CN112348234A - Insect situation prediction method, terminal and storage medium - Google Patents
Insect situation prediction method, terminal and storage medium Download PDFInfo
- Publication number
- CN112348234A CN112348234A CN202011125552.7A CN202011125552A CN112348234A CN 112348234 A CN112348234 A CN 112348234A CN 202011125552 A CN202011125552 A CN 202011125552A CN 112348234 A CN112348234 A CN 112348234A
- Authority
- CN
- China
- Prior art keywords
- insect
- ovary
- ovarian
- development
- insects
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 241000238631 Hexapoda Species 0.000 title claims abstract description 149
- 238000000034 method Methods 0.000 title claims abstract description 47
- 241000607479 Yersinia pestis Species 0.000 claims abstract description 203
- 230000006408 female gonad development Effects 0.000 claims abstract description 117
- 230000002611 ovarian Effects 0.000 claims abstract description 97
- 210000001672 ovary Anatomy 0.000 claims abstract description 80
- 238000012545 processing Methods 0.000 claims abstract description 8
- 210000002468 fat body Anatomy 0.000 claims description 94
- 235000013601 eggs Nutrition 0.000 claims description 67
- 238000004590 computer program Methods 0.000 claims description 22
- 210000000683 abdominal cavity Anatomy 0.000 claims description 21
- 230000012447 hatching Effects 0.000 claims description 11
- 102000002322 Egg Proteins Human genes 0.000 claims description 9
- 108010000912 Egg Proteins Proteins 0.000 claims description 9
- 238000013508 migration Methods 0.000 claims description 9
- 230000005012 migration Effects 0.000 claims description 9
- 210000004681 ovum Anatomy 0.000 claims description 9
- 230000037237 body shape Effects 0.000 claims description 8
- 230000003187 abdominal effect Effects 0.000 claims description 7
- 238000012549 training Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 3
- 238000011161 development Methods 0.000 description 14
- 230000018109 developmental process Effects 0.000 description 14
- 241000255777 Lepidoptera Species 0.000 description 10
- 230000000694 effects Effects 0.000 description 8
- 239000008187 granular material Substances 0.000 description 8
- 238000011534 incubation Methods 0.000 description 8
- 239000002245 particle Substances 0.000 description 8
- 239000000575 pesticide Substances 0.000 description 8
- 241000282414 Homo sapiens Species 0.000 description 7
- 241000098289 Cnaphalocrocis medinalis Species 0.000 description 6
- 241000196324 Embryophyta Species 0.000 description 6
- 238000013145 classification model Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 244000144987 brood Species 0.000 description 5
- 238000001514 detection method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000001850 reproductive effect Effects 0.000 description 5
- 239000003814 drug Substances 0.000 description 4
- 238000003062 neural network model Methods 0.000 description 4
- 210000001015 abdomen Anatomy 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 229940079593 drug Drugs 0.000 description 3
- 241000256251 Spodoptera frugiperda Species 0.000 description 2
- 210000000593 adipose tissue white Anatomy 0.000 description 2
- 230000002411 adverse Effects 0.000 description 2
- 244000052616 bacterial pathogen Species 0.000 description 2
- 235000013339 cereals Nutrition 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000017448 oviposition Effects 0.000 description 2
- 230000008635 plant growth Effects 0.000 description 2
- 241000894007 species Species 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- KEQXNNJHMWSZHK-UHFFFAOYSA-L 1,3,2,4$l^{2}-dioxathiaplumbetane 2,2-dioxide Chemical compound [Pb+2].[O-]S([O-])(=O)=O KEQXNNJHMWSZHK-UHFFFAOYSA-L 0.000 description 1
- 241000426497 Chilo suppressalis Species 0.000 description 1
- 241000008892 Cnaphalocrocis patnalis Species 0.000 description 1
- 241001147381 Helicoverpa armigera Species 0.000 description 1
- 241001477931 Mythimna unipuncta Species 0.000 description 1
- 206010039509 Scab Diseases 0.000 description 1
- 241001249129 Scirpophaga incertulas Species 0.000 description 1
- 241000985245 Spodoptera litura Species 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 238000009395 breeding Methods 0.000 description 1
- 230000001488 breeding effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 235000021186 dishes Nutrition 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 210000004392 genitalia Anatomy 0.000 description 1
- 230000012010 growth Effects 0.000 description 1
- 235000012907 honey Nutrition 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000001617 migratory effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 210000003101 oviduct Anatomy 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000033458 reproduction Effects 0.000 description 1
- 230000010539 reproductive behavior Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Agronomy & Crop Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Multimedia (AREA)
- Life Sciences & Earth Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Catching Or Destruction (AREA)
Abstract
The application is applicable to the technical field of image processing, and provides an insect pest situation prediction method, a terminal and a storage medium. The method comprises the following steps: acquiring an ovary image of the insect; determining the ovarian development grade of the insect according to the ovarian image; and predicting the insect situation of the insects according to the ovary development grade. By adopting the technical scheme, the accuracy of insect situation prediction can be improved.
Description
Technical Field
The application belongs to the technical field of image processing, and particularly relates to an insect pest situation prediction method, a terminal and a storage medium.
Background
Insect pests refer to the damage that harmful insects cause to the growth of plants. If the control measures are not taken for the pests in time, the crop yield can be reduced, the vegetation withers, and the germs carried by the pests can cause diseases to human beings. Therefore, pest control is a very important task. In order to control pests, it is often necessary to know the pest situation of pests.
However, the existing insect situation prediction method has insufficient accuracy, so that the insect pest control effect is poor.
