjustment information, further, if multiple insect pests appear in crops in a target area, the pesticide control effect indexes corresponding to the insect pests after the pesticides are applied are obtained through analysis and calculation according to image information of the insect pests on the crops in the target area, and the reasonable proportion of the pesticides during combined control of the insect pests is determined through the obtained pesticide control effect indexes corresponding to the insect pests.
Fig. 4 shows a block diagram of a pesticide application system based on pesticide resistance monitoring according to the present invention.
The second aspect of the present invention also provides a pesticide application system 4 based on pesticide resistance monitoring, the system comprising: a memory 41, a processor 42, the memory including a pesticide application method program based on pesticide resistance monitoring, the method program based on pesticide resistance monitoring application realizing the following steps when executed by the processor:
acquiring crop image information in a target area;
establishing a neural network model, and performing initialization learning;
identifying plant diseases and insect pests by the neural network model and determining the proportion concentration of the pesticide by combining big data, and applying;
collecting pest-damaged crop information in a target area after a preset time;
calculating and generating a crop disease index and a pesticide control effect index according to the pest-damaged crop information;
and analyzing and generating pesticide resistance information through the pesticide control effect index, and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
It should be noted that the pesticide ratio concentration is determined by identifying the plant diseases and insect pests through the neural network model and combining big data, and the pesticide ratio concentration is applied, specifically: the method comprises the steps of preprocessing acquired crop frame image information in a target area, wherein the preprocessing comprises image filtering, image segmentation, feature extraction and the like, the processed crop frame image information is led into a trained neural network model, the neural network model identifies the types of diseases and insect pests of crops through analysis and judgment, and the pesticide proportioning concentration is determined according to the output result of the neural network model by combining a big data knowledge system, wherein the big data knowledge system consists of a disease and insect pest database, a disease and insect pest historical control scheme and a pesticide application database.
It should be noted that, the establishing of the neural network model and the initial learning are specifically: using crop pest related data or importing the crop pest related data into a pest database to obtain original data, grouping the obtained data into training sets through preprocessing, importing different groups of training sets into a neural network model for full learning, calculating initial learning rates of different groups of training sets, adjusting the neural network model according to the initial learning rates, generating different groups of output results after multiple times of learning, comparing the output results of the different groups of training sets to obtain an output result deviation rate, judging whether the output result deviation rate is smaller than a preset output result deviation rate threshold value, and if so, finishing the training of the neural network model.
According to the embodiment of the invention, the pesticide resistance analysis according to the pest-damaged crop information specifically comprises the following steps:
acquiring crop image information in a target area after pesticide application;
the crop image information is pre-processed and then is led into the neural network model;
the neural network model identifies crops suffering from diseases and insect pests, and counts the number of the crops suffering from the diseases and insect pests;
comparing the actual pest-suffered crop quantity information with preset quantity information after pesticide application to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate or not;
and if so, calculating and generating a crop disease index and a pesticide control effect index according to the image information of the crops suffering from the insect pests.
The method includes the steps of obtaining crop image information in a target area after pesticide application, preprocessing the image information, filtering out a background image through an edge-based monitoring algorithm, extracting leaf and fruit frame image data information of crops through an image segmentation algorithm, identifying and judging the extracted frame image data information through a neural network model, identifying the crops as the crops suffering from insect pests when the leaves or the fruits suffer from the insect pests, and grading according to the occupied area of the insect pest areas of the crops suffering from the insect pests.
According to the embodiment of the invention, the pesticide resistance information is generated by analyzing the crop disease index and the pesticide control effect index, and the pesticide proportioning concentration is adjusted according to the pesticide resistance information, specifically:
acquiring image information of crops suffering from insect pests;
preprocessing the image information of the crops suffering from the insect pests frame by frame, and extracting the area occupied by the leaf or fruit scab area;
calculating according to the area occupied by the scab area to obtain the disease index of the crops;
calculating to obtain a pesticide control effect index according to the crop disease index, and analyzing the pesticide control effect index to obtain pesticide resistance information;
and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
In the scheme, the crop disease condition index is obtained by calculation according to the area occupied by the scab area, specifically, classification is carried out according to the area occupied by the scab area of the pest-damaged crop, and the classified crop is matched with indexes of different levels, wherein the crop disease condition index calculation formula specifically comprises the following steps:
wherein beta represents the disease index of the crops, b represents the number of the crops suffering from the insect pests at each level, lambda represents the indexes of each level after the crops suffering from the insect pests are classified, and n represents the total number of the crops in the target area.
