CN115271209A - Method, device, equipment and medium for predicting plant diseases and insect pests - Google Patents
Method, device, equipment and medium for predicting plant diseases and insect pests Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a medium for predicting plant diseases and insect pests. The method comprises the following steps: acquiring first historical data; predicting the type and the outbreak time of the pest and disease damage outbreak according to the first historical data; determining the interval time between the burst time and the current time according to the burst time; and determining a corresponding pest prevention suggestion according to the interval time and the type of the pest outbreak. According to the embodiment of the invention, the accuracy of disease and insect pest prediction can be improved, and a real-time and accurate disease and insect pest prevention suggestion is realized.
Description
Technical Field
The invention relates to the technical field of agriculture, in particular to a method, a device, equipment and a medium for predicting plant diseases and insect pests.
Background
Pests and diseases have been one of the major disasters affecting crop yield and growth during crop growth.
Traditional plant diseases and insect pests discernment and prediction mainly rely on peasant and expert's experience, utilize local meteorological change, judge possibility and the type that the plant diseases and insect pests appear, and corresponding prevention and treatment measures are refabricated afterwards, and the rate of accuracy is lower, easily causes the waste and the loss of manpower, material resources resource.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for predicting plant diseases and insect pests, which can predict the types and the outbreak time of plant diseases and insect pests and determine suggestions for preventing the plant diseases and the insect pests.
According to an aspect of the present invention, there is provided a method for predicting a pest, comprising:
acquiring first historical data;
predicting the type and the outbreak time of the pest and disease damage outbreak according to the first historical data;
determining the interval time between the burst time and the current time according to the burst time;
and determining a corresponding pest and disease prevention suggestion according to the interval time and the type of the pest and disease outbreak.
According to another aspect of the present invention, there is provided a pest prediction device, including:
the first data acquisition module is used for acquiring first historical data;
the prediction module is used for predicting the type and the outbreak time of the pest and disease damage outbreak according to the first historical data;
the interval time determining module is used for determining the interval time between the burst time and the current time according to the burst time;
and the prevention suggestion determining module is used for determining corresponding prevention suggestion of plant diseases and insect pests according to the interval time and the type of the plant disease and insect pest outbreak.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a pest prediction method according to any embodiment of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the pest prediction method according to any one of the embodiments of the present invention when executed.
According to another aspect of the present invention there is provided a computer program product comprising a computer program which when executed by a processor implements a method of pest prediction according to any one of the embodiments of the present invention.
According to the technical scheme of the embodiment of the invention, the type and the outbreak time of the pest and disease outbreak are predicted through the first historical data, the corresponding pest and disease prevention suggestion is determined according to the interval time between the outbreak time and the current time and the type of the pest and disease outbreak, the type of the pest and disease outbreak and the corresponding specific outbreak time are predicted, the accuracy of the pest and disease prediction can be improved, the corresponding pest and disease prevention suggestion is determined jointly according to the interval time between the outbreak time and the current time and by combining the type of the pest and disease outbreak, the accuracy of the pest and disease prevention suggestion can be improved, the pest and disease can be prevented accurately in real time, and waste and loss of manpower and material resources are reduced.
It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present invention, nor are they intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a pest and disease damage prediction method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a pest and disease damage prediction method provided by the second embodiment of the invention;
FIG. 3 is a flow chart of a pest and disease damage prediction method provided by the third embodiment of the invention;
fig. 4 is a schematic structural diagram of a pest and disease damage prediction device according to the fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device that implements the pest and disease damage prediction method according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and the like of the related data all conform to the regulations of related laws and regulations and do not violate the common customs of the public order.
Example one
Fig. 1 is a flowchart of a pest prediction method according to an embodiment of the present invention, where the present embodiment is applicable to a situation of predicting a pest of a plant crop, and the method may be implemented by a pest prediction device, and the pest prediction device may be implemented in a form of hardware and/or software, and may be configured in an electronic device with computing capability. As shown in fig. 1, the method includes:
and S110, acquiring first historical data.
The first historical data refers to agricultural four-situation data collected in a first historical time period, and the first historical time period is before the current time and can be adjacent to the current time. The agricultural four-condition data comprises data of at least one type of the types such as atmospheric temperature, atmospheric humidity, illumination, atmospheric pressure, carbon dioxide concentration, soil temperature, soil pH value, plant diseases and insect pests type, plant diseases and insect pests number, fruit diameter and the like. First historical data can gather through the sensor of fixed position, also can gather through mobile device, and mobile device can be the unmanned aerial vehicle who installs the sensor. Illustratively, the current time is 3 months and 1 day 2020. The first historical data may be agricultural four-season data collected during months 1 to 2 of 2020.
Specifically, the first history data may be acquired through a database or a storage device.
And S120, predicting the type and the outbreak time of the pest and disease damage outbreak according to the first historical data.