Disclosure of Invention
The embodiment of the application provides an insect condition prediction method, a terminal and a storage medium, and can solve the problem of insufficient accuracy of the existing insect condition prediction method.
A first aspect of an embodiment of the present application provides an insect situation prediction method, where the insect situation prediction method includes:
acquiring an ovary image of the insect;
determining the ovarian development grade of the insect according to the ovarian image;
and predicting the insect situation of the insects according to the ovary development grade.
A second aspect of the embodiments of the present application provides an insect situation prediction apparatus, including:
the acquisition unit is used for acquiring an ovary image of the insect;
a determining unit, which is used for determining the ovary development grade of the insect according to the ovary image;
and the prediction unit is used for predicting the insect situation of the insect according to the ovary development grade.
A third aspect of the embodiments of the present application provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method when executing the computer program.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the above method.
A fifth aspect of embodiments of the present application provides a computer program product, which when run on a terminal, causes the terminal to perform the steps of the method.
In an embodiment of the present application, the terminal acquires an image of an ovary of the insect. Then, according to the ovary image, determining the ovary development grade of the insect. Then, insect pest situation prediction can be carried out according to the ovary development grade. Because the ovary development level marks the ovary development degree of the insect, the embodiment of the application considers the reproduction characteristics of the insect and can accurately predict the insect situation according to the reproduction situation of the insect. When the method is applied to pests, the pest situation of the pests can be predicted, and then the pest control work is guided, so that the workload of plant protection workers can be reduced, and the influence of the pests on human lives and properties can be reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a first implementation of a method for predicting insect pest situation according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of an embodiment of the present application for determining ovarian developmental grade;
FIG. 3 is a schematic diagram of determining ovarian developmental levels using a neural network model provided by an embodiment of the present application;
FIG. 4 is a schematic flow chart of a second implementation of a method for predicting insect pest situation according to an embodiment of the present disclosure;
FIG. 5 is a schematic flow chart for predicting brooding peak and incubation peak provided in the embodiments of the present application;
FIG. 6 is a schematic flow chart illustrating an implementation of predicting the number of eggs laid by the present application;
fig. 7 is a schematic structural diagram of an insect situation prediction apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Insect pests refer to the damage that harmful insects cause to the growth of plants. If the control measures are not taken for the pests in time, the crop yield can be reduced, the vegetation withers, and the germs carried by the pests can cause diseases to human beings. Therefore, pest control is a very important task. In order to control pests, it is often necessary to know the pest situation of pests.
However, the existing insect situation prediction method has insufficient accuracy, so that the insect pest control effect is poor.
In the embodiment of the application, the terminal acquires an ovary image of the insect, and determines the ovary development grade of the insect according to the ovary image. Then, according to the ovarian development grade, insect situation prediction of the insects can be carried out. Therefore, in the embodiments of the present application, considering the reproductive characteristics of insects, it is possible to analyze the development degree of ovaries of insects which are bred using ovaries, determine the development grade of ovaries from the ovary images, and predict the insect situation of the insects. The method can be applied to any insects with ovaries, so that workers such as agricultural workers and forestry workers can judge the future activities of the insects and take certain intervention measures according to the predicted insect situations. When the method is applied to pests, the pest situation of the pests can be predicted, so that workers can accurately use the pesticide according to the pest situation, the workload of the workers is reduced, the influence of the pests on human life and property is reduced, and scientific pesticide use can be realized based on the pest situation.
In order to explain the technical solution of the present application, the following description will be given by way of specific examples, taking the predicted insect as an example of a pest.
Fig. 1 shows a schematic flow chart of an implementation of the insect pest situation prediction method provided in the embodiment of the present application, where the method can be applied to a terminal and is suitable for a situation where accuracy of insect pest situation prediction needs to be improved.
Specifically, the insect pest situation prediction method may include the following steps S101 to S103.
Step S101, acquiring an ovary image of pests.
Wherein the above-mentioned pests mean insects which bring adverse effects on human life or property. The species of pests may not be the same under different circumstances. However, generally, if pests multiply in large quantities, they are easily caused, resulting in reduction of plants, ecological damage, and adverse effects on human lives and properties.
In order to avoid mass propagation of pests, in the embodiment of the application, an ovary image of the pest needs to be acquired, the pest situation of the pest is predicted according to the ovary image, and then the pest control is performed according to the pest situation.
The acquisition mode of the ovarian image can be selected by workers according to actual conditions.
In some embodiments of the present application, workers may trap pests in areas where pest control work may be needed. After the worker catches the pest, the terminal can utilize image acquisition equipment such as a stereoscopic microscope to collect the ovary image of the caught pest.
And step S102, determining the ovarian development grade of the pests according to the ovarian image.
In the embodiment of the application, after the ovary image of the pest is obtained, the ovary of the pest can be identified through target detection, and then the ovary development grade of the pest is determined.
Wherein, the ovary development grade is used for marking the development degree of the ovaries of the pests. Different ovarian development grades correspond to different pest ovarian development degrees. That is, different degrees of development correspond to one ovarian development grade from the time when the pest does not form eggs to the time when the pest completes laying eggs.
And step S103, predicting the pest situation of the pests according to the ovary development grade.
In the embodiment of the application, the insect situation prediction refers to prediction of behavior activities of pests, and specific prediction contents can be selected by an administrator according to actual needs. For example, one or more behavioral activities of reproductive behavior, migration activity of the pest may be predicted.
In the embodiment of the application, the ovarian development degree of the pests can be known according to the ovarian development grade of the pests, so that the pest situation of the pests can be predicted according to the reproductive characteristics of the pests, and then the workers can accurately take the medicines according to the pest situation of the pests.
In some embodiments of the present application, after determining the development degree of ovaries of the pest, the ovum holding data of the pest may be determined, for example, one or more of the ovum holding time, the ovum holding number and the ovum holding location of the pest may be calculated. Then, according to the ovum holding data of the pests, the pest situation of the pests can be accurately predicted.
In the embodiment of the application, the terminal acquires an ovary image of the pest. Then, determining the ovarian development grade of the pests according to the ovarian image. Then, the insect pest situation of the insect pest can be predicted according to the ovary development grade. Because the ovary development level marks the ovary development degree of the pest, the embodiment of the application considers the reproduction characteristic of the pest, can accurately predict the pest situation according to the reproduction situation of the pest, enables the worker to perform pest control according to the predicted pest situation, can greatly reduce the workload of plant protection workers, and reduces the influence of the pest on the lives and properties of human beings.
In practical application, the variety of pests is various, and lepidoptera pests are one of the pests. Lepidoptera insects mainly include two types of insects, namely moths and butterflies. The lepidopteran pests with large body shapes can eat up the leaves or bore the branches and trunks. While the lepidoptera pests with smaller body sizes tend to curl leaves, affix leaves, scab, spit and net or dig into plant tissues for feeding. Lepidoptera pests such as Spodoptera frugiperda, Heliothis armigera, Spodoptera litura, armyworm, Chilo suppressalis, Tryporyza incertulas, and Cnaphalocrocis medinalis can all cause damage to plants.
Therefore, in order to prevent lepidopteran pests from harming plants, as shown in fig. 2, in some embodiments of the present application, when the pests are lepidopteran pests, the determining of the ovarian development grade of the pests based on the ovarian images may specifically include steps S201 to S202.
Step S201, identifying abdominal cavity color, ovarian duct characteristics and fat body characteristics of pests according to the ovarian image.
Wherein the abdominal color is a color of an abdomen of the lepidopteran pest. The abdomen of a lepidopteran pest is its reproductive center. Generally, female lepidopteran pests have an external genitalia in the abdomen, which is used to lay eggs.
The above characteristics of the ovarian duct refer to those of the ovarian duct of lepidopteran pests and the egg granules in the ovarian duct. The ovarian duct characteristics may specifically include ovarian duct color, ovarian duct status, ovarian duct length, egg location, egg status, and the like.
The above-mentioned fatty body characteristics refer to those of fatty bodies of lepidopteran pests. The fat body is an important structure in the body of the lepidoptera pests and plays an extremely important role in the growth, development and reproduction processes of the lepidoptera pests. The fat body characteristics may specifically include the number of fat bodies, the shape of the fat bodies, the color of the fat bodies, the state of the fat bodies, and the like.
Step S202, determining the development grade of the ovary according to the color of the abdominal cavity, the characteristics of the ovarian duct and the characteristics of the fat body.
In an embodiment of the application, after the abdominal color, the ovarian duct characteristic, and the fat body characteristic of the pest are obtained, the terminal may determine the ovarian development degree of the pest according to the abdominal color, the ovarian duct characteristic, and the fat body characteristic of the pest, so as to obtain the ovarian development grade of the pest.
Specifically, in some embodiments of the present application, the ovarian development level of the pest can be obtained by determining the ovarian development degree of the pest according to the abdominal cavity color, the ovarian tube color and the fat body number.
For example, fat bodies with the abdominal cavity being milky white in color, the ovarian tube being transparent in color, and the number of fat bodies being greater than a first preset threshold are determined as a grade indicating that no egg is formed in the ovary of the pest. And determining the fat bodies with the abdominal cavity color of milk white, the ovarian tube color of white and the fat body number larger than a second preset threshold as a second grade, wherein the second grade indicates that the ovaries of the pests are distinguishable. And determining the fat body with the abdominal cavity color of yellow white, the ovarian tube color of white to yellow and the fat body number smaller than a third preset threshold as a third level, wherein the third level indicates that the ovaries of the pests are mature and do not lay eggs. Fat bodies with the abdominal cavity color of yellow-white, the ovarian tube color of white to yellow and the fat body number smaller than a fourth preset threshold are determined as four grades, and the grades indicate that eggs in the ovaries of the pests are partially produced. And determining the fat body with yellow abdominal cavity color, dark yellow ovarian tube color and fat body quantity smaller than a fifth preset threshold as a fifth grade, wherein the fifth grade indicates that the ovaries of the pests are laid and only a small amount of eggs are left.
In some embodiments of the present application, in order to more accurately determine the ovarian development grade, the ovarian tube characteristics may include the ovarian tube color, the ovarian tube length, the ovarian tube status, the egg granule position, and the egg granule status, and the fat body characteristics may include the fat body number, the fat body shape, the fat body color, and the fat body status. By simultaneously referring to the abdominal cavity color, the ovarian tube state, the ovarian tube length, the ovarian particle position, the ovarian particle state, the fat body number, the fat body shape, the fat body color and the fat body state, the ovarian development grade can be accurately determined.
Specifically, in some embodiments of the present application, if the abdominal color is a first color, the ovarian tube color is a second color, the ovarian tube length is in a first length range, the egg state is a first egg state, the fat body number is greater than a first predetermined number, the fat body shape is a first shape, the fat body color is a third color, and the fat body state is a first fat body state, the ovarian development grade is determined as one grade. The rating indicates that eggs are not formed in the ovaries of the pest.
The first color, the second color, the first length range, the first egg granule state, the first preset number, the first shape, the third color and the first fatty body state can be adjusted according to actual conditions, for example, the first length range can be adjusted according to the size of pests. The source of this data can be obtained by workers capturing the pest with ovaries in an egg-unformed state and analyzing the ovaries of the pest.
