Method, system and storage medium for evaluating weed control effect of herbicides

Abstract

The present invention relates to a method, system and storage medium for evaluating the weed control effect of a herbicide, and belongs to the technical field. The present invention obtains environmental factor information of the current area to be applied, and screens according to other herbicide types except the current herbicide type and the environmental factor information of the current area to be applied, obtains the screened herbicide type, and finally obtains the component data information of the current herbicide type, and performs cross analysis according to the component data information of the current herbicide type and the screened herbicide type, obtains the cross analysis result, and generates the final herbicide type according to the cross analysis result. The present invention evaluates the herbicide performance characteristic data of each herbicide, thereby obtaining the drug resistance to the current herbicide in the target area, and then recommends a herbicide that is more suitable for the target area in combination with the actual environment, so that the herbicide treatment effect is better.

Description

Technical Field

[0001] The present invention relates to the technical field of herbicides, and in particular to a method, system and storage medium for evaluating the weeding effect of a herbicide. Background Art

[0002] As the most effective means of weed control, chemical control plays an important role in promoting agricultural production. With the continuous and widespread use of herbicides, weeds are becoming more and more resistant to herbicides. The emergence and rapid spread of resistant weeds have posed new challenges to the weed control system based on chemical weed control. The emergence of resistance has led to a decline in the effectiveness of weed control, which, if not adjusted, will seriously affect the growth and yield of related plants. Summary of the invention

[0003] The present invention overcomes the shortcomings of the prior art and provides a method, system and storage medium for evaluating the weed control effect of a herbicide.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention provides a method for evaluating the weed control effect of a herbicide, comprising the following steps:

[0006] Obtaining the herbicide performance characteristic data of the current herbicide in each target area, and obtaining the herbicide performance membership information in each target area by evaluating the herbicide performance characteristic data;

[0007] By performing resistance identification on the weed control performance membership information in the target area, a resistance identification result is obtained, and other herbicide types other than the current herbicide type are initialized based on the resistance identification result;

[0008] Obtaining environmental factor information of the current area to be sprayed, and filtering according to other herbicide types except the current herbicide type and the environmental factor information of the current area to be sprayed, to obtain the filtered herbicide type;

[0009] The component data information of the current herbicide type is obtained, and a cross-analysis is performed based on the component data information of the current herbicide type and the types of the screened herbicides to obtain the cross-analysis result, and a final herbicide type is generated based on the cross-analysis result.

[0010] Furthermore, in the present method, the herbicidal performance characteristic data of the current herbicide in each target area is obtained, and the herbicidal performance membership information in each target area is obtained by evaluating the herbicidal performance characteristic data, specifically including:

[0011] Acquire the herbicide performance characteristic data of the current herbicide in each target area, introduce a decision tree algorithm, construct sample data according to the herbicide performance characteristic data of the current herbicide in each target area, and input the sample data into the decision tree algorithm;

[0012] Set the splitting criteria, build a root node based on the sample data, and perform initial splitting on the root node to generate several leaf nodes. Then, judge each leaf node based on the splitting criteria to determine whether there are at least two types of sample data in the leaf node.

[0013] When there are at least two types of sample data in a leaf node, continue to split the leaf node until there is only one type of sample data in the leaf node, and then output the leaf node;

[0014] When there is only one type of sample data in a leaf node, the leaf node is output, and the weeding performance membership information in the target area is obtained according to the leaf node, the weeding performance membership information in the target area is counted, and the weeding performance membership information in each target area is output.

[0015] Furthermore, in this method, by performing resistance identification on the weed control performance membership information in the target area, a resistance identification result is obtained, and other herbicide types other than the current herbicide type are initialized based on the resistance identification result, specifically including:

[0016] Setting a weeding performance membership evaluation index, and determining whether the weeding performance membership information in the target area is greater than the weeding performance membership evaluation index;

[0017] When the weed control performance membership information in the target area is greater than the weed control performance membership evaluation index, the corresponding target area is regarded as an area where resistance is generated, and a resistance identification result is generated;

[0018] When the weed control performance membership information in the target area is not greater than the weed control performance membership evaluation index, the corresponding target area is regarded as a non-resistant area, and the current herbicide is regarded as the recommended drug for the current target area;

[0019] When the resistance identification result is greater than the preset resistance identification result, the weed type in the target area is obtained, and other herbicide types except the current herbicide type are obtained according to the weed type in the target area through big data retrieval.