In the scheme, the index of the pesticide control effect is calculated according to the index of the disease condition of the crops, and the calculation formula of the index of the pesticide control effect is specifically as follows:
wherein f represents the index of the control effect of the pesticide, betaqIndicating the index of disease of the crop prior to pesticide application, betapIndicating the index of disease condition of the crops after pesticide application.
According to the embodiment of the invention, the method further comprises the following steps:
pesticide resistance information is generated through the pesticide control effect index analysis;
comparing the pesticide resistance information with preset resistance information to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate or not;
and if the index is larger than the preset value, combining the pesticide resistance information with the crop disease index to generate concentration adjustment information, and adjusting the pesticide proportioning concentration through the concentration adjustment information.
It should be noted that, the pesticide control effect indexes obtained through multiple calculations are analyzed, the pesticide control effect indexes obtained through multiple calculations are screened and sorted to obtain effective collection values, the effective collection values are subjected to mean value processing, pesticide resistance information is generated through correlation calculation according to the obtained pesticide control effect index mean values, the pesticide resistance information is combined with the disease index of crops to generate concentration adjustment information, the pesticide proportioning concentration is adjusted through the concentration adjustment information, further, if multiple insect pests appear in crops in a target area, the pesticide control effect indexes corresponding to the insect pests after the pesticides are applied are obtained through analysis and calculation according to image information of the insect pests on the crops in the target area, and the reasonable proportion of the pesticides during combined control of the insect pests is determined through the obtained pesticide control effect indexes corresponding to the insect pests.
The third aspect of the present invention also provides a computer readable storage medium, which includes a program of a method for pesticide application based on pesticide resistance monitoring, and when the program of the method for pesticide application based on pesticide resistance monitoring is executed by a processor, the steps of the method for pesticide application based on pesticide resistance monitoring as described in any one of the above are realized.
The invention discloses a pesticide application method, a pesticide application system and a readable storage medium based on pesticide resistance monitoring, which are used for acquiring crop image information in a target area; establishing a neural network model for pest and disease identification, and training the neural network model; introducing crop image information in the target area into the neural network model, carrying out pesticide proportioning and spraying by combining a big data system according to an output result of the neural network model, collecting pest-damaged crop quantity information in the target area after a preset time after pesticide application, and comparing actual pest-damaged crop quantity information with preset pest-damaged crop quantity information after pesticide application to obtain a deviation ratio; judging whether the deviation rate is greater than a preset deviation rate or not; if the pest ratio is larger than the set value, the disease index of the crops and the index of the pesticide control effect are obtained through analysis and calculation, pest pesticide resistance information is generated, and the pesticide proportioning concentration is adjusted according to the pest pesticide resistance information, so that scientific control of the crop pests is realized.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method of pesticide application based on pesticide resistance monitoring, comprising:
acquiring crop image information in a target area;
establishing a neural network model, and performing initialization learning;
identifying plant diseases and insect pests by the neural network model and determining the proportion concentration of the pesticide by combining big data, and applying;
collecting pest-damaged crop information in a target area after a preset time;
calculating and generating a crop disease index and a pesticide control effect index according to the pest-damaged crop information;
and analyzing and generating pesticide resistance information through the pesticide control effect index, and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
2. The pesticide application method based on pesticide resistance monitoring as claimed in claim 1, wherein the crop disease index and the pesticide control effect index are calculated according to the pest-damaged crop information, and specifically:
acquiring crop image information in a target area after pesticide application;
preprocessing the crop image information and then introducing the preprocessed crop image information into the neural network model;
the neural network model identifies crops suffering from diseases and insect pests, and counts the number of the crops suffering from the diseases and insect pests;
comparing the actual pest-suffered crop quantity information with preset quantity information after pesticide application to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate or not;
and if so, calculating and generating a crop disease index and a pesticide control effect index according to the image information of the crops suffering from the insect pests.