The type of outbreak of a pest refers to the type of pest that has exploded during the outbreak of a pest. The number of species of outbreak is at least one. Specifically, the pest species at least include tea lesser leafhopper, corn worm, wheat straw rust pathogenic bacteria and the like, and correspondingly, the type of pest outbreak is at least one of the pest species. The outbreak time refers to the specific date when the pest breaks out. The number of burst times is at least one. The type of an outbreak corresponds to at least one outbreak time.
Specifically, according to the first historical data, at least one pest and disease outbreak type in a future time period and an outbreak time corresponding to each pest and disease outbreak type can be predicted. Illustratively, according to the first historical data, the first historical data can be divided according to the types of the agricultural four-situation data to obtain at least one group of data, for each group of data, a statistical value is obtained, and the type of at least one pest and disease outbreak in a future time period is predicted according to the statistical value, wherein the statistical value can be at least one of an average value, a maximum value, a minimum value, a mode and the like. The future time period refers to a time period with the current time as the starting time, and the length of the future time period can be set according to actual conditions. Illustratively, the current time is 3 months and 1 day 2020, and the length of the future time period is 3 months, and according to the first historical data, it is predicted that: the disease and pest outbreak species is tea lesser leafhopper, and the outbreak time is 3 months and 3 days in 2020; the disease and pest outbreak type is wheat rust pathogen, and the outbreak time is 3 months and 25 days in 2020.
And S130, determining the interval time between the explosion time and the current time according to the explosion time.
The interval time refers to the length of time between the burst time and the current time. Illustratively, the outbreak period is 3 and 15 days 2020, the current period is 3 and 4 days 2020, with an interval of 11 days. The number of intervals is at least one. Specifically, according to at least one burst time, the interval time between each burst time and the current time is determined.
And S140, determining a corresponding pest prevention suggestion according to the interval time and the type of the pest outbreak.
The suggestion for preventing diseases and insect pests refers to the suggestion about preventive measures when the diseases and insect pests are prevented. The number of recommendations for pest prevention is at least one. Aiming at the type of disease and pest outbreak, different disease and pest prevention suggestions are provided at different intervals, and each interval corresponds to one disease and pest prevention suggestion.
Specifically, the pest and disease prevention suggestions can be screened according to the interval time to obtain a screening result, and the corresponding pest and disease prevention suggestions are determined from the screening result according to the types of pest outbreaks; and screening the pest prevention suggestions according to the types of pest outbreaks to obtain screening results, and determining the corresponding pest prevention suggestions in the screening results according to the interval time. Illustratively, the corresponding pest prevention advice can be determined by means of a table look-up according to the interval time and the type of pest outbreak.
According to the technical scheme of the embodiment of the invention, the type and the outbreak time of the pest outbreak are predicted through the first historical data, the corresponding pest prevention suggestion is determined according to the interval time between the outbreak time and the current time and the type of the pest outbreak, the type of the pest outbreak and the corresponding specific outbreak time are predicted, the accuracy of the pest prediction can be improved, the corresponding pest prevention suggestion is determined jointly according to the interval time between the outbreak time and the current time and in combination with the type of the pest outbreak, the accuracy of the pest prevention suggestion can be improved, the pest can be prevented accurately in real time, and waste and loss of manpower and material resources are reduced.
On the basis of the above embodiment, the determining a corresponding pest prevention suggestion according to the interval time and the type of the pest outbreak includes: matching the interval time with the pest and disease damage grade to obtain a matching result; determining a proposal for preventing the plant diseases and insect pests according to the matching result and the types of the plant diseases and insect pests outbreak; the pest recommendation includes fertilization prevention, pesticide application prevention, or mixed prevention.
Pest ratings are used to characterize the urgency of pest outbreak. Higher pest grade indicates more urgent pest outbreak and less time intervals. The pest grade can be determined according to a preset grade division rule, wherein the grade division rule is used for indicating the range of the interval time corresponding to each grade. Illustratively, when the interval time is less than 10, the pest grade is serious pest; when the interval time is more than or equal to 10 and less than 30, the pest grade is medium pest; when the interval time is more than or equal to 30 and less than 60, the pest grade is a slight grade; when the interval time is more than or equal to 60, the pest grade is no pest temporarily.
For example, the same crop may have different types of outbreaks, in which case the grading rules for different types of outbreaks and pest grades may be the same. For different planting crops, different types of pest and disease outbreaks exist, in this case, the grading rules of the pest and disease grades can be different for different types of pest and disease outbreaks, but after grading, the grades are the same in number, and specifically, the grading rules can be determined according to the farming season of the planting crops, namely the farming time of the planting crops.
The matching result refers to the pest grade corresponding to the interval time. The number of matching results is at least one. The fertilization prevention refers to prevention of pest outbreak by means of fertilization, and specifically, the fertilization prevention includes types and usage amounts of fertilizers. The pesticide application prevention refers to prevention of pest outbreak by means of pesticide application, and specifically comprises the type and the dosage of a pesticide. The mixed prevention refers to prevention of pest outbreak through combined action of fertilizers and medicines, and specifically, the mixed prevention comprises the types and the use amounts of the fertilizers and the medicines.