[0020] Furthermore, in the present method, environmental factor information of the current area to be sprayed is obtained, and other herbicide types except the current herbicide type and the environmental factor information of the current area to be sprayed are screened to obtain the screened herbicide types, specifically including:

[0021] Obtain the herbicide performance characteristic data of different herbicide types under various environmental factor information through big data, and import the herbicide performance characteristic data of different herbicide types under various environmental factor information into the graph neural network;

[0022] Taking the environmental factor as the first node, the herbicide type as the second node, and the herbicide performance characteristic data as the third node, a topological structure diagram is constructed based on the first node, the second node, and the third node, and the topological structure diagram is input into the knowledge graph for storage;

[0023] Obtain environmental factor information of the current area to be sprayed, and input the environmental factor information of the current area to be sprayed and other herbicide types except the current herbicide type into the knowledge graph to obtain the herbicide performance characteristic data of each herbicide type under the environmental factor information of the current area to be sprayed;

[0024] The herbicides whose weeding performance characteristic data are greater than a preset weeding performance characteristic threshold are obtained, and the herbicides whose weeding performance characteristic data are greater than the preset weeding performance characteristic threshold are output as the types of the screened herbicides.

[0025] Furthermore, in this method, component data information of the current herbicide type is obtained, and cross-analysis is performed based on the component data information of the current herbicide type and the screened herbicide type to obtain cross-analysis results, specifically including:

[0026] Obtaining component data information of the current herbicide type and component data information of the filtered herbicide type;

[0027] According to the component data information of the current herbicide type and the component data information of the screened herbicide type, 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 are obtained;

[0028] Calculate the Mahalanobis distance value between the effective component of the current herbicide type in the weeding process and the effective component of the screened herbicide type in the weeding process, and determine whether the Mahalanobis distance value is greater than a preset Mahalanobis distance threshold;

[0029] When the Mahalanobis distance value is not greater than the preset Mahalanobis distance threshold, the Mahalanobis distance value is used as the intersection scoring result, and the intersection scoring result is output as the intersection analysis result.

[0030] Furthermore, in this method, the final herbicide type is generated according to the cross-analysis results, specifically including:

[0031] Preset the intersectionality scoring threshold indicator and determine whether the intersectionality analysis result is greater than the intersectionality scoring threshold indicator;

[0032] When the intersection analysis result is greater than the intersection score threshold index, the corresponding herbicide type is eliminated, and the screened herbicide type is updated;

[0033] When the intersection analysis result is not greater than the intersection score threshold index, the corresponding herbicide type is used as the recommended herbicide type, and the recommended herbicide type is statistically calculated to generate the final herbicide type.

[0034] A second aspect of the present invention provides a herbicide weeding effect evaluation system, the system comprising a memory and a processor, the memory comprising a herbicide weeding effect evaluation method program, and when the herbicide weeding effect evaluation method program is executed by the processor, the following steps are implemented:

[0035] Obtaining the herbicide performance characteristic data of the current herbicide in each target area, and obtaining the herbicide performance membership information in each target area by evaluating the herbicide performance characteristic data;

[0036] By performing resistance identification on the weed control performance membership information in the target area, a resistance identification result is obtained, and other herbicide types other than the current herbicide type are initialized based on the resistance identification result;

[0037] Obtaining environmental factor information of the current area to be sprayed, and filtering according to other herbicide types except the current herbicide type and the environmental factor information of the current area to be sprayed, to obtain the filtered herbicide type;

[0038] The component data information of the current herbicide type is obtained, and a cross-analysis is performed based on the component data information of the current herbicide type and the types of the screened herbicides to obtain the cross-analysis result, and a final herbicide type is generated based on the cross-analysis result.

[0039] A third aspect of the present invention provides a computer-readable storage medium, which includes a program for evaluating the weeding effect of a herbicide. When the program for evaluating the weeding effect of a herbicide is executed by a processor, the steps of any one of the methods for evaluating the weeding effect of a herbicide are implemented.

[0040] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0041] The present invention obtains the herbicidal performance characteristic data of the current herbicide in each target area, and obtains the herbicidal performance membership information in each target area by evaluating the herbicidal performance characteristic data, and then performs resistance identification on the herbicidal performance membership information in the target area to obtain the resistance identification result, and initializes other herbicide types except the current herbicide type based on the resistance identification result, thereby obtaining the environmental factor information of the current area to be applied, and screening according to other herbicide types except the current herbicide type and the environmental factor information of the current area to be applied, obtaining the screened herbicide type, and finally obtaining the component data information of the current herbicide type, and performing cross analysis according to the component data information of the current herbicide type and the screened herbicide type, obtaining the cross analysis result, and generating the final herbicide type according to the cross analysis result. The present invention evaluates the herbicidal performance characteristic data of each herbicide, thereby obtaining the resistance to the current herbicide in the target area, and then recommends a herbicide that is more suitable for the target area in combination with the actual environment, so that the herbicide treatment effect is better. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0043] Figure 1 The overall method flow chart of the method for evaluating the weed control effect of a herbicide is shown;

[0044] Figure 2 A first method flow chart of a method for evaluating the weed control effect of a herbicide is shown;

[0045] Figure 3 A second method flow chart of the method for evaluating the herbicidal effect of a herbicide is shown;

[0046] Figure 4 A system block diagram of a herbicide weed control effect evaluation system is shown. DETAILED DESCRIPTION

[0047] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0049] like Figure 1 As shown, the first aspect of the present invention provides a method for evaluating the weed control effect of a herbicide, comprising the following steps:

[0050] S102: obtaining the herbicidal performance characteristic data of the current herbicide in each target area, and obtaining the herbicidal performance membership information in each target area by evaluating the herbicidal performance characteristic data;

[0051] S104: performing resistance identification on the weed control performance membership information in the target area to obtain resistance identification results, and initializing other herbicide types except the current herbicide type based on the resistance identification results;

[0052] S106: Obtaining environmental factor information of the current area to be sprayed, and screening according to other herbicide types except the current herbicide type and the environmental factor information of the current area to be sprayed, to obtain the screened herbicide type;

[0053] S108: Obtaining component data information of the current herbicide type, and performing cross-analysis based on the component data information of the current herbicide type and the screened herbicide types, obtaining cross-analysis results, and generating a final herbicide type based on the cross-analysis results.

[0054] It should be noted that the present invention evaluates the herbicidal performance characteristic data of each herbicide to obtain the resistance to the current herbicide in the target area, and then recommends a herbicide that is more suitable for the target area in combination with the actual environment, so that the herbicide has a better control effect. Among them, the herbicidal performance characteristic data includes data such as the amount of weeds killed per unit area and the amount of weeds killed per unit herbicide dosage.

[0055] like Figure 2 As shown, further, in this method, the herbicidal performance characteristic data of the current herbicide in each target area is obtained, and the herbicidal performance membership information in each target area is obtained by evaluating the herbicidal performance characteristic data, specifically including:

[0056] S202: obtaining the herbicidal performance characteristic data of the current herbicide in each target area, introducing a decision tree algorithm, constructing sample data according to the herbicidal performance characteristic data of the current herbicide in each target area, and inputting the sample data into the decision tree algorithm;

[0057] S204: Setting a splitting criterion, constructing a root node based on the sample data, and performing initial splitting on the root node to generate a number of leaf nodes, and judging each leaf node based on the splitting criterion to determine whether there are at least two types of sample data in the leaf node;

[0058] S206: When there are at least two types of sample data in the leaf node, continue to split the leaf node until there are only one type of sample data in the leaf node, and output the leaf node;

[0059] S208: When there is only one type of sample data in a leaf node, the leaf node is output, and the weeding performance membership information in the target area is obtained according to the leaf node, the weeding performance membership information in the target area is counted, and the weeding performance membership information in each target area is output.

[0060] It should be noted that the decision tree algorithm can quickly obtain the weeding performance membership information in the target area, wherein the weeding performance membership information includes data such as low weeding performance membership, medium weeding performance membership, and high weeding performance membership.

[0061] Furthermore, in this method, by performing resistance identification on the weed control performance membership information in the target area, a resistance identification result is obtained, and other herbicide types other than the current herbicide type are initialized based on the resistance identification result, specifically including:

[0062] Setting a weeding performance membership evaluation index, and determining whether the weeding performance membership information in the target area is greater than the weeding performance membership evaluation index;

[0063] When the weed control performance membership information in the target area is greater than the weed control performance membership evaluation index, the corresponding target area is regarded as an area where resistance is generated, and a resistance identification result is generated;

[0064] When the weed control performance membership information in the target area is not greater than the weed control performance membership evaluation index, the corresponding target area is regarded as a non-resistant area, and the current herbicide is regarded as the recommended drug for the current target area;

[0065] When the resistance identification result is greater than the preset resistance identification result, the weed type in the target area is obtained, and other herbicide types except the current herbicide type are obtained according to the weed type in the target area through big data retrieval.

[0066] It should be noted that the weed control performance membership evaluation index can be set to a medium weed control performance membership. When the weed control performance membership information in the target area is greater than the weed control performance membership evaluation index, it indicates that drug resistance has occurred.

[0067] like Figure 3 As shown, further, in this method, the environmental factor information of the current area to be applied is obtained, and the herbicide types other than the current herbicide type and the environmental factor information of the current area to be applied are screened to obtain the screened herbicide types, specifically including:

[0068] S302: obtaining the herbicide performance characteristic data of different herbicide types under each environmental factor information through big data, and importing the herbicide performance characteristic data of different herbicide types under each environmental factor information into the graph neural network;

[0069] S304: taking the environmental factor as the first node, the herbicide type as the second node, and the herbicide performance characteristic data as the 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 the knowledge graph for storage;

[0070] S306: Obtain environmental factor information of the current area to be sprayed, and input the environmental factor information of the current area to be sprayed and other herbicide types except the current herbicide type into the knowledge graph to obtain the herbicide performance characteristic data of each herbicide type under the environmental factor information of the current area to be sprayed;

[0071] S308: Acquire herbicides whose weeding performance characteristic data is greater than a preset weeding performance characteristic threshold, and output the herbicides whose weeding performance characteristic data is greater than the preset weeding performance characteristic threshold as the type of screened herbicides.

[0072] It should be noted that due to the influence of the chemical components in the herbicide, the herbicidal performance characteristic data of different herbicide types under different environmental factors are different. This method can further improve the rationality of herbicide recommendations.

[0073] Furthermore, in this method, component data information of the current herbicide type is obtained, and cross-analysis is performed based on the component data information of the current herbicide type and the screened herbicide type to obtain cross-analysis results, specifically including:

[0074] Obtaining component data information of the current herbicide type and component data information of the filtered herbicide type;

[0075] According to the component data information of the current herbicide type and the component data information of the screened herbicide type, 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 are obtained;

[0076] Calculate the Mahalanobis distance value between the effective component of the current herbicide type in the weeding process and the effective component of the screened herbicide type in the weeding process, and determine whether the Mahalanobis distance value is greater than a preset Mahalanobis distance threshold;

[0077] When the Mahalanobis distance value is not greater than the preset Mahalanobis distance threshold, the Mahalanobis distance value is used as the intersection scoring result, and the intersection scoring result is output as the intersection analysis result.

[0078] It should be noted that when the Mahalanobis distance value is not greater than the preset Mahalanobis distance threshold, it indicates that the crossover between the herbicides is high and they are not suitable to continue to be used as herbicides in the target area.

[0079] Furthermore, in this method, the final herbicide type is generated according to the cross-analysis results, specifically including:

[0080] Preset the intersectionality scoring threshold indicator and determine whether the intersectionality analysis result is greater than the intersectionality scoring threshold indicator;

[0081] When the intersection analysis result is greater than the intersection score threshold index, the corresponding herbicide type is eliminated, and the screened herbicide type is updated;

[0082] When the intersection analysis result is not greater than the intersection score threshold index, the corresponding herbicide type is used as the recommended herbicide type, and the recommended herbicide type is statistically calculated to generate the final herbicide type.

[0083] It should be noted that when the Mahalanobis distance value is not greater than the preset Mahalanobis distance threshold, it indicates that the two types of herbicides are very similar. This method can further screen out more reasonable herbicides to improve the weed control effect.

[0084] In addition, the method further comprises the following steps:

[0085] Acquire the weed types in the target area, initialize final combination information of herbicide types according to the weed types in the target area, and determine whether the final combination information of herbicide types is at least two herbicide types;

[0086] If the final combination information of herbicide types is at least two herbicide types, a genetic algorithm is introduced, a genetic generation is set according to the genetic algorithm, and a search is performed through big data to determine whether there is interaction between the herbicide types in the final combination information of herbicide types;

[0087] When there is interaction between herbicide types in the final combination information of herbicide types, performing genetic iteration according to the genetic algorithm to adjust the herbicide types in the final combination information of herbicide types until there is no interaction between the herbicide types in the final combination information of herbicide types;

[0088] When there is no interaction between the herbicide types in the final combination information of the herbicide types, the final combination information of the herbicide types is output, and relevant recommendation information is generated according to the final combination information of the herbicide types.

[0089] It should be noted that since multiple herbicides may be needed in the target area, and there may be certain interactions (physical reactions, chemical reactions, etc.) between the herbicides due to their different chemical compositions, which may lead to a decrease in herbicidal performance, this method can further improve the rationality of herbicide selection.

[0090] like Figure 4 As shown, the second aspect of the present invention provides a herbicide weeding effect evaluation system 4, the system 4 includes a memory 41 and a processor 42, the memory 41 includes a herbicide weeding effect evaluation method program, and when the herbicide weeding effect evaluation method program is executed by the processor 42, the following steps are implemented:

[0091] Obtaining the herbicide performance characteristic data of the current herbicide in each target area, and obtaining the herbicide performance membership information in each target area by evaluating the herbicide performance characteristic data;

[0092] By performing resistance identification on the weed control performance membership information in the target area, a resistance identification result is obtained, and other herbicide types other than the current herbicide type are initialized based on the resistance identification result;

[0093] Obtaining environmental factor information of the current area to be sprayed, and filtering according to other herbicide types except the current herbicide type and the environmental factor information of the current area to be sprayed, to obtain the filtered herbicide type;

[0094] The component data information of the current herbicide type is obtained, and a cross-analysis is performed based on the component data information of the current herbicide type and the types of the screened herbicides to obtain the cross-analysis result, and a final herbicide type is generated based on the cross-analysis result.

[0095] Furthermore, in this system, the herbicidal performance characteristic data of the current herbicide in each target area is obtained, and the herbicidal performance membership information in each target area is obtained by evaluating the herbicidal performance characteristic data, specifically including:

[0096] Acquire the herbicide performance characteristic data of the current herbicide in each target area, introduce a decision tree algorithm, construct sample data according to the herbicide performance characteristic data of the current herbicide in each target area, and input the sample data into the decision tree algorithm;

[0097] Set the splitting criteria, build a root node based on the sample data, and perform initial splitting on the root node to generate several leaf nodes. Then, judge each leaf node based on the splitting criteria to determine whether there are at least two types of sample data in the leaf node.

[0098] When there are at least two types of sample data in a leaf node, continue to split the leaf node until there is only one type of sample data in the leaf node, and then output the leaf node;

[0099] When there is only one type of sample data in a leaf node, the leaf node is output, and the weeding performance membership information in the target area is obtained according to the leaf node, the weeding performance membership information in the target area is counted, and the weeding performance membership information in each target area is output.

[0100] Furthermore, in this system, by performing resistance identification on the weed control performance membership information in the target area, a resistance identification result is obtained, and other herbicide types other than the current herbicide type are initialized based on the resistance identification result, specifically including:

[0101] Setting a weeding performance membership evaluation index, and determining whether the weeding performance membership information in the target area is greater than the weeding performance membership evaluation index;

[0102] When the weed control performance membership information in the target area is greater than the weed control performance membership evaluation index, the corresponding target area is regarded as an area where resistance is generated, and a resistance identification result is generated;

[0103] When the weed control performance membership information in the target area is not greater than the weed control performance membership evaluation index, the corresponding target area is regarded as a non-resistant area, and the current herbicide is regarded as the recommended drug for the current target area;

[0104] When the resistance identification result is greater than the preset resistance identification result, the weed type in the target area is obtained, and other herbicide types except the current herbicide type are obtained according to the weed type in the target area through big data retrieval.

