CN113408849B - Method and system for evaluating mixed pesticide effect of fenpyroximate and pesticide - Google Patents
Method and system for evaluating mixed pesticide effect of fenpyroximate and pesticide Download PDFInfo
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Abstract
The invention discloses a method and a system for evaluating the pesticide effect of fenpyroximate and pesticide, wherein the method comprises the following steps: obtaining a first mixed pesticide for a first plant, wherein the first mixed pesticide comprises first fenpyroximate, the first plant has a first disease and a second disease, and the second disease is within a disease treatment range of the first fenpyroximate; inputting the first fenpyroximate with very hot first diseases into a pesticide screening model to obtain first pesticides for treating the first diseases; obtaining a first desired therapeutic effect; obtaining first pesticide composition information; obtaining a first inhibiting component; obtaining a first site of action of the inhibitory component; inputting the first inhibition component, the first action position and the first fenpyroximate into an actual drug effect evaluation model to obtain a first actual drug effect evaluation result; obtaining first difference information according to the first actual efficacy evaluation result and the first expected treatment effect; constructing a first evaluation mapping function; and inputting the first difference information into a first evaluation mapping function to obtain a second actual drug effect evaluation result.
Description
Technical Field
The invention relates to the field of pesticide evaluation, in particular to a method and a system for evaluating the mixed pesticide effect of fenpyroximate and pesticide.
Background
Fenpyroximate is a common oxime acaricide, is suitable for preventing and controlling tetranychus urticae koch and tetranychus urticae koch on various plants, has strong contact poisoning effect, and when the fenpyroximate is used in the agricultural field, 5% of fenpyroximate suspending agent is mixed with pesticide to prepare mixed pesticide with the concentration of 20-50mg/kg so as to prevent and control various phytophagous mites. The action mechanism of the pesticide is different from that of a common pesticide in that the pesticide interferes the nerve physiological activity of mites and has an inhibiting effect on stimulating nerve conduction, the mites, nymphs, insects and larvae can generate paralysis after being contacted with the pesticide, and the mites, the nymphs, the insects and the larvae die after 2 to 4 days without eating the pesticide.
In the process of implementing the technical scheme of the invention in the embodiment of the present application, the inventor of the present application finds that the above-mentioned technology has at least the following technical problems:
in the prior art, the pesticide effect evaluation method aiming at the mixed pesticide of fenpyroximate and pesticide is only limited to analyzing the growth condition of crops to judge the pesticide effect after spraying, but cannot predict the pesticide effect in advance through scientific and accurate evaluation before use.
Disclosure of Invention
The embodiment of the application provides a method and a system for evaluating the pesticide effect of the fenpyroximate and pesticide mixture, solves the technical problems that in the prior art, the pesticide effect evaluation method of the fenpyroximate and pesticide mixture is only limited to analyzing the growth condition of crops after spraying to judge the pesticide effect and cannot predict the pesticide effect in advance through scientific and accurate evaluation before use, and the technical effect of predicting the pesticide effect of the fenpyroximate and pesticide mixture is realized by analyzing pesticide effect influence factors such as preparation proportion, action position and environmental information, so that the technical purpose of maximizing the pesticide effect of the first mixed pesticide is achieved.
In view of the above problems, the embodiments of the present application provide a method and a system for evaluating the efficacy of fenpyroximate and pesticide in combination.
In a first aspect, the present application provides a method for evaluating the efficacy of fenpyroximate in combination with a pesticide, wherein the method comprises: obtaining a first mixed pesticide comprising first fenpyroximate, wherein the first mixed pesticide is applied to a first plant, the first plant has a first disease and a second disease, and the second disease belongs to a disease treatment range of the first fenpyroximate; inputting the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide, wherein the first pesticide is used for treating the first disease; obtaining a first desired therapeutic effect of the first plant; obtaining component information of the first pesticide; obtaining a first inhibition component of the first pesticide and the first fenpyroximate according to the component information of the first pesticide; obtaining a first action position of the inhibiting component according to the first inhibiting component; inputting the first inhibiting component, the first action position and the first fenpyroximate into an actual efficacy evaluation model to obtain a first actual efficacy evaluation result; obtaining first difference information according to the first actual efficacy evaluation result and the first expected treatment effect; constructing a first evaluation mapping function; and inputting the first difference information into the first evaluation mapping function to obtain a second actual efficacy evaluation result.
In another aspect, the present application further provides a mixed pesticide effect evaluation system of fenpyroximate and pesticide, wherein the system comprises: a first obtaining unit for obtaining a first mixed pesticide including first fenpyroximate, wherein the first mixed pesticide is applied to a first plant having a first disease and a second disease within a disease treatment range of the first fenpyroximate; a second obtaining unit, wherein the second obtaining unit is used for inputting the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide, and the first pesticide is used for treating the first disease; a third obtaining unit for obtaining a first desired therapeutic effect of the first plant; a fourth obtaining unit for obtaining ingredient information of the first pesticide; a fifth obtaining unit, configured to obtain a first inhibitory component of the first pesticide and the first fenpyroximate according to component information of the first pesticide; a sixth obtaining unit for obtaining a first action position of the suppressing component based on the first suppressing component; a first execution unit, wherein the first execution unit is used for inputting the first inhibition component, the first action position and the first fenpyroximate into an actual drug effect evaluation model to obtain a first actual drug effect evaluation result; a seventh obtaining unit configured to obtain first difference information based on the first actual efficacy evaluation result and the first expected therapeutic effect; a first construction unit for constructing a first evaluation mapping function; an eighth obtaining unit, configured to input the first difference information into the first evaluation mapping function, and obtain a second actual efficacy evaluation result.
On the other hand, the embodiment of the application also provides a mixed pesticide effect evaluation system of fenpyroximate and pesticide, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the method of the first aspect.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the embodiment of the application provides a method and a system for evaluating the pesticide effect of fenpyroximate and pesticide mixture, wherein a first mixed pesticide is obtained and comprises first fenpyroximate, the first mixed pesticide is used for a first plant, the first plant has a first disease and a second disease, and the second disease belongs to the disease treatment range of the first fenpyroximate; inputting the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide, wherein the first pesticide is used for treating the first disease; obtaining a first desired therapeutic effect of said first plant; obtaining component information of the first pesticide; obtaining a first inhibition component of the first pesticide and the first fenpyroximate according to the component information of the first pesticide; obtaining a first site of action of the inhibitory component from the first inhibitory component; inputting the first inhibiting component, the first action position and the first fenpyroximate into an actual efficacy evaluation model to obtain a first actual efficacy evaluation result; obtaining first difference information according to the first actual efficacy evaluation result and the first expected treatment effect; constructing a first evaluation mapping function; and inputting the first difference information into the first evaluation mapping function to obtain a second actual pesticide effect evaluation result to evaluate the pesticide effect of the fenpyroximate and the pesticide in detail and accurately, so that the pesticide effect of the fenpyroximate and the pesticide is evaluated by combining various factors before spraying the pesticide, the pesticide effect of the first mixed pesticide is maximized, and the technical effects of the cultivation efficiency and the planting survival rate of crops are further improved. So as to realize assisting the planting and cultivation of crops through intelligent means.