And if the abdominal cavity color is the fourth color, the ovarian tube color is the fifth color, the ovarian tube length is in the second length range, the egg state is the second egg state, the fat body number is larger than the second preset number, the fat body shape is the second shape, the fat body color is the sixth color, and the fat body state is the second fat body state, determining the ovarian development grade as the second grade. The grade indicates that the ovaries of the pest are distinguishable.
The fourth color, the fifth color, the second length range, the second egg particle state, the second preset number, the second shape, the sixth color and the second fat body state can be adjusted according to actual conditions.
And if the abdominal cavity color is a seventh color, the ovarian tube color is an eighth color, the ovarian tube length is in a third length range, the egg particle state is a third egg particle state, the egg particle position is a first position, the fat body number is less than a third preset number, the fat body color is a ninth color, and the fat body state is a third fat body state, determining the ovarian development grade as the third grade. The grade indicates that the ovaries of the pest have matured and have not spawned.
The seventh color, the eighth color, the third length range, the third egg particle state, the first position, the third preset number, the ninth color and the third fat body state can be adjusted according to actual conditions.
And if the color of the abdominal cavity is the tenth color, the color of the ovarian tube is the eleventh color, the length of the ovarian tube is in the fourth length range, the state of the ovarian granules is in the fourth ovarian granule state, and the number of fat bodies is less than the fourth preset number, determining the development grade of the ovary as the fourth grade. The grade indicates that eggs have partially laid in the ovaries of the pest.
The tenth color, the eleventh color, the fourth length range, and the fourth preset number may also be adjusted according to actual situations.
And if the color of the abdominal cavity is the twelfth color, the color of the ovarian tube is the thirteenth color, the state of the ovarian tube is the first state of the ovarian tube, the length of the ovarian tube is in the fifth length range, and the number of fat bodies is less than the fifth preset number, determining the development grade of the ovary as the second grade. The rating indicates that eggs are laid out in the ovaries of the pest, with only a few eggs left.
The thirteenth color, the first ovarian duct state, the fifth length range and the fifth preset number can be adjusted according to actual conditions.
Specifically, in one embodiment of the present application, the ovarian development levels corresponding to different abdominal color, ovarian duct characteristics, and adipose body characteristics are shown in the following table:
if the abdominal cavity is milky white, the ovarian tube is transparent, the length of the ovarian tube is more than or equal to 3 cm and less than or equal to 4 cm, the egg particle state is in a non-segment shape, the fat body number is more than a first preset number, the fat body shape is circular or elliptical, the fat body color is milky white, and the fat body state is fat body fullness, the ovarian development grade is determined as first grade. Wherein, when the number of the fat bodies is larger than the first preset number, the number of the fat bodies is large. Whether the fat body state is full can be judged according to the thickness of the fat body, for example, when the thickness of all the fat bodies is larger than a first preset thickness, the fat body state is determined to be full.
If the abdominal cavity is milky white, the ovarian tube is white, the length of the ovarian tube is more than or equal to 4 cm and less than or equal to 6 cm, the egg is in a segmented state, the fat body is more than a second preset number, the fat body is in a circular or elliptical shape, the fat body is milky white, and the fat body is in a partially unsaturated state, the egg is formed and the egg is distinguishable, and the ovarian development grade of the pest is determined as the second grade. When the number of the fat bodies is larger than the second preset number, the number of the fat bodies is large, and the value of the second preset number can be the same as that of the first preset number. Whether the fat state is partially unsaturated can be determined according to the thickness of the fat, for example, when the thickness of the existing part of the fat is smaller than a second preset thickness, the fat state is determined to be partially unsaturated.
If the abdominal cavity color is yellowish white, the ovarian tube color is faint yellow, the length of the ovarian tube is more than or equal to 7 cm and less than or equal to 10 cm, the position of the egg is close to the lateral oviduct, the state of the egg is that the egg is tightly arranged and has a stacking and overlapping phenomenon, the number of fat bodies is less than a third preset number, the color of the fat bodies is transparent or semitransparent, and the state of the fat bodies is that the fat bodies are withered and flat, the situation that the eggs are mature and not lay eggs is indicated, and the ovarian development grade of the pests is determined to be three grades. Wherein, when the number of the fat bodies is less than the third preset number, it means that the number of the fat bodies is less. The state of the eggs can be determined according to the distance between the eggs, and if the distance between the eggs is smaller than a first distance threshold value, the state of the eggs is determined to be that the eggs are tightly arranged and have a stacking and overlapping phenomenon. Whether the fat body state is that the fat grains are withered and flat can be judged according to the thickness of the fat body, for example, when the thickness of all the part of the fat body is smaller than a third preset thickness, the fat body state is confirmed to be that the fat grains are withered and flat.
If the color of the abdominal cavity is light yellow, the color of the ovarian tube is light yellow, the length of the ovarian tube is more than or equal to 4 cm and less than or equal to 10 cm, the state of the egg granules is that the egg granules are not tightly arranged, and the number of milky fat bodies is less than a fourth preset number, the egg is partially produced, and the ovarian development grade of the pest is determined to be level four. Wherein, when the amount of the milky white fat body is less than the fourth preset amount, the milky white fat body is less. The above-mentioned state of the eggs may be determined according to the distance between the eggs, and if the distance between the eggs is greater than a second distance threshold, the state of the eggs is determined as the eggs are not closely arranged.