[0105] Furthermore, in the present system, environmental factor information of the current area to be sprayed is obtained, and other herbicide types other than the current herbicide type and environmental factor information of the current area to be sprayed are screened to obtain the screened herbicide types, specifically including:

[0106] Obtain the herbicide performance characteristic data of different herbicide types under various environmental factor information through big data, and import the herbicide performance characteristic data of different herbicide types under various environmental factor information into the graph neural network;

[0107] Taking the environmental factor as the first node, the herbicide type as the second node, and the herbicide performance characteristic data as the third node, a topological structure diagram is constructed based on the first node, the second node, and the third node, and the topological structure diagram is input into the knowledge graph for storage;

[0108] Obtain environmental factor information of the current area to be sprayed, and input the environmental factor information of the current area to be sprayed and other herbicide types except the current herbicide type into the knowledge graph to obtain the herbicide performance characteristic data of each herbicide type under the environmental factor information of the current area to be sprayed;

[0109] The herbicides whose weeding performance characteristic data are greater than a preset weeding performance characteristic threshold are obtained, and the herbicides whose weeding performance characteristic data are greater than the preset weeding performance characteristic threshold are output as the types of the screened herbicides.

[0110] Furthermore, in the present system, component data information of the current herbicide type is obtained, and cross-analysis is performed based on the component data information of the current herbicide type and the screened herbicide type to obtain cross-analysis results, specifically including:

[0111] Obtaining component data information of the current herbicide type and component data information of the filtered herbicide type;

[0112] According to the component data information of the current herbicide type and the component data information of the screened herbicide type, 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 are obtained;

[0113] Calculate the Mahalanobis distance value between the effective component of the current herbicide type in the weeding process and the effective component of the screened herbicide type in the weeding process, and determine whether the Mahalanobis distance value is greater than a preset Mahalanobis distance threshold;

[0114] When the Mahalanobis distance value is not greater than the preset Mahalanobis distance threshold, the Mahalanobis distance value is used as the intersection scoring result, and the intersection scoring result is output as the intersection analysis result.

[0115] Furthermore, in this system, the final herbicide type is generated according to the cross-analysis results, including:

[0116] Preset the intersectionality scoring threshold indicator and determine whether the intersectionality analysis result is greater than the intersectionality scoring threshold indicator;

[0117] When the intersection analysis result is greater than the intersection score threshold index, the corresponding herbicide type is eliminated, and the screened herbicide type is updated;

[0118] When the intersection analysis result is not greater than the intersection score threshold index, the corresponding herbicide type is used as the recommended herbicide type, and the recommended herbicide type is statistically calculated to generate the final herbicide type.

[0119] A third aspect of the present invention provides a computer-readable storage medium, which includes a program for evaluating the weeding effect of a herbicide. When the program for evaluating the weeding effect of a herbicide is executed by a processor, the steps of any one of the methods for evaluating the weeding effect of a herbicide are implemented.

[0120] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0121] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0122] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0123] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0124] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0125] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating the weed control effect of a herbicide, characterized in that: The following steps are involved: Acquire the herbicidal performance characteristic data of the current herbicide in each target area, and acquire the herbicidal performance membership information in each target area by evaluating the herbicidal performance characteristic data; Obtaining a resistance identification result by performing resistance identification on the weed control performance membership information in the target area, and initializing other herbicide types except the current herbicide type based on the resistance identification result; Acquire environmental factor information of the current area to be sprayed, and screen other herbicide types except the current herbicide type and the environmental factor information of the current area to be sprayed, to obtain the screened herbicide type; Obtaining component data information of the current herbicide type, performing cross-analysis based on the component data information of the current herbicide type and the screened herbicide types, obtaining cross-analysis results, and generating a final herbicide type based on the cross-analysis results; By performing resistance identification on the weed control performance membership information in the target area, obtaining a resistance identification result, and initializing other herbicide types except the current herbicide type based on the resistance identification result, specifically including: Setting a weeding performance membership evaluation index, and determining whether the weeding performance membership information in the target area is greater than the weeding performance membership evaluation index; When the weed control performance membership information in the target area is greater than the weed control performance membership evaluation index, the corresponding target area is regarded as an area where drug resistance is generated, and a drug resistance identification result is generated; When the weed control performance membership information in the target area is not greater than the weed control performance membership evaluation index, the corresponding target area is regarded as a non-resistant area, and the current herbicide is regarded as a recommended drug for the current target area; When the pesticide resistance identification result is greater than a preset pesticide resistance identification result, the weed type in the target area is obtained, and other herbicide types except the current herbicide type are obtained according to the weed type in the target area through big data retrieval.