The foregoing is a summary of the present disclosure, and embodiments of the present disclosure are described below to make the technical means of the present disclosure more clearly understood.
Drawings
FIG. 1 is a schematic flow chart of a method for evaluating the efficacy of fenpyroximate and pesticide in combination according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a mixed pesticide effect evaluation system of fenpyroximate and pesticide in the embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of the reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a first executing unit 17, a seventh obtaining unit 18, a first constructing unit 19, an eighth obtaining unit 20, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 305.
Detailed Description
The embodiment of the application provides a method and a system for evaluating the pesticide effect of the mixed fenpyroximate and pesticide, solves the technical problem that the pesticide effect cannot be predicted in advance through scientific and accurate evaluation before the mixed fenpyroximate and pesticide is used in the prior art, and realizes the technical effect of predicting the pesticide effect of the mixed fenpyroximate and pesticide in advance by analyzing pesticide effect influence factors such as preparation proportion, action position and environmental information, thereby achieving the technical purpose of maximizing the pesticide effect of the mixed fenpyroximate and pesticide.
Hereinafter, example embodiments of the present application will be described in detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and it is to be understood that the present application is not limited by the example embodiments described herein.
Summary of the application
The preparation proportion of the fenpyroximate and the pesticide, the action position and the action environment of the mixed pesticide and the like all influence the mixed pesticide effect of the fenpyroximate and the pesticide, the pesticide effect evaluation method aiming at the mixed pesticide effect of the fenpyroximate and the pesticide in the prior art is only limited to analyzing the growth condition of crops to judge the pesticide effect after spraying, and the technical problem that the pesticide effect cannot be predicted in advance through scientific and accurate evaluation before use, so that the pesticide cannot be reasonably used and the pesticide effect is not fully exerted exists.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the application provides a fenpyroximate and pesticide mixed pesticide effect evaluation method, wherein the method is applied to a fenpyroximate and pesticide mixed pesticide effect evaluation device, and the method comprises the following steps: obtaining a first mixed pesticide comprising first fenpyroximate, wherein the first mixed pesticide is applied to a first plant, the first plant has a first disease and a second disease, and the second disease belongs to a disease treatment range of the first fenpyroximate; inputting the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide, wherein the first pesticide is used for treating the first disease; obtaining a first desired therapeutic effect of the first plant; obtaining component information of the first pesticide; obtaining a first inhibition component of the first pesticide and the first fenpyroximate according to the component information of the first pesticide; obtaining a first site of action of the inhibitory component from the first inhibitory component; inputting the first inhibiting component, the first action position and the first fenpyroximate into an actual efficacy evaluation model to obtain a first actual efficacy evaluation result; obtaining first difference information according to the first actual efficacy evaluation result and the first expected treatment effect; constructing a first evaluation mapping function; and inputting the first difference information into the first evaluation mapping function to obtain a second actual efficacy evaluation result.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, the present application provides a method for evaluating the efficacy of a mixture of fenpyroximate and a pesticide, wherein the method comprises the following steps:
step S100: obtaining a first mixed pesticide comprising first fenpyroximate, wherein the first mixed pesticide is applied to a first plant, the first plant has a first disease and a second disease, and the second disease belongs to a disease treatment range of the first fenpyroximate;
specifically, the first mixed pesticide is a mixed drug of the first fenpyroximate and a pesticide, the fenpyroximate is used as a common acaricide, and the pesticide can be mixed with the fenpyroximate to increase the acaricidal effect. The first plant is an acting object of the first mixed pesticide, the first plant has a plurality of disease types such as rust disease, anthracnose, damping off and the like, the first disease and the second disease are two different disease types, and the second disease belongs to the disease treatment range of the first fenpyroximate. For example, the second disease may be leaf curl and withered and yellow abscission caused by tetranychus cinnabarinus, or the phenomena of uneven leaf surface, loss of luster, swelling and distortion caused by gall mites. By obtaining the first mixed pesticide and specifically analyzing the components of the first mixed pesticide, the accuracy of the evaluation of the pesticide effect of the mixture of the fenpyroximate and the pesticide is ensured.
Step S200: inputting the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide, wherein the first pesticide is used for treating the first disease;
specifically, the pesticide screening model is a machine learning model and has the characteristics of continuously supervising and learning training data and acquiring experience to process the data. The first disease and the first fenpyroximate are input into the pesticide screening model, and each data is processed by the model, so that the most suitable first pesticide type is determined according to the disease characteristics, the corresponding preparation scheme and the like. For example, for the phenomenon that the growth of crops is influenced by absorbing the nutrition of the crops due to the clustering of peripheral weeds, the first pesticide can be herbicides such as acetochlor and alachlor; for the phenomenon that beetles, noseworms and the like eat the leaves of crops to cause the death of the crops, the first pesticide can be an insecticide containing efficient cyhalothrin, deltamethrin and the like.
Further, step S200 in the embodiment of the present application further includes:
step S210: inputting the first disease and the first fenpyroximate into the pesticide screening model as input data;
step S220: the pesticide screening model is obtained by training a plurality of groups of training data until convergence, wherein each group of the plurality of groups of training data comprises the first disease, the first fenpyroximate and identification information for identifying a first pesticide;
step S230: obtaining output data of the pesticide screening model, wherein the output data comprises the first pesticide.
Specifically, the pesticide screening model is a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely interconnecting a large number of simple processing units (called neurons), reflects many basic characteristics of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. And inputting the first disease and the first fenpyroximate into a neural network model through training of a large amount of training data, and outputting a first pesticide.
Furthermore, the training process is essentially a supervised learning process, each group of supervised data comprises the first disease, the first fenpyroximate and identification information for identifying a first pesticide, the first disease and the first fenpyroximate are input into a neural network model, the neural network model is continuously self-corrected and adjusted according to the identification information for identifying the first pesticide, and the group of supervised learning is ended until the obtained output result is consistent with the identification information, and the next group of data supervised learning is carried out; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through supervised learning of the neural network model, the neural network model can process the input information more accurately, so that a more accurate and suitable first pesticide can be obtained, the purpose of inputting the first disease and the first fenpyroximate to obtain the first pesticide can be achieved, meanwhile, the neural network model is added, the efficiency and the accuracy of a data operation processing result are improved, and a basis is tamped for providing a more accurate and suitable first pesticide.
Step S300: obtaining a first desired therapeutic effect of the first plant;
further, step S300 in the embodiment of the present application further includes:
step S310: obtaining characteristic information of the first plant;
step S320: obtaining planting season information of the first plant;
step S330: obtaining a first reference factor according to the characteristic information of the first plant, the planting season information and the growth environment information;
step S340: respectively obtaining a first damage grade and a second damage grade of the first disease and the second disease to the first plant;
step S350: mapping the first hazard level and the second hazard level respectively to obtain a first weight value and a second weight value;
step S360: obtaining first input information according to the first weight value and the first reference factor;
step S370: obtaining second input information according to the second weight value and the first reference factor;
step S380: inputting the first input information and the second input information into an expected treatment effect estimation model to obtain a first expected treatment effect of the first plant.
Specifically, the first expected therapeutic effect is an estimated action effect of the first plant obtained by using the first mixed pesticide in the growing environment through an expected therapeutic effect estimation model. Obtaining a first reference factor according to the characteristic information of the first plant, the planting season information and the growth environment information, grading the first disease and the second disease to obtain a first hazard grade and a second hazard grade of the first plant, and weighting the first hazard grade and the second hazard grade respectively to obtain a first weight value and a second weight value. And respectively obtaining first input information and second input information through the first weight value and the second weight value and the first reference factor, and inputting the first input information and the second input information into an expected treatment effect estimation model to obtain a first expected treatment effect of the first plant. The technical effect of predicting the pesticide effect of the first mixed pesticide on the first plant is achieved.
Step S400: obtaining component information of the first pesticide;
step S500: obtaining a first inhibition component of the first pesticide and the first fenpyroximate according to the component information of the first pesticide;
specifically, the ingredient information of the first pesticide is a specific chemical ingredient contained in the first pesticide, and when the first pesticide is mixed and used with the first fenpyroximate, the pesticide effect is negatively affected due to the presence of a certain ingredient in the first mixed pesticide, and the ingredient negatively affecting the pesticide effect is the first inhibiting ingredient. The first inhibiting component may be a component contained in the first pesticide itself, or may be a component newly generated by a chemical reaction when the first pesticide and the first fenpyroximate are mixed and acted on. According to the first inhibiting component, the information of the influence of the existence of the inhibiting component on the pesticide effect can be obtained when the first pesticide and the first fenpyroximate are mixed for use, so that the final pesticide effect can be predicted more accurately.
Step S600: obtaining a first site of action of the inhibitory component from the first inhibitory component;
step S700: inputting the first inhibiting component, the first action position and the first fenpyroximate into an actual efficacy evaluation model to obtain a first actual efficacy evaluation result;
specifically, the first action site of the inhibited component is a specific action site of the first inhibited component on crops, such as roots, stems and leaves of plants, the actual pesticide effect evaluation model is a model for performing targeted analysis on pesticide effect monitoring data of the first mixed pesticide, and the first inhibited component, the first action site and the first fenpyroximate are input into the actual pesticide effect evaluation model for performing actual pesticide effect data analysis, so that a first actual pesticide effect evaluation result is obtained. In detail, the first actual pesticide effect evaluation result can fit pesticide effect data information of the first mixed pesticide and complete corresponding analysis, so that a platform built by a computer can conveniently process the pesticide effect data information, wherein the actual pesticide effect evaluation model is a model built on the basis of a neural network model, the neural network is an operation model formed by interconnection of a large number of neurons, the output of the network is expressed according to a logic strategy of the connection mode of the network, the output information has accuracy through training of the actual pesticide effect evaluation model, and then the pesticide effect comprehensive analysis result of the first mixed pesticide is accurately obtained, so that the pesticide effect data of the first mixed pesticide can be accurately analyzed, and the technical effect of analyzing the accuracy is improved.
Step S800: obtaining first difference information according to the first actual efficacy evaluation result and the first expected treatment effect;
step S900: constructing a first evaluation mapping function;
step S1000: and inputting the first difference information into the first evaluation mapping function to obtain a second actual efficacy evaluation result.
Specifically, the first difference information is a difference between the first actual drug efficacy evaluation result and the first expected therapeutic effect, the first evaluation mapping function is a functional correspondence between the first difference information and the second actual drug efficacy evaluation result, and the first actual drug efficacy evaluation result is corrected by inputting the first difference information into the first evaluation mapping function, thereby obtaining a second actual drug efficacy evaluation result. Because reverse thinking is adopted to take the first difference information as an evaluation basis, and an actual drug effect evaluation result is continuously corrected to obtain a second actual drug effect evaluation result, the difference between a predicted value and an actual value is ensured to be within a stable range, the influence of other interference factors on the actual drug effect evaluation accuracy is controllable, and the technical effect of accurately estimating the mixed drug effect of the fenpyroximate and the pesticide is achieved.
Further, the embodiment of the present application further includes a step S1100, where the step S1100 includes:
step S1110: obtaining first influence factor information of the first pesticide;
step S1120: judging whether the temperature information and the humidity information exist in the first influence factor information or not;
step S1130: if the temperature information and the humidity information exist in the first influence factor information, acquiring the growth environment of the first plant;
step S1140: obtaining real-time temperature information and real-time humidity information of the growing environment according to the growing environment of the first plant;
step S1150: acquiring a first influence parameter according to the real-time temperature information and the real-time humidity information;
step S1160: and adjusting the first actual drug effect evaluation result according to the first influence parameter to obtain a third actual drug effect evaluation result.
Specifically, the first influence factor information is external factors that can affect the pesticide effect of the first pesticide, such as temperature, humidity, atmospheric pressure, and the like, and when the first influence factor information includes temperature and humidity, which indicates that real-time temperature and real-time humidity can affect the pesticide effect of the first pesticide, a growth environment of the first plant needs to be obtained, where the growth environment of the first plant is an external environment of the first plant during the growth process, such as climate, temperature, humidity, or illumination degree. The method comprises the steps of obtaining real-time temperature information and real-time humidity information of a growing environment through the growing environment of a first plant, obtaining a first influence parameter according to the real-time temperature information and the real-time humidity information, wherein the first influence parameter is a related parameter which inevitably influences the pesticide effect of a first pesticide under the growing environment of the first plant, and adjusting a first actual pesticide effect evaluation result according to the first influence parameter to obtain a third actual pesticide effect evaluation result. The influence parameters of the plant growth environment on the first pesticide are extracted to correct the actual pesticide effect evaluation result, so that the error of the actual pesticide effect evaluation caused by the influence of the external environment is avoided in advance, the accuracy of the actual pesticide effect evaluation is improved, and the technical effect of accurately estimating the actual pesticide effect is achieved.