Specifically, matching is performed according to the interval time and pest and disease damage grades to obtain at least one matching result, according to the types of pest and disease damage outbreaks, the matching result corresponding to the type of each pest and disease damage outbreak is determined in the at least one matching result, according to the matching result corresponding to the type of each pest and disease damage outbreak, pest and disease damage prevention suggestions corresponding to the type of each pest and disease damage outbreak can be determined in a table look-up mode and the like, wherein one type of each pest and disease damage outbreak corresponds to one pest and disease damage prevention suggestion.
The matching result is obtained by matching the interval time with the pest and disease damage grade, and the pest and disease damage prevention suggestion is determined by combining the type of pest and disease damage outbreak, so that different pest and disease damage prevention suggestions can be provided aiming at different types of pest and disease damage outbreaks and different interval times, the accuracy of determining the pest and disease damage prevention suggestion is improved, the pest and disease damage prevention effect is improved, and the influence of pest and disease damage is reduced.
On the basis of the embodiment, the determining a pest prevention suggestion according to the matching result and the type of pest outbreak comprises the following steps: acquiring associated interval time, wherein the associated interval time is a plurality of interval time with a time length difference value less than or equal to a preset threshold value; determining the incidence relation between the matching results and the priority of the matching results according to the matching results and the types of the pest outbreaks; and determining a pest prevention suggestion based on the association relationship among the matching results, the associated interval time and the priority of the matching results.
The time length difference refers to the difference between two adjacent intervals. The two adjacent intervals refer to two adjacent intervals after the intervals are sequenced. Illustratively, the two adjacent intervals are 21 days and 14 days, and the difference between the two adjacent intervals is 21-14=7 days. The preset threshold is a judgment basis for determining the interval time as a key interval time. Specifically, when the time length difference is less than or equal to the preset threshold, the interval time corresponding to the time length difference is determined as the associated interval time. Illustratively, the preset threshold is 7 days, the interval time is 3 days, 9 days, 15 days and 30 days, the time length difference between two adjacent interval times is calculated, and when the time length difference is less than or equal to 7, the interval time is determined as the associated interval time, and the interval time with 3 days, 9 days and 15 days as the associated interval time is obtained.
The priority of the matching result is used to indicate an order in which pest prevention advice is determined, wherein the pest prevention advice corresponds to the kind of pest outbreak. The priority of the matching result can be determined according to the actual situation, and exemplarily, the priority of the matching result can be determined according to pest grades corresponding to different pest outbreak types. Specifically, the matching results are sorted according to the priority, and the pest and disease prevention suggestions corresponding to the matching results are sequentially determined. The incidence relation between the matching results is used for showing that the same, similar or conflicting relations exist between the disease and insect pest prevention suggestions corresponding to the matching results. The association between the matching results can be preset according to actual conditions. Illustratively, for at least one pest and disease outbreak type of the same planting type crops, the corresponding matching results have the same incidence relation; for the types of disease and pest outbreaks of different planting crops, the corresponding matching results do not have an incidence relation or have a conflict relation.
Specifically, the priority of the matching result and the pest prevention suggestion corresponding to the matching result are determined according to the matching result and the type of pest outbreak. According to the pest prevention suggestion, the incidence relation between the matching results can be determined. And determining a matching result corresponding to the associated interval time based on the associated interval time. And in the matching results corresponding to the associated interval time, determining the suggestions for preventing plant diseases and insect pests in turn according to the priority of the matching results for the matching results without the association relation. And determining the matching result with the association relation as an association matching result in the matching results corresponding to the associated interval time. And fusing at least one disease and pest prevention suggestion corresponding to the correlation matching result, and determining the fused result as the disease and pest prevention suggestion of the correlation matching result. Illustratively, in at least one pest and disease prevention suggestion corresponding to the association matching result, if all the pest and disease prevention suggestions are fertilizer application prevention, the same or similar relationship exists between the pest and disease prevention suggestions corresponding to the association matching result, the fertilizer and the amount corresponding to at least one fertilizer application prevention can be combined to be used as new fertilizer application prevention, and the new fertilizer application prevention is determined to be the pest and disease prevention suggestion of the matching result; when at least one disease and pest prevention suggestion corresponding to the correlation matching result contains mixed prevention and/or drug prevention, the fact that similar or conflicting relations exist between the disease and pest prevention suggestions corresponding to the correlation matching result is shown, the disease and pest prevention suggestions contain at least one drug, whether the drugs have conflicting relations or not is determined, if yes, the types and the dosage of the drugs can be changed and used for updating the disease and pest prevention suggestions, the updated disease and pest prevention suggestions are combined to obtain a new disease and pest prevention suggestion, the new disease and pest prevention suggestion is determined to be the disease and pest prevention suggestion of the correlation matching result, and if not, the at least one disease and pest prevention suggestion can be directly combined to obtain the new disease and pest prevention suggestion and the new disease and pest prevention suggestion is determined to be the disease and pest prevention suggestion of the correlation matching result. The conflict relationship between the medicines indicates that the medicines can harm the environment or planting crops after being taken simultaneously or reduce the effect of the medicines after being taken simultaneously. The judgment condition whether there is a conflicting relationship between the medicines may be set in advance.
