CN118779739B - Herbicide weeding effect evaluation method, system and storage medium - Google Patents
Herbicide weeding effect evaluation method, system and storage medium Download PDFInfo
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- 230000002363 herbicidal effect Effects 0.000 title claims abstract description 337
- 239000004009 herbicide Substances 0.000 title claims abstract description 329
- 238000009333 weeding Methods 0.000 title claims abstract description 142
- 230000000694 effects Effects 0.000 title claims abstract description 21
- 238000003860 storage Methods 0.000 title claims abstract description 19
- 238000011156 evaluation Methods 0.000 title claims description 38
- 238000004458 analytical method Methods 0.000 claims abstract description 52
- 238000000034 method Methods 0.000 claims abstract description 50
- 230000007613 environmental effect Effects 0.000 claims abstract description 39
- 206010059866 Drug resistance Diseases 0.000 claims description 41
- 241000196324 Embryophyta Species 0.000 claims description 38
- 238000003066 decision tree Methods 0.000 claims description 9
- 239000003814 drug Substances 0.000 claims description 8
- 229940079593 drug Drugs 0.000 claims description 8
- 239000000575 pesticide Substances 0.000 claims 4
- 239000004480 active ingredient Substances 0.000 claims 2
- 238000012216 screening Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 7
- 230000002068 genetic effect Effects 0.000 description 5
- 230000003993 interaction Effects 0.000 description 5
- 239000000126 substance Substances 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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Abstract
The invention relates to a method, a system and a storage medium for evaluating the weeding effect of herbicide, belonging to the technical field, the invention obtains the environmental factor information of the current area to be applied with the herbicide, screens according to the types of other herbicides except the current herbicide type and the environmental factor information of the current area to be applied with the herbicide, and finally, acquiring the component data information of the current herbicide type, performing cross analysis according to the component data information of the current herbicide type and the type of the screened herbicide, acquiring a cross analysis result, and generating a final herbicide type according to the cross analysis result. According to the invention, the herbicide resistance of the target area to the current herbicide is obtained by evaluating the herbicide performance characteristic data of each herbicide, and the actual environment is combined to recommend the herbicide which meets the target area better, so that the treatment effect of the herbicide is better.
Description
Technical Field
The invention relates to the technical field of herbicides, in particular to a herbicide weeding effect evaluation method, a herbicide weeding effect evaluation system and a herbicide storage medium.
Background
Chemical control plays an important role in promoting agricultural production as the most effective means for controlling weeds. As herbicides continue to be widely used, weeds are becoming increasingly common for resistance to herbicides. The creation and rapid spread of resistant weeds presents new challenges to chemical herbicidal based weed management systems. The generation of drug resistance causes the prevention and control effect of weeds to be reduced, and if the weeds are not regulated, the growth and the yield of related plants are seriously affected.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a weeding effect evaluation method, a weeding effect evaluation system and a weeding effect evaluation storage medium of a herbicide.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides a method for evaluating the weeding effect of a herbicide, which comprises the following steps:
the weeding performance characteristic data of the current herbicide in each target area are obtained, and weeding performance membership information in each target area is obtained by evaluating the weeding performance characteristic data;
The method comprises the steps of carrying out drug resistance identification on weeding performance membership information in a target area, obtaining a drug resistance identification result, and initializing other herbicide types except the current herbicide type based on the drug resistance identification result;
Acquiring environmental factor information of a current herbicide to be applied area, and screening according to other herbicide types except the current herbicide type and the environmental factor information of the current herbicide to be applied area to acquire the screened herbicide type;
Component data information of the current herbicide type is obtained, cross analysis is carried out according to the component data information of the current herbicide type and the type of the screened herbicide, cross analysis results are obtained, and final herbicide types are generated according to the cross analysis results.
Further, in the method, the weeding performance characteristic data of the current herbicide in each target area is obtained, and weeding performance membership information in each target area is obtained by evaluating the weeding performance characteristic data, and the method specifically comprises the following steps:
The method comprises the steps of obtaining weeding performance characteristic data of current herbicides in each target area, introducing a decision tree algorithm, constructing sample data according to the weeding performance characteristic data of the current herbicides in each target area, and inputting the sample data into the decision tree algorithm;
setting a splitting standard, constructing a root node based on sample data, carrying out initialization splitting on the root node to generate a plurality of leaf nodes, judging each leaf node based on the splitting standard, and judging whether at least two types of sample data exist in the leaf nodes;
when at least two types of sample data exist in the leaf node, continuously splitting the leaf node until only one type of sample data exists in the leaf node, and outputting the leaf node;
When only one type of sample data exists in the leaf nodes, outputting the leaf nodes, acquiring weeding performance membership information in the target area according to the leaf nodes, counting the weeding performance membership information in the target area, and outputting the weeding performance membership information in each target area.
Further, in the method, by performing drug resistance identification on the weeding performance membership information in the target area, a drug resistance identification result is obtained, and other herbicide types except the current herbicide type are initialized based on the drug resistance identification result, which specifically comprises the following steps:
setting a weeding performance membership evaluation index, and judging whether weeding performance membership information in a target area is larger than the weeding performance membership evaluation index;
When the weeding performance membership information in the target area is larger than the weeding performance membership evaluation index, taking the corresponding target area as an area for generating the drug resistance, and generating a drug resistance identification result;
When the weeding performance membership degree information in the target area is not more than the weeding performance membership degree evaluation index, taking the corresponding target area as a non-drug-resistant area, and taking the current herbicide as a recommended drug of the current target area;
When the drug resistance identification result is larger than the preset drug resistance identification result, the weed type in the target area is obtained, and other herbicide types except the current herbicide type are obtained through big data retrieval according to the weed type in the target area.
Further, in the method, environmental factor information of a current herbicide to be applied area is obtained, screening is performed according to other herbicide types except the current herbicide type and the environmental factor information of the current herbicide to be applied area, and the herbicide type after screening is obtained, which specifically comprises:
The weeding performance characteristic data of different herbicide types under the environmental factor information are obtained through the big data, and the weeding performance characteristic data of different herbicide types under the environmental factor information are imported into the graphic neural network;
Taking the environmental factors as a first node, the herbicide type as a second node and the weeding performance characteristic data as a third node, constructing a topological structure diagram based on the first node, the second node and the third node, and inputting the topological structure diagram into a knowledge graph for storage;
Acquiring environmental factor information of a current area to be applied with the herbicide, inputting the environmental factor information of the current area to be applied with the herbicide and other herbicide types except the current herbicide type into a knowledge graph, and acquiring weeding performance characteristic data of each herbicide type under the environmental factor information of the current area to be applied with the herbicide;
And obtaining the herbicide with the weeding performance characteristic data larger than the preset weeding performance characteristic threshold, and outputting the herbicide with the weeding performance characteristic data larger than the preset weeding performance characteristic threshold as the type of the screened herbicide.
Further, in the method, component data information of the current herbicide type is obtained, and cross analysis is performed according to the component data information of the current herbicide type and the screened herbicide type, so as to obtain a cross analysis result, which specifically comprises the following steps:
Acquiring component data information of the current herbicide type and component data information of the type of the screened herbicide;
Acquiring the effective components of the current herbicide type in the weeding process and the effective components of the screened herbicide type in the weeding process according to the component data information of the current herbicide type and the component data information of the screened herbicide type;
calculating a mahalanobis distance value between an effective component of the current herbicide type in the weeding process and an effective component of the screened herbicide type in the weeding process, and judging whether the mahalanobis distance value is larger than a preset mahalanobis distance threshold value or not;
And when the mahalanobis distance value is not larger than the preset mahalanobis distance threshold, taking the mahalanobis distance value as a cross scoring result, and outputting a cross scoring junction as a cross analysis result.
Further, in the present method, the final herbicide type is generated according to the cross-sex analysis result, specifically including:
Presetting a cross score threshold index, and judging whether a cross analysis result is larger than the cross score threshold index;
When the cross analysis result is larger than the cross scoring threshold index, the corresponding herbicide type is removed, and the screened herbicide type is updated;
and when the cross analysis result is not greater than the cross scoring threshold index, taking the corresponding herbicide type as the recommended herbicide type, and counting the recommended herbicide type to generate the final herbicide type.
The second aspect of the present invention provides a herbicidal effect evaluation system of a herbicide, the system comprising a memory and a processor, the memory including a herbicidal effect evaluation method program of the herbicide, the herbicidal effect evaluation method program of the herbicide, when executed by the processor, effecting the steps of:
the weeding performance characteristic data of the current herbicide in each target area are obtained, and weeding performance membership information in each target area is obtained by evaluating the weeding performance characteristic data;
The method comprises the steps of carrying out drug resistance identification on weeding performance membership information in a target area, obtaining a drug resistance identification result, and initializing other herbicide types except the current herbicide type based on the drug resistance identification result;
Acquiring environmental factor information of a current herbicide to be applied area, and screening according to other herbicide types except the current herbicide type and the environmental factor information of the current herbicide to be applied area to acquire the screened herbicide type;
Component data information of the current herbicide type is obtained, cross analysis is carried out according to the component data information of the current herbicide type and the type of the screened herbicide, cross analysis results are obtained, and final herbicide types are generated according to the cross analysis results.
A third aspect of the present invention provides a computer-readable storage medium including therein a herbicidal effect evaluation method program of a herbicide, the herbicidal effect evaluation method program of a herbicide, when executed by a processor, implementing the steps of the herbicidal effect evaluation method of any one of the herbicides.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
According to the invention, the weeding performance characteristic data of the current herbicide in each target area are obtained, the weeding performance characteristic data are evaluated to obtain weeding performance membership information in each target area, then the herbicide resistance identification is carried out on the weeding performance membership information in the target area, a drug resistance identification result is obtained, other herbicide types except the current herbicide type are initialized based on the drug resistance identification result, so that the environment factor information of the current herbicide to be applied area is obtained, screening is carried out according to the other herbicide types except the current herbicide type and the environment factor information of the current herbicide to be applied area, the screened herbicide type is obtained, finally the component data information of the current herbicide type is obtained, cross analysis is carried out according to the component data information of the current herbicide type and the screened herbicide type, and the cross analysis result is obtained, so that the final herbicide type is generated according to the cross analysis result. According to the invention, the herbicide resistance of the target area to the current herbicide is obtained by evaluating the herbicide performance characteristic data of each herbicide, and the actual environment is combined to recommend the herbicide which meets the target area better, so that the treatment effect of the herbicide is better.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall method flowchart of a herbicidal effect evaluation method of herbicides;
FIG. 2 shows a first method flowchart of a herbicidal effect evaluation method of herbicides;
FIG. 3 shows a second method flow chart of a method for evaluating the herbicidal effect of a herbicide;
Fig. 4 shows a system block diagram of the herbicidal effect evaluation system of the herbicide.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention provides a herbicidal effect evaluation method of a herbicide, comprising the steps of:
S102, acquiring weeding performance characteristic data of a current herbicide in each target area, and acquiring weeding performance membership information in each target area by evaluating the weeding performance characteristic data;
S104, carrying out drug resistance identification on the weeding performance membership information in the target area to obtain a drug resistance identification result, and initializing other herbicide types except the current herbicide type based on the drug resistance identification result;
S106, acquiring environmental factor information of a current herbicide to be applied area, and screening according to other herbicide types except the current herbicide type and the environmental factor information of the current herbicide to be applied area to acquire the screened herbicide type;
S108, acquiring component data information of the current herbicide type, performing cross analysis according to the component data information of the current herbicide type and the screened herbicide type, acquiring a cross analysis result, and generating a final herbicide type according to the cross analysis result.
The herbicide resistance of the target area to the current herbicide is obtained by evaluating the herbicide performance characteristic data of each herbicide, and the actual environment is combined to recommend the herbicide which meets the target area better, so that the control effect of the herbicide is better. The weeding performance characteristic data comprise weeding amount in unit area, weeding amount of unit weeding amount and the like.
As shown in fig. 2, further, in the present method, weeding performance characteristic data of a current herbicide in each target area is obtained, and weeding performance membership information in each target area is obtained by evaluating the weeding performance characteristic data, which specifically includes:
S202, obtaining weeding performance characteristic data of current herbicides in each target area, introducing a decision tree algorithm, constructing sample data according to the weeding performance characteristic data of the current herbicides in each target area, and inputting the sample data into the decision tree algorithm;
S204, setting a splitting standard, constructing a root node based on sample data, carrying out initialization splitting on the root node to generate a plurality of leaf nodes, judging each leaf node based on the splitting standard, and judging whether at least two types of sample data exist in the leaf nodes;
S206, when at least two types of sample data exist in the leaf node, continuously splitting the leaf node until only one type of sample data exists in the leaf node, and outputting the leaf node;
And S208, when only one type of sample data exists in the leaf nodes, outputting the leaf nodes, acquiring the weeding performance membership information in the target area according to the leaf nodes, counting the weeding performance membership information in the target area, and outputting the weeding performance membership information in each target area.
It should be noted that, the information of the membership degree of the weeding performance in the target area can be obtained rapidly through a decision tree algorithm, wherein the information of the membership degree of the weeding performance comprises data of low membership degree of the weeding performance, medium membership degree of the weeding performance, high membership degree of the weeding performance and the like.
Further, in the method, by performing drug resistance identification on the weeding performance membership information in the target area, a drug resistance identification result is obtained, and other herbicide types except the current herbicide type are initialized based on the drug resistance identification result, which specifically comprises the following steps:
setting a weeding performance membership evaluation index, and judging whether weeding performance membership information in a target area is larger than the weeding performance membership evaluation index;
When the weeding performance membership information in the target area is larger than the weeding performance membership evaluation index, taking the corresponding target area as an area for generating the drug resistance, and generating a drug resistance identification result;
When the weeding performance membership degree information in the target area is not more than the weeding performance membership degree evaluation index, taking the corresponding target area as a non-drug-resistant area, and taking the current herbicide as a recommended drug of the current target area;
When the drug resistance identification result is larger than the preset drug resistance identification result, the weed type in the target area is obtained, and other herbicide types except the current herbicide type are obtained through big data retrieval according to the weed type in the target area.
It should be noted that, the weeding performance membership evaluation index may be set to be a middle weeding performance membership, and when the weeding performance membership information in the target area is greater than the weeding performance membership evaluation index, the generation of drug resistance is described.
As shown in fig. 3, in the method, further, environmental factor information of a current area to be applied is obtained, and screening is performed according to other herbicide types except the current herbicide type and the environmental factor information of the current area to be applied, so as to obtain the screened herbicide type, which specifically includes:
S302, obtaining weeding performance characteristic data of different herbicide types under each environmental factor information through big data, and importing the weeding performance characteristic data of different herbicide types under each environmental factor information into a graph neural network;
S304, taking an environmental factor as a first node, a herbicide type as a second node and weeding performance characteristic data as a third node, constructing a topological structure diagram based on the first node, the second node and the third node, and inputting the topological structure diagram into a knowledge graph for storage;
S306, acquiring environmental factor information of a current area to be applied, inputting the environmental factor information of the current area to be applied and other herbicide types except the current herbicide type into a knowledge graph, and acquiring weeding performance characteristic data of each herbicide type under the environmental factor information of the current area to be applied;
And S308, acquiring herbicide with the weeding performance characteristic data larger than a preset weeding performance characteristic threshold, and outputting the herbicide with the weeding performance characteristic data larger than the preset weeding performance characteristic threshold as the type of the screened herbicide.
It should be noted that, due to the influence of chemical components in the herbicide, the herbicide performance characteristic data of different herbicide types under different environmental factors are different, and the recommended rationality of the herbicide can be further improved by the method.
Further, in the method, component data information of the current herbicide type is obtained, and cross analysis is performed according to the component data information of the current herbicide type and the screened herbicide type, so as to obtain a cross analysis result, which specifically comprises the following steps:
Acquiring component data information of the current herbicide type and component data information of the type of the screened herbicide;
Acquiring the effective components of the current herbicide type in the weeding process and the effective components of the screened herbicide type in the weeding process according to the component data information of the current herbicide type and the component data information of the screened herbicide type;
calculating a mahalanobis distance value between an effective component of the current herbicide type in the weeding process and an effective component of the screened herbicide type in the weeding process, and judging whether the mahalanobis distance value is larger than a preset mahalanobis distance threshold value or not;
And when the mahalanobis distance value is not larger than the preset mahalanobis distance threshold, taking the mahalanobis distance value as a cross scoring result, and outputting a cross scoring junction as a cross analysis result.
When the mahalanobis distance value is not greater than the preset mahalanobis distance threshold, the cross property between herbicides is high, and the herbicide is not suitable for continuing as the herbicide of the target area.
Further, in the present method, the final herbicide type is generated according to the cross-sex analysis result, specifically including:
Presetting a cross score threshold index, and judging whether a cross analysis result is larger than the cross score threshold index;
When the cross analysis result is larger than the cross scoring threshold index, the corresponding herbicide type is removed, and the screened herbicide type is updated;
and when the cross analysis result is not greater than the cross scoring threshold index, taking the corresponding herbicide type as the recommended herbicide type, and counting the recommended herbicide type to generate the final herbicide type.
When the Marshall distance value is not larger than the preset Marshall distance threshold, the two types of herbicides are similar, and more reasonable herbicides can be further screened out by the method, so that the control effect on weeding is improved.
Furthermore, the method comprises the following steps:
acquiring weed types in a target area, initializing the combined information of the final herbicide types according to the weed types in the target area, and judging whether the combined information of the final herbicide types is at least two herbicide types or not;
If the combination information of the final herbicide types is at least two herbicide types, introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, searching through big data, and judging whether interaction exists between the herbicide types in the combination information of the final herbicide types;
When the interaction exists between the herbicide types in the combined information of the final herbicide types, carrying out genetic iteration according to the genetic algorithm, and adjusting the herbicide types in the combined information of the final herbicide types until no interaction exists between the herbicide types in the combined information of the final herbicide types;
outputting the combination information of the final herbicide types when no interaction exists between the herbicide types in the combination information of the final herbicide types, and generating related recommended information according to the combination information of the final herbicide types.
It should be noted that, since multiple herbicides may be required in the target area, there may be a certain interaction (physical reaction, chemical reaction, etc.) between the herbicides due to the difference of chemical components, and thus the herbicidal performance may be reduced, and the rationality of selecting the herbicide may be further improved by the method.
As shown in fig. 4, the second aspect of the present invention provides a herbicidal effect evaluation system 4 for herbicides, the system 4 comprising a memory 41 and a processor 42, the memory 41 comprising a herbicidal effect evaluation method program for herbicides, which when executed by the processor 42, implements the steps of:
the weeding performance characteristic data of the current herbicide in each target area are obtained, and weeding performance membership information in each target area is obtained by evaluating the weeding performance characteristic data;
The method comprises the steps of carrying out drug resistance identification on weeding performance membership information in a target area, obtaining a drug resistance identification result, and initializing other herbicide types except the current herbicide type based on the drug resistance identification result;
Acquiring environmental factor information of a current herbicide to be applied area, and screening according to other herbicide types except the current herbicide type and the environmental factor information of the current herbicide to be applied area to acquire the screened herbicide type;
Component data information of the current herbicide type is obtained, cross analysis is carried out according to the component data information of the current herbicide type and the type of the screened herbicide, cross analysis results are obtained, and final herbicide types are generated according to the cross analysis results.
Further, in the present system, weeding performance characteristic data of the current herbicide in each target area is obtained, and weeding performance membership information in each target area is obtained by evaluating the weeding performance characteristic data, which specifically includes:
The method comprises the steps of obtaining weeding performance characteristic data of current herbicides in each target area, introducing a decision tree algorithm, constructing sample data according to the weeding performance characteristic data of the current herbicides in each target area, and inputting the sample data into the decision tree algorithm;
setting a splitting standard, constructing a root node based on sample data, carrying out initialization splitting on the root node to generate a plurality of leaf nodes, judging each leaf node based on the splitting standard, and judging whether at least two types of sample data exist in the leaf nodes;
when at least two types of sample data exist in the leaf node, continuously splitting the leaf node until only one type of sample data exists in the leaf node, and outputting the leaf node;
When only one type of sample data exists in the leaf nodes, outputting the leaf nodes, acquiring weeding performance membership information in the target area according to the leaf nodes, counting the weeding performance membership information in the target area, and outputting the weeding performance membership information in each target area.
Further, in the present system, by performing drug resistance recognition on the herbicide performance membership information in the target area, a drug resistance recognition result is obtained, and other herbicide types than the current herbicide type are initialized based on the drug resistance recognition result, specifically including:
setting a weeding performance membership evaluation index, and judging whether weeding performance membership information in a target area is larger than the weeding performance membership evaluation index;
When the weeding performance membership information in the target area is larger than the weeding performance membership evaluation index, taking the corresponding target area as an area for generating the drug resistance, and generating a drug resistance identification result;
When the weeding performance membership degree information in the target area is not more than the weeding performance membership degree evaluation index, taking the corresponding target area as a non-drug-resistant area, and taking the current herbicide as a recommended drug of the current target area;
When the drug resistance identification result is larger than the preset drug resistance identification result, the weed type in the target area is obtained, and other herbicide types except the current herbicide type are obtained through big data retrieval according to the weed type in the target area.
Further, in the system, environmental factor information of a current herbicide to be applied area is obtained, screening is performed according to other herbicide types except the current herbicide type and the environmental factor information of the current herbicide to be applied area, and the herbicide type after screening is obtained, which specifically comprises:
The weeding performance characteristic data of different herbicide types under the environmental factor information are obtained through the big data, and the weeding performance characteristic data of different herbicide types under the environmental factor information are imported into the graphic neural network;
Taking the environmental factors as a first node, the herbicide type as a second node and the weeding performance characteristic data as a third node, constructing a topological structure diagram based on the first node, the second node and the third node, and inputting the topological structure diagram into a knowledge graph for storage;
Acquiring environmental factor information of a current area to be applied with the herbicide, inputting the environmental factor information of the current area to be applied with the herbicide and other herbicide types except the current herbicide type into a knowledge graph, and acquiring weeding performance characteristic data of each herbicide type under the environmental factor information of the current area to be applied with the herbicide;
And obtaining the herbicide with the weeding performance characteristic data larger than the preset weeding performance characteristic threshold, and outputting the herbicide with the weeding performance characteristic data larger than the preset weeding performance characteristic threshold as the type of the screened herbicide.
Further, in the system, component data information of the current herbicide type is obtained, and cross analysis is performed according to the component data information of the current herbicide type and the screened herbicide type, so as to obtain a cross analysis result, which specifically comprises the following steps:
Acquiring component data information of the current herbicide type and component data information of the type of the screened herbicide;
Acquiring the effective components of the current herbicide type in the weeding process and the effective components of the screened herbicide type in the weeding process according to the component data information of the current herbicide type and the component data information of the screened herbicide type;
calculating a mahalanobis distance value between an effective component of the current herbicide type in the weeding process and an effective component of the screened herbicide type in the weeding process, and judging whether the mahalanobis distance value is larger than a preset mahalanobis distance threshold value or not;
And when the mahalanobis distance value is not larger than the preset mahalanobis distance threshold, taking the mahalanobis distance value as a cross scoring result, and outputting a cross scoring junction as a cross analysis result.
Further, in the present system, the generation of the final herbicide type based on the results of the crossover analysis specifically includes:
Presetting a cross score threshold index, and judging whether a cross analysis result is larger than the cross score threshold index;
When the cross analysis result is larger than the cross scoring threshold index, the corresponding herbicide type is removed, and the screened herbicide type is updated;
and when the cross analysis result is not greater than the cross scoring threshold index, taking the corresponding herbicide type as the recommended herbicide type, and counting the recommended herbicide type to generate the final herbicide type.
A third aspect of the present invention provides a computer-readable storage medium including therein a herbicidal effect evaluation method program of a herbicide, the herbicidal effect evaluation method program of a herbicide, when executed by a processor, implementing the steps of the herbicidal effect evaluation method of any one of the herbicides.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is merely a logical function division, and there may be additional divisions of actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described as separate components may or may not be physically separate, and components displayed as units may or may not be physical units, may be located in one place or distributed on a plurality of network units, and may select some or all of the units according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of hardware plus a form of software functional unit.
It will be appreciated by those of ordinary skill in the art that implementing all or part of the steps of the above method embodiments may be implemented by hardware associated with program instructions, where the above program may be stored in a computer readable storage medium, where the program when executed performs the steps comprising the above method embodiments, where the above storage medium includes a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic or optical disk, or other various media that may store program code.
Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. The storage medium includes various media capable of storing program codes such as a removable storage device, a ROM, a RAM, a magnetic disk or an optical disk.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
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| CN117672450A (en) * | 2023-12-06 | 2024-03-08 | 中科(安徽)智慧健康创新研究院有限公司 | A personalized drug recommendation method and system based on knowledge graph |
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| CN110231441A (en) * | 2019-07-02 | 2019-09-13 | 中国农业科学院特产研究所 | A kind of Appraisal of Herbicides method |
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| CN117672450A (en) * | 2023-12-06 | 2024-03-08 | 中科(安徽)智慧健康创新研究院有限公司 | A personalized drug recommendation method and system based on knowledge graph |
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