2. The method for evaluating the weed control effect of a herbicide according to claim 1, characterized in that: Obtaining the herbicidal performance characteristic data of the current herbicide in each target area, and obtaining the herbicidal performance membership information in each target area by evaluating the herbicidal performance characteristic data, specifically including: Acquire the herbicidal performance characteristic data of the current herbicide in each target area, introduce a decision tree algorithm, construct sample data according to the herbicidal performance characteristic data of the current herbicide in each target area, and input the sample data into the decision tree algorithm; Setting a splitting criterion, constructing a root node based on the sample data, and performing initial splitting on the root node to generate a plurality of leaf nodes, and judging each leaf node based on the splitting criterion to determine whether there are at least two types of sample data in the leaf node; When there are at least two types of sample data in the leaf node, continue to split the leaf node until there is only one type of sample data in the leaf node, and output the leaf node; When there is only one type of sample data in the leaf node, the leaf node is output, and the weeding performance membership information in the target area is obtained according to the leaf node, the weeding performance membership information in the target area is counted, and the weeding performance membership information in each target area is output.

3. The method for evaluating the weed control effect of a herbicide according to claim 1, characterized in that: Obtaining component data information of the current herbicide type, and performing cross-analysis based on the component data information of the current herbicide type and the screened herbicide types to obtain cross-analysis results, specifically including: Obtaining component data information of the current herbicide type and component data information of the filtered herbicide type; According to the component data information of the current herbicide type and the component data information of the screened herbicide type, 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 are obtained; Calculating the Mahalanobis distance value between the active ingredient of the current herbicide type during the weeding process and the active ingredient of the screened herbicide type during the weeding process, and determining whether the Mahalanobis distance value is greater than a preset Mahalanobis distance threshold; When the Mahalanobis distance value is not greater than a preset Mahalanobis distance threshold, the Mahalanobis distance value is used as an intersection scoring result, and the intersection scoring result is output as an intersection analysis result.

4. The method for evaluating the weed control effect of a herbicide according to claim 1, characterized in that: The final herbicide type is generated according to the cross-analysis results, including: Preset an intersectionality scoring threshold indicator, and determine whether the intersectionality analysis result is greater than the intersectionality scoring threshold indicator; When the intersection analysis result is greater than the intersection scoring threshold index, the corresponding herbicide type is eliminated, and the screened herbicide type is updated; When the intersection analysis result is not greater than the intersection scoring threshold index, the corresponding herbicide type is used as the recommended herbicide type, and the recommended herbicide type is statistically calculated to generate a final herbicide type.

5. A system for evaluating the weed control effect of a herbicide, characterized in that: The system includes a memory and a processor. The memory includes a herbicide herbicidal effect evaluation method program. When the herbicide herbicidal effect evaluation method program is executed by the processor, the following steps are implemented: Acquire the herbicidal performance characteristic data of the current herbicide in each target area, and acquire the herbicidal performance membership information in each target area by evaluating the herbicidal performance characteristic data; Obtaining a resistance identification result by performing resistance identification on the weed control performance membership information in the target area, and initializing other herbicide types except the current herbicide type based on the resistance identification result; Acquire environmental factor information of the current area to be sprayed, and screen other herbicide types except the current herbicide type and the environmental factor information of the current area to be sprayed, to obtain the screened herbicide type; Obtaining component data information of the current herbicide type, performing cross-analysis based on the component data information of the current herbicide type and the screened herbicide types, obtaining cross-analysis results, and generating a final herbicide type based on the cross-analysis results; By performing resistance identification on the weed control performance membership information in the target area, obtaining a resistance identification result, and initializing other herbicide types except the current herbicide type based on the resistance identification result, specifically including: Setting a weeding performance membership evaluation index, and determining whether the weeding performance membership information in the target area is greater than the weeding performance membership evaluation index; When the weed control performance membership information in the target area is greater than the weed control performance membership evaluation index, the corresponding target area is regarded as an area where drug resistance is generated, and a drug resistance identification result is generated; When the weed control performance membership information in the target area is not greater than the weed control performance membership evaluation index, the corresponding target area is regarded as a non-resistant area, and the current herbicide is regarded as a recommended drug for the current target area; When the pesticide resistance identification result is greater than a preset pesticide resistance identification result, the weed type in the target area is obtained, and other herbicide types except the current herbicide type are obtained according to the weed type in the target area through big data retrieval.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a herbicide herbicidal effect evaluation method program, and when the herbicide herbicidal effect evaluation method program is executed by a processor, the steps of the herbicide herbicidal effect evaluation method according to any one of claims 1 to 4 are implemented.

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