If the color of the abdominal cavity is yellow, the color of the ovarian tube is dark yellow, the state of the ovarian tube is that the ovarian tube is atrophied, the length of the ovarian tube is less than the preset ovarian tube length, and the number of fat bodies is less than the fifth preset number, the egg is laid, only a small number of eggs are left, and the ovarian development grade of the pest is determined to be five. When the length of the ovarian duct is smaller than the preset length of the ovarian duct, the length of the ovarian duct is short, and the value of the length can be adjusted according to the actual situation, for example, 4 cm. When the number of fat bodies is less than the fifth preset number, it means that the number of fat bodies is small.
In order to determine the ovarian grade of the ovary more efficiently and accurately, in some embodiments of the present application, the terminal may input the ovarian image into a pre-trained ovarian grading model for processing, and obtain the ovarian development grade output by the ovarian grading model. The ovary grading model is obtained by training ovary images of sample insects with different ovary development degrees.
Specifically, the worker can collect the pests at different ovarian development degrees in advance, dissect the pests under a stereoscope, and shoot the pests to obtain the ovarian images of the pests at different ovarian development degrees.
For example, a worker can raise 6000 spodoptera frugiperda larvae in batch by using feed in an indoor constant-temperature incubator to pupate the larvae, divide males and females according to wing shapes after adults emerge, place the divided females into a yarn cage with honey water (50 females/cage), take out 250 females every day according to different day ages of 1-12 days, place the females in culture dishes paved with sponge and clear water respectively to dissect the females, and place the dissected ovaries under a stereomicroscope to shoot so as to obtain ovaries images of pests with different ovarian development degrees.
Then, marking the ovary pictures according to the abdominal cavity color, the ovarian duct characteristics and the fat body characteristics, wherein each grade is at least 500, and obtaining an accurate lepidoptera pest ovary grading training set. Next, an ovarian grading model of lepidopteran pests is trained using the training set.
In an embodiment of the present application, the ovarian hierarchy model may be a neural network model.
As a preferred embodiment of the present application, the ovarian grade model may comprise a target detection model and a classification model. The target detection model can be a Yolo-V3 target detection model, and the classification model can be a ResNet-50 model or a DenseNet-121 model. On the basis of the classification model, the ovary classification model can also increase an attention suggestion network, and improve the accuracy of ovary classification.
Specifically, as shown in fig. 3, after the ovary image is input into the ovary classification model, the target detection model can detect the ovary, and the classification model determines the ovary development grade of the ovary.
In the embodiment of the application, the ovary image is input into the ovary grading model which is trained in advance, and the terminal can efficiently determine the ovary development grade when the ovary development grade output by the ovary grading model is obtained, so that the process does not need related professionals to participate, and the operation efficiency can be effectively improved.
In practical application, in order to predict the pest situation more accurately, the breeding situation of a plurality of pest areas is often required to be known so as to predict the pest situation of a certain area. Therefore, in some embodiments of the present application, as shown in fig. 4, the insect pest situation prediction method includes steps S401 to S403.
Step S401, ovarian images of a plurality of pests are respectively obtained.
And step S402, determining the ovary development grade of each pest according to the ovary image.
And S403, predicting the pest situation of the pests according to the ovary development grade of each pest.
In steps S401 to S402, the detailed operation for each pest can refer to the description of fig. 1 to fig. 2, which is not repeated herein.
That is, in some embodiments of the present application, the terminal may determine the ovarian development levels of multiple pests simultaneously according to the ovarian images of the pests, and then complete the insect situation prediction for one region according to the ovarian development levels of the pests.
Specifically, the specific implementation manner of the insect situation prediction can be selected by the worker according to the actual situation.
In some embodiments of the present application, the pests are located in the first zone. In this case, the terminal may predict the incubation peak period and the brooding peak period of the pest in the first region.
The size and the division form of the first area are not limited in the present application, and the first area may be, for example, one village or one-mu field.
Specifically, as shown in fig. 5, the predicting pest situation according to the ovarian development grade of each pest may include: step S501 to step S503.
Step S501, screening out a target ovary development grade from the ovary development grades.
Wherein the target ovary development grade is one of the ovary development grades with the largest number of pests corresponding to the pests.
In some embodiments of the application, after acquiring the ovary images of the plurality of pests in the first region respectively, and determining the ovary development levels of the pests according to the ovary images, the number of the pests corresponding to each ovary development level may be counted, and then the target ovary development level with the largest number of the pests is screened from all the ovary development levels.
Step S502, determining the spawning time corresponding to the development grade of the target ovary.
Wherein, the spawning time refers to the time of the target ovary development level pest spawning.
Specifically, in some embodiments of the present application, the oviposition time corresponding to the target ovarian development level may be determined according to the environmental data of the first region.
Taking lepidoptera pests as an example, under the environmental conditions of temperature (25 +/-1) DEG C, relative humidity of 75% +/-5% and photoperiod L/D of 16 h/8 h, the average day ages required by the pests with the ovary development grades from five to one for completely producing eggs are 1.22 days, 2.24 days, 4.26 days, 6.68 days and 9.08 days respectively. That is, lepidopteran pests take about 9 days from the first order of ovarian development to complete egg laying.
And S503, determining the brooding peak period and the hatching peak period of the pests in the first area according to the brooding time.
Wherein, the spawning peak period refers to the time of concentrated spawning of pests in the first area. The incubation peak period refers to the time when pests in the first area are hatched intensively. In the embodiment of the application, according to the spawning time corresponding to the development grade of the target ovary, the peak spawning time of the pests in the first area can be determined. And determining the incubation peak period according to the incubation peak period.
Taking lepidopteran pests as an example, the hatching time of eggs is from laying eggs to hatching larvae, and 2 to 8.4 days are required at different temperatures.