Further, the embodiment of the present application further includes step S1200, where step S1200 includes:
step 1210: obtaining a predetermined pollution source database according to the first fenpyroximate and the first pesticide;
step S1220: judging whether a first pollution source in the preset pollution source database exists in the growth environment or not;
step S1230: if a first pollution source in the preset pollution source database exists in the growth environment, obtaining a second influence parameter according to the first pollution source;
step S1240: and adjusting the first actual drug effect evaluation result according to the second influence parameter to obtain a fourth actual drug effect evaluation result.
Specifically, the predetermined pollution source database is a sum of pollution sources which can affect the actual drug effect, when a first pollution source in the predetermined pollution source database exists in the growth environment of the first plant, it is indicated that the first pollution source can affect the actual drug effect, a second influence parameter is obtained according to the first pollution source, and the first actual drug effect evaluation result is adjusted according to the second influence parameter, so as to obtain a fourth actual drug effect evaluation result. The pollution source in the plant growth environment is extracted and used as an influence parameter to judge the influence on the actual pesticide effect, so that the first actual pesticide effect evaluation result is corrected to obtain a fourth actual pesticide effect evaluation result, the error on the actual pesticide effect evaluation result due to the influence of the pollution source on the actual pesticide effect in the growth environment is avoided, the accuracy of actual pesticide effect evaluation is improved, and the technical effect of accurately estimating the actual pesticide effect is achieved.
Further, the embodiment of the present application further includes step S1300, where step S1300 includes:
step 1310: obtaining actual proportioning information of the first fenpyroximate and the first pesticide;
step S1320: obtaining a drug effect component ratio curve;
step S1330: obtaining first ratio pharmacodynamic information according to the actual ratio information and the pharmacodynamic component ratio curve;
step S1340: and correcting the second actual drug effect evaluation result according to the first ratio drug effect information to obtain a fifth actual drug effect evaluation result.
Specifically, the actual proportioning information is the concentration proportion of the first fenpyroximate and the first pesticide respectively in the first mixed pesticide, the effective component proportioning curve is a curve relation graph among ideal effects of the first fenpyroximate, the first pesticide and the first mixed pesticide, and the ideal effect of the first fenpyroximate or the first pesticide at any concentration can be known in detail through the effective component proportioning curve. And obtaining first ratio pharmacodynamic information according to the actual ratio information and the pharmacodynamic component ratio curve, wherein the first ratio pharmacodynamic information is ideal pharmacodynamic under actual ratio concentration, and correcting the second actual pharmacodynamic evaluation result according to the first ratio pharmacodynamic information to obtain a fifth actual pharmacodynamic evaluation result. The ratio content of fenpyroximate in the first mixed pesticide directly influences the mite removing efficiency of the first mixed pesticide. When the content of the fenpyroximate in the first mixed pesticide is low, the first mixed pesticide cannot play a role in removing mites; when the content of fenpyroximate in the first mixed pesticide is high, the effect is saturated, and medicine waste is generated. When the pesticide is used daily, the content of the fenpyroximate accounts for about 5% of the concentration of the first mixed pesticide, namely the content is the better proportion, so that the mite killing effect can be achieved, and waste can not be caused. The actual drug effect evaluation result is corrected according to the ideal drug effect, so that the influence on the actual drug effect evaluation result due to the error generated in the proportioning process is avoided, the accuracy of the actual drug effect evaluation is improved, and the technical effect of accurately estimating the actual drug effect is achieved.
Further, the embodiment of the present application further includes step S1400, where step S1400 includes:
step S1410: obtaining a predetermined treatment duration;
step S1420: acquiring a temperature and humidity change curve of the preset treatment duration;
step S1430: obtaining a second actual drug effect evaluation result set corresponding to the temperature and humidity change curve;
step S1440: and executing a first operation instruction on the second actual drug effect evaluation result set to obtain a sixth actual drug effect evaluation result, wherein the first operation instruction is used for carrying out average operation on the second actual drug effect evaluation result set.
Specifically, the preset treatment time is a short period of use time of the first mixed pesticide, the temperature and humidity change curve is a data set of continuous temperature and humidity change of the first plant in the growth environment within the preset treatment time, the temperature and humidity change condition in the treatment time is determined by extracting the treatment time, and a second actual pesticide effect evaluation result set is correspondingly obtained by combining the influence of the temperature and humidity on the actual pesticide effect. And executing a first operation instruction on the second actual drug effect evaluation result set to obtain a sixth actual drug effect evaluation result, wherein the first operation instruction is used for carrying out average operation on the second actual drug effect evaluation result set. From the perspective of the average value, by means of the method for re-averaging the efficacy set in the preset treatment duration, stable efficacy data capable of representing the whole time period is obtained, the influence of individual extreme temperature and humidity values in the preset treatment duration on final evaluation in the efficacy evaluation process is avoided, the inaccuracy of efficacy evaluation is avoided, the accuracy of actual efficacy evaluation is improved, and the technical effect of accurately estimating the actual efficacy is achieved.
In summary, the processing method of kumquat provided by the embodiment of the application has the following technical effects:
1. the embodiment of the application provides a method and a system for evaluating the pesticide effect of fenpyroximate and pesticide mixture, wherein a first mixed pesticide is obtained and comprises first fenpyroximate, the first mixed pesticide is used for a first plant, the first plant has a first disease and a second disease, and the second disease belongs to the disease treatment range of the first fenpyroximate; inputting the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide, wherein the first pesticide is used for treating the first disease; obtaining a first desired therapeutic effect of the first plant; obtaining component information of the first pesticide; obtaining a first inhibition component of the first pesticide and the first fenpyroximate according to the component information of the first pesticide; obtaining a first site of action of the inhibitory component from the first inhibitory component; inputting the first inhibiting component, the first action position and the first fenpyroximate into an actual efficacy evaluation model to obtain a first actual efficacy evaluation result; obtaining first difference information according to the first actual efficacy evaluation result and the first expected treatment effect; constructing a first evaluation mapping function; and inputting the first difference information into the first evaluation mapping function to obtain a second actual pesticide effect evaluation result to evaluate the pesticide effect of the fenpyroximate and the pesticide in detail and accurately, so that the pesticide effect of the fenpyroximate and the pesticide is evaluated by combining various factors before the pesticide is sprayed, the pesticide effect of the fenpyroximate and the pesticide is maximized, and the technical effects of the cultivation efficiency and the planting survival rate of crops are further improved. So as to realize assisting the planting and cultivation of crops through intelligent means.
