CN114586760B - Pesticide spraying method and system based on big data and readable storage medium - Google Patents
Pesticide spraying method and system based on big data and readable storage medium Download PDFInfo
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- CN114586760B CN114586760B CN202210318386.5A CN202210318386A CN114586760B CN 114586760 B CN114586760 B CN 114586760B CN 202210318386 A CN202210318386 A CN 202210318386A CN 114586760 B CN114586760 B CN 114586760B
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Abstract
According to the pesticide spraying method and system based on big data and the readable storage medium, the data of the preset area is detected through the detection device, pest information of the preset area is comprehensively known, pesticide spraying types of the preset area are obtained through analysis, and accurate pesticide application to pests is guaranteed. This application is still through testing the test area, through obtaining the value to experimental pesticide blowout value and experimental pesticide and analyzing, obtains every actual pesticide degree of spraying accuracy value of predetermineeing the region and sends and predetermine the terminal to carry out the accurate of pesticide and spray. According to the method, the pesticide residue in agricultural products is reduced and the quality of the living standard of people is improved by controlling the types and the using amount of the sprayed pesticides.
Description
Technical Field
The application relates to the field of data processing and data transmission, in particular to a pesticide spraying method, system and readable storage medium based on big data.
Background
The agricultural products are one of the products with the largest daily consumption and the most serious pollution degree. Wherein, as the farmer reuses a pesticide, the drug resistance of the pests is continuously enhanced, which causes poor control effect and increases the dosage; or the pesticide residue is too high because farmers do not know the novel pests or do not know the prevention method, apply the pesticide blindly, increase the pesticide dosage or mix and spray a plurality of pesticides.
Accordingly, there are deficiencies in the art and improvements are needed.
Disclosure of Invention
In view of the above problems, it is an object of the present invention to provide a pesticide spraying method, system and readable storage medium based on big data, which can control the amount of pesticide precisely to control pests.
The invention provides a pesticide spraying method based on big data, which comprises the following steps:
acquiring detection data of a detection device;
pest data information of a preset area is obtained according to the detection data;
performing data analysis according to the pest data information to obtain spraying pesticide type information of a preset area;
analyzing according to the spraying value of the test pesticide in the test area and the obtained value of the test pesticide in the test area to obtain the accurate value information of the concentration of the sprayed pesticide in the preset area;
sending the information of the types of the sprayed pesticides and the accurate values of the concentrations of the sprayed pesticides in the preset area to a terminal for displaying;
the detection devices are arranged in a preset area and are not less than 1, and the detection devices are used for acquiring detection data of the preset area.
In this scheme, still include:
acquiring image information of pests;
comparing and analyzing the image information of the pests with preset pest type image information to obtain similarity information;
judging whether the similarity is greater than a first preset threshold value or not, if so, determining that the pests are of the corresponding image types;
and sending the pest species information to a terminal for displaying.
In this scheme, still include:
acquiring behavior information of pests;
obtaining damage information of the pests to crops according to the pest behavior information;
analyzing damage of the pests to crops to obtain destructive power information of the pests;
judging whether the destructive power of the pests is greater than a second preset threshold value or not, and if so, obtaining pest killing information;
and sending the pest killing information to a server to perform pesticide matching.
In this scheme, still include:
acquiring density information of pests;
judging whether the density of the pests is greater than a third preset threshold value or not, and if so, obtaining pest killing information;
and sending the pest eliminating information to a server to carry out pesticide matching.
In this scheme, still include:
acquiring historical data information of pests;
analyzing the historical data of the pests to obtain historical time information of the pest disasters;
obtaining corresponding pest prevention time information according to the history time of pest disasters;
and sending the pest prevention time information to a terminal for displaying.
In this scheme, still include:
acquiring pest data after pesticide spraying, and setting the pest data as second pest data;
analyzing the second pest data to obtain second pest density information;
judging whether the density of the second pests is smaller than a third preset threshold value or not; if so, obtaining the effective pesticide spraying fruit information.
The invention provides a pesticide spraying system based on big data, which comprises a memory and a processor, wherein the memory comprises a pesticide spraying program based on the big data, and the pesticide spraying program based on the big data realizes the following steps when being executed by the processor:
acquiring detection data of a detection device;
pest data information of a preset area is obtained according to the detection data;
performing data analysis according to the pest data information to obtain spraying pesticide type information of a preset area;
analyzing according to the spraying value of the test pesticide in the test area and the obtained value of the test pesticide in the test area to obtain the accurate value information of the concentration of the sprayed pesticide in the preset area;
sending the information of the types of the sprayed pesticides and the accurate values of the concentrations of the sprayed pesticides in the preset area to a terminal for displaying;
the detection devices are arranged in a preset area and are not less than 1, and the detection devices are used for acquiring detection data of the preset area.
In this scheme, still include:
acquiring image information of pests;
comparing and analyzing the image information of the pests with preset pest type image information to obtain similarity information;
judging whether the similarity is greater than a first preset threshold value or not, if so, determining that the pests are of the corresponding image types;
and sending the pest species information to a terminal for displaying.
In this scheme, still include:
acquiring behavior information of pests;
obtaining damage information of the pests to crops according to the pest behavior information;
analyzing damage of the pests to crops to obtain destructive power information of the pests;
judging whether the destructive power of the pests is greater than a second preset threshold value or not, and if so, obtaining pest killing information;
and sending the pest killing information to a server to match pesticides.
In this scheme, still include:
acquiring density information of pests;
judging whether the density of the pests is greater than a third preset threshold value, if so, obtaining pest killing information;
and sending the pest killing information to a server to perform pesticide matching.
In this scheme, still include:
acquiring historical data information of pests;
analyzing the historical data of the pests to obtain historical time information of the pest disasters;
obtaining corresponding pest prevention time information according to the history time of pest disasters;
and sending the pest prevention time information to a terminal for displaying.
In this scheme, still include:
acquiring pest data after pesticide spraying, and setting the pest data as second pest data;
analyzing the second pest data to obtain second pest density information;
judging whether the density of the second pests is smaller than a third preset threshold value or not; if so, obtaining the effective pesticide spraying fruit information.
In a third aspect, the invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a big data-based pesticide spraying method program, and when the big data-based pesticide spraying method program is executed by a processor, the steps of the big data-based pesticide spraying method are implemented as described in any one of the above.
According to the pesticide spraying method and system based on big data and the readable storage medium, the data of the preset area is detected through the detection device, pest information of the preset area is comprehensively known, pesticide spraying types of the preset area are obtained through analysis, and accurate pesticide application to pests is guaranteed. This application is still through testing the test area, through obtaining the value to experimental pesticide blowout value and experimental pesticide and analyzing, obtains every actual pesticide concentration accurate value that sprays that predetermines the region and sends and predetermine the terminal to carry out the accurate of pesticide and spray. According to the method, the pesticide residue in agricultural products is reduced and the quality of the living standard of people is improved by controlling the types and the using amount of the sprayed pesticides.
Drawings
FIG. 1 shows a flow chart of a big data based pesticide spraying method of the present invention;
fig. 2 shows a block diagram of a big data based pesticide spray system of the present invention.
Detailed Description
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 otherwise than as 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 big data-based pesticide spraying method according to the present invention.
As shown in fig. 1, the invention discloses a pesticide spraying method based on big data, which comprises the following steps:
s102, acquiring detection data of a detection device;
s104, pest data information of a preset area is obtained according to the detection data;
s106, performing data analysis according to the pest data information to obtain spraying pesticide type information of a preset area;
s108, analyzing according to the spraying value of the test pesticide in the test area and the obtained value of the test pesticide in the test area to obtain accurate value information of the concentration of the sprayed pesticide in the preset area;
and S110, sending the information of the spraying pesticide type and the spraying pesticide concentration accurate value of the preset area to a terminal for displaying.
The pest information of the preset area is comprehensively known, whether the pests in the preset area are harmful or potentially harmful to crops in the preset area is analyzed and obtained, if yes, the pests are matched with the preset pesticide types, if the matching is unsuccessful, an alarm device is triggered to obtain that the pests are new-variety pests, the pest information in the new varieties is reported to relevant units for caution, and if the matching is successful, the pesticide types obtained through matching are sent to a terminal, and a worker selects a proper pesticide type. After the information of the types of the sprayed pesticides in the preset areas is obtained, the accurate value of the actual sprayed pesticide concentration of each preset area is obtained by testing the test areas and analyzing the sprayed value and the obtained value of the test pesticides. For example; setting the spraying value of the experimental pesticide in the test area as C1, the acquisition value of the experimental pesticide as C2, the expected value of the pesticide sprayed to crops as C3, and the accurate value of the actual spraying pesticide concentration in the preset area as C4, wherein the volatilization rate of the sprayed pesticide in the air in the test is R = (C1-C2)/C1, and the volatilization rate of the sprayed pesticide in the air is equal, then:
c4= C3/(1-R) = (C1 × C3)/C2. And sending the actual spraying pesticide concentration value C4 of the preset area to a preset terminal for pesticide spraying.
It should be noted that the number of the detection devices is not less than 1, and the detection devices are arranged in the preset area and are used for acquiring detection data of the preset area.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring image information of pests;
comparing and analyzing the image information of the pests with preset pest type image information to obtain similarity information;
judging whether the similarity is larger than a first preset threshold value, if so, determining that the pests are of the corresponding image types;
and sending the pest species information to a terminal for displaying.
It should be noted that, the pest image obtained by the detection device is compared and analyzed with a preset pest image, the pest type is determined through similarity, if all the similarities are lower than a first preset threshold, the pest is obtained as a new species pest or a variant pest, the pest is sent to a terminal to warn, if one of the similarities obtained from the pest image is greater than the first preset threshold, the pest is obtained as corresponding picture type information, if more than two pieces of similarity information are greater than the first preset threshold, the pest image is sent to a specialist identification end to determine the pest type, for example, the preset first preset threshold is 90%, the image similarity between the obtained pest image and the preset pest type a is 95%, and the image similarity between the obtained pest image and the preset pest type B is 85%, so that the pest is obtained as a type.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring behavior information of pests;
obtaining damage information of the pests to crops according to the pest behavior information;
analyzing damage of the pests to crops to obtain destructive power information of the pests;
judging whether the destructive power of the pests is greater than a second preset threshold value or not, and if so, obtaining pest killing information;
and sending the pest killing information to a server to match pesticides.
It should be noted that, by means of the behavior information of the pests, the real-time understanding of the behavior of the pests is realized, and the damage of the pests to crops in the preset area can be clearly judged. The damage of the pests to the crops comprises direct damage and indirect damage, wherein the direct damage is damage of the pests to the crops directly, for example, the pests A eat wheat leaves, and the indirect damage is damage of the pests which does not cause direct damage to the crops, for example, the pests B suck nutrition on the wheat leaves, the pests B do not damage the crops directly, but influence the growth of the crops by sucking the nutrition on the wheat leaves. Evaluating and analyzing the damage of the crop by the pests to obtain the destructive power value of the pests, for example, if the direct damage power of the pest A is 70 minutes and the indirect damage power of the pest A is 10 minutes, the comprehensive destructive power value of the pest A is 80 minutes, and if the second preset threshold value is 70 minutes, the comprehensive destructive power of the pest A is greater than the second preset threshold value, the existence of the pest A and the threat to the crop growth require killing of the pest A.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring density information of pests;
judging whether the density of the pests is greater than a third preset threshold value or not, and if so, obtaining pest killing information;
and sending the pest killing information to a server to perform pesticide matching.
It should be noted that the preset area itself is a very large ecosystem, and has many biological chains, and many pests exist, but as long as the density is not increased violently and the damage to crops is not great, pesticide spraying treatment is not needed, and the density of the pests is the number of pests in a unit area range, for example, the pest density preset threshold value of the preset area is 15, and the pest density is maintained between 10 and 15 according to the pest density information, and control information that the pest density does not deviate from the preset area is obtained, so pesticide spraying is not needed.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring historical data information of pests;
analyzing the historical data of the pests to obtain historical time information of the pest disasters;
obtaining corresponding pest prevention time information according to the history time of pest disasters;
and sending the pest prevention time information to a terminal for displaying.
Acquiring the history data information of the pests corresponds to acquiring the disease history of the crop, and acquiring information about what pests are present in the disease history or are likely to grow in the disease history at that time. For example: in the ear stage of rice, rice planthoppers and rice leaf rollers are used as main control objects, and even if the pests possibly have little influence on rice at the present stage, the pests need to be sprayed with corresponding pesticides for prevention, and management of crops is performed by a control combination method.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring pest data after pesticide spraying, and setting the pest data as second pest data;
analyzing the second pest data to obtain second pest density information;
judging whether the density of the second pests is smaller than a third preset threshold value or not; if so, obtaining the effective pesticide spraying fruit information.
It is noted that the agricultural product is sprayed with the agricultural chemicalObtaining second pest data information of the preset area through the detection device, and analyzing the second pest data to obtain second pest density information, for example, setting the monitoring area as W, W =3m 2 If the density of the detection area is set as P, P =30/3=10 harmful insects A/m 2 The third preset threshold is 15/m 2 Obtaining the pesticide spraying effect, if P>15 pieces/m 2 And then the pesticide spray does not reach the target effect.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring picture information of crops in a preset area;
judging whether the crops in the preset area have complete information or not, and if not, obtaining the harmful insect information of the area;
and sending the harmful insect information of the preset area to a terminal for displaying.
The picture of the crop includes tissue organs of the crop, such as leaves, stems, and fruits of the crop, and whether the crop is damaged by the pest is determined by judging the integrity of the tissue organs of the crop.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring weather state information;
extracting wind power, temperature and humidity information in the weather state information;
judging whether the weather state is in a preset range, if so, obtaining that the weather state accords with pesticide spraying information;
and sending the weather state according with the pesticide spraying information to a terminal for prompting.
It should be noted that the weather state information of the preset area is acquired through real-time weather forecast or field investigation and monitoring, the temperature, humidity and wind power information in the weather forecast is extracted, and whether pesticide spraying is facilitated or not is judged according to the temperature, humidity and wind power information in the weather forecast. When the temperature is too high or low, be unfavorable for the drug effect performance of pesticide, rainy day or humidity are too big then reduce the drug effect through washing to the pesticide, and the liquid medicine is blown away easily to strong wind weather for the property of a medicine volatilizes too greatly, reduces the property of a medicine. For example, the preset range includes that wind power is set to be 1-6 levels, the temperature is 15-30 ℃, the humidity is 50% -80%, when one or two pieces of acquired weather state information meet requirements, the weather does not meet pesticide spraying, when the temperature is 20 ℃, the humidity is 70% and the wind power is 4 levels, the weather is in the preset range at the same time, pesticide spraying can be met, and pesticide effect is maximized.
According to the embodiment of the invention, the method further comprises the following steps:
obtaining pest growth rate information of a preset area according to pest density change information in the area;
drawing a pest density change curve graph according to the growth rate of the pests;
prejudging future pest density information of the preset area according to the pest density change curve graph;
judging whether the future pest density of the preset area is greater than a preset third preset threshold value or not, and if so, obtaining information for preventing the pests in advance;
and sending the pest prevention information to a terminal for displaying.
It should be noted that, the pests in the crops are all present in small quantity from the beginning, then the quantity is increased by propagation, if there is a natural enemy or environmental influence, the propagation speed is slowed down or negative increase is realized, if there is no natural enemy or environmental influence, the quantity of the pests can be increased at high speed, in order to make the crops have better protection, advance prevention of the pests is needed, for example, the density of the pests A obtained by the first detection is 4/m 2 The pest density obtained by the second detection is 8 pests/m at the interval of 5 days 2 And detecting for the third time at the interval of 5 days to obtain the density of the pests of 15/m 2 Although the density of the pest is not greater than the third preset threshold, it can be analyzed from the pest density variation graph that the density increase of the pest a is exponentially violent, and the pest a needs to be prevented in advance.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring pest information of a region adjacent to a preset region;
obtaining pest prevention and control information corresponding to a preset area according to the pest information of the adjacent areas;
and sending the pest prevention and control information corresponding to the preset area to a terminal for displaying.
The pest is alive, and can move and spread between the regions of the crops without obstruction, when pest disasters of the adjacent regions are monitored, the information is sent to the terminal to prompt the user to which the adjacent regions belong, the information is processed in time, the information is deployed in advance in the preset regions according to the pest matching corresponding prevention and control measures, and pest prevention work is well done.
Fig. 2 shows a block diagram of a big data based pesticide spray system of the present invention.
As shown in fig. 2, a second aspect of the present invention provides a big data based pesticide spraying system, which includes a memory and a processor, wherein the memory includes a big data based pesticide spraying program, and the big data based pesticide spraying program, when executed by the processor, implements the following steps:
acquiring detection data of a detection device;
pest data information of a preset area is obtained according to the detection data;
performing data analysis according to the pest data information to obtain spraying pesticide type information of a preset area;
analyzing according to the spraying value of the test pesticide in the test area and the obtained value of the test pesticide in the test area to obtain the accurate value information of the concentration of the sprayed pesticide in the preset area;
and sending the spraying pesticide type and the spraying pesticide concentration accurate value information of the preset area to a terminal for displaying.
It should be noted that, by comprehensively knowing pest information of a preset area, whether pests in the preset area are harmful or potentially harmful to crops in the preset area is obtained through analysis, if yes, the pests are matched with preset pesticide types, if the matching is not successful, an alarm device is triggered to obtain that the pests are new species, pest information in the new species is reported to relevant units for warning, if the matching is successful, the pesticide types obtained through matching are sent to a terminal, and a worker selects proper pesticide types. After the information of the types of the sprayed pesticides in the preset areas is obtained, the actual sprayed pesticide concentration accurate value of each preset area is obtained by testing the test areas and analyzing the sprayed value and the obtained value of the test pesticides. For example; setting an experimental pesticide spraying value of a test area as C1, setting an experimental pesticide obtaining value as C2, setting an expected value of spraying pesticide to crops as C3, and setting an actual spraying pesticide concentration accurate value of a preset area as C4, wherein the volatilization rate R = (C1-C2)/C1 of the sprayed pesticide in the air in the test is equal, and then:
c4= C3/(1-R) = (C1 x C3)/C2. And sending the actual spraying pesticide concentration value C4 of the preset area to a preset terminal for pesticide spraying.
It should be noted that the number of the detection devices is not less than 1, and the detection devices are arranged in the preset area and are used for acquiring detection data of the preset area.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring image information of pests;
comparing and analyzing the image information of the pests with preset pest type image information to obtain similarity information;
judging whether the similarity is greater than a first preset threshold value or not, if so, determining that the pests are of the corresponding image types;
and sending the pest species information to a terminal for displaying.
It should be noted that, the pest image obtained by the detection device is compared and analyzed with a preset pest image, the pest type is determined according to the similarity, if all the similarities are lower than a first preset threshold, the pest is obtained as a new-species pest or a variant pest, the pest is sent to the terminal to warn, if one of the similarities obtained from the pest image is greater than the first preset threshold, the pest is obtained as corresponding picture type information, if more than two pieces of similarity information are greater than the first preset threshold, the pest image is sent to the expert identification terminal to determine the pest type, for example, the preset first preset threshold is 90%, the image similarity between the obtained pest image and the preset pest type a is 95%, and the image similarity between the obtained pest image and the preset pest type B is 85%, so that the pest belongs to the a type.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring behavior information of pests;
obtaining damage information of the pests to crops according to the pest behavior information;
analyzing the damage of the pests to crops to obtain destructive power information of the pests;
judging whether the destructive power of the pests is greater than a second preset threshold value or not, and if so, obtaining pest killing information;
and sending the pest killing information to a server to perform pesticide matching.
It should be noted that, by means of the behavior information of the pests, the real-time understanding of the behavior of the pests is realized, and the damage of the pests to crops in the preset area can be clearly judged. The damage of the pests to the crops comprises direct damage and indirect damage, wherein the direct damage is damage of the pests to the crops directly, for example, the pests A eat wheat leaves, and the indirect damage is damage of the pests without causing direct damage to the crops, for example, the pests B suck nutrition on the wheat leaves, the pests B do not damage the crops directly, but the pests suck the nutrition on the wheat leaves to influence the growth of the crops. And evaluating and analyzing the damage of the pests to crops to obtain the destructive power value of the pests, wherein for example, if the direct damage power of the pest A is 70 minutes and the indirect damage power of the pest A is 10 minutes, the comprehensive destructive power value of the pest A is 80 minutes, and if the second preset threshold value is 70 minutes, the comprehensive destructive power of the pest A is greater than the second preset threshold value, the existence of the pest A and the growth of the crops are threatened, and the pest A needs to be killed.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring density information of pests;
judging whether the density of the pests is greater than a third preset threshold value or not, and if so, obtaining pest killing information;
and sending the pest killing information to a server to perform pesticide matching.
It should be noted that the preset area itself is a very large ecosystem, and has many biological chains, many pests exist but as long as the density is not increased violently and the damage to crops is not great, pesticide spraying treatment is not needed, the density of the pests is the number of pests in a unit area range, for example, the pest density of the preset area is preset at a threshold value of 15, the pest density is obtained according to the pest density information and maintained at 10-15, and control information that the pest density does not depart from the preset area is obtained, so pesticide spraying is not needed.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring historical data information of pests;
analyzing the historical data of the pests to obtain historical time information of the pest disasters;
obtaining corresponding pest prevention time information according to the history time of pest disasters;
and sending the pest prevention time information to a terminal for displaying.
Acquiring the history data information of the pests corresponds to acquiring the disease history of the crop, and acquiring information about what pests are present in the disease history or are likely to grow in the disease history at that time. For example: in the ear stage of rice, rice planthoppers and rice leaf rollers are used as main control targets, and even though the pest may not greatly affect rice at the present stage, the pest needs to be prevented by spraying corresponding pesticides, and management of crops is performed by a control combination method.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring pest data after pesticide spraying, and setting the pest data as second pest data;
analyzing the second pest data to obtain second pest density information;
judging whether the density of the second pests is smaller than a third preset threshold value or not; if yes, the effective pesticide spraying effect information is obtained.
It should be noted that after the agricultural chemical is sprayed on the crops, the second pest data information of the preset area is continuously obtained through the detection device, and the second pest density information is obtained through analyzing the second pest data, for example, the monitoring area is set as W, W =3m 2 If the density of the detection area is set as P, P =30/3=10 harmful insects A/m 2 If the fourth predetermined threshold is 15/m 2 Obtaining the pesticide spraying effect, if P>15 pieces/m 2 And then the pesticide spray does not reach the target effect.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring picture information of crops in a preset area;
judging whether the crops in the preset area have complete information or not, and if not, obtaining the harmful insect information of the area;
and sending the harmful insect information of the preset area to a terminal for displaying.
The picture of the crop includes tissue and organs of the crop such as leaves, stems, and fruits of the crop, and whether the crop is damaged by the pest is determined by judging the integrity of the tissue and organ of the crop.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring weather state information;
extracting wind power, temperature and humidity information in the weather state information;
judging whether the weather state is in a preset range, if so, obtaining that the weather state accords with pesticide spraying information;
and sending the weather state according with the pesticide spraying information to a terminal for prompting.
It should be noted that the weather state information of the preset area is acquired through real-time weather forecast or field investigation and monitoring, the temperature, humidity and wind power information in the weather forecast is extracted, and whether pesticide spraying is facilitated or not is judged according to the temperature, humidity and wind power information in the weather forecast. When the temperature is too high or low, be unfavorable for the drug effect performance of pesticide, rainy day or humidity are too big then reduce the drug effect through washing to the pesticide, and the liquid medicine is blown away easily to strong wind weather for the property of a medicine volatilizes too greatly, reduces the property of a medicine. For example, the preset range includes that wind power is set to be 1-6 grade, the temperature is 15-30 ℃, the humidity is 50% -80%, when one or two pieces of acquired weather state information meet the requirements, the weather does not meet pesticide spraying, when the temperature is 20 ℃, the humidity is 70% and the wind power is 4 grade, the weather is in the preset range at the same time, the pesticide spraying can be met, and the pesticide effect is maximized.
According to the embodiment of the invention, the method further comprises the following steps:
obtaining pest growth rate information of a preset region according to pest density change information in the region;
drawing a pest density change curve graph according to the growth rate of the pests;
prejudging future pest density information of the preset area according to the pest density change curve graph;
judging whether the future pest density of the preset area is greater than a preset third preset threshold value or not, and if so, obtaining information for preventing the pests in advance;
and sending the pest information for prevention in advance to a terminal for display.
It should be noted that, the pests in the crops are all present in small quantity from the beginning, then the quantity is increased by propagation, if there is a natural enemy or environmental influence, the propagation speed is slowed down or negative increase is realized, if there is no natural enemy or environmental influence, the quantity of the pests can be increased at high speed, in order to make the crops have better protection, advance prevention of the pests is needed, for example, the density of the pests A obtained by the first detection is 4/m 2 At an interval of 5 daysIn the second detection, the density of the pests is 8 pests/m 2 And detecting for the third time at an interval of 5 days to obtain the density of the pests of 15 pests/m 2 Although the density of the pest is not greater than the third preset threshold, it can be analyzed from the pest density variation graph that the density increase of the pest a is exponentially violent, and the pest a needs to be prevented in advance.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring pest information of a region adjacent to a preset region;
obtaining pest control information corresponding to a preset area according to the pest information of the adjacent area;
and sending the pest prevention and control information corresponding to the preset area to a terminal for displaying.
The pest is alive, and can move and spread between the regions of the crops without obstruction, when pest disasters of the adjacent regions are monitored, the information is sent to the terminal to prompt the user to which the adjacent regions belong, the information is processed in time, the information is deployed in advance in the preset regions according to the pest matching corresponding prevention and control measures, and pest prevention work is well done.
In a third aspect, the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a big data-based pesticide spraying method program, and when the big data-based pesticide spraying method program is executed by a processor, the steps of a big data-based pesticide spraying method as described in any one of the above are implemented.
According to the pesticide spraying method and system based on big data and the readable storage medium, the data of the preset area is detected through the detection device, pest information of the preset area is comprehensively known, pesticide spraying types of the preset area are obtained through analysis, and accurate pesticide application to pests is guaranteed. This application is still through testing the test area, through obtaining the value to experimental pesticide blowout value and experimental pesticide and analyzing, obtains every actual pesticide concentration accurate value that sprays that predetermines the region and sends and predetermine the terminal to carry out the accurate of pesticide and spray. The application reduces the pesticide residue in agricultural products and improves the quality of the people's living standard by controlling the types and the using amounts of the sprayed pesticides.
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 functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used 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 of implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer-readable storage medium, and when executed, executes the steps including the method embodiments; 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.
Claims (7)
1. A pesticide spraying method based on big data is characterized by comprising the following steps:
acquiring detection data of a detection device;
pest data information of a preset area is obtained according to the detection data;
performing data analysis according to the pest data information to obtain spraying pesticide type information of a preset area;
analyzing according to the spraying value of the pesticide to be tested in the test area and the obtained value of the pesticide to be tested in the test area to obtain the accurate value information of the concentration of the sprayed pesticide in the preset area;
sending the information of the types of the sprayed pesticides and the accurate values of the concentrations of the sprayed pesticides in the preset area to a terminal for displaying;
the detection devices are arranged in a preset area and are not less than 1, and are used for acquiring detection data of the preset area;
after the information of the types of the sprayed pesticides in the preset areas is obtained, the accurate value of the actual sprayed pesticide concentration of each preset area is obtained by testing the test areas and analyzing the sprayed value and the obtained value of the tested pesticides; setting an experimental pesticide spraying value of a test area as C1, setting an experimental pesticide obtaining value as C2, setting an expected value of spraying pesticide to crops as C3, and setting an actual spraying pesticide concentration accurate value of a preset area as C4, wherein the volatilization rate R = (C1-C2)/C1 of the sprayed pesticide in the air in the test;
C4＝C3/(1-R)＝(C1*C3)/C2；
sending the actual spraying pesticide concentration value C4 of the preset area to a preset terminal for pesticide spraying;
obtaining damage information of the pests to crops according to the pest behavior information;
analyzing the damage of the pests to crops to obtain destructive power information of the pests;
judging whether the destructive power of the pests is greater than a second preset threshold value or not, and if so, obtaining pest killing information;
sending the pest killing information to a server for pesticide matching;
acquiring pest data after pesticide spraying, and setting the pest data as second pest data;
analyzing the second pest data to obtain second pest density information;
judging whether the second pest density is smaller than a third preset threshold value or not; if so, obtaining effective pesticide spraying fruit information;
further comprising:
acquiring picture information of crops in a preset area;
judging whether the crops in the preset area have complete information or not, and if not, obtaining the harmful insect information of the area;
sending the harmful insect information of the preset area to a terminal for displaying;
further comprising:
acquiring weather state information;
extracting wind power, temperature and humidity information in the weather state information;
judging whether the weather state is in a preset range, if so, obtaining that the weather state accords with pesticide spraying information;
and sending the weather state according with the pesticide spraying information to a terminal for prompting.
2. The big data-based pesticide spraying method according to claim 1, characterized by further comprising:
acquiring image information of pests;
comparing and analyzing the image information of the pests with preset pest type image information to obtain similarity information;
judging whether the similarity is greater than a first preset threshold value or not, if so, determining that the pests are of the corresponding image types;
and sending the pest species information to a terminal for displaying.
3. The big data-based pesticide spraying method according to claim 1, characterized by further comprising:
acquiring density information of pests;
judging whether the density of the pests is greater than a third preset threshold value or not, and if so, obtaining pest killing information;
and sending the pest killing information to a server to match pesticides.
4. The big data-based pesticide spraying method according to claim 1, characterized by further comprising:
acquiring historical data information of pests;
analyzing the historical data of the pests to obtain historical time information of the pest disasters;
obtaining corresponding pest prevention time information according to the historical time information of the pest disasters;
and sending the pest prevention time information to a terminal for displaying.