The incidence relation between the priority of the matching result and the matching result is determined according to the matching result and the type of the pest outbreak, and the pest prevention suggestion is determined by combining the associated interval time, so that the occurrence of conflict among the pest prevention suggestions corresponding to the type of each pest outbreak can be reduced when the time length difference among a plurality of interval times is small, namely the pest outbreaks are concentrated, and the accuracy and the effectiveness of determining the pest prevention suggestion are improved.
Example two
Fig. 2 is a flowchart of a pest and disease damage prediction method provided in the second embodiment of the present invention, where on the basis of the second embodiment, the present embodiment further includes: acquiring second historical data, historical outbreak pest and disease damage types and historical outbreak time related to the second historical data, wherein one historical outbreak pest and disease damage type corresponds to one historical outbreak time, and the second historical data comprises at least one unit time of agricultural four-condition data; processing the historical outbreak time corresponding to the historical outbreak pest type to obtain a historical pest numerical value corresponding to the historical outbreak pest type, unit time corresponding to an integer part in the historical pest numerical value and days corresponding to a decimal part in the historical pest numerical value; training a training model according to the second historical data, the historical outbreak pest type correlated with the second historical data and the historical pest numerical value to obtain a prediction model. As shown in fig. 2, the method includes:
s210, second historical data, historical outbreak pest and disease damage types and historical outbreak time related to the second historical data are obtained, wherein one historical outbreak pest and disease damage type corresponds to one historical outbreak time, and the second historical data comprise at least one unit time of agricultural four-condition data.
The second historical data refers to agricultural four-situation data collected in a second historical time period, wherein the second historical time period is before and adjacent to the first historical time period. The historical outbreak pest species refers to the species of pest outbreak prior to the current time. The unit time may be ten days, one month may be divided into the first, middle and last ten days, and the total number of the ten days is 10 days or 11 days. The length of the second historical time period is integral multiple of one unit time, and the second historical data comprises agricultural four-situation data of at least one unit time. The historical outbreak pest type associated with the second historical data is the type of pest outbreak within the second historical time. The second historical data correlates the number of historical outbreak pest species to at least one. The historical outbreak time is the time when the historical outbreak pest species outbreak. One historical outbreak pest species corresponds to one historical outbreak time.
Specifically, the second historical data, the historical outbreak pest and disease category associated with the second historical data and the historical outbreak time can be acquired through a database or a storage device.
S220, processing historical outbreak time corresponding to the historical outbreak pest type to obtain a historical pest numerical value corresponding to the historical outbreak pest type, unit time corresponding to an integral part in the historical pest numerical value, and days corresponding to a decimal part in the historical pest numerical value.
Historical pest values are used to indicate historical outbreak times. The integral part of the historical pest numerical value corresponds to unit time, and the decimal part of the historical pest numerical value corresponds to days. And in a second historical time period, optionally selecting the time of one day as standard time, and determining historical pest numerical values corresponding to historical outbreak pest types by taking the standard time as the starting time. The selected standard time is different, and the historical pest numerical values corresponding to the historical outbreak pest types are different.
Specifically, a month to which the standard time belongs, a first day of the standard time in the month to which the standard time belongs, a month to which the historical outbreak time belongs and a second day of the historical outbreak time in the month to which the historical outbreak time belongs are determined, and the historical pest numerical value is determined through a calculation formula of an integer part in the historical pest numerical value and a calculation formula of a decimal part in the historical pest numerical value. Illustratively, the standard time is day B of month A of X years, the month to which the standard time belongs is A, and the first day is B; the historical outbreak time is X years, C month and D day, the month to which the historical outbreak time period belongs is C, and the second day is D. Integer part = | second day/10-first day/10 | + (month to which historical outbreak time belongs-month to which standard time belongs) × 3+1, fractional part = (| second day-first day | + 1)/10, historical pest number = integer part + fractional part. Illustratively, the standard time is 3 months and 1 day in 2020 year, the historical outbreak time is 5 months and 9 days in 2020 year, the month to which the standard time belongs is 3, the first day of the standard time in the month to which the standard time belongs is 1, the month to which the historical outbreak time belongs is 5, the second day of the historical outbreak time in the month to which the historical outbreak time belongs is 9, an integral part in the historical pest and disease damage value = |9/10-1/10| + (5-3) =3+ 1=7, a decimal part in the historical pest and disease damage value = (| 9-1| + 1)/10 =0.9, and the historical pest and disease damage value =7+0.9=7.9.
And S230, training a training model according to the second historical data, the historical outbreak pest and disease damage type associated with the second historical data and the historical pest and disease damage numerical value to obtain a prediction model.
Specifically, in the second historical data, the second historical data between standard times is used as input of a training model, historical outbreak pest types and historical pest numerical values related to the second historical data after the standard times are used as output of the training model, the training model is trained to obtain parameters of the training model after the training is finished, and a prediction model is determined according to the parameters of the training model. The number of predictive models is at least one. Specifically, when the number of the prediction models is one, at least one historical outbreak pest type and a historical pest numerical value corresponding to each historical non-outbreak pest type can be obtained in a prediction mode. When the number of the prediction models is multiple, one prediction model obtains a historical outbreak pest type and a historical pest numerical value.
S240, acquiring first historical data.
And S250, predicting the type and the outbreak time of the pest and disease damage outbreak according to the first historical data.
And S260, determining the interval time between the explosion time and the current time according to the explosion time.
And S270, determining a corresponding pest and disease prevention suggestion according to the interval time and the type of the pest and disease outbreak.
According to the technical scheme of the embodiment of the invention, the historical outbreak time corresponding to the historical outbreak pest type is processed to obtain the historical pest numerical value corresponding to the historical outbreak pest type, the training model is trained according to the second historical data, the historical outbreak pest type associated with the second historical data and the historical pest numerical value to obtain the prediction model, and the historical outbreak time is processed to obtain the historical pest numerical value, so that the training difficulty of the training model and the parameters of the training model can be reduced. The second historical data are divided through optional standard time, the training model can be trained for multiple times by using the same group of second historical data, and the data volume in the training process can be reduced.
On the basis of the above embodiment, the training a training model according to the second historical data, the historical outbreak pest type associated with the second historical data, and the historical pest numerical value to obtain a prediction model includes: determining third history data corresponding to the historical outbreak pest and disease damage type in the second history data based on the historical outbreak pest and disease damage type; training at least one training model based on third history data corresponding to each pest type and historical pest numerical values corresponding to each pest type to obtain a prediction model; the prediction model corresponds to the historical outbreak pest type.
The third history data is history data corresponding to one history outbreak pest and disease damage type in the second history data. And the third history data corresponding to different historical outbreak pest species can be repeated. Specifically, different types of data in the agricultural four-condition data are factors causing pest outbreaks of different historical outbreak pest types, and the second historical data can be screened according to the corresponding relationship between the types of the agricultural four-condition data and the historical outbreak pest types to obtain third historical data. The corresponding relation between the types of the agricultural four-condition data and the types of the historical outbreak plant diseases and insect pests can be set according to actual conditions. Illustratively, the data of the atmospheric temperature and the data of the soil temperature in the agricultural four-story data are factors causing tea leafhopper outbreak, and the data of the atmospheric temperature and the data of the soil temperature may be screened from the second history data as the third history data based on the atmospheric temperature and the soil temperature.
Specifically, third history data corresponding to each historical outbreak pest is determined in the second history data based on at least one historical outbreak pest type. And training the training model to obtain a prediction model based on third history data corresponding to the historical outbreak of plant diseases and insect pests and historical plant disease and insect pest numerical values corresponding to the historical outbreak of plant diseases and insect pests. The prediction model corresponds to the historical outbreak pest and disease category.
By determining third history data corresponding to historical outbreak pest types in the second history data, training the training model based on the third history data corresponding to the historical outbreak pest types and the historical pest data corresponding to the historical outbreak pest types, a prediction model can be obtained for each historical outbreak pest type, the prediction function of the prediction model can be reduced, and the accuracy of the prediction model can be improved.
EXAMPLE III
Fig. 3 is a flowchart of a pest and disease damage prediction method provided by the third embodiment of the present invention, and in this embodiment, based on the above embodiment, the predicting of the type and the outbreak time of a pest and disease damage outbreak according to the first historical data is embodied as follows: obtaining at least one predicted value according to the at least one prediction model based on the first historical data; the predicted value corresponds to the pest and disease type; and acquiring unit time corresponding to the integer part of the predicted value and days corresponding to the decimal part of the predicted value, and predicting the outbreak time of the pest and disease outbreak according to the current time. As shown in fig. 3, the method includes:
s310, acquiring first historical data.
S320, obtaining at least one predicted value according to the at least one prediction model based on the first historical data; the predicted value corresponds to the type of pest outbreak.
The prediction value is an output result of the prediction model and indicates the burst time. At least one predicted value may be obtained by a predictive model. One predicted value corresponds to the kind of pest outbreak.
Specifically, the first historical data is used as the input of the prediction model, and at least one predicted value output by the prediction model is obtained.
S330, unit time corresponding to the integer part of the predicted value and days corresponding to the decimal part of the predicted value are obtained, and the outbreak time of the pest outbreak is predicted according to the current time.
And determining the integral part of the predicted value as unit time, rounding up ten times of the decimal part of the predicted value as days, starting from the current time, adding the unit time and the days, and determining that the pest and disease outbreak is outbreak time.
Specifically, the month to which the burst time belongs = the month to which the current time belongs + unit time/3, the number of days of the burst time = the number of days × 10+ the number of days of the current time, and the month to which the burst time belongs and the number of days of the burst time constitute the burst time. Illustratively, the predicted value is 7.83, the current time is 3 months and 1 day, the unit time is 7, the number of days is 0.83, the current time month is 3, the current time day is 1, the month to which the burst time belongs =3+7/3=5, the number of days of the burst time =0.83 × 10+1=9, and the burst time is 5 months and 9 days.
S340, determining the interval time between the explosion time and the current time according to the explosion time.
And S350, determining a corresponding pest prevention suggestion according to the interval time and the type of the pest outbreak.
According to the technical scheme, based on the first historical data, at least one predicted value is obtained according to at least one prediction model, one predicted value can be obtained through one prediction model, multiple predicted values can be obtained through one prediction model, the application scenes of the prediction model can be increased, the outbreak time of the disease and pest outbreak can be determined according to the current time and the predicted values, and the accuracy of determining the outbreak time can be improved.
On the basis of the above embodiments, the pests include tea lesser leafhoppers; the first historical data includes atmospheric temperature data and soil temperature data.
The atmospheric temperature data refers to data obtained by collecting atmospheric temperature in a first historical time period. The soil temperature data refers to data obtained by collecting the temperature of the soil in a first historical time period.
In an alternative embodiment, the pest is a tea lesser leafhopper; the first historical data are atmospheric temperature data and soil temperature data, and the pest and disease damage prediction method specifically comprises the following steps:
the current time is 3 months and 1 day in 2020, and the atmospheric temperature data and the soil temperature data of each day in 1 month and 2 months of a place are acquired. The method comprises the steps of obtaining an atmospheric temperature data average value and a soil temperature data average value, wherein the atmospheric temperature data and the soil temperature data are counted to obtain the average value of the atmospheric temperature data and the average value of the soil temperature data. Illustratively, the average of the atmospheric temperature data =33.128 and the average of the soil temperature data =35.682.
And inputting the average value of the atmospheric temperature data and the average value of the soil temperature data into a prediction model to obtain a predicted value. Wherein, the prediction model is a regression equation Z = -0.316X +0.107Y +14.484, wherein X is the average value of the atmospheric temperature data, Y is the average value of the soil temperature data, and Z is the predicted value. Substituting the average value X =33.128 of the atmospheric temperature data and the average value Y =35.682 of the soil temperature data into the regression equation to obtain Z =7.83.
Optionally, the burst time is composed of the month of the burst time = the month to which the current time belongs + unit time/3, the number of days of the burst time = the number of days × 10+ the number of days of the current time. Burst times of 5 months and 9 months were obtained.
Optionally, the integer portions 1, 2, 3, … and 12 of Z correspond to 12 time periods in the first 3 th, middle 3 rd, last 3 rd, … …, last 6 th, middle 6 th and last 6 th months, respectively, so that Z =7.83 represents that the pest outbreak time is in the first 5 th month, and each time period corresponds to 10 (or 11) days, the corresponding dates are: 0.83 + 10+1=9, and the burst time is No. 9 at month 5.
The interval time between the burst time and the current time is 31+30+9=70 days.
Inquiring a suggestion table for preventing the plant diseases and insect pests of the tea lesser leafhoppers according to the outbreak time and the current time interval:
TABLE 1 Pest and pest prevention suggestion table for tea lesser leafhopper
According to table 2, the pest grade can be determined as temporary pest absence, and the corresponding pest prevention suggestion is suggestion 4.
Optionally, for the prediction model Z = -0.316x +0.107y +14.484, the atmospheric temperature data and the soil temperature data in 2019 and 2018 can be obtained as the second historical data, and the historical outbreak time of tea lesser leafhoppers in 2019 and 2018.
And (4) optionally selecting one day from 2019 and 2018 as standard time, and processing the historical outbreak time to obtain the historical pest and disease damage numerical value corresponding to the tea lesser leafhopper. And taking the average value of the atmospheric temperature data before the standard time and the average value of the soil temperature data before the standard time as the input of a training model, and taking the historical pest and disease numerical value as the output to train the training model. Wherein the training model is Z = aX + bY + c, and a, b and c are parameters of the training model.
After the training is completed, parameters of the training model are obtained as a =0.316, b =0.107, c =14.484. And determining the prediction model as Z = -0.316X +0.107Y +14.484 according to the parameters of the training model.
The method has the advantages that the type and the outbreak time of the pest and disease outbreak are predicted through the first historical data, the corresponding pest and disease prevention suggestions are determined according to the interval time between the outbreak time and the current time and the type of the pest and disease outbreak, the type of the pest and disease outbreak and the corresponding specific outbreak time are predicted, the accuracy of the pest and disease prediction can be improved, the corresponding pest and disease prevention suggestions are determined jointly according to the interval time between the outbreak time and the current time and in combination with the type of the pest and disease outbreak, the accuracy of the pest and disease prevention suggestions can be improved, the pest and disease can be prevented accurately in real time, and waste and loss of manpower and material resources are reduced.
Example four
Fig. 4 is a schematic structural diagram of a pest and disease damage prediction device provided by the fourth embodiment of the present invention. As shown in fig. 4, the apparatus includes: a first data acquisition module 401, a prediction module 402, an interval time determination module 403 and a prevention suggestion determination module 404.
The first data obtaining module 401 is configured to obtain first history data;
a prediction module 402, configured to predict a type and a burst time of pest outbreak according to the first historical data;
an interval determining module 403, configured to determine, according to the burst time, an interval between the burst time and a current time;
and a prevention suggestion determining module 404, configured to determine a corresponding prevention suggestion for pest and disease damage according to the interval time and the type of pest outbreak.
According to the technical scheme of the embodiment of the invention, the type and the outbreak time of the pest and disease outbreak are predicted through the first historical data, the corresponding pest and disease prevention suggestion is determined according to the interval time between the outbreak time and the current time and the type of the pest and disease outbreak, the type of the pest and disease outbreak and the corresponding specific outbreak time are predicted, the accuracy of the pest and disease prediction can be improved, the corresponding pest and disease prevention suggestion is determined jointly according to the interval time between the outbreak time and the current time and by combining the type of the pest and disease outbreak, the accuracy of the pest and disease prevention suggestion can be improved, the pest and disease can be prevented accurately in real time, and waste and loss of manpower and material resources are reduced.
Optionally, the prevention recommendation determining module 404 includes:
the matching unit is used for matching the interval time with the pest and disease damage grade to obtain a matching result;
the prevention suggestion determining unit is used for determining a prevention suggestion of the plant diseases and insect pests according to the matching result and the types of the plant diseases and insect pests outbreak; the pest recommendation includes fertilization prevention, pesticide application prevention, or mixed prevention.
Optionally, the prevention recommendation determining unit includes:
the association interval time acquisition subunit is used for acquiring association interval time, wherein the association interval time is a plurality of interval time with the time length difference value smaller than or equal to a preset threshold value;
the matching result subunit is used for determining the incidence relation between the matching results and the priority of the matching results according to the matching results and the types of the pest outbreaks;
and the prevention suggestion determination subunit is used for determining a pest and disease prevention suggestion based on the association relationship among the matching results, the associated interval time and the priority of the matching results.
Optionally, the apparatus further comprises:
the second data acquisition module is used for acquiring second historical data, historical outbreak pest and disease damage types and historical outbreak time which are related to the second historical data, wherein one historical outbreak pest and disease damage type corresponds to one historical outbreak time, and the second historical data comprises agricultural four-situation data of at least one unit time;
the historical pest and disease damage numerical value determining module is used for processing the historical outbreak time corresponding to the historical outbreak pest and disease damage type to obtain a historical pest and disease damage numerical value corresponding to the historical outbreak pest and disease damage type, unit time corresponding to an integer part in the historical pest and disease damage numerical value and days corresponding to a decimal part in the historical pest and disease damage numerical value;
and the training module is used for training a training model according to the second historical data, the historical outbreak pest and disease damage type associated with the second historical data and the historical pest and disease damage numerical value to obtain a prediction model.
Optionally, the training module includes:
a third data determining unit, configured to determine, based on the historical outbreak pest type, third historical data corresponding to the historical outbreak pest type in the second historical data;
the training unit is used for training at least one training model based on third history data corresponding to each historical outbreak pest type and historical pest numerical values corresponding to each historical outbreak pest type to obtain a prediction model; the prediction model corresponds to the historical outbreak pest and disease category.
Optionally, the prediction module 402 includes:
a predicted value determining unit, configured to obtain at least one predicted value according to the at least one prediction model based on the first historical data; the predicted value corresponds to the pest and disease type;
and the prediction unit is used for acquiring unit time corresponding to the integer part of the predicted value and days corresponding to the decimal part of the predicted value, and predicting the outbreak time of the pest and disease outbreak according to the current time.
Optionally, the pest comprises tea lesser leafhopper; the first historical data includes atmospheric temperature data and soil temperature data.
The pest and disease damage prediction device provided by the embodiment of the invention can execute the pest and disease damage prediction method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. Processor 11 performs the various methods and processes described above, such as pest prediction methods.
In some embodiments, the pest prediction method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the pest prediction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the pest prediction method by any other suitable means (e.g., by way of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server may be a cloud Server, which is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the conventional physical host and VPS (Virtual Private Server) service.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for predicting plant diseases and insect pests, which is characterized by comprising the following steps:
acquiring first historical data;
predicting the type and the outbreak time of the pest and disease damage outbreak according to the first historical data;
determining the interval time between the burst time and the current time according to the burst time;
and determining a corresponding pest and disease prevention suggestion according to the interval time and the type of the pest and disease outbreak.
2. The method of claim 1, wherein said determining a corresponding pest prevention recommendation based on said interval and said type of said outbreak comprises:
matching the interval time with the pest and disease damage grade to obtain a matching result;
determining a proposal for preventing the plant diseases and insect pests according to the matching result and the types of the plant diseases and insect pests outbreak; the pest recommendation includes fertilization prevention, pesticide application prevention, or mixed prevention.
3. The method of claim 2, wherein determining a pest prevention recommendation based on the match and the type of outbreak comprises:
acquiring associated interval time, wherein the associated interval time is a plurality of interval time with a time length difference value less than or equal to a preset threshold value;
determining the incidence relation between the matching results and the priority of the matching results according to the matching results and the types of the pest outbreaks;
and determining a pest and disease prevention suggestion based on the association relationship among the matching results, the associated interval time and the priority of the matching results.
4. The method of claim 1, further comprising:
acquiring second historical data, historical outbreak pest and disease damage types and historical outbreak time associated with the second historical data, wherein one historical outbreak pest and disease damage type corresponds to one historical outbreak time, and the second historical data comprises agricultural four-condition data of at least one unit time;
processing the historical outbreak time corresponding to the historical outbreak pest type to obtain a historical pest numerical value corresponding to the historical outbreak pest type, unit time corresponding to an integer part in the historical pest numerical value and days corresponding to a decimal part in the historical pest numerical value;
and training a training model according to the second historical data, the historical outbreak pest type associated with the second historical data and the historical pest numerical value to obtain a prediction model.
5. The method of claim 4, wherein training a training model based on the second historical data, historical outbreak pest type associated with the second historical data, and the historical pest value to obtain a prediction model comprises:
determining third history data corresponding to the historical outbreak pest and disease damage type in the second history data based on the historical outbreak pest and disease damage type;
training at least one training model based on third history data corresponding to each historical outbreak pest type and a historical pest numerical value corresponding to each historical outbreak pest type to obtain a prediction model; the prediction model corresponds to the historical outbreak pest type.
6. The method according to claim 1, wherein predicting the type and timing of a pest outbreak based on the first historical data comprises:
obtaining at least one predicted value according to the at least one prediction model based on the first historical data; the predicted value corresponds to the type of the plant diseases and insect pests;
and acquiring unit time corresponding to the integer part of the predicted value and days corresponding to the decimal part of the predicted value, and predicting the outbreak time of the pest and disease outbreak according to the current time.
7. The method of claim 1, wherein the pest comprises a tea lesser leafhopper; the first historical data includes atmospheric temperature data and soil temperature data.
8. A plant disease and insect pest prediction device, comprising:
the first data acquisition module is used for acquiring first historical data;
the prediction module is used for predicting the type and the outbreak time of the pest and disease damage outbreak according to the first historical data;
the interval time determining module is used for determining the interval time between the burst time and the current time according to the burst time;
and the prevention suggestion determining module is used for determining corresponding prevention suggestion of plant diseases and insect pests according to the interval time and the type of the plant disease and insect pest outbreak.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the pest prediction method of any one of claims 1-7.
10. A computer readable storage medium having stored thereon computer instructions for causing a processor to execute a method of pest prediction according to any one of claims 1 to 7.
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| CN117408364A (en) * | 2023-08-08 | 2024-01-16 | 湖北泰跃卫星技术发展股份有限公司 | Crop disease and pest index prediction method and device based on model fusion |
| CN117408364B (en) * | 2023-08-08 | 2024-05-24 | 湖北泰跃卫星技术发展股份有限公司 | Crop disease and pest index prediction method and device based on model fusion |
| CN117391265A (en) * | 2023-12-13 | 2024-01-12 | 金乡县林业保护和发展服务中心(金乡县湿地保护中心、金乡县野生动植物保护中心、金乡县国有白洼林场) | Forestry pest hazard risk prediction method based on big data analysis |
| CN117391265B (en) * | 2023-12-13 | 2024-03-05 | 金乡县林业保护和发展服务中心(金乡县湿地保护中心、金乡县野生动植物保护中心、金乡县国有白洼林场) | Forestry pest hazard risk prediction method based on big data analysis |
| CN117852726A (en) * | 2024-03-07 | 2024-04-09 | 中国农业科学院烟草研究所(中国烟草总公司青州烟草研究所) | Vegetable pest outbreak epidemic prediction method, medium and system |
| CN117852726B (en) * | 2024-03-07 | 2024-06-04 | 中国农业科学院烟草研究所(中国烟草总公司青州烟草研究所) | Vegetable pest outbreak epidemic prediction method, medium and system |
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