2. And inputting the first disease and the first fenpyroximate as input data into the pesticide screening model to obtain the first pesticide, wherein the pesticide screening model is a machine learning model, and the first pesticide can be obtained more accurately based on a mode that the machine learning model can continuously learn and obtain experience to process data.
Example two
Based on the same inventive concept as the method for evaluating the mixed pesticide effect of fenpyroximate and pesticide in the previous embodiment, the invention also provides a system for evaluating the mixed pesticide effect of fenpyroximate and pesticide, as shown in fig. 2, the system comprises:
a first obtaining unit 11, wherein the first obtaining unit 11 is configured to obtain a first mixed pesticide including first fenpyroximate, and the first mixed pesticide is applied to a first plant having a first disease and a second disease within a disease treatment range of the first fenpyroximate;
a second obtaining unit 12, where the second obtaining unit 12 is configured to input the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide, and the first pesticide is used to treat the first disease;
a third obtaining unit 13, the third obtaining unit 13 being adapted to obtain a first desired therapeutic effect of the first plant;
a fourth obtaining unit 14, wherein the fourth obtaining unit 14 is used for obtaining the component information of the first pesticide;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to obtain the first pesticide and the first inhibitory component of the first fenpyroximate according to the component information of the first pesticide;
a sixth obtaining unit 16, the sixth obtaining unit 16 being configured to obtain a first action position of the suppression component based on the first suppression component;
a first execution unit 17, wherein the first execution unit 17 is configured to input the first inhibitory component, the first action site, and the first fenpyroximate into an actual efficacy evaluation model to obtain a first actual efficacy evaluation result;
a seventh obtaining unit 18, wherein the seventh obtaining unit 18 is configured to obtain first difference information according to the first actual efficacy evaluation result and the first expected therapeutic effect;
a first construction unit 19, said first construction unit 19 being configured to construct a first evaluation mapping function;
an eighth obtaining unit 20, where the eighth obtaining unit 20 is configured to input the first difference information into the first evaluation mapping function to obtain a second actual efficacy evaluation result.
Further, the system further comprises:
a ninth obtaining unit for obtaining first influence factor information of the first pesticide;
a first judging unit configured to judge whether temperature information and humidity information exist in the first influence factor information;
a tenth obtaining unit configured to obtain a growing environment of the first plant when the temperature information and the humidity information exist in the first influence factor information;
an eleventh obtaining unit, configured to obtain real-time temperature information and real-time humidity information of a growing environment according to the growing environment of the first plant;
a twelfth obtaining unit, configured to obtain the first influence parameter according to the real-time temperature information and the real-time humidity information;
a thirteenth obtaining unit, configured to adjust the first actual drug efficacy evaluation result according to the first influence parameter, and obtain a third actual drug efficacy evaluation result.
Further, the apparatus further comprises:
a fourteenth obtaining unit, configured to obtain a predetermined pollution source database according to the first fenpyroximate and the first pesticide;
a second judging unit, configured to judge whether a first pollution source in the predetermined pollution source database exists in the growth environment;
a fifteenth obtaining unit, configured to, when a first pollution source in the predetermined pollution source database exists in the growth environment, obtain a second influence parameter according to the first pollution source;
a sixteenth obtaining unit, configured to adjust the first actual drug efficacy evaluation result according to the second influence parameter, and obtain a fourth actual drug efficacy evaluation result.
Further, the apparatus further comprises:
a seventeenth obtaining unit for obtaining characteristic information of the first plant;
an eighteenth obtaining unit for obtaining planting season information of the first plant;
a nineteenth obtaining unit configured to obtain a first reference factor based on the characteristic information of the first plant, the planting season information, and the growing environment;
a twentieth obtaining unit, configured to obtain a first hazard level and a second hazard level of the first disease and the second disease to the first plant, respectively;
a twenty-first obtaining unit, configured to map the first hazard level and the second hazard level respectively to obtain a first weight value and a second weight value;
a twenty-second obtaining unit, configured to obtain first input information according to the first weight value and the first influence factor;
a twenty-third obtaining unit configured to obtain second input information according to the second weight value and the first influence factor;
a second execution unit, configured to input the first input information and the second input information into an expected therapeutic effect estimation model to obtain a first expected therapeutic effect of the first plant.
Further, the apparatus further comprises:
a twenty-fourth obtaining unit, configured to obtain actual ratio information of the first fenpyroximate and the first pesticide;
a twenty-fifth obtaining unit, configured to obtain a pharmacodynamic component matching curve;
a twenty-sixth obtaining unit, configured to obtain first matching pharmacodynamic information according to the actual matching information and the pharmacodynamic component matching curve;
a twenty-seventh obtaining unit, configured to correct the second actual drug efficacy evaluation result according to the first ratio drug efficacy information, and obtain a fifth actual drug efficacy evaluation result.
Further, the apparatus further comprises:
a twenty-eighth obtaining unit for obtaining a predetermined treatment duration;
a twenty-ninth obtaining unit, configured to obtain a temperature and humidity change curve of the predetermined treatment duration;
a thirtieth obtaining unit, configured to obtain a second actual drug effect evaluation result set corresponding to the temperature and humidity change curve;
a third execution unit, configured to execute a first operation instruction on the second actual drug efficacy evaluation result set to obtain a sixth actual drug efficacy evaluation result, where the first operation instruction is used to perform an average operation on the second actual drug efficacy evaluation result set.
Further, the apparatus further comprises:
the fourth execution unit is used for inputting the first disease and the first fenpyroximate into the pesticide screening model by taking the first disease and the first fenpyroximate as input data;
a thirty-first obtaining unit, configured to obtain data that the pesticide screening model converges through multiple sets of training, where each set of the multiple sets of training data includes the first disease, the first fenpyroximate, and identification information for identifying a first pesticide;
a thirty-second obtaining unit for obtaining output data of the pesticide screening model, the output data including the first pesticide.