5. The pesticide spraying system based on the big data is characterized by comprising a memory and a processor, wherein the memory comprises a pesticide spraying method program based on the big data, and the processor executes the pesticide spraying method program based on the big data to realize the following steps:
acquiring detection data of a detection device;
pest data information of a preset area is obtained according to the detection data;
performing data analysis according to the pest data information to obtain spraying pesticide type information of a preset area;
analyzing according to the spraying value of the test pesticide in the test area and the obtained value of the test pesticide in the test area to obtain the accurate value information of the concentration of the sprayed pesticide in the preset area;
sending the information of the types of the sprayed pesticides and the accurate values of the concentrations of the sprayed pesticides in the preset area to a terminal for displaying;
the detection devices are arranged in the preset area and are not less than 1, and are used for acquiring detection data of the preset area;
after the type information of the sprayed pesticides in the preset areas is obtained, the accurate value of the actual sprayed pesticide concentration of each preset area is obtained by testing the test areas and analyzing the spraying value and the obtained value of the tested pesticides; setting the spraying value of the experimental pesticide in the test area as C1, the acquisition value of the experimental pesticide as C2, the expected value of the pesticide sprayed to crops as C3, and the accurate value of the actual spraying pesticide concentration in the preset area as C4, wherein the volatilization rate of the sprayed pesticide in the air in the test is R = (C1-C2)/C1;
C4＝C3/(1-R)＝(C1*C3)/C2；
sending the actual spraying pesticide concentration value C4 of the preset area to a preset terminal for pesticide spraying;
obtaining damage information of the pests to crops according to the pest behavior information;
analyzing the damage of the pests to crops to obtain destructive power information of the pests;
judging whether the destructive power of the pests is greater than a second preset threshold value or not, and if so, obtaining pest killing information;
sending the pest killing information to a server for pesticide matching;
acquiring pest data after pesticide spraying, and setting the pest data as second pest data;
analyzing the second pest data to obtain second pest density information;
judging whether the second pest density is smaller than a third preset threshold value or not; if so, obtaining effective pesticide spraying fruit information;
further comprising:
acquiring picture information of crops in a preset area;
judging whether the crops in the preset area have complete information or not, and if not, obtaining the harmful insect information of the area;
sending the harmful insect information of the preset area to a terminal for displaying;
further comprising:
acquiring weather state information;
extracting wind power, temperature and humidity information in the weather state information;
judging whether the weather state is in a preset range, if so, obtaining that the weather state accords with pesticide spraying information;
and sending the weather state according with the pesticide spraying information to a terminal for prompting.
6. The big data-based pesticide spraying system as claimed in claim 5, further comprising:
acquiring image information of pests;
comparing and analyzing the image information of the pests with preset pest type image information to obtain similarity information;
judging whether the similarity is greater than a first preset threshold value or not, if so, determining that the pests are of the corresponding image types;
and sending the pest species information to a terminal for displaying.
7. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a big data-based pesticide spraying method program, and when the big data-based pesticide spraying method program is executed by a processor, the steps of a big data-based pesticide spraying method according to any one of claims 1 to 4 are realized.
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| CN116259078B (en) * | 2023-05-16 | 2023-07-28 | 浙江天演维真网络科技股份有限公司 | Pesticide recommendation method, device, equipment and storage medium |
| CN118378783A (en) * | 2024-04-17 | 2024-07-23 | 深圳市佰乐弗电子科技有限公司 | Insect capturing visual monitoring system based on Internet |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109984105A (en) * | 2018-12-29 | 2019-07-09 | 南京林业大学工程规划设计院有限公司 | A kind of intelligent monitoring system of Landscape Construction |
| CN110263200A (en) * | 2019-06-13 | 2019-09-20 | 浙江省检验检疫科学技术研究院 | A kind of intelligence agriculture and forestry injurious insect standardization investigation method and system |
| CN110516712A (en) * | 2019-08-01 | 2019-11-29 | 仲恺农业工程学院 | Insect pest image recognition method, insect pest monitoring method, insect pest image recognition device, insect pest monitoring equipment and insect pest monitoring medium |
| KR20200044216A (en) * | 2018-10-11 | 2020-04-29 | 순천대학교 산학협력단 | System and method for predicting the occurrence of pests using Big Data |
| CN113095555A (en) * | 2021-03-31 | 2021-07-09 | 佛山一本农业科技有限公司 | Crop disease and insect pest monitoring method and system based on Internet of things and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113207511A (en) * | 2021-03-31 | 2021-08-06 | 广西中烟工业有限责任公司 | Pesticide application method and system based on pesticide resistance monitoring and readable storage medium |
-
2022
- 2022-03-29 CN CN202210318386.5A patent/CN114586760B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20200044216A (en) * | 2018-10-11 | 2020-04-29 | 순천대학교 산학협력단 | System and method for predicting the occurrence of pests using Big Data |
| CN109984105A (en) * | 2018-12-29 | 2019-07-09 | 南京林业大学工程规划设计院有限公司 | A kind of intelligent monitoring system of Landscape Construction |
| CN110263200A (en) * | 2019-06-13 | 2019-09-20 | 浙江省检验检疫科学技术研究院 | A kind of intelligence agriculture and forestry injurious insect standardization investigation method and system |
| CN110516712A (en) * | 2019-08-01 | 2019-11-29 | 仲恺农业工程学院 | Insect pest image recognition method, insect pest monitoring method, insect pest image recognition device, insect pest monitoring equipment and insect pest monitoring medium |
| CN113095555A (en) * | 2021-03-31 | 2021-07-09 | 佛山一本农业科技有限公司 | Crop disease and insect pest monitoring method and system based on Internet of things and storage medium |
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