3. The pesticide application method based on pesticide resistance monitoring as claimed in claim 2, wherein the pesticide resistance information is generated through analysis of the crop disease index and the pesticide control effect index, and the pesticide mixture ratio concentration is adjusted according to the pesticide resistance information, specifically:
acquiring image information of crops suffering from insect pests;
preprocessing the image information of the crops suffering from the insect pests frame by frame, and extracting the area occupied by the leaf or fruit scab area;
calculating according to the area occupied by the scab area to obtain the disease index of the crops;
calculating to obtain a pesticide control effect index according to the crop disease index, and analyzing the pesticide control effect index to obtain pesticide resistance information;
and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
4. The pesticide application method based on pesticide resistance monitoring as claimed in claim 3, wherein the disease condition index of the crops is obtained by calculation according to the area occupied by the scab area, specifically, the crops subjected to pest insect damage are classified according to the area occupied by the scab area, and the classified crops are matched with the indexes of each level, wherein the crop disease condition index calculation formula specifically comprises:
wherein beta represents the disease index of the crops, b represents the number of the crops suffering from the insect pests at each level, lambda represents the indexes of each level after the crops suffering from the insect pests are classified, and n represents the total number of the crops in the target area.
5. The pesticide application method based on pesticide resistance monitoring as claimed in claim 3, wherein the pesticide control effect index is calculated according to the crop disease index, and the calculation formula of the pesticide control effect index is specifically as follows:
wherein f represents the index of the control effect of the pesticide, betaqIndicating the index of disease of the crop prior to pesticide application, betapIndicating the index of disease condition of the crops after pesticide application.
6. The pesticide application method based on pesticide resistance monitoring as claimed in claim 1, further comprising:
pesticide resistance information is generated through the pesticide control effect index analysis;
comparing the pesticide resistance information with preset resistance information to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate or not;
and if the index is larger than the preset value, combining the pesticide resistance information with the crop disease index to generate concentration adjustment information, and adjusting the pesticide proportioning concentration through the concentration adjustment information.
7. A pesticide application system based on pesticide resistance monitoring, the system comprising: the storage comprises a pesticide application method program based on pesticide resistance monitoring, and the processor is used for realizing the following steps when the processor executes the pesticide application method program based on pesticide resistance monitoring:
acquiring crop image information in a target area;
establishing a neural network model, and performing initialization learning;
identifying plant diseases and insect pests by the neural network model and determining the proportion concentration of the pesticide by combining big data, and applying;
collecting pest-damaged crop information in a target area after a preset time;
calculating and generating a crop disease index and a pesticide control effect index according to the pest-damaged crop information;
and analyzing and generating pesticide resistance information through the pesticide control effect index, and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
8. The pesticide application system based on pesticide resistance monitoring as claimed in claim 7, wherein the pesticide resistance is analyzed according to pest-affected crop information, and specifically comprises:
acquiring crop image information in a target area after pesticide application;
preprocessing the crop image information and then introducing the preprocessed crop image information into the neural network model;
the neural network model identifies crops suffering from diseases and insect pests, and counts the number of the crops suffering from the diseases and insect pests;
comparing the actual pest-suffered crop quantity information with preset quantity information after pesticide application to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate or not;
and if so, calculating and generating a crop disease index and a pesticide control effect index according to the image information of the crops suffering from the insect pests.