The aforementioned fenpyroximate and pesticide mixed efficacy evaluation method and specific example in the first embodiment of fig. 1 are also applicable to the fenpyroximate and pesticide mixed efficacy evaluation system in this embodiment, and through the aforementioned detailed description of the fenpyroximate and pesticide mixed efficacy evaluation method, a person skilled in the art can clearly know the fenpyroximate and pesticide mixed efficacy evaluation system in this embodiment, so for the brevity of the description, detailed description is omitted here.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the fenpyroximate and pesticide mixed pesticide effect evaluation method in the previous embodiment, the invention also provides a fenpyroximate and pesticide mixed pesticide effect evaluation system, wherein a computer program is stored on the fenpyroximate and pesticide mixed pesticide effect evaluation system, and when the computer program is executed by a processor, the steps of any one of the methods for the fenpyroximate and pesticide mixed pesticide effect evaluation method are realized.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The application provides a fenpyroximate and pesticide mixed pesticide effect evaluation method, wherein the method is applied to a fenpyroximate and pesticide mixed pesticide effect evaluation system, and the method comprises the following steps: obtaining a first mixed pesticide comprising first fenpyroximate, wherein the first mixed pesticide is applied to a first plant having a first disease and a second disease, and the second disease is within a disease treatment range of the first fenpyroximate; inputting the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide, wherein the first pesticide is used for treating the first disease; obtaining a first desired therapeutic effect of the first plant; obtaining component information of the first pesticide; obtaining a first inhibition component of the first pesticide and the first fenpyroximate according to the component information of the first pesticide; obtaining a first action position of the inhibiting component according to the first inhibiting component; inputting the first inhibiting component, the first action position and the first fenpyroximate into an actual efficacy evaluation model to obtain a first actual efficacy evaluation result; obtaining first difference information according to the first actual efficacy evaluation result and the first expected treatment effect; constructing a first evaluation mapping function; and inputting the first difference information into the first evaluation mapping function to obtain a second actual efficacy evaluation result.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A method for evaluating the efficacy of fenpyroximate mixed with pesticide, wherein the method comprises the following steps:
obtaining a first mixed pesticide comprising first fenpyroximate, wherein the first mixed pesticide is applied to a first plant, the first plant has a first disease and a second disease, and the second disease belongs to a disease treatment range of the first fenpyroximate;
inputting the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide, wherein the first pesticide is used for treating the first disease;
obtaining a first desired therapeutic effect of the first plant;
obtaining component information of the first pesticide;
obtaining a first inhibition component of the first pesticide and the first fenpyroximate according to the component information of the first pesticide;
obtaining a first site of action of the inhibitory component from the first inhibitory component;
inputting the first inhibiting component, the first action position and the first fenpyroximate into an actual pesticide effect evaluation model to obtain a first actual pesticide effect evaluation result, wherein the first action position of the inhibiting component is a specific action part of the first inhibiting component on crops, the actual pesticide effect evaluation model is a model for performing targeted analysis on pesticide effect monitoring data of a first mixed pesticide, and the first inhibiting component, the first action position and the first fenpyroximate are input into the actual pesticide effect evaluation model to perform actual pesticide effect data analysis, so that the first actual pesticide effect evaluation result is obtained;
obtaining first difference information according to the first actual efficacy evaluation result and the first expected treatment effect;
constructing a first evaluation mapping function;
and inputting the first difference information into the first evaluation mapping function to obtain a second actual efficacy evaluation result.
2. The method of claim 1, wherein obtaining the first actual efficacy assessment result comprises:
obtaining first influence factor information of the first pesticide;
judging whether the temperature information and the humidity information exist in the first influence factor information or not;
if the temperature information and the humidity information exist in the first influence factor information, acquiring the growth environment of the first plant;
obtaining real-time temperature information and real-time humidity information of the growing environment according to the growing environment of the first plant;
acquiring a first influence parameter according to the real-time temperature information and the real-time humidity information;
and adjusting the first actual drug effect evaluation result according to the first influence parameter to obtain a third actual drug effect evaluation result.
3. The method of claim 2, wherein the method comprises:
obtaining a predetermined pollution source database according to the first fenpyroximate and the first pesticide;
judging whether a first pollution source in the preset pollution source database exists in the growth environment or not;
if a first pollution source in the preset pollution source database exists in the growth environment, obtaining a second influence parameter according to the first pollution source;
and adjusting the first actual drug effect evaluation result according to the second influence parameter to obtain a fourth actual drug effect evaluation result.
4. The method of claim 2, wherein said obtaining a first desired therapeutic effect of said first plant comprises:
obtaining characteristic information of the first plant;
obtaining planting season information of the first plant;
obtaining a first reference factor according to the characteristic information of the first plant, the planting season information and the growing environment;
respectively obtaining a first damage grade and a second damage grade of the first plant caused by the first disease and the second disease;
mapping the first hazard level and the second hazard level respectively to obtain a first weight value and a second weight value;
obtaining first input information according to the first weight value and the first influence factor;
obtaining second input information according to the second weight value and the first influence factor;
inputting the first input information and the second input information into an expected treatment effect estimation model to obtain a first expected treatment effect of the first plant.
5. The method of claim 1, wherein the method comprises:
obtaining actual proportioning information of the first fenpyroximate and the first pesticide;
obtaining a drug effect component ratio curve;
obtaining first ratio pharmacodynamic information according to the actual ratio information and the pharmacodynamic component ratio curve;
and correcting the second actual drug effect evaluation result according to the first ratio drug effect information to obtain a fifth actual drug effect evaluation result.
6. The method of claim 2, wherein the method comprises:
obtaining a predetermined treatment duration;
acquiring a temperature and humidity change curve of the preset treatment duration;
obtaining a second actual drug effect evaluation result set corresponding to the temperature and humidity change curve;
and executing a first operation instruction on the second actual drug effect evaluation result set to obtain a sixth actual drug effect evaluation result, wherein the first operation instruction is used for carrying out average operation on the second actual drug effect evaluation result set.
7. The method of claim 1, wherein the inputting the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide for treating the first disease comprises:
inputting the first disease and the first fenpyroximate into the pesticide screening model as input data;
the pesticide screening model is obtained by training a plurality of groups of training data until convergence, wherein each group of the plurality of groups of training data comprises the first disease, the first fenpyroximate and identification information for identifying a first pesticide;
obtaining output data of the pesticide screening model, wherein the output data comprises the first pesticide.
8. A system for evaluating the efficacy of fenpyroximate in combination with a pesticide, for use in the method according to any one of claims 1 to 7, wherein the system comprises:
a first obtaining unit for obtaining a first mixed pesticide including first fenpyroximate, wherein the first mixed pesticide is applied to a first plant having a first disease and a second disease within a disease treatment range of the first fenpyroximate;
a second obtaining unit, wherein the second obtaining unit is used for inputting the first disease and the first fenpyroximate into a pesticide screening model to obtain a first pesticide, and the first pesticide is used for treating the first disease;
a third obtaining unit for obtaining a first desired therapeutic effect of the first plant;
a fourth obtaining unit for obtaining ingredient information of the first pesticide;
a fifth obtaining unit, configured to obtain a first inhibitory component of the first pesticide and the first fenpyroximate according to component information of the first pesticide;
a sixth obtaining unit for obtaining a first action position of the suppressing component based on the first suppressing component;
a first execution unit, configured to input the first inhibitory component, the first action site, and the first fenpyroximate into an actual efficacy evaluation model to obtain a first actual efficacy evaluation result, where the first action site of the inhibitory component is a specific action site of the first inhibitory component on a crop, and the actual efficacy evaluation model is a model for performing targeted analysis on efficacy monitoring data of a first mixed pesticide, and input the first inhibitory component, the first action site, and the first fenpyroximate into the actual efficacy evaluation model for performing actual efficacy data analysis, thereby obtaining the first actual efficacy evaluation result;
a seventh obtaining unit configured to obtain first difference information according to the first actual efficacy evaluation result and the first expected therapeutic effect;
a first construction unit for constructing a first evaluation mapping function;
an eighth obtaining unit, configured to input the first difference information into the first evaluation mapping function, and obtain a second actual efficacy evaluation result.
9. A system for assessing the efficacy of a combination of fenpyroximate and a pesticide comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1-7 when executing the program.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104542707A (en) * | 2013-12-31 | 2015-04-29 | 同济大学 | Nano material synergistic pesticide antibacterial and continuous photocatalytic degradation residue-removing technical method |
| CN105145605A (en) * | 2015-10-08 | 2015-12-16 | 广西南宁益土生物科技有限责任公司 | Pesticide composition containing fenpyroximate and flufenoxuron |
| CN108651474A (en) * | 2018-06-19 | 2018-10-16 | 广西南宁益土生物科技有限责任公司 | A kind of miticide composition containing fenpyroximate and fluazinam |
| CN110651784A (en) * | 2018-06-28 | 2020-01-07 | 江苏龙灯化学有限公司 | Preparation method of pesticide emulsion in water, emulsion in water prepared based on method and application of emulsion in water |
| CN111213642A (en) * | 2020-02-20 | 2020-06-02 | 广西益才农业发展有限公司 | Insecticidal composition, preparation containing insecticidal composition and application of insecticidal composition |
| CN111713505A (en) * | 2020-07-14 | 2020-09-29 | 江苏省激素研究所股份有限公司 | Nicosulfuron with acaricidal function and preparation method thereof |
| CN111855830A (en) * | 2020-03-26 | 2020-10-30 | 浙江省农业科学院 | Method for detecting strawberry pesticide residues based on color-quality combination technology |
| CN111899837A (en) * | 2020-08-17 | 2020-11-06 | 江苏达实久信数字医疗科技有限公司 | Operation report coordination method and system based on digital operating room |
| CN112233752A (en) * | 2020-12-14 | 2021-01-15 | 强基(上海)医疗器械有限公司 | Information processing method and device for endoscope examination report |
| CN112438254A (en) * | 2020-12-04 | 2021-03-05 | 中化化工科学技术研究总院有限公司 | Pesticide dispersing auxiliary and pesticide composition containing same |
| CN112529898A (en) * | 2020-12-28 | 2021-03-19 | 柳州市汇方科技有限公司 | Rice flour bacteriostasis method and device |
Family Cites Families (41)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050164955A1 (en) * | 2003-11-06 | 2005-07-28 | Gross Richard A. | Antifungal properties of various forms of sophorolipids |
| EP2154974A2 (en) * | 2007-05-18 | 2010-02-24 | AgION Technologies, Inc. | Bioactive acid agrichemical compositions and use thereof |
| EP2170066B1 (en) * | 2007-07-27 | 2012-02-29 | Dow AgroSciences LLC | Pesticides and uses thereof |
| BR112013006612A2 (en) * | 2010-09-22 | 2017-10-24 | Bayer Ip Gmbh | use of biological or chemical control agents for insect and nematode control in resistant crops |
| EP3222146B1 (en) * | 2011-01-28 | 2019-03-13 | Deepak Pranjivandas Shah | Pesticide composition comprising sulphur, an acaricide and an agrochemical excipient |
| AR093996A1 (en) * | 2012-12-18 | 2015-07-01 | Bayer Cropscience Ag | BACTERICIDAL COMBINATIONS AND BINARY FUNGICIDES |
| BR112015021757B8 (en) * | 2013-03-14 | 2022-08-23 | Dow Agrosciences Llc | MOLECULE PRESENTING CERTAIN PESTICIDE UTILITIES, AND PROCESS |
| CN103503912A (en) * | 2013-09-29 | 2014-01-15 | 江志鑫 | Acaricidal composite composition |
| BR122020026778B1 (en) * | 2013-10-18 | 2021-09-28 | Basf Agrochemical Products B.V. | MIXTURE OF PESTICIDES, METHODS FOR PROTECTION OF VEGETABLES, INSECT CONTROL AND PROTECTION OF VEGETABLE PROPAGATION MATERIAL, USE OF A MIXTURE AND PESTICIDE COMPOSITION |
| CN103636634A (en) * | 2013-11-19 | 2014-03-19 | 广西田园生化股份有限公司 | Ultra-low volume liquid containing fenpyroximate |
| AU2014372693A1 (en) * | 2013-12-23 | 2016-06-16 | Syngenta Participations Ag | Insecticidal compounds |
| CN104273139A (en) * | 2014-09-15 | 2015-01-14 | 广东中迅农科股份有限公司 | Mite-killing composition containing fenpyroximate and ivermectin |
| CN104650064B (en) * | 2014-12-17 | 2017-12-05 | 南开大学 | A kind of aminothiazole carboxylic acid derivates and its production and use |
| US20180153201A1 (en) * | 2015-05-29 | 2018-06-07 | Saraya Co., Ltd. | Agrochemical-free siraitia grosvenorii extract, and method for preparing same |
| CN106689204A (en) * | 2015-07-20 | 2017-05-24 | 江苏龙灯化学有限公司 | Composition for preventing and treating crop diseases and pests |
| CN105519569A (en) * | 2015-10-22 | 2016-04-27 | 陕西嘉益蓝德生物工程有限公司 | Systematic pesticide for whole course of apple plantation period and apple plantation period whole course systematic protection method |
| CN108368064B (en) * | 2015-11-30 | 2022-04-26 | 北兴化学工业株式会社 | 1,2, 3-triazole derivative and insecticide and acaricide comprising the same as active ingredient |
| EP3205209A1 (en) * | 2016-02-09 | 2017-08-16 | Basf Se | Mixtures and compositions comprising paenibacillus strains or metabolites thereof and other biopesticides |
| US10709138B2 (en) * | 2016-04-28 | 2020-07-14 | Kumiai Chemical Industry Co., Ltd. | Herbicidal agrichemical composition and herbicidal method using same |
| CN106490069A (en) * | 2016-10-19 | 2017-03-15 | 罗友祥 | Chinese herbal medicine synthetic drug fertilizer and its preparation method and application |
| CN106512845A (en) * | 2016-10-25 | 2017-03-22 | 北京小米移动软件有限公司 | Method and device for preparation of solution |
| BR102018000183B1 (en) * | 2017-01-05 | 2023-04-25 | Dow Agrosciences Llc | PICOLINAMIDES, COMPOSITION FOR CONTROLLING A FUNGAL PATHOGEN, AND METHOD FOR CONTROLLING AND PREVENTING A FUNGAL ATTACK ON A PLANT |
| CN111362933A (en) * | 2017-05-16 | 2020-07-03 | 南开大学 | α -amino acrylic acid microbicide and preparation method and application thereof |
| CN109691467A (en) * | 2017-10-23 | 2019-04-30 | 上海崇明低碳农业科技有限公司 | A kind of application of bacillus subtilis microbial preparation in prevention and treatment crop powdery mildew |
| CN108041031A (en) * | 2017-12-05 | 2018-05-18 | 徐州农丰生物化工有限公司 | A kind of fenpyroximate nicotine recompounding suspension and preparation method thereof |
| CN109369256B (en) * | 2018-09-06 | 2021-09-14 | 广西乐土生物科技有限公司 | Composite microbial fertilizer with insecticidal activity |
| CN109221291A (en) * | 2018-10-30 | 2019-01-18 | 吴秋雨 | A kind of novel green composite pesticide formula |
| CN111264529A (en) * | 2018-12-04 | 2020-06-12 | 浙江省化工研究院有限公司 | Composition containing fenpyroximate |
| CN109453315A (en) * | 2018-12-26 | 2019-03-12 | 李克峰 | For removing composition, preparation method, health care product, diet product or the drug of vivotoxin |
| CN109776327A (en) * | 2019-02-27 | 2019-05-21 | 黄山学院 | First chlorine chrysanthemumic acid polyfluoro benzylalcohol chrysanthemum ester type compound, preparation method and application |
| CN109970653A (en) * | 2019-05-17 | 2019-07-05 | 南开大学 | Methoxy acrylate derivative and its preparation method and application of the one kind containing difluoromethyl pyrazole |
| CN110241172A (en) * | 2019-05-22 | 2019-09-17 | 桂林理工大学 | A method of predicting that typical antibiotic and combined toxicity of pesticides interact |
| CN110275001B (en) * | 2019-07-31 | 2023-01-03 | 四川省农业科学院生物技术核技术研究所 | Device and method for measuring and evaluating drug effect of drug on root-knot nematodes |
| CN110717903A (en) * | 2019-09-30 | 2020-01-21 | 天津大学 | Method for detecting crop diseases by using computer vision technology |
| CN111165485A (en) * | 2020-02-25 | 2020-05-19 | 本生(惠州)生物技术有限公司 | Diatomite-beauveria bassiana pesticide composition and application thereof |
| CN111213665A (en) * | 2020-02-26 | 2020-06-02 | 广西益才农业发展有限公司 | Pesticide composition containing diatomite, preparation method and application thereof |
| CN111899894B (en) * | 2020-08-03 | 2021-06-25 | 东南大学 | System and method for evaluating prognosis drug effect of depression patient |
| CN112514903B (en) * | 2020-12-05 | 2022-09-16 | 青岛滕润翔检测评价有限公司 | Pesticide composition containing fenpyroximate and application thereof |
| CN112597827A (en) * | 2020-12-11 | 2021-04-02 | 西北农林科技大学 | Plant phenological period prediction method and system based on big data |
| CN112561369A (en) * | 2020-12-23 | 2021-03-26 | 徐靖玮 | Supply chain financial service method and system based on cloud platform |
| CN112785267B (en) * | 2021-01-23 | 2022-04-19 | 南京利特嘉软件科技有限公司 | Flight information management method and system based on MVC framework technology |
-
2021
- 2021-05-13 CN CN202110520519.2A patent/CN113408849B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104542707A (en) * | 2013-12-31 | 2015-04-29 | 同济大学 | Nano material synergistic pesticide antibacterial and continuous photocatalytic degradation residue-removing technical method |
| CN105145605A (en) * | 2015-10-08 | 2015-12-16 | 广西南宁益土生物科技有限责任公司 | Pesticide composition containing fenpyroximate and flufenoxuron |
| CN108651474A (en) * | 2018-06-19 | 2018-10-16 | 广西南宁益土生物科技有限责任公司 | A kind of miticide composition containing fenpyroximate and fluazinam |
| CN110651784A (en) * | 2018-06-28 | 2020-01-07 | 江苏龙灯化学有限公司 | Preparation method of pesticide emulsion in water, emulsion in water prepared based on method and application of emulsion in water |
| CN111213642A (en) * | 2020-02-20 | 2020-06-02 | 广西益才农业发展有限公司 | Insecticidal composition, preparation containing insecticidal composition and application of insecticidal composition |
| CN111855830A (en) * | 2020-03-26 | 2020-10-30 | 浙江省农业科学院 | Method for detecting strawberry pesticide residues based on color-quality combination technology |
| CN111713505A (en) * | 2020-07-14 | 2020-09-29 | 江苏省激素研究所股份有限公司 | Nicosulfuron with acaricidal function and preparation method thereof |
| CN111899837A (en) * | 2020-08-17 | 2020-11-06 | 江苏达实久信数字医疗科技有限公司 | Operation report coordination method and system based on digital operating room |
| CN112438254A (en) * | 2020-12-04 | 2021-03-05 | 中化化工科学技术研究总院有限公司 | Pesticide dispersing auxiliary and pesticide composition containing same |
| CN112233752A (en) * | 2020-12-14 | 2021-01-15 | 强基(上海)医疗器械有限公司 | Information processing method and device for endoscope examination report |
| CN112529898A (en) * | 2020-12-28 | 2021-03-19 | 柳州市汇方科技有限公司 | Rice flour bacteriostasis method and device |
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