In the embodiment of the application, a target ovary development grade is selected from the ovary development grades, and the target ovary development grade is the one with the largest number of pests corresponding to the pests in the ovary development grades. And then determining the spawning time corresponding to the development grade of the target ovary, and determining the spawning peak time and the hatching peak time of the pests in the first region according to the spawning time. According to the embodiment of the application, the brood peak time and the hatching peak time can be accurately predicted according to the reproductive capacity of pests by utilizing the development grade of the ovaries of the pests, so that workers can control the pests according to the brood peak time and the hatching peak time, and the pertinence and the accuracy of pest control are improved.
In addition, the pesticide is applied in the brooding peak period and the hatching peak period of the pests, the killing work of the pests can be completed with the least dosage, the scientific and reasonable pesticide application can be realized, the accurate pesticide application is promoted, the pesticide application cost is saved, and the influence of the pesticide on the ecological environment is also reduced.
In other embodiments of the present application, the terminal may also predict the number of eggs laid by the pest. Specifically, as shown in fig. 6, the predicting pest situation according to the ovarian development grade of each pest may further include: step S601 to step S602.
Step S601, acquiring an egg number model.
The above-mentioned oviform model may be a mathematical model, a neural network model, etc., and may be obtained in advance through statistical analysis or training of the neural network model.
Step S602, inputting the target ovary development grade into an egg-taking number model to obtain the egg-taking number of the insects in the first area.
Wherein, the number of the spawns is the number of the final spawns of the target ovary development level.
Taking lepidoptera pests as an example, the above-mentioned oviparous model may be: 354.06+1199.31 x (-253.82) x2Wherein y is the amount of brood and x is the ovarian development grade.
According to the embodiment of the application, the number of the eggs of the pests in the first region is calculated by acquiring the number model of the eggs, inputting the development grade of the target ovary into the number model of the eggs, so that the number of the eggs can be accurately predicted according to the reproductive capacity of the pests, workers can control the pests according to the number of the eggs, and the pertinence and the accuracy of pest control are improved.
In other embodiments of the present application, the migration of pests can also be predicted. Specifically, the terminal may count the ovarian development levels of each pest in the first region at preset time intervals to determine the migration condition of the pest.
Specifically, in some embodiments of the present application, images of ovaries of the same species of pests may be obtained at preset time intervals, and the number of pests corresponding to each ovarian development level at a plurality of time points within a period of time may be determined. If the ovary development grade is low (grade 1 and grade 2) and the duration is long, the pests in the area are in the state of external migration. Therefore, a second area adjacent to the first area can be warned to control pests in the second area.
In some embodiments of the present application, the insect pest situation prediction methods described above may be used in combination, so that the number of brood, the brood peak time, the incubation peak time, and the migratory situation of the pest in the first area may be predicted, and further guidance for pest control is provided for the worker.
Taking the cnaphalocrocis medinalis as an example, if the proportion of the cnaphalocrocis medinalis with the ovary development grade of the first grade in the local area is always high, for example, more than 50 percent, and the cnaphalocrocis medinalis is still maintained at the level for 4 to 5 days, the cnaphalocrocis medinalis imagoes in the local area are in massive migration, so that the cnaphalocrocis medinalis does not cause excessive damage to the local area, generally can not completely prevent and treat the local area, and can give an early warning to other areas adjacent to the local area. If the proportion of the rice leaf rollers with higher ovarian development grade in the local area is high, the proper control period can be determined within 10 days of the incubation peak period according to the brooding peak period and the incubation peak period, and scientific medicine application is carried out according to the brooding amount.
In the embodiment of the application, the ovary development grade is given through image recognition, a more accurate reproduction potential prediction report is rapidly provided, the pest situation peak is predicted, and then control measures are taken in advance to guide farmers to prevent and treat, so that the effects of guiding scientific and reasonable medication, promoting accurate medication and controlling major pests in advance are achieved, and meanwhile, the environmental pollution caused by misuse and abuse of pesticides is also reduced.
It should be noted that, for simplicity of description, the foregoing method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts, as some steps may, in accordance with the present application, occur in other orders.
Fig. 7 is a schematic structural diagram of an insect situation prediction apparatus 700 according to an embodiment of the present application, where the insect situation prediction apparatus 700 is configured on a terminal. The insect pest situation prediction apparatus 700 may include: an acquisition unit 701, a determination unit 702, and a prediction unit 703.
An acquisition unit 701 for acquiring an ovary image of an insect;
a determining unit 702, configured to determine an ovarian development grade of the insect according to the ovarian image;
a prediction unit 703 for performing insect pest situation prediction of the insect according to the ovarian development grade.
In some embodiments of the present application, the insect is a lepidopteran insect; the determining unit 702 is further specifically configured to: identifying abdominal color, ovarian duct characteristics and fat body characteristics of the insects according to the ovarian image; and determining the ovarian development grade according to the abdominal cavity color, the ovarian duct characteristics and the fat body characteristics.
In some embodiments of the present application, the ovarian tube characteristics include at least one of ovarian tube color, ovarian tube length, ovarian tube status, egg location, and egg status, and the fat body characteristics include at least one of fat body number, fat body shape, fat body color, and fat body status.
In some embodiments of the present application, the determining unit 702 is further specifically configured to: inputting the ovary images into a pre-trained ovary grading model for processing, wherein the ovary grading model is obtained by training the ovary images of sample insects with different ovary development degrees; and acquiring the ovary development grade output by the ovary grading model.