9. The pesticide application system based on pesticide resistance monitoring of claim 7, wherein the pesticide proportioning concentration is adjusted according to pesticide resistance information of insect pests, and the pesticide proportioning concentration is specifically as follows:
acquiring image information of crops suffering from insect pests;
preprocessing the image information of the crops suffering from the insect pests frame by frame, and extracting the area occupied by the leaf or fruit scab area;
calculating according to the area occupied by the scab area to obtain the disease index of the crops;
calculating to obtain a pesticide control effect index according to the crop disease index, and analyzing the pesticide control effect index to obtain pesticide resistance information;
and adjusting the pesticide proportioning concentration according to the pesticide resistance information.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium includes therein a pesticide application method program based on pesticide resistance monitoring, which when executed by a processor, implements the steps of the pesticide application method based on pesticide resistance monitoring as recited in any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110347359.6A CN113207511A (en) | 2021-03-31 | 2021-03-31 | Pesticide application method and system based on pesticide resistance monitoring and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110347359.6A CN113207511A (en) | 2021-03-31 | 2021-03-31 | Pesticide application method and system based on pesticide resistance monitoring and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113207511A true CN113207511A (en) | 2021-08-06 |
Family
ID=77086213
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110347359.6A Pending CN113207511A (en) | 2021-03-31 | 2021-03-31 | Pesticide application method and system based on pesticide resistance monitoring and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113207511A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114586760A (en) * | 2022-03-29 | 2022-06-07 | 广东省农业科学院植物保护研究所 | Pesticide spraying method and system based on big data and readable storage medium |
| CN115358883A (en) * | 2022-09-15 | 2022-11-18 | 河北省农林科学院植物保护研究所 | Method and system for dynamically monitoring resistance of cotton aphids and recommending control pesticide |
| CN117132934A (en) * | 2023-08-28 | 2023-11-28 | 广东省农业科学院设施农业研究所 | System and method for supervising drug delivery safety interval period |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1543784A (en) * | 2003-11-14 | 2004-11-10 | 中国科学院合肥智能机械研究所 | Device for monitoring crop diseases and insect pests and generating pesticide prescription |
| US20100273885A1 (en) * | 2009-04-20 | 2010-10-28 | Davis P. Davis Ph.D. LLC | Low environmental impact pesticide made from "gras" ingredients for use against coqui frogs and other species |
| CN104751122A (en) * | 2015-03-05 | 2015-07-01 | 北京农业信息技术研究中心 | Computing method and system for crop disease index |
| CN105446309A (en) * | 2015-12-31 | 2016-03-30 | 西北工业大学 | Agricultural intelligent analysis monitoring system based on IOT (Internet of Things) |
| CN106614761A (en) * | 2016-11-15 | 2017-05-10 | 山西大学 | Plant-sourced pesticide for controlling tomato canker disease and preparation method thereof |
| CN108541683A (en) * | 2018-04-18 | 2018-09-18 | 济南浪潮高新科技投资发展有限公司 | A kind of unmanned plane pesticide spraying system based on convolutional neural networks chip |
| CN110915782A (en) * | 2019-11-15 | 2020-03-27 | 西安和光明宸科技有限公司 | Adjustable plant pesticide spraying system and pesticide spraying method |
| CN112084225A (en) * | 2020-09-16 | 2020-12-15 | 苏州众智诺成信息科技有限公司 | Intelligent processing method and system of big data based sharing platform and readable storage medium |
-
2021
- 2021-03-31 CN CN202110347359.6A patent/CN113207511A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1543784A (en) * | 2003-11-14 | 2004-11-10 | 中国科学院合肥智能机械研究所 | Device for monitoring crop diseases and insect pests and generating pesticide prescription |
| US20100273885A1 (en) * | 2009-04-20 | 2010-10-28 | Davis P. Davis Ph.D. LLC | Low environmental impact pesticide made from "gras" ingredients for use against coqui frogs and other species |
| CN104751122A (en) * | 2015-03-05 | 2015-07-01 | 北京农业信息技术研究中心 | Computing method and system for crop disease index |
| CN105446309A (en) * | 2015-12-31 | 2016-03-30 | 西北工业大学 | Agricultural intelligent analysis monitoring system based on IOT (Internet of Things) |
| CN106614761A (en) * | 2016-11-15 | 2017-05-10 | 山西大学 | Plant-sourced pesticide for controlling tomato canker disease and preparation method thereof |
| CN108541683A (en) * | 2018-04-18 | 2018-09-18 | 济南浪潮高新科技投资发展有限公司 | A kind of unmanned plane pesticide spraying system based on convolutional neural networks chip |
| CN110915782A (en) * | 2019-11-15 | 2020-03-27 | 西安和光明宸科技有限公司 | Adjustable plant pesticide spraying system and pesticide spraying method |
| CN112084225A (en) * | 2020-09-16 | 2020-12-15 | 苏州众智诺成信息科技有限公司 | Intelligent processing method and system of big data based sharing platform and readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 辽宁省林业学校: "《森林病理学》", 中国林业出版社, pages: 435 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114586760A (en) * | 2022-03-29 | 2022-06-07 | 广东省农业科学院植物保护研究所 | Pesticide spraying method and system based on big data and readable storage medium |
| CN115358883A (en) * | 2022-09-15 | 2022-11-18 | 河北省农林科学院植物保护研究所 | Method and system for dynamically monitoring resistance of cotton aphids and recommending control pesticide |
| CN115358883B (en) * | 2022-09-15 | 2023-10-24 | 河北省农林科学院植物保护研究所 | Dynamic resistance monitoring and prevention and control drug recommendation method and system for cotton aphids |
| CN117132934A (en) * | 2023-08-28 | 2023-11-28 | 广东省农业科学院设施农业研究所 | System and method for supervising drug delivery safety interval period |
| CN117132934B (en) * | 2023-08-28 | 2024-03-01 | 广东省农业科学院设施农业研究所 | System and method for supervising drug delivery safety interval period |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113095555B (en) | Crop pest monitoring method, system and storage medium based on Internet of things | |
| CN113207511A (en) | Pesticide application method and system based on pesticide resistance monitoring and readable storage medium | |
| CN109308697B (en) | Leaf disease identification method based on machine learning algorithm | |
| Seng et al. | Computer vision and machine learning for viticulture technology | |
| CN114445785B (en) | Internet of things-based litchi insect pest monitoring and early warning method and system and storage medium | |
| Wu et al. | Detection and counting of banana bunches by integrating deep learning and classic image-processing algorithms | |
| Jaware et al. | Crop disease detection using image segmentation | |
| CN114550108B (en) | Spodoptera frugiperda identification and early warning method and system | |
| CN110458109A (en) | A kind of tealeaves disease recognition system and working method based on image recognition technology | |
| CN107468260A (en) | A kind of brain electricity analytical device and analysis method for judging ANIMAL PSYCHE state | |
| CN115879773B (en) | Intelligent disease and pest early warning method and system based on Internet of things | |
| CN109101891A (en) | A kind of rice pest detection system and its detection method merging artificial intelligence | |
| CN116630663A (en) | Intelligent pesticide application method and system based on Internet of things | |
| CN107844771A (en) | Method, system, computer installation and the storage medium of crop production management | |
| CN116740644A (en) | Comprehensive evaluation and control method and system for plant diseases and insect pests in passion fruit cultivation process | |
| CN117173400B (en) | Low-carbon treatment scheme recommendation method and system for litchi insect pest | |
| CN114266979A (en) | Tea tree pest and disease intelligent identification method, device, equipment and storage medium | |
| CN113379188A (en) | Tobacco crop rotation planting method and system based on Internet of things | |
| CN117541423A (en) | Aphis gossypii harm monitoring method and system based on fusion map features | |
| CN117371604A (en) | Agricultural production prediction method and system based on intelligent perception | |
| CN113435825A (en) | Intelligent management method, system and storage medium based on soil-borne disease control | |
| CN116740645A (en) | Fruit fly monitoring and comprehensive prevention and control method, system and storage medium based on Internet of things | |
| Rony et al. | BottleNet18: Deep Learning-Based Bottle Gourd Leaf Disease Classification | |
| CN113378004B (en) | FANet-based farmer work behavior recognition method and FANet-based farmer work behavior recognition device apparatus and medium | |
| Dahiya et al. | An effective detection of litchi disease using deep learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |