CN114638445B - Method, device, medium, and electronic device for crop disease prevention - Google Patents
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Abstract
The embodiment of the disclosure provides a method, a device and a medium for crop disease prevention and electronic equipment, and relates to the field of crop planting. Wherein the method comprises the following steps: in the growth period of the target crops, acquiring the hours within the disease infection temperature threshold of the ith day and acquiring the hours within the disease infection temperature threshold of the (i + 1) th day; determining the disease infection probability P of the (i + 1) th day according to the relative air humidity of the (i) th day, the relative air humidity of the (i + 1) th day and the relative air humidity at which the diseases begin to infect, wherein i is a natural number; predicting the number of hours of disease infection on the i +1 th day based on the number of hours within the disease infection temperature threshold on the i th day, the number of hours within the disease infection temperature threshold on the i +1 th day and the disease infection probability on the i +1 th day; and determining the infection risk index of the disease to the target crop according to the disease infection hours of the i +1 th day. By the scheme, the day-by-day infection probability of the crop diseases can be obtained more accurately, so that the crop diseases can be prevented accurately.
Description
Technical Field
The present disclosure relates to the field of crop planting, and in particular, to a method, an apparatus, a medium, and an electronic device for preventing crop diseases.
Background
Crop diseases are one of important reasons for reducing the yield of grains, and effective disease early warning has important significance for guaranteeing the yield and quality of crops and guiding scientific and green prevention and control of diseases. Particularly, the occurrence and prevalence of the climate type diseases are influenced by meteorological factors such as field temperature, humidity and the like, so that the climate type diseases are difficult to prevent. Therefore, the forecasting and early warning method based on the meteorological factors has high feasibility.
Currently, a more common climate type disease prediction model generally uses a multivariate regression analysis method, a gray neural network combination method, a bp neural network method, a remote sensing method and other methods, mainly considered factors include air temperature, humidity, precipitation, a growth period, leaf area indexes, remote sensing NDVI, RVI, DVI indexes and the like, and the model can be roughly divided into three types: the system comprises a monitoring and predicting model established based on pathogenic bacteria information, a monitoring and predicting model established based on meteorological factor information and a monitoring and predicting model established based on crop canopy spectrum information. Because of the difference of the crop variety, climate condition, cultivation and cultivation system, physiological race composition of germs and the like in each area, the current monitoring and early warning of climate type diseases are carried out aiming at specific areas. Moreover, most of the existing models rely on the point location information of bacterial sources or meteorological factors, the occurrence and development of climatic diseases are a dynamic mechanism process, the infection of the climatic diseases is closely related to the physiological characteristics of host plants, and the point location information hardly reflects the space dynamic distribution rule of the diseases.
Disclosure of Invention
The present disclosure is directed to a method, an apparatus, a medium, and an electronic device for crop disease prevention. According to the displayed content, the method can at least more accurately obtain the day-by-day infection probability of the crop diseases, so that the crop diseases can be accurately prevented, and the loss of the crop yield is reduced.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure. According to one aspect of the present disclosure, there is provided a method of crop disease prevention comprising: in the growth period of the target crops, acquiring the hours within the disease infection temperature threshold of the ith day and acquiring the hours within the disease infection temperature threshold of the (i + 1) th day; determining the disease infection probability P of the (i + 1) th day according to the relative air humidity of the (i) th day, the relative air humidity of the (i + 1) th day and the relative air humidity at which the disease starts to infect, wherein i is a natural number; predicting the number of hours of disease infection on the i +1 th day based on the number of hours within the disease infection temperature threshold on the i th day, the number of hours within the disease infection temperature threshold on the i +1 th day and the disease infection probability on the i +1 th day; and determining the infection risk index of the disease to the target crop according to the disease infection hours of the i +1 th day.
According to another aspect of the present disclosure, there is provided a device for crop disease prevention, comprising: an acquisition module: the method is used for acquiring the hours within the disease infection temperature threshold of the ith day and acquiring the hours within the disease infection temperature threshold of the (i + 1) th day in the growth period of a target crop; a first determination module: determining the disease infection probability P of the i +1 th day according to the relative air humidity of the i th day, the relative air humidity of the i +1 th day and the relative air humidity at which the disease starts to infect, wherein i is a natural number; a prediction module: and predicting the number of hours of disease infection on the i +1 th day based on the number of hours within the disease infection temperature threshold on the i th day, the number of hours within the disease infection temperature threshold on the i +1 th day and the disease infection probability on the i th day. A second determination module: determining the infection risk index of the disease to the target crop according to the number of disease infection hours of the i +1 th day.
According to yet another aspect of the present disclosure, there is provided an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for crop disease prevention as in the above embodiments when executing the computer program.
According to still another aspect of the present disclosure, there is provided a readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of crop disease prevention as in the above embodiments.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in some embodiments of the present disclosure, the following processes are performed, including: and in the growth period of the target crops, acquiring the hours within the disease infection temperature threshold of the ith day and the hours within the disease infection temperature threshold of the (i + 1) th day, and determining the disease infection probability P of the (i + 1) th day according to the relative air humidity of the ith day, the relative air humidity of the (i + 1) th day and the relative air humidity at which the diseases begin to infect, wherein i is a natural number. And further, predicting the number of hours of disease infection on the i +1 th day based on the number of hours within the disease infection temperature threshold on the i th day, the number of hours within the disease infection temperature threshold on the i +1 th day and the disease infection probability on the i +1 th day. And finally, determining the infection risk index of the disease to the target crop according to the disease infection hours of the (i + 1) th day. By the scheme, the day-by-day infection probability of the crop diseases can be more accurately obtained, so that the diseases of the target crops can be accurately prevented, and the yield loss of the target crops is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 schematically shows a schematic view of an exemplary application scenario to which the method of crop disease prevention of an embodiment of the present disclosure may be applied.
Fig. 2 schematically shows a flow diagram of a method of crop disease prevention according to an exemplary embodiment of the present disclosure.
Fig. 3 schematically illustrates a flow chart for obtaining crop growth periods in an exemplary embodiment according to the present disclosure.
Fig. 4 schematically illustrates a flow chart for determining a disaster area of a crop according to an exemplary embodiment of the present disclosure.
Fig. 5 schematically shows a structural view of an apparatus for crop disease prevention according to an exemplary embodiment of the present disclosure.
Fig. 6 schematically shows a structural view of an apparatus for crop disease prevention according to another exemplary embodiment of the present disclosure.
FIG. 7 schematically shows a block diagram of an electronic device in an exemplary embodiment according to the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present disclosure more clear, embodiments of the present disclosure will be described in further detail below with reference to the accompanying drawings.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
In the description of the present disclosure, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present disclosure can be understood in specific instances by those of ordinary skill in the art. Further, in the description of the present disclosure, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Referring to fig. 1, a schematic diagram of an exemplary application scenario in which the method of crop disease prevention of an embodiment of the present disclosure may be applied is schematically illustrated.
As shown in fig. 1, includes a crop 110, an environmental measurement device 120, and a server 130.
The crops 110 include, but are not limited to, rice and wheat, the environment measuring device 120 at least has a function of collecting an environment temperature and a function of collecting an environment humidity, and the server 130 is configured to process environment data collected by the environment measuring device 120.
Illustratively, fig. 2 schematically shows a flow diagram of a method of crop disease prevention according to an exemplary embodiment of the present disclosure.
Specifically, referring to fig. 2, the method of crop disease prevention shown in this figure comprises:
s210, in the growth period of the target crop, obtaining the hours within the disease infection temperature threshold of the ith day and obtaining the hours within the disease infection temperature threshold of the (i + 1) th day.
In an exemplary embodiment, referring to fig. 1, the number of hours within the disease infestation temperature threshold of day i and the number of hours within the disease infestation temperature threshold of day i +1 are obtained by the environmental measurement device 120 during the growth period of the target crop 110.
Illustratively, when the target crop plant 110 is rice, the disease is rice blast, which is also known as rice blast fever, kowter blast, etc., and is a disease caused by pathogenic rice blast and occurring in rice. In the case where the target crop 110 is wheat, the disease is gibberellic disease, which is also known as ear rot, wheat straw blight, wheat head rot, red wheat head, and red head malaria, and is a disease caused by infection with a variety of fusarium species and occurring on wheat.
Illustratively, the above-described obtaining of the number of hours within the temperature threshold of disease infestation on day i ， The method comprises the following steps:
wherein n =23, CT is the number of hours that the temperature of the ith day is within the disease infection threshold value, and CT is j Used for judging whether the temperature of the ith day at the jth hour is within the disease infection temperature threshold value Th j Is the jth hour temperature; th min Is the lowest temperature, Th, of disease onset max The highest temperature at which the disease occurs. For example, when the target crop 110 is rice, if the disease is rice blast, the Th is min The minimum temperature of blast is 25 deg.C, and the above Th max The highest temperature of the blast attack is 28 ℃; when the target crop 110 is wheat and the disease is gibberellic disease, the Th is min The lowest temperature of the onset of gibberellic disease (24 ℃), above Th max The highest temperature of the onset of head blight was 29 ℃.
As an example, as a refinement to the above scheme, the hourly temperature statistics may be refined to the minute-by-minute temperature statistics, which is not limited by the present disclosure.
Through the steps, the environmental temperature of the crops in one day can be refined, the more accurate hours of the crops in the disease infection threshold value can be obtained, the statistical error caused by temperature mutation is solved, and the probability of the crops being infected by the diseases can be more accurately predicted.
And S220, determining the disease infection probability P of the (i + 1) th day according to the relative air humidity of the (i) th day, the relative air humidity of the (i + 1) th day and the relative air humidity at which the disease starts to infect, wherein i is a natural number.
In an exemplary embodiment, referring to fig. 1, the server 130 determines the disease infection probability P on the i +1 th day, i being a natural number, according to the relative humidity of the air on the i th day, the relative humidity of the air on the i +1 th day, and according to the relative humidity of the air at which the disease starts to infect.
Exemplarily, the determining the disease infection probability P for the i +1 th day according to the relative humidity of the air on the i th day, the relative humidity of the air on the i +1 th day, and the relative humidity of the air at which the disease starts to infect includes:
wherein P is the infection probability of the disease on the ith day and RH 0 The relative humidity of the air at which the disease begins to infect,
is the mean value of relative humidity of air, RH t Relative humidity of air, RH, day i y The relative humidity of the air on day i + 1. For example, the relative humidity of the air at which rice blast begins to infect is 77%, and the relative humidity of the air at which wheat scab begins to infect is 70%.
Illustratively, in the process of collecting the relative humidity of the air, a Markov chain of crop fungus attack can be built based on a fungus growth cycle model, a continuous-time Markov Monte Carlo model is built by using a Monte Carlo method, and the disease infection probability P is simulated and calculated through the continuous-time Markov Monte Carlo model.
Through the steps, the relative humidity of the air in the previous day is combined with the relative humidity of the air in the current day, the average relative humidity of the air in two adjacent days is calculated, and the average relative humidity of the air is compared with the relative humidity of the air in which the diseases begin to infect, so that the infection probability of the diseases in the current day can be calculated more accurately.
And S230, predicting the number of hours of disease infection on the i +1 th day based on the number of hours within the disease infection temperature threshold on the i th day, the number of hours within the disease infection temperature threshold on the i +1 th day and the disease infection probability on the i +1 th day.
In an exemplary embodiment, referring to fig. 1, server 130 predicts the number of disease-infecting hours on day i +1 based on the number of hours within the disease-infecting temperature threshold on day i and the number of hours within the disease-infecting temperature threshold on day i +1 described above, and the disease-infecting probability on day i.
The step of predicting the number of infected disease hours of the (i + 1) th day comprises the following steps:
wherein R is r CT is the number of hours that the disease may be infected t The number of hours that the temperature of day i +1 is within the disease infection threshold, CT y The number of hours that the temperature of day i is within the disease infestation threshold.
Through the steps, the hours of the temperature in the previous day within the disease infection threshold value and the hours of the temperature in the current day within the disease infection threshold value are combined, so that the hours of possible infection of the disease in the current day can be predicted more accurately.
S240, determining the infection risk index of the disease to the target crop according to the disease infection hours of the (i + 1) th day.
In an exemplary embodiment, referring to fig. 1, server 130 determines a disease risk of infestation index for the target crop based on the number of disease infestation hours on day i +1 as described above.
Wherein, the determining the infection risk index of the disease to the target crop according to the disease infection hours of the i +1 th day comprises the following steps:
wherein R is dl For a daily infection risk rating, max 1 Is 15, R fl Predicting risk level for bureau, max 2 Is 170. The bureau predicted risk level refers to: and (4) evaluating the comprehensive disease infection risk of the whole infection period by the ith day.
Illustratively, the disease risk index of each day of the critical period is defined as low risk, medium risk, higher risk, high risk and extremely high risk by combining with the national standard of crop disease prediction technical specification (GB/T15790 2009 rice blast prediction survey specification). And accumulating and calculating the disease fixed station predicted risk index by utilizing the disease risk index of the key period of the whole infection window and combining the disease fixed station historical data, and normalizing the disease fixed station predicted risk index into 1-5 grades which respectively represent low risk, medium risk, higher risk, high risk and extremely high risk.
Illustratively, the above is low risk at Rdl ∈ [0,2), medium risk at Rdl ∈ [2, 4%), high risk at Rdl ∈ [4, 6%), high risk at Rdl ∈ [6, 8%), very high risk at Rdl ≧ 8, low risk at Rfl ∈ [0, 2%), medium risk at Rfl ∈ [2, 4%), high risk at Rfl ∈ [4, 6%), high risk at Rfl ∈ [6, 8%), and very high risk at Rfl ≧ 8.
In the technical scheme provided by the embodiment shown in fig. 2, in the growth period of the target crop, the number of hours within the disease infection temperature threshold of the ith day and the number of hours within the disease infection temperature threshold of the (i + 1) th day are obtained, and the disease infection probability of the (i + 1) th day is further determined according to the relative air humidity of the ith day, the relative air humidity of the (i + 1) th day and the relative air humidity at which the disease starts to infect. And predicting the number of hours of disease infection on the (i + 1) th day based on the number of hours in the disease infection temperature threshold of the (i) th day, the number of hours in the disease infection temperature threshold of the (i + 1) th day and the disease infection probability of the (i + 1) th day, and finally determining the infection risk index of the disease on the target crop according to the number of hours of disease infection on the (i + 1) th day. Through the steps, the day-by-day infection probability of the crop diseases can be obtained more accurately, so that the crop diseases can be prevented accurately, and the loss of crop yield is reduced.
The following describes in detail the specific implementation of each step of the embodiment shown in fig. 2 with reference to the embodiments shown in fig. 3 to 4:
in an exemplary embodiment, fig. 3 schematically illustrates a flow chart for obtaining crop growth periods in an exemplary embodiment according to the present disclosure. Referring to fig. 3, the method of crop disease prevention shown in this figure comprises:
s310, obtaining growth environment data of the target crop, wherein the growth environment data comprises: sowing date, effective accumulated temperature and multispectral remote sensing monitoring data.
In an exemplary embodiment, referring to fig. 1, growth environment data for a target crop 110 is obtained by an environment measuring device 120. The effective accumulated temperature is the sum of effective temperatures of the target crop 110 in a certain growth period or all growth periods, i.e., the sum of differences between daily average temperature and biological zero temperature in a certain period of time. The multispectral remote sensing is a remote sensing technology for synchronously imaging ground objects by using a sensor with more than two spectral channels, and can divide electromagnetic wave information of reflected radiation of an object into a plurality of spectral segments for receiving and recording.
And S320, calculating the window period of the target crop based on the growth environment data.
In an exemplary embodiment, referring to FIG. 1, the server 130 calculates a window period for the target crop 110 based on the growth environment data described above. Wherein, the window period is: the critical period of disease prevalence, i.e., the critical period of pathogen infestation on crops. For rice, the window period refers to the period when a single ear begins to extend out of the last 1 leaf and expose grains after the differentiation of the rice ears is completed; in the case of wheat, the window period refers to the heading and flowering period, i.e., the phenomenon that the ears of the cereal crops are completely developed and extend out of the top leaves along with the elongation of the stems is called heading, and when the cereal crops are flowering, the stigma extends out and pollen scatters, and is called flowering.
S330, determining the window period and the preset number of days before the window period as the growth period.
In an exemplary embodiment, the server 130 determines the window period, and a preset number of days before the window period, as the birth period.
For example, the preset number of days before the window period can be set according to different crops.
According to the technical scheme provided by the embodiment shown in fig. 3, the window period of the target crop is calculated based on the growth environment data by acquiring the growth environment data of the target crop. And finally, determining the window period and the preset number of days before the window period as the growth period. Through the steps, the window period of the target crop can be predicted, so that the high-incidence period of the crop infected by the disease is predicted, and early warning for the arrival of the high-incidence period of the crop disease is realized.
Illustratively, fig. 4 schematically shows a flow chart for performing the determination of the disaster area of the crop according to an exemplary embodiment of the present disclosure.
Referring to fig. 4, in S410, the kind of a target crop is identified.
In an exemplary embodiment, the category of the target crop is identified by the server 130.
In S420, the disaster area of the target crop is determined based on the type of the target crop and the infection risk index.
In an exemplary embodiment, the server 130 determines the disaster area of the target crop 110 based on the type of the target crop 110 and the risk of infestation index.
Through the steps, the crop disaster situations caused by diseases can be quantified, a plurality of crop disaster situations which are possibly infected by the diseases can be compared to determine the crop which is possibly seriously damaged in the future, and the crop which is possibly seriously damaged is preferentially prevented from the diseases, so that the influence of the diseases on the crop is reduced to the maximum extent. For example, in a case where there are two wheat fields and rice fields having the same area, the server 130 determines that the risk index of rice blast infection is high risk and the risk index of wheat scab infection is low risk, and thus the server 130 determines that the affected area of rice in the future is much larger than that of wheat. Furthermore, the manpower and material resources for preventing the crop diseases can be reasonably distributed according to the comparison result of the damaged areas of the rice and the wheat so as to reduce the influence of the diseases on the crop yield to the maximum extent.
According to the technical scheme provided by the embodiment shown in fig. 4, the type of the target crop is firstly identified, then the disaster area of the target crop is determined based on the type of the target crop and the infection risk index, the crop disaster situation caused by diseases is quantized, a plurality of crop disaster situations which are possibly infected by the diseases can be compared to determine the crop which is possibly seriously damaged in the future, and the crop which is possibly seriously damaged is preferentially prevented from being damaged, so that the influence of the diseases on the crop is reduced to the greatest extent.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 5 is a view schematically showing a structure of an apparatus for crop disease prevention according to an exemplary embodiment of the present disclosure. Referring to fig. 5, there is shown a crop disease prevention apparatus 500 comprising: an acquisition module 510, a first determination module 520, a prediction module 530, a second determination module 540.
Specifically, the obtaining module 510 is configured to obtain the number of hours within the disease infection temperature threshold of the ith day and obtain the number of hours within the disease infection temperature threshold of the (i + 1) th day in the growth period of the target crop.
The first determining module 520 is configured to determine the disease infection probability P on the i +1 th day, where i is a natural number, according to the relative humidity of the air on the i th day, the relative humidity of the air on the i +1 th day, and the relative humidity of the air at which the disease starts to infect.
The prediction module 530 is configured to predict the number of hours of disease infection on the i +1 th day based on the number of hours within the disease infection temperature threshold on the i th day, the number of hours within the disease infection temperature threshold on the i +1 th day, and the disease infection probability on the i +1 th day.
The second determining module 540 is configured to determine an infection risk index of the disease on the target crop according to the number of disease infection hours on the i +1 th day.
In an exemplary embodiment, based on the foregoing solution, the obtaining module 510 is further configured to: and acquiring growth environment data of the target crops. Wherein the growth environment data comprises: sowing date, effective accumulated temperature and multispectral remote sensing monitoring data.
In an exemplary embodiment, based on the foregoing scheme, the first determining module 520 is further configured to: calculating the window period of the target crop based on the growth environment data; and determining the window period and the preset number of days before the window period as the growth period.
In an exemplary embodiment, based on the foregoing, fig. 6 schematically shows a structural view of an apparatus for crop disease prevention according to another exemplary embodiment of the present disclosure.
In an exemplary embodiment, based on the foregoing solution, the apparatus further includes: an identification module 650.
The identification module 650 is configured to: and identifying the type of the target crop.
In an exemplary embodiment, based on the foregoing solution, the apparatus further includes: a third determination module 660.
The third determining module 660 is configured to: and determining the disaster area of the target crop based on the type of the target crop and the infection risk index.
It should be noted that, when the apparatus for preventing crop diseases provided in the foregoing embodiment executes the method for preventing crop diseases, only the division of the above functional modules is taken as an example, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the apparatus may be divided into different functional modules to complete all or part of the functions described above. In addition, the crop disease prevention device provided in the above embodiment and the crop disease prevention method embodiment belong to the same concept, and therefore, for details that are not disclosed in the device embodiment of the present disclosure, please refer to the above crop disease prevention method embodiment of the present disclosure, which is not described herein again.
The above-mentioned serial numbers of the embodiments of the present disclosure are merely for description and do not represent the merits of the embodiments.
The disclosed embodiments also provide a readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method of any of the preceding embodiments. The readable storage medium may include, but is not limited to, any type of disk including floppy disks, optical disks, DVD (Digital Video disk), CD-ROMs (Compact disk Read-Only Memory), microdrive, and magneto-optical disks, ROMs (Read-Only Memory), RAMs (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), DRAM (Dynamic Random Access Memory), VRAM (Video RAM), flash Memory devices, magnetic or optical cards, nanosystems, or any type of media or device suitable for storing instructions and/or data.
The embodiment of the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of any of the above-mentioned embodiments of the method are implemented.
FIG. 7 schematically illustrates a block diagram of an electronic device in an exemplary embodiment according to the present disclosure. Referring to fig. 7, an electronic device 700 includes: a processor 710 and a memory 720.
In the embodiment of the present disclosure, the processor 710 is a control center of a computer system, and may be a processor of a physical machine or a processor of a virtual machine. Processor 710 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 710 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 710 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state.
In an embodiment of the present disclosure, the processor 710 is specifically configured to:
in the growth period of target crops, acquiring the hours within the disease infection temperature threshold of the ith day and acquiring the hours within the disease infection temperature threshold of the (i + 1) th day; determining the disease infection probability P of the i +1 th day according to the relative air humidity of the i th day, the relative air humidity of the i +1 th day and the relative air humidity at which the disease starts to infect, wherein i is a natural number; predicting the number of hours of disease infection on the i +1 th day based on the number of hours within the disease infection temperature threshold on the i th day, the number of hours within the disease infection temperature threshold on the i +1 th day and the disease infection probability on the i +1 th day; and determining the infection risk index of the disease to the target crop according to the disease infection hours of the i +1 th day.
Further, before the obtaining of the number of hours within the disease infection temperature threshold of the ith day and the number of hours within the disease infection temperature threshold of the (i + 1) th day in the first growth period of the target crop, the method further comprises: acquiring growth environment data of the target crops; calculating the window period of the target crop based on the growth environment data; determining the window period and the preset number of days before the window period as the growth period; wherein the growth environment data comprises: sowing date, effective accumulated temperature and multispectral remote sensing monitoring data.
Further, the number of hours within the infection temperature threshold of the disease on the ith day is obtained ， The method comprises the following steps:
wherein n =23, CT is the number of hours that the temperature of the ith day is within the disease infection threshold value, and CT is j Used for judging whether the temperature of the ith day at the jth hour is within the disease infection temperature threshold value Th j Is the jth hour temperature; th min Is the lowest temperature, Th, of disease onset max The highest temperature at which the disease occurs.
Further, the determining the disease infection probability P for the i +1 th day according to the relative air humidity of the i th day, the relative air humidity of the i +1 th day, and the relative air humidity at which the disease starts to infect includes:
wherein P is the infection probability of the disease on the ith day and RH 0 The relative humidity of the air at which the disease begins to infect,
is the mean value of relative humidity of air, RH t Relative humidity of air, RH, day i y The relative humidity of the air on day i + 1.
Further, the predicting the number of hours of disease infection on the i +1 th day based on the number of hours within the disease infection temperature threshold on the i th day, the number of hours within the disease infection temperature threshold on the i +1 th day, and the disease infection probability on the i +1 th day includes:
wherein R is r CT is the number of hours that the disease may be infected t The number of hours that the temperature is within the disease infection threshold value on the (i + 1) th day, CT y The number of hours that the temperature of day i is within the disease infestation threshold.
Further, the determining the infection risk index of the disease to the target crop according to the disease infection hours of the i +1 th day comprises the following steps:
wherein R is dl For a daily infection risk rating, max 1 Is 15, R fl Predicting risk level for bureau, max 2 Is 170.
Further, after determining the infection risk index of the disease to the target crop, the method further comprises the following steps: identifying the type of the target crop; and determining the disaster area of the target crop based on the type of the target crop and the infection risk index.
In some embodiments, the electronic device 700 further comprises: a peripheral interface 730 and at least one peripheral. Processor 710, memory 720 and peripheral interface 730 may be connected by buses or signal lines. Various peripheral devices may be connected to peripheral interface 730 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of a display 740, a camera 750, and an audio circuit 760.
The display screen 740 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When display 740 is a touch display, display 740 also has the ability to capture touch signals on or over the surface of display 740. The touch signal may be input to the processor 710 as a control signal for processing. At this point, the display 740 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments of the present disclosure, the display 740 may be one, providing a front panel of the electronic device 700; in other embodiments of the present disclosure, the display 740 may be at least two, respectively disposed on different surfaces of the electronic device 700 or in a foldable design; in still other embodiments of the present disclosure, the display 740 may be a flexible display disposed on a curved surface or a folded surface of the electronic device 700. Even more, the display 740 may be configured in a non-rectangular irregular pattern, i.e., a shaped screen. The Display 740 can be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), and other materials.
The camera 750 is used to capture images or video. Optionally, the camera 750 includes a front camera and a rear camera. Generally, a front camera is disposed on a front panel of an electronic apparatus, and a rear camera is disposed on a rear surface of the electronic apparatus. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments of the present disclosure, camera 750 may also include a flash. The flash lamp can be a single-color temperature flash lamp or a double-color temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.
The power supply 770 is used to power the various components in the electronic device 700. The power source 770 may be alternating current, direct current, disposable or rechargeable. When the power supply 770 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charge technology.
The block diagram of the electronic device 700 shown in the embodiments of the present disclosure is not intended to limit the electronic device 700, and the electronic device 700 may include more or fewer components than those shown, or may combine some of the components, or may employ a different arrangement of components.
In the present disclosure, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or order; the term "plurality" means two or more unless expressly limited otherwise. The terms "mounted," "connected," "fixed," and the like are to be construed broadly, and for example, "connected" may be a fixed connection, a removable connection, or an integral connection; "coupled" may be direct or indirect through an intermediary. The specific meaning of the above terms in the present disclosure can be understood by those of ordinary skill in the art as appropriate.
In the description of the present disclosure, it is to be understood that the terms "upper", "lower", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present disclosure and simplifying the description, but do not indicate or imply that the referred device or unit must have a specific direction, be configured and operated in a specific orientation, and thus, should not be construed as limiting the present disclosure.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present disclosure, and all the changes or substitutions should be covered within the scope of the present disclosure. Accordingly, equivalents may be resorted to as falling within the scope of the disclosure as claimed.
Claims (8)
1. A method of disease prevention in a crop, the method comprising:
in the growth period of the target crops, acquiring the hours within the disease infection temperature threshold of the ith day and acquiring the hours within the disease infection temperature threshold of the (i + 1) th day;
determining the disease infection probability P of the (i + 1) th day according to the relative air humidity of the (i) th day, the relative air humidity of the (i + 1) th day and the relative air humidity at which the disease starts to infect, wherein i is a natural number;
predicting the number of hours of disease infection on the i +1 th day based on the number of hours in the disease infection temperature threshold on the i th day, the number of hours in the disease infection temperature threshold on the i +1 th day and the disease infection probability on the i +1 th day;
determining an infection risk index of the disease to the target crop according to the number of disease infection hours of the (i + 1) th day;
determining the disease infection probability P of the i +1 th day according to the relative air humidity of the i th day, the relative air humidity of the i +1 th day and the relative air humidity at which the disease starts to infect, wherein the determining comprises the following steps:
wherein P is the infection probability of the disease on the ith day and RH 0 The relative humidity of the air at which the disease begins to infect,
is the mean value of relative humidity of air, RH t Relative humidity of air, RH, day i y Relative humidity of air for day i + 1;
predicting the number of hours of disease infection on the i +1 th day based on the number of hours in the disease infection temperature threshold on the i th day, the number of hours in the disease infection temperature threshold on the i +1 th day and the disease infection probability on the i th day, wherein the predicting comprises the following steps:
wherein R is r CT is the number of hours that the disease may be infected t The number of hours that the temperature of day i +1 is within the disease infection threshold, CT y The number of hours that the temperature of day i is within the disease infestation threshold.
2. The method of crop disease prevention according to claim 1, further comprising, prior to obtaining the number of hours within the ith day disease infestation temperature threshold and the number of hours within the i +1 th day disease infestation temperature threshold during the first growth period of the target crop:
acquiring growth environment data of the target crops;
calculating a window period for the target crop based on the growth environment data;
determining the window period and a preset number of days before the window period as the growth period;
wherein the growth environment data comprises: sowing date, effective accumulated temperature and multispectral remote sensing monitoring data.
3. The method of crop disease prevention according to claim 1, wherein the obtaining the number of hours within the disease infestation temperature threshold for day i comprises:
wherein n =23, CT is the number of hours that the temperature of the ith day is within the disease infection threshold value, and CT is j Used for judging whether the temperature of the ith day at the jth hour is within the disease infection temperature threshold value Th j Is the jth hour temperature; th min Is the lowest temperature, Th, of disease onset max The highest temperature at which the disease occurs.
4. The method of crop disease prevention according to claim 1, wherein the determining of the risk of infection index of the disease to the target crop based on the number of disease infection hours on day i +1 comprises:
wherein R is dl For a daily infection risk rating, max 1 Is 15, R fl Predicting risk level for bureau, max 2 Is 170.
5. The method of disease prevention in crops of any of claims 1 to 4, wherein after said determining the risk of infestation index of said disease in said target crop, further comprising:
identifying a species of the target crop;
and determining the disaster area of the target crop based on the type of the target crop and the infection risk index.
6. A crop disease prevention apparatus, comprising:
an acquisition module: the method is used for acquiring the hours within the disease infection temperature threshold of the ith day and acquiring the hours within the disease infection temperature threshold of the (i + 1) th day in the growth period of a target crop;
a first determination module: determining the disease infection probability P of the (i + 1) th day according to the relative air humidity of the (i) th day, the relative air humidity of the (i + 1) th day and the relative air humidity at which the disease starts to infect, wherein i is a natural number;
a prediction module: the method is used for predicting the number of infected disease hours of the i +1 th day based on the number of hours within the disease infection temperature threshold of the i th day, the number of hours within the disease infection temperature threshold of the i +1 th day and the disease infection probability of the i +1 th day;
a second determination module: determining an infection risk index of the disease to the target crop according to the number of disease infection hours of the i +1 th day;
the first determining module is specifically configured to:
wherein P is the infection probability of the disease on the ith day and RH 0 The relative humidity of the air at which the disease begins to infect,
is the mean value of relative humidity of air, RH t Relative humidity of air, RH, day i y Relative humidity of air for day i + 1;
the prediction module is specifically configured to:
wherein R is r CT is the number of hours that the disease may be infected t The number of hours that the temperature of day i +1 is within the disease infection threshold, CT y The number of hours that the temperature of day i is within the disease infestation threshold.
7. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements a method of crop disease prevention as claimed in any one of claims 1 to 5.
8. A readable storage medium on which a computer program is stored, the computer program, when executed by a processor, implementing a method of crop disease prevention as claimed in any one of claims 1 to 5.
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