In some embodiments of the present application, the obtaining unit 701 is further specifically configured to: respectively acquiring ovary images of a plurality of insects; the determining unit 702 is further specifically configured to: determining the ovarian development grade of each insect according to the ovarian image; the prediction unit 703 is further specifically configured to: and predicting the insect situation of the insects according to the ovary development grade of each insect.
In some embodiments of the present application, the insect is located in a first area; the insect situation prediction comprises prediction of brooding peak and hatching peak of the insects; the prediction unit 703 is further specifically configured to: screening a target ovary development grade from the ovary development grades, wherein the target ovary development grade is one of the ovary development grades which corresponds to the largest number of insects; determining the spawning time corresponding to the target ovary development grade; determining the brooding peak and the hatching peak of the insects in the first region according to the brooding time.
In some embodiments of the present application, the insect situation prediction further comprises a prediction of the number of eggs laid by the insect; the prediction unit 703 is further specifically configured to: acquiring an egg-carrying number model; inputting the target ovary development grade into the ovum number model to obtain the ovum number of the insects in the first region.
In some embodiments of the present application, the above insect situation prediction further comprises a prediction of migration of the insect; said insects being located in a first region; the prediction unit 703 is further specifically configured to: and counting the ovary development grades of the insects in the first region at preset time intervals to determine the migration condition of the insects.
It should be noted that, for convenience and simplicity of description, the specific working process of the insect situation prediction apparatus 700 may refer to the corresponding process of the method described in fig. 1 to fig. 6, and is not described herein again.
Fig. 8 is a schematic diagram of a terminal according to an embodiment of the present application. The terminal 8 may include: a processor 80, a memory 81 and a computer program 82, such as an insect situation prediction apparatus program, stored in said memory 81 and operable on said processor 80. The processor 80, when executing the computer program 82, implements the steps in each of the insect pest situation prediction method embodiments described above, such as the steps S101 to S103 shown in fig. 1. Alternatively, the processor 80, when executing the computer program 82, implements the functions of each module/unit in each device embodiment described above, for example, the functions of the units 701 to 703 shown in fig. 7.
The computer program may be divided into one or more modules/units, which are stored in the memory 81 and executed by the processor 80 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program in the terminal.
For example, the computer program may be partitioned into an acquisition unit, a determination unit, and a prediction unit. The specific functions of each unit are as follows: the acquisition unit is used for acquiring an ovary image of the insect; a determining unit, which is used for determining the ovary development grade of the insect according to the ovary image; and the prediction unit is used for predicting the insect situation of the insect according to the ovary development grade.
The terminal may include, but is not limited to, a processor 80, a memory 81. Those skilled in the art will appreciate that fig. 8 is merely an example of a terminal and is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or different components, e.g., the terminal may also include input-output devices, network access devices, buses, etc.
The Processor 80 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 81 may be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The memory 81 may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are equipped on the terminal. Further, the memory 81 may also include both an internal storage unit and an external storage device of the terminal. The memory 81 is used for storing the computer program and other programs and data required by the terminal. The memory 81 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An insect pest situation prediction method is characterized by comprising the following steps:
acquiring an ovary image of the insect;
determining the ovarian development grade of the insect according to the ovarian image;
and predicting the insect situation of the insects according to the ovary development grade.
2. The method of predicting the insect situation according to claim 1, wherein the insect is a lepidopteran insect; determining the ovarian development grade of the insect according to the ovarian image, wherein the determining comprises the following steps:
identifying abdominal color, ovarian duct characteristics and fat body characteristics of the insects according to the ovarian image;
and determining the ovarian development grade according to the abdominal cavity color, the ovarian duct characteristics and the fat body characteristics.
3. The insect situation prediction method of claim 2 wherein the ovarian tube characteristics comprise at least one of ovarian tube color, ovarian tube length, ovarian tube status, egg location and egg status, and the fat body characteristics comprise at least one of fat body number, fat body shape, fat body color and fat body status.
4. The method of claim 1, wherein said determining an ovarian development level of said insect based on said ovarian image comprises:
inputting the ovary images into a pre-trained ovary grading model for processing, wherein the ovary grading model is obtained by training the ovary images of sample insects with different ovary development degrees;
and acquiring the ovary development grade output by the ovary grading model.
5. The method of claim 1, wherein the number of the insects is a plurality, the obtaining an ovary image of the insect, determining an ovary development grade of the insect according to the ovary image, and predicting the insect pest situation according to the ovary development grade comprises:
respectively acquiring ovary images of a plurality of insects;
determining the ovarian development grade of each insect according to the ovarian image;
and predicting the insect situation of the insects according to the ovary development grade of each insect.
6. The method of predicting insect pest situation according to claim 5, wherein the insect is located in a first area; the insect situation prediction comprises prediction of brooding peak and hatching peak of the insect;
the insect situation prediction of the insects is carried out according to the ovary development grade of each insect, and comprises the following steps:
screening a target ovary development grade from the ovary development grades, wherein the target ovary development grade is one of the ovary development grades which corresponds to the largest number of insects;
determining the spawning time corresponding to the target ovary development grade;
determining the brooding peak and the hatching peak of the insects in the first region according to the brooding time.
7. The insect pest situation prediction method according to claim 6, wherein the insect pest situation prediction further comprises a prediction of the number of eggs laid by the insect;
the predicting the insect situation of the insects according to the ovary development grade of each insect further comprises:
acquiring an egg-carrying number model;
inputting the target ovary development grade into the ovum number model to obtain the ovum number of the insects in the first region.
8. The insect condition prediction method according to claim 5, wherein the insect condition prediction further comprises a prediction of migration of the insect; the insect is located in a first area;
the predicting the insect situation of the insects according to the ovary development grade of each insect further comprises:
and counting the ovary development grades of the insects in the first region at preset time intervals to determine the migration condition of the insects.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011125552.7A CN112348234A (en) | 2020-10-20 | 2020-10-20 | Insect situation prediction method, terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011125552.7A CN112348234A (en) | 2020-10-20 | 2020-10-20 | Insect situation prediction method, terminal and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112348234A true CN112348234A (en) | 2021-02-09 |
Family
ID=74358878
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011125552.7A Pending CN112348234A (en) | 2020-10-20 | 2020-10-20 | Insect situation prediction method, terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112348234A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203290099U (en) * | 2013-05-06 | 2013-11-20 | 上海创塔电子科技有限公司 | Insect monitoring management system |
| CN104137821A (en) * | 2013-05-06 | 2014-11-12 | 上海创塔电子科技有限公司 | Insect pest situation forecasting management system and forecasting management method |
| CN104539916A (en) * | 2015-02-02 | 2015-04-22 | 广西牛博物联信息技术有限公司 | Remote early warning system for Pseudonoorda minor Munroe |
| CN107463958A (en) * | 2017-08-04 | 2017-12-12 | 成都东谷利农农业科技有限公司 | Insect identifies method for early warning and system |
| CN109377038A (en) * | 2018-10-12 | 2019-02-22 | 郴州市农业科学研究所 | Southern peach pest and disease monitoring method |
-
2020
- 2020-10-20 CN CN202011125552.7A patent/CN112348234A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203290099U (en) * | 2013-05-06 | 2013-11-20 | 上海创塔电子科技有限公司 | Insect monitoring management system |
| CN104137821A (en) * | 2013-05-06 | 2014-11-12 | 上海创塔电子科技有限公司 | Insect pest situation forecasting management system and forecasting management method |
| CN104539916A (en) * | 2015-02-02 | 2015-04-22 | 广西牛博物联信息技术有限公司 | Remote early warning system for Pseudonoorda minor Munroe |
| CN107463958A (en) * | 2017-08-04 | 2017-12-12 | 成都东谷利农农业科技有限公司 | Insect identifies method for early warning and system |
| CN109377038A (en) * | 2018-10-12 | 2019-02-22 | 郴州市农业科学研究所 | Southern peach pest and disease monitoring method |
Non-Patent Citations (2)
| Title |
|---|
| 安瑞军: "《植物保护学》", 内蒙古科学技术出版社, pages: 146 - 147 * |
| 王小奇: "《昆虫生态学理论与实践》", 31 July 2020, 内蒙古科学技术出版社, pages: 146 - 147 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Calovi et al. | Summer weather conditions influence winter survival of honey bees (Apis mellifera) in the northeastern United States | |
| Henderson et al. | Ecological methods | |
| Nürnberger et al. | Seasonal timing in honey bee colonies: phenology shifts affect honey stores and varroa infestation levels | |
| Einum et al. | The spatial scale of density-dependent growth and implications for dispersal from nests in juvenile Atlantic salmon | |
| Wolf et al. | A model for the genetic evaluation of number of clinical mastitis cases per lactation in Czech Holstein cows | |
| Bussan | Can cattle grazing benefit grassland butterflies? | |
| Dorigo et al. | In vitro larval rearing protocol for the stingless bee species Melipona scutellaris for toxicological studies | |
| Stygar et al. | Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring | |
| Sibanda et al. | Flock use of the range is associated with the use of different components of a multi-tier aviary system in commercial free-range laying hens | |
| Jones et al. | Pollinator nutrition and its role in merging the dual objectives of pollinator health and optimal crop production | |
| Heldbjerg et al. | Common Starlings (Sturnus vulgaris) increasingly select for grazed areas with increasing distance-to-nest | |
| Facchini et al. | Investigating genetic and phenotypic variability of queen bees: Morphological and reproductive traits | |
| Anderson et al. | Variation in phenology and density differentially affects predator–prey interactions between salamanders | |
| Reinders et al. | Reduced susceptibility of western corn rootworm (Diabrotica virgifera virgifera LeConte) populations to Cry34/35Ab1-expressing maize in northeast Nebraska | |
| Lowry et al. | Optimizing the timing of management interventions against fall armyworm in African smallholder maize: modelling the pattern of larval population emergence and development | |
| Zelenka et al. | An agent-based algorithm resembles behaviour of tree-dwelling bats under fission–fusion dynamics | |
| Butler et al. | Do contemporary age-growth models overlook life-history complexities in protandrous fishes? A case study on the large protandrous polynemid, the giant African threadfin Polydactylus quadrifilis | |
| Ferreira et al. | Toward greater realism in inclusive fitness models: the case of caste fate conflict in insect societies | |
| CN112348234A (en) | Insect situation prediction method, terminal and storage medium | |
| Lundin et al. | Historical change and drivers of insect pest abundances in red clover seed production | |
| Reinders et al. | Characterizing the sublethal effects of SmartStax PRO dietary exposure on life history traits of the western corn rootworm, Diabrotica virgifera virgifera LeConte | |
| Hirt-Chabbert et al. | Effect of size grading on the growth performance of shortfin eel (Anguilla australis) during its yellow stage | |
| Thomson et al. | Manipulating individual decisions and environmental conditions reveal individual quality in decision-making and non-lethal costs of predation risk | |
| Sanané et al. | High-throughput feeding bioassay for lepidoptera larvae | |
| Huynh et al. | Development of a nondiapausing strain of northern corn rootworm with rearing techniques for both diapausing and nondiapausing strains |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |