CN116738185A

CN116738185A - AI algorithm construction method for intelligent cultivation

Info

Publication number: CN116738185A
Application number: CN202310881962.1A
Authority: CN
Inventors: 陈丽园
Original assignee: Huiliantong Industrial Supply Chain Digital Technology Xiamen Co ltd
Current assignee: Huiliantong Industrial Supply Chain Digital Technology Xiamen Co ltd
Priority date: 2023-07-18
Filing date: 2023-07-18
Publication date: 2023-09-12
Anticipated expiration: 2043-07-18
Also published as: CN116738185B

Abstract

The application discloses an AI algorithm construction method for intelligent cultivation, which comprises the following steps: determining a first cultivation parameter and a first AI algorithm corresponding to the first cultivation parameter; determining a second cultivation parameter and a second AI algorithm corresponding to the second cultivation parameter; determining a third cultivation parameter and a third AI algorithm corresponding to the third cultivation parameter; the third AI algorithm is configured to determine the third cultivation parameter based on the first cultivation parameter and/or the second cultivation parameter, the third cultivation parameter being a cultivation parameter based on the environmental dimension and/or the artificial dimension; and constructing a target AI algorithm based on the first AI algorithm, the second AI algorithm, the third AI algorithm, the target culture variety and the target parameter relationship. The method can improve the adaptation degree of the AI algorithm and intelligent cultivation, and improve the accuracy and applicability of the AI algorithm.

Description

AI algorithm construction method for intelligent cultivation

Technical Field

The application relates to the technical field of intelligent cultivation, in particular to an AI algorithm construction method for intelligent cultivation.

Background

Along with the development of intelligent cultivation, more and more cultivation industries are integrated into intelligent cultivation. In the breeding industry, intelligent breeding can analyze breeding data through an AI (Artificial Intelligence ) algorithm; or predicting the cultivation data; or forecast cultivation parameters, etc. to save a lot of labor costs.

Currently, in intelligent cultivation techniques involving AI algorithms, it is common to target one cultivar; or a special AI algorithm is formulated for a specific cultivation environment; that is, one AI algorithm is directed to one cultivation scene, which is an algorithm selected from a large number of AI algorithms provided. In this way, the adaptation degree of the AI algorithm and the intelligent cultivation scene is not very high, and the accuracy and the applicability of the AI algorithm are poor.

Disclosure of Invention

The application aims to provide an AI algorithm construction method for intelligent cultivation, which can improve the adaptation degree of the AI algorithm and intelligent cultivation, and improve the accuracy and the applicability of the AI algorithm.

To achieve the above object, an embodiment of the present application provides an AI algorithm construction method for smart cultivation, including: determining a first cultivation parameter and a first AI algorithm corresponding to the first cultivation parameter; the first AI algorithm is used for determining the first culture parameter based on target culture data, wherein the first culture parameter is a culture parameter of an environmental dimension; determining a second cultivation parameter and a second AI algorithm corresponding to the second cultivation parameter; the second AI algorithm is used for determining the second culture parameter based on the target culture data, wherein the second culture parameter is a culture parameter with an artificial dimension; determining a third cultivation parameter and a third AI algorithm corresponding to the third cultivation parameter; the third AI algorithm is configured to determine the third cultivation parameter based on the first cultivation parameter and/or the second cultivation parameter, the third cultivation parameter being a cultivation parameter based on the environmental dimension and/or the artificial dimension; constructing a target AI algorithm based on the first AI algorithm, the second AI algorithm, the third AI algorithm, the target cultivar and a target parameter relationship; the target AI algorithm is used for determining the cultivation parameters of the target cultivation varieties, and the target parameter relation is expressed as follows: Wherein, the->Representing said first cultivation parameter, < >>Representing the second cultivation parameter, +.>Representing the third cultivation parameter in question,represents a first weight, ++>Represents a second weight, ++>Representing a first influence value,/->Representing a second influence value,/->Representing a third influence value,/->Representing a preset impact value.

In one possible implementation manner, the determining the first cultivation parameter and the first AI algorithm corresponding to the first cultivation parameter includes: acquiring a plurality of first preset culture parameters; the first preset culture parameters are culture parameters of environmental dimensions; determining direct influence values of the first preset culture parameters on the target culture varieties; determining indirect influence values of the plurality of first preset culture parameters on the target culture varieties; the indirect influence value is determined based on direct influence values of the plurality of first preset culture parameters on a first associated culture variety of the target culture variety; the similarity between the culture environment of the first associated culture variety and the culture environment of the target culture variety is larger than a first preset similarity; determining an integrated influence value of the plurality of first preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety; and determining the first culture parameters from the plurality of first preset culture parameters based on the integration influence values.

In one possible implementation manner, the determining the first cultivation parameter and the first AI algorithm corresponding to the first cultivation parameter includes: acquiring a plurality of first preset AI algorithms; the first preset AI algorithms respectively correspond to different cultivars; determining the first AI algorithm from the plurality of first preset AI algorithms; among the first preset AI algorithms, the first AI algorithm has the highest number of corresponding cultivars, and the cultivation environment of the corresponding cultivars accords with preset cultivation environment conditions.

In one possible implementation manner, the determining the second cultivation parameter and the second AI algorithm corresponding to the second cultivation parameter includes: obtaining a plurality of second preset culture parameters; the plurality of second preset culture parameters are culture parameters with artificial dimensions; determining direct influence values of the second preset culture parameters on the target culture varieties; determining indirect influence values of the second preset culture parameters on the target culture varieties; the indirect influence value is determined based on direct influence values of the plurality of second preset culture parameters on a second associated culture variety of the target culture variety; the similarity between the artificial culture condition of the second associated culture variety and the artificial culture condition of the target culture variety is greater than a second preset similarity; determining an integrated influence value of the plurality of second preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety; and determining the second culture parameters from the second preset culture parameters based on the integration influence values.

In one possible implementation manner, the determining the second cultivation parameter and the second AI algorithm corresponding to the second cultivation parameter includes: acquiring a plurality of second preset AI algorithms; the plurality of second preset AI algorithms respectively correspond to different cultivars; determining a second AI algorithm from the plurality of second preset AI algorithms; the second AI algorithm is the highest in the number of corresponding cultivars among the second preset AI algorithms, and the artificial cultivation conditions of the corresponding cultivars accord with preset artificial cultivation conditions.

In one possible implementation manner, the determining a third cultivation parameter and a third AI algorithm corresponding to the third cultivation parameter includes: obtaining a plurality of third preset culture parameters; the plurality of third preset culture parameters are culture parameters determined based on the first preset culture parameters and/or the second preset culture parameters; determining direct influence values of the plurality of third preset culture parameters on the target culture varieties; determining indirect influence values of the plurality of third preset culture parameters on the target culture varieties; the indirect influence value is determined based on the direct influence value of the plurality of first preset culture parameters on a third associated culture variety of the target culture variety; the similarity between the cultivation environment of the third associated cultivation variety and the cultivation environment of the target cultivation variety is greater than a first preset similarity, and/or the similarity between the artificial cultivation condition of the third associated variety and the artificial cultivation condition of the target cultivation variety is greater than a second preset similarity; determining an integrated influence value of the plurality of third preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety; and determining the third culture parameters from the third preset culture parameters based on the integration influence values.

In one possible implementation manner, the determining a third cultivation parameter and a third AI algorithm corresponding to the third cultivation parameter includes: acquiring a plurality of third preset AI algorithms; the plurality of third preset AI algorithms respectively correspond to different cultivars; determining a third AI algorithm from the plurality of third preset AI algorithms; the third AI algorithm is the highest in the number of the corresponding cultivation varieties among the third preset AI algorithms, the cultivation environment of the corresponding cultivation varieties accords with preset cultivation environment conditions, and/or the artificial cultivation conditions of the corresponding cultivation varieties accord with preset artificial cultivation conditions.

In one possible implementation manner, the constructing a target AI algorithm based on the first AI algorithm, the second AI algorithm, the third AI algorithm, the target cultivar, and a target parameter relationship includes: determining connection relations among the first AI algorithm, the second AI algorithm and the third AI algorithm based on the target parameter relation; the connection relationship is used for indicating the relationship between the input and the output of each AI algorithm; based on the connection relation, connecting the first AI algorithm, the second AI algorithm and the third AI algorithm to construct an initial target AI algorithm; and determining the target AI algorithm based on a preset AI algorithm corresponding to the target culture variety and the initial target AI algorithm.

In one possible implementation manner, the determining the target AI algorithm based on the preset AI algorithm corresponding to the target cultivar and the initial target AI algorithm includes: determining whether at least one of the first culture parameter, the second culture parameter and the third culture parameter is included in input parameters and output parameters corresponding to the preset AI algorithm; if yes, judging whether the preset AI algorithm has an association relation with at least one AI algorithm of the first AI algorithm, the second AI algorithm and the third AI algorithm; if yes, determining the target AI algorithm based on the initial target AI algorithm; if not, adjusting the initial target AI algorithm based on the preset AI algorithm, and determining the target AI algorithm based on the adjusted AI algorithm.

In one possible implementation manner, the AI algorithm construction method for intelligent cultivation further includes: obtaining sample culture data; determining a first sample culture parameter based on the sample culture data and the target AI algorithm; determining a second sample culture parameter based on the sample culture data and a preset smart culture model; the model algorithm corresponding to the preset intelligent culture model is different from the target AI algorithm; and optimizing the target AI algorithm and the preset intelligent culture model based on the first sample culture parameter, the second sample culture parameter and the real culture parameter corresponding to the sample culture parameter.

Compared with the prior art, the AI algorithm construction method for intelligent cultivation provided by the embodiment of the application has the advantages that on one hand, corresponding AI algorithms are respectively determined according to different cultivation parameters, so that the diversity of the AI algorithms can be improved, and on the basis of higher diversity of the AI algorithms, the accuracy of the AI algorithms and the adaptation degree with an intelligent scene are both improved. On the other hand, based on a plurality of AI algorithms, the target AI algorithm is integrated and constructed by combining the relations among the input objects corresponding to the AI algorithms, so that the adaptation degree of the constructed target AI algorithm and the target culture variety is improved. Furthermore, on the basis of improving the adaptation degree and the accuracy, the applicability of the AI algorithm is correspondingly improved. Therefore, the AI algorithm construction method for intelligent cultivation can improve the adaptation degree of the AI algorithm and intelligent cultivation, and improve the accuracy and the applicability of the AI algorithm.

Drawings

FIG. 1 is a flowchart of an AI algorithm construction method for smart farming according to one embodiment of the application;

FIG. 2 is a schematic diagram of AI algorithm connections according to one embodiment of the application;

FIG. 3 is a schematic diagram of the construction of an AI algorithm building apparatus for smart farming according to one embodiment of the application;

Fig. 4 is a schematic structural view of a terminal device according to an embodiment of the present application.

Detailed Description

The following detailed description of embodiments of the application is, therefore, to be taken in conjunction with the accompanying drawings, and it is to be understood that the scope of the application is not limited to the specific embodiments.

Throughout the specification and claims, unless explicitly stated otherwise, the term "comprise" or variations thereof such as "comprises" or "comprising", etc. will be understood to include the stated element or component without excluding other elements or components.

The technical scheme provided by the embodiment of the application can be applied to intelligent cultivation scenes, and cultivation data are analyzed through an AI (Artificial Intelligence ) algorithm in the intelligent cultivation scenes; or predicting the cultivation data; or forecast cultivation parameters, etc. to save a lot of labor costs.

And, in different smart cultivation scenes, the smart cultivation items involved may be correspondingly changed for the distinction of cultivation environments, cultivation varieties and the like. Thus, different AI algorithms may be designed for different smart farming scenarios so that they may be applicable to these scenarios.

Currently, a large number of AI algorithms have been generated for these smart farming scenarios, typically for one farming variety; or a special AI algorithm is formulated for a specific cultivation environment; that is, one AI algorithm is directed to one farming scenario.

Therefore, a large number of AI algorithms cannot be effectively applied, so that the adaptation degree of the AI algorithm and a smart cultivation scene is not very high, and the accuracy and the applicability of the AI algorithm are poor.

Based on the above, the embodiment of the application provides an AI algorithm construction method for intelligent cultivation, on one hand, corresponding AI algorithms are respectively determined according to different cultivation parameters, so that the diversity of the AI algorithms can be improved, and on the basis of higher diversity of the AI algorithms, the accuracy of the AI algorithms and the adaptation degree with intelligent scenes are both improved. On the other hand, based on a plurality of AI algorithms, the target AI algorithm is integrated and constructed by combining the relations among the input objects corresponding to the AI algorithms, so that the adaptation degree of the constructed target AI algorithm and the target culture variety is improved.

Referring next to fig. 1, a flowchart of an AI algorithm construction method for smart cultivation according to an embodiment of the present application is provided, where the construction method includes:

Step 101, determining a first cultivation parameter and a first AI algorithm corresponding to the first cultivation parameter. The first AI algorithm is used for determining a first culture parameter based on the target culture data, wherein the first culture parameter is a culture parameter of an environmental dimension.

In some embodiments, the target farming data may be considered input data to a first AI algorithm, and the specific value of the first farming parameter may be considered output data of the first AI algorithm, such that the first AI algorithm may determine the value of the first farming parameter based on the target farming data.

In some embodiments, the first culture parameter is a culture parameter of an environmental dimension, which may be understood as an environmental factor influence. For example: the cultivation temperature, cultivation humidity, etc. can be regarded as cultivation parameters in the environmental dimension.

In some embodiments, the target farming data may be farming data for an intended target farming breed, such as: the cultivation quantity, cultivation period and the like.

As an alternative embodiment, step 101 includes: acquiring a plurality of first preset culture parameters; the first preset culture parameters are culture parameters of environmental dimensions; determining direct influence values of a plurality of first preset culture parameters on a target culture variety; determining indirect influence values of a plurality of first preset culture parameters on a target culture variety; the indirect influence value is determined based on direct influence values of a plurality of first preset culture parameters on a first associated culture variety of the target culture variety; the similarity between the culture environment of the first associated culture variety and the culture environment of the target culture variety is greater than a first preset similarity; determining an integrated influence value of a plurality of first preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety; and determining the first culture parameters from a plurality of first preset culture parameters based on the integrated influence values.

In some embodiments, determining the first cultivation parameter may be understood as determining a cultivation parameter item corresponding to the target cultivation breed from among cultivation parameter items in a plurality of selectable environmental dimensions.

In some embodiments, the first preset culture parameters may be culture parameters in a preset environmental dimension, which may or may not have a relationship with the target culture variety, so it is necessary to further determine whether the first preset culture parameters may be applied to the target culture variety.

In some embodiments, the direct impact value of the plurality of first preset culture parameters on the target culture variety may be determined according to the historical culture parameters corresponding to the target culture variety. If the historical cultivation parameters corresponding to the target cultivation variety include a certain first preset cultivation parameter, the first preset cultivation parameter may have an influence value (for example, 90, and up to 100) on the target cultivation variety. If the historical cultivation parameters corresponding to the target cultivation variety include the relevant cultivation parameters of a certain first preset cultivation parameter, the first preset cultivation parameter may have an influence value (for example, 70, and up to 100) on the target cultivation variety. If none of the above conditions is concerned, the corresponding impact value may be between 0 and 50, and at most 100.

In some embodiments, the similarity between the breeding environment of the first associated breeding species and the breeding environment of the target breeding species is greater than a first preset similarity; the first preset similarity may be set according to different application scenarios, for example, ninety percent. For example: the similarity between the fish pond culture environment and the freshwater culture environment can reach eighty percent.

In some embodiments, the similarity between the culture environments may be determined based on such data as humidity, temperature, primary materials, etc. of the culture environments.

In some embodiments, based on the direct impact value and the indirect impact value on the target cultivar, weighted integration may be performed according to a preset weight value, and the determined impact value is an integrated impact value.

Further, based on the integration influence value, a first preset culture parameter having an integration influence value greater than the preset influence value may be determined as the first culture parameter. The preset influence value may be set according to different application scenarios, for example, may be 85, and may be up to 100.

As an alternative embodiment, step 101 further comprises: acquiring a plurality of first preset AI algorithms; the first preset AI algorithms respectively correspond to different breeding varieties; determining a first AI algorithm from a plurality of first preset AI algorithms; among the first preset AI algorithms, the first AI algorithm has the highest number of corresponding cultivars, and the cultivation environment of the corresponding cultivars accords with the preset cultivation environment conditions.

In some embodiments, the plurality of first preset AI algorithms may be understood as algorithms corresponding to different cultivars and applicable to the determination of the first cultivation parameters, which are algorithms that have been configured in advance.

In some embodiments, each of the first preset AI algorithms may correspond to one cultivar or may correspond to a plurality of cultivars, which is not limited herein.

Further, a first AI algorithm is determined from a plurality of first preset AI algorithms. In some embodiments, the AI algorithm with the highest number of corresponding cultivars may be determined from the above algorithm, and then the AI algorithm with the cultivation environment of the cultivar conforming to the preset cultivation environment condition may be determined from the AI algorithms.

It will be appreciated that the preset cultivation environment condition may be a cultivation environment condition corresponding to the target cultivation breed, and the relevant data of the cultivation environment with the minimum requirement required by the target cultivation breed may be defined in the conditions.

Step 102, determining a second cultivation parameter and a second AI algorithm corresponding to the second cultivation parameter. The second AI algorithm is used for determining a second cultivation parameter based on the target cultivation data, wherein the second cultivation parameter is a cultivation parameter in an artificial dimension.

In some embodiments, the target farming data may be considered input data to a second AI algorithm, and the specific value of the second farming parameter may be considered output data from the second AI algorithm, such that the second AI algorithm may determine the value of the first farming parameter based on the target farming data.

In some embodiments, the second culture parameter is a culture parameter of an artificial dimension, which may be understood as being influenced by an artificial factor. For example: the artificial feeding time, the artificial fishing times, the artificial fishing quantity and the like can be regarded as artificial dimension cultivation parameters.

As an alternative embodiment, step 102 includes: obtaining a plurality of second preset culture parameters; the plurality of second preset culture parameters are culture parameters in artificial dimension; determining direct influence values of a plurality of second preset culture parameters on the target culture varieties; determining indirect influence values of a plurality of second preset culture parameters on the target culture varieties; the indirect influence value is determined based on direct influence values of a plurality of second preset culture parameters on a second associated culture variety of the target culture variety; the similarity between the artificial culture condition of the second associated culture variety and the artificial culture condition of the target culture variety is greater than a second preset similarity; determining an integrated influence value of a plurality of second preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety; and determining a second cultivation parameter from a plurality of second preset cultivation parameters based on the integrated influence value.

In some embodiments, determining the second cultivation parameter may be understood as determining a cultivation parameter item corresponding to the target cultivation breed from among cultivation parameter items in a plurality of selectable artificial dimensions.

In some embodiments, the plurality of second preset culture parameters may be a plurality of culture parameters in a preset artificial dimension, which may or may not have a relationship with the target culture variety, so it is necessary to further determine whether the plurality of culture parameters may be applied to the target culture variety.

In some embodiments, the direct impact value of the plurality of second preset culture parameters on the target culture variety may be determined according to the historical culture parameters corresponding to the target culture variety. If the historical cultivation parameters corresponding to the target cultivation variety include a certain second preset cultivation parameter, the second preset cultivation parameter may correspond to an influence value (for example, 90, and up to 100) on the target cultivation variety. If the historical cultivation parameters corresponding to the target cultivation variety include the relevant cultivation parameters of a certain second preset cultivation parameter, the second preset cultivation parameter may have an influence value (for example, 70, and up to 100) on the target cultivation variety. If none of the above conditions is concerned, the corresponding impact value may be between 0 and 50, and at most 100.

In some embodiments, the similarity between the artificial breeding conditions of the second associated cultivar and the artificial breeding conditions of the target cultivar is greater than a second preset similarity; the second preset similarity may be set according to different application scenarios, for example, ninety percent.

In some embodiments, the similarity between artificial breeding conditions may be determined from various data involved in the artificial breeding conditions.

Further, based on the integration influence value, a second preset cultivation parameter having an integration influence value greater than the preset influence value may be determined as the second cultivation parameter. The preset influence value may be set according to different application scenarios, for example, may be 85, and may be up to 100.

As an alternative embodiment, the steps further comprise: acquiring a plurality of second preset AI algorithms; the plurality of second preset AI algorithms respectively correspond to different breeding varieties; determining a second AI algorithm from a plurality of second preset AI algorithms; among the second preset AI algorithms, the number of the corresponding cultivars is highest, and the artificial cultivation conditions of the corresponding cultivars meet preset artificial cultivation conditions.

In some embodiments, the plurality of second preset AI algorithms may be understood as algorithms corresponding to different cultivars and applicable to the determination of the second cultivation parameters, which are algorithms that have been configured in advance.

In some embodiments, each of the second preset AI algorithms may correspond to one cultivar or may correspond to a plurality of cultivars, which is not limited herein.

Further, a second AI algorithm is determined from a plurality of second preset AI algorithms. In some embodiments, the AI algorithm with the highest number of corresponding cultivars may be determined from the above algorithm, and then the AI algorithm with the artificial cultivation condition of the cultivar conforming to the preset artificial cultivation condition may be determined from the AI algorithms.

It is understood that the preset artificial breeding condition may be an artificial breeding condition corresponding to the target breeding variety, and the condition may be related data of the minimum required artificial breeding condition required by the target breeding variety.

And step 103, determining a third cultivation parameter and a third AI algorithm corresponding to the third cultivation parameter. The third AI algorithm is used for determining a third culture parameter based on the first culture parameter and/or the second culture parameter, and the third culture parameter is a culture parameter based on the environmental dimension and/or the artificial dimension.

In some embodiments, the first and/or second culture parameters may be considered input data of a third AI algorithm, and a particular value of the third culture parameter may be considered output data of the third AI algorithm, whereby the third AI algorithm may determine a value of the third culture parameter based on the second culture parameter and/or the second culture parameter.

In some embodiments, the third culturing parameter is a culturing parameter based on an environmental dimension and/or an artificial dimension, which can be understood to be influenced by both dimensions; or affected by only one of the dimensions. For example: the cultivation period is cultivation parameters based on the environmental dimension and the artificial dimension.

Further, for the third culture parameter, it may be necessary to determine with the first culture parameter and the second culture parameter.

As an alternative embodiment, step 103 includes: obtaining a plurality of third preset culture parameters; the plurality of third preset culture parameters are culture parameters determined based on the first preset culture parameters and/or the second preset culture parameters; determining direct influence values of a plurality of third preset culture parameters on the target culture varieties; determining indirect influence values of a plurality of third preset culture parameters on the target culture varieties; the indirect influence value is determined based on the direct influence values of the first preset culture parameters on the third associated culture varieties of the target culture varieties; the similarity between the cultivation environment of the third associated cultivation variety and the cultivation environment of the target cultivation variety is greater than the first preset similarity, and/or the similarity between the artificial cultivation condition of the third associated variety and the artificial cultivation condition of the target cultivation variety is greater than the second preset similarity; determining an integrated influence value of a plurality of third preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety; and determining a third cultivation parameter from a plurality of third preset cultivation parameters based on the integrated influence value.

In some embodiments, determining the third cultivation parameter may be understood as determining a cultivation parameter item corresponding to the target cultivation breed from a plurality of selectable cultivation parameter items based on the environmental dimension and/or the artificial dimension.

In some embodiments, the plurality of third preset farming parameters are farming parameters determined based on the first preset farming parameters and/or the second preset farming parameters; the plurality of third preset culture parameters can be determined according to the dimension association between the environment dimension corresponding to the first preset culture parameters and the artificial dimension corresponding to the second preset culture parameters.

In some embodiments, the direct impact value of the third preset culture parameters on the target culture variety may be determined according to the historical culture parameters corresponding to the target culture variety. If the historical cultivation parameters corresponding to the target cultivation variety include a third preset cultivation parameter, the third preset cultivation parameter may have an influence value (for example, 90, and up to 100) on the target cultivation variety. If the historical cultivation parameters corresponding to the target cultivation variety include the relevant cultivation parameters of a third preset cultivation parameter, the third preset cultivation parameter may have an influence value (for example, 70, and up to 100) on the target cultivation variety. If none of the above conditions is concerned, the corresponding impact value may be between 0 and 50, and at most 100.

In some embodiments, the similarity between the cultivation environment of the third associated cultivar and the cultivation environment of the target cultivar is greater than the first preset similarity, and/or the similarity between the artificial cultivation condition of the third associated cultivar and the artificial cultivation condition of the target cultivar is greater than the second preset similarity; the first preset similarity and the second preset similarity may be described with reference to the foregoing embodiments.

Further, based on the integration influence value, a third preset cultivation parameter having an integration influence value greater than the preset influence value may be determined as the third cultivation parameter. The preset influence value may be set according to different application scenarios, for example, may be 85, and may be up to 100.

As an alternative embodiment, step 103 further comprises: acquiring a plurality of third preset AI algorithms; the plurality of third preset AI algorithms respectively correspond to different breeding varieties; determining a third AI algorithm from a plurality of third preset AI algorithms; the third AI algorithm is the highest in the number of the corresponding cultivation varieties among the third preset AI algorithms, the cultivation environment of the corresponding cultivation varieties accords with preset cultivation environment conditions, and/or the artificial cultivation conditions of the corresponding cultivation varieties accord with preset artificial cultivation conditions.

In some embodiments, the plurality of third preset AI algorithms may be understood as algorithms corresponding to different cultivars and applicable to the determination of the third cultivation parameters, which are algorithms that have been configured in advance.

In some embodiments, each third preset AI algorithm may correspond to one cultivar or may correspond to a plurality of cultivars, which is not limited herein.

Further, a third AI algorithm is determined from a plurality of third preset AI algorithms. In some embodiments, the AI algorithm with the highest number of the corresponding cultivars can be determined from the above AI algorithms, the AI algorithm with the artificial cultivation condition of the cultivar conforming to the preset artificial cultivation condition can be determined from the AI algorithms, and finally the AI algorithm with the cultivation environment of the cultivar conforming to the preset cultivation environment condition can be determined from the AI algorithms.

In the embodiment of the present application, the first preset AI algorithm, the second preset AI algorithm, and the third preset AI algorithm may be algorithms selected from among mature algorithms in the art; or an artificial intelligence algorithm designed according to a specific application scene.

For example, for the first preset AI algorithm, training, parameter adjustment, optimization and the like of the AI algorithm may be performed for a plurality of cultivars, so as to obtain the first preset AI algorithm meeting the conditions. That is, the form and the acquisition mode of the three algorithms are not limited in the embodiment of the present application.

And 104, constructing a target AI algorithm based on the first AI algorithm, the second AI algorithm, the third AI algorithm, the target culture variety and the target parameter relationship. The target parameter relation is a relation among the first cultivation parameter, the second cultivation parameter and the third cultivation parameter, and the target AI algorithm is used for determining the cultivation parameters of the target cultivation variety.

In some embodiments, the target parameter relationship is expressed as:wherein, the->Representing the first cultivation parameter->Representing a second cultivation parameter,/->Representing a third cultivation parameter,/->Represents a first weight, ++>Represents a second weight, ++>Representing a first influence value,/->Representing a second influence value,/->Representing a third influence value,/->Representing a preset impact value.

In some embodiments, the first weight and the second weight may be configured in combination with different application scenarios, and represent the relevance between the first cultivation parameter and the second cultivation parameter and the third cultivation parameter, where the stronger the relevance is, the higher the corresponding weight value is.

In some embodiments, the first influence value, the second influence value and the third influence value respectively represent influence of each cultivation parameter on the target cultivation variety, and the higher the influence is, the larger the influence value of the response is, and the influence value can be determined by combining historical data, which is not limited herein.

In some embodiments, the preset influence value may be configured in combination with different application scenarios, which represents an influence value with a larger influence on the target cultivar.

Thus, based on the relationship between the three cultivation parameters, the corresponding connection relationship can be determined. For example, if the third farming parameter is determined based on the first farming parameter and the second farming parameter, then the third AI algorithm has a connection relationship with both the first AI algorithm and the second AI algorithm; and, between the input and the output, a corresponding weight value definition is also added.

If the third cultivation parameter is determined based on the first cultivation parameter or the second cultivation parameter, the third AI algorithm has a connection relationship with the first AI algorithm or the second AI algorithm; and, between the input and the output, a corresponding weight value definition is also added.

Thus, as an alternative embodiment, step 104 includes: determining a connection relation of a first AI algorithm, a second AI algorithm and a third AI algorithm based on the target parameter relation; the connection relationship is used for indicating the relationship between the input and the output of each AI algorithm; based on the connection relation, the first AI algorithm, the second AI algorithm and the third AI algorithm are connected to construct an initial target AI algorithm; and determining the target AI algorithm based on a preset AI algorithm corresponding to the target culture variety and an initial target AI algorithm.

As shown in fig. 2, an algorithm connection relationship is provided in an embodiment of the present application, in which input data of a first AI algorithm and input data of a second AI algorithm are the same, and outputs of the first AI algorithm and the second AI algorithm are both connected to an input of a third AI algorithm.

It will be appreciated that in different application scenarios, other algorithm connection relationships may be also possible based on different target parameter relationships, which is not limited herein.

Therefore, the three AI algorithms are connected based on the connection relation, and an initial target AI algorithm can be constructed.

Further, determining the target AI algorithm based on the preset AI algorithm and the initial target AI algorithm corresponding to the target cultivar includes: determining whether at least one of the first cultivation parameter, the second cultivation parameter and the third cultivation parameter is included in input parameters and output parameters corresponding to a preset AI algorithm; if yes, judging whether the preset AI algorithm has an association relation with at least one AI algorithm in the first AI algorithm, the second AI algorithm and the third AI algorithm; if yes, determining a target AI algorithm based on the initial target AI algorithm; if not, the initial target AI algorithm is adjusted based on the preset AI algorithm, and the target AI algorithm is determined based on the adjusted AI algorithm.

In some embodiments, the algorithms may have an association relationship between them, where the complexity of the AI algorithms is similar, the types of AI algorithms are the same or similar, and so on.

In some embodiments, determining the target AI algorithm based on the initial target AI algorithm includes: the initial target AI algorithm is perfected, for example, some basic data preprocessing algorithms are added, and the algorithm after perfecting is determined as the target AI algorithm.

In some embodiments, the preset AI algorithm may be a preset AI algorithm that matches the target cultivar.

In some embodiments, adjusting the initial target AI algorithm based on the preset AI algorithm includes: and adding a preset AI algorithm into the initial target AI algorithm, wherein the preset AI algorithm and other AI algorithms are in parallel relation. Therefore, in the final target AI algorithm, after the output results of the preset AI algorithm and the third AI algorithm are integrated, the final algorithm result can be output.

In some embodiments, the construction method further comprises: obtaining sample culture data; determining a first sample culture parameter based on the sample culture data and a target AI algorithm; determining a second sample culture parameter based on the sample culture data and a preset smart culture model; the model algorithm corresponding to the preset intelligent culture model is different from the target AI algorithm; and optimizing a target AI algorithm and the preset intelligent culture model based on the first sample culture parameter, the second sample culture parameter and the real culture parameter corresponding to the sample culture parameter.

In some embodiments, the sample culture data is input into a target AI algorithm, which outputs the first sample culture parameter. And inputting the sample culture data into the smart culture model, and outputting the second sample culture parameters.

The smart culture model may be an artificial intelligent model obtained in advance through model training or the like, and may be a neural network model, a random forest model or the like.

Further, the sample cultivation parameter may correspond to a real cultivation parameter, i.e. the sample cultivation data is implemented, and the data of the corresponding cultivation parameter is obtained.

In some embodiments, the first sample culture parameter is compared to the actual culture parameter if the match is high, for example: the parameter values are similar; the target AI algorithm need not be optimized.

If the degree of matching is low, for example: if the parameter values differ far, the target AI algorithm needs to be optimized, and the optimization method comprises the following steps: optimizing functions, parameters, algorithm circulation modes and the like therein.

In some embodiments, the second sample culture parameter is compared to the real culture parameter if the match is high, for example: the parameter values are similar; there is no need to optimize the smart culture model.

If the degree of matching is low, for example: if the parameter values differ far, the intelligent culture model needs to be optimized in the following ways: retraining with the new training data set, etc.

According to the embodiment of the application, on one hand, the corresponding AI algorithm is respectively determined according to different types of cultivation parameters, so that the diversity of the AI algorithm can be improved, and on the basis of higher diversity of the AI algorithm, the accuracy of the AI algorithm and the adaptation degree with an intelligent scene are both improved. On the other hand, based on a plurality of AI algorithms, the target AI algorithm is integrated and constructed by combining the relations among the input objects corresponding to the AI algorithms, so that the adaptation degree of the constructed target AI algorithm and the target culture variety is improved. Furthermore, on the basis of improving the adaptation degree and the accuracy, the applicability of the AI algorithm is correspondingly improved. Therefore, the AI algorithm construction method for intelligent cultivation can improve the adaptation degree of the AI algorithm and intelligent cultivation, and improve the accuracy and the applicability of the AI algorithm.

Referring to fig. 3, a schematic structural diagram of an AI algorithm building apparatus for smart cultivation according to an embodiment of the present application includes:

a determining unit 301, configured to determine a first cultivation parameter and a first AI algorithm corresponding to the first cultivation parameter; the first AI algorithm is used for determining the first culture parameter based on target culture data, wherein the first culture parameter is a culture parameter of an environmental dimension; determining a second cultivation parameter and a second AI algorithm corresponding to the second cultivation parameter; the second AI algorithm is used for determining the second culture parameter based on the target culture data, wherein the second culture parameter is a culture parameter with an artificial dimension; determining a third cultivation parameter and a third AI algorithm corresponding to the third cultivation parameter; the third AI algorithm is configured to determine the third cultivation parameter based on the first cultivation parameter and/or the second cultivation parameter, the third cultivation parameter being a cultivation parameter based on the environmental dimension and/or the artificial dimension; a construction unit 302, configured to construct a target AI algorithm based on the first AI algorithm, the second AI algorithm, the third AI algorithm, the target cultivar, and a target parameter relationship; the target AI algorithm is used for determining the cultivation parameters of the target cultivation varieties, and the target parameter relation is expressed as follows: Wherein, the->Representing said first cultivation parameter, < >>Representing the second cultivation parameter, +.>Representing the third cultivation parameter, +.>Represents a first weight, ++>Representing the second weight of the first weight,representing a first influence value,/->Representing a second influence value,/->Representing a third influence value,/->Representing a preset impact value.

In some embodiments, the determining unit 301 is further configured to: acquiring a plurality of first preset culture parameters; the first preset culture parameters are culture parameters of environmental dimensions; determining direct influence values of the first preset culture parameters on the target culture varieties; determining indirect influence values of the plurality of first preset culture parameters on the target culture varieties; the indirect influence value is determined based on direct influence values of the plurality of first preset culture parameters on a first associated culture variety of the target culture variety; the similarity between the culture environment of the first associated culture variety and the culture environment of the target culture variety is larger than a first preset similarity; determining an integrated influence value of the plurality of first preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety; and determining the first culture parameters from the plurality of first preset culture parameters based on the integration influence values.

In some embodiments, the determining unit 301 is further configured to: acquiring a plurality of first preset AI algorithms; the first preset AI algorithms respectively correspond to different cultivars; determining the first AI algorithm from the plurality of first preset AI algorithms; among the first preset AI algorithms, the first AI algorithm has the highest number of corresponding cultivars, and the cultivation environment of the corresponding cultivars accords with preset cultivation environment conditions.

In some embodiments, the determining unit 301 is further configured to: obtaining a plurality of second preset culture parameters; the plurality of second preset culture parameters are culture parameters with artificial dimensions; determining direct influence values of the second preset culture parameters on the target culture varieties; determining indirect influence values of the second preset culture parameters on the target culture varieties; the indirect influence value is determined based on direct influence values of the plurality of second preset culture parameters on a second associated culture variety of the target culture variety; the similarity between the artificial culture condition of the second associated culture variety and the artificial culture condition of the target culture variety is greater than a second preset similarity; determining an integrated influence value of the plurality of second preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety; and determining the second culture parameters from the second preset culture parameters based on the integration influence values.

In some embodiments, the determining unit 301 is further configured to: acquiring a plurality of second preset AI algorithms; the plurality of second preset AI algorithms respectively correspond to different cultivars; determining a second AI algorithm from the plurality of second preset AI algorithms; the second AI algorithm is the highest in the number of corresponding cultivars among the second preset AI algorithms, and the artificial cultivation conditions of the corresponding cultivars accord with preset artificial cultivation conditions.

In some embodiments, the determining unit 301 is further configured to: obtaining a plurality of third preset culture parameters; the plurality of third preset culture parameters are culture parameters determined based on the first preset culture parameters and/or the second preset culture parameters; determining direct influence values of the plurality of third preset culture parameters on the target culture varieties; determining indirect influence values of the plurality of third preset culture parameters on the target culture varieties; the indirect influence value is determined based on the direct influence value of the plurality of first preset culture parameters on a third associated culture variety of the target culture variety; the similarity between the cultivation environment of the third associated cultivation variety and the cultivation environment of the target cultivation variety is greater than a first preset similarity, and/or the similarity between the artificial cultivation condition of the third associated variety and the artificial cultivation condition of the target cultivation variety is greater than a second preset similarity; determining an integrated influence value of the plurality of third preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety; and determining the third culture parameters from the third preset culture parameters based on the integration influence values.

In some embodiments, the determining unit 301 is further configured to: acquiring a plurality of third preset AI algorithms; the plurality of third preset AI algorithms respectively correspond to different cultivars; determining a third AI algorithm from the plurality of third preset AI algorithms; the third AI algorithm is the highest in the number of the corresponding cultivation varieties among the third preset AI algorithms, the cultivation environment of the corresponding cultivation varieties accords with preset cultivation environment conditions, and/or the artificial cultivation conditions of the corresponding cultivation varieties accord with preset artificial cultivation conditions.

In some embodiments, the construction unit 302 is further to: determining connection relations among the first AI algorithm, the second AI algorithm and the third AI algorithm based on the target parameter relation; the connection relationship is used for indicating the relationship between the input and the output of each AI algorithm; based on the connection relation, connecting the first AI algorithm, the second AI algorithm and the third AI algorithm to construct an initial target AI algorithm; and determining the target AI algorithm based on a preset AI algorithm corresponding to the target culture variety and the initial target AI algorithm.

In some embodiments, the construction unit 302 is further configured to: determining whether at least one of the first culture parameter, the second culture parameter and the third culture parameter is included in input parameters and output parameters corresponding to the preset AI algorithm; if yes, judging whether the preset AI algorithm has an association relation with at least one AI algorithm of the first AI algorithm, the second AI algorithm and the third AI algorithm; if yes, determining the target AI algorithm based on the initial target AI algorithm; if not, adjusting the initial target AI algorithm based on the preset AI algorithm, and determining the target AI algorithm based on the adjusted AI algorithm.

As shown in fig. 4, the embodiment of the present application further provides a terminal device, which includes a processor 401 and a memory 402, where the processor 401 is communicatively connected to the memory 402, and the terminal device may be used as an execution body of the aforementioned AI algorithm construction method for smart cultivation.

The processor 401 and the memory 402 are directly or indirectly electrically connected to each other to realize data transmission or interaction. For example, electrical connections may be made between these elements through one or more communication buses or signal buses. The aforementioned AI algorithm construction method for smart farming includes at least one software function module, which may be stored in the memory 402 in the form of software or firmware (firmware), respectively.

The processor 401 may be an integrated circuit chip having signal processing capabilities. The processor 401 may be a general-purpose processor including a CPU (Central Processing Unit ), NP (Network Processor, network processor), etc.; but may be a digital signal processor, an application specific integrated circuit, an off-the-shelf programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. Which may implement or perform the disclosed methods, steps, and logic blocks in embodiments of the application. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 402 may store various software programs and modules, such as program instructions/modules corresponding to the image processing methods and apparatuses provided in the embodiments of the present application. The processor 401 executes various functional applications and data processing, i.e., implements the methods of embodiments of the present application, by running software programs and modules stored in the memory 402.

Memory 402 may include, but is not limited to, RAM (Random Access Memory ), ROM (Read Only Memory), PROM (Programmable Read-Only Memory, programmable Read Only Memory), EPROM (Erasable Programmable Read-Only Memory, erasable Read Only Memory), EEPROM (Electric Erasable Programmable Read-Only Memory, electrically erasable Read Only Memory), and the like.

It will be appreciated that the configuration shown in fig. 4 is merely illustrative, and that the terminal device may also include more or fewer components than shown in fig. 4, or have a different configuration than shown in fig. 4.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The foregoing descriptions of specific exemplary embodiments of the present application are presented for purposes of illustration and description. It is not intended to limit the application to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain the specific principles of the application and its practical application to thereby enable one skilled in the art to make and utilize the application in various exemplary embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the application be defined by the claims and their equivalents.

Claims

1. An AI algorithm construction method for intelligent cultivation is characterized by comprising the following steps:

Determining a first cultivation parameter and a first AI algorithm corresponding to the first cultivation parameter; the first AI algorithm is used for determining the first culture parameter based on target culture data, wherein the first culture parameter is a culture parameter of an environmental dimension;

determining a second cultivation parameter and a second AI algorithm corresponding to the second cultivation parameter; the second AI algorithm is used for determining the second culture parameter based on the target culture data, wherein the second culture parameter is a culture parameter with an artificial dimension;

determining a third cultivation parameter and a third AI algorithm corresponding to the third cultivation parameter; the third AI algorithm is configured to determine the third cultivation parameter based on the first cultivation parameter and/or the second cultivation parameter, the third cultivation parameter being a cultivation parameter based on the environmental dimension and/or the artificial dimension;

constructing a target AI algorithm based on the first AI algorithm, the second AI algorithm, the third AI algorithm, the target cultivar and a target parameter relationship; the target AI algorithm is used for determining the cultivation parameters of the target cultivation varieties, and the target parameter relation is expressed as follows:wherein, the->Representing said first cultivation parameter, < > >Representing the second cultivation parameter, +.>Representing the third cultivation parameter, +.>Represents a first weight, ++>Represents a second weight, ++>Representing a first influence value,/->Representing a second influence value,/->Representing a third influence value,/->Representing a preset impact value.

2. The AI algorithm construction method for smart farming according to claim 1, wherein the determining a first farming parameter and a first AI algorithm corresponding to the first farming parameter comprises:

acquiring a plurality of first preset culture parameters; the first preset culture parameters are culture parameters of environmental dimensions;

determining direct influence values of the first preset culture parameters on the target culture varieties;

determining indirect influence values of the plurality of first preset culture parameters on the target culture varieties; the indirect influence value is determined based on direct influence values of the plurality of first preset culture parameters on a first associated culture variety of the target culture variety; the similarity between the culture environment of the first associated culture variety and the culture environment of the target culture variety is larger than a first preset similarity;

determining an integrated influence value of the plurality of first preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety;

And determining the first culture parameters from the plurality of first preset culture parameters based on the integration influence values.

3. The AI algorithm construction method for smart farming according to claim 1, wherein the determining a first farming parameter and a first AI algorithm corresponding to the first farming parameter comprises:

acquiring a plurality of first preset AI algorithms; the first preset AI algorithms respectively correspond to different cultivars;

determining the first AI algorithm from the plurality of first preset AI algorithms; among the first preset AI algorithms, the first AI algorithm has the highest number of corresponding cultivars, and the cultivation environment of the corresponding cultivars accords with preset cultivation environment conditions.

4. The AI algorithm construction method for smart farming according to claim 1, wherein the determining a second farming parameter and a second AI algorithm corresponding to the second farming parameter comprises:

obtaining a plurality of second preset culture parameters; the plurality of second preset culture parameters are culture parameters with artificial dimensions;

determining direct influence values of the second preset culture parameters on the target culture varieties;

Determining indirect influence values of the second preset culture parameters on the target culture varieties; the indirect influence value is determined based on direct influence values of the plurality of second preset culture parameters on a second associated culture variety of the target culture variety; the similarity between the artificial culture condition of the second associated culture variety and the artificial culture condition of the target culture variety is greater than a second preset similarity;

determining an integrated influence value of the plurality of second preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety;

and determining the second culture parameters from the second preset culture parameters based on the integration influence values.

5. The AI algorithm construction method for smart farming according to claim 1, wherein the determining a second farming parameter and a second AI algorithm corresponding to the second farming parameter comprises:

acquiring a plurality of second preset AI algorithms; the plurality of second preset AI algorithms respectively correspond to different cultivars;

determining a second AI algorithm from the plurality of second preset AI algorithms; the second AI algorithm is the highest in the number of corresponding cultivars among the second preset AI algorithms, and the artificial cultivation conditions of the corresponding cultivars accord with preset artificial cultivation conditions.

6. The AI algorithm construction method for smart farming according to claim 1, wherein the determining a third farming parameter and a third AI algorithm corresponding to the third farming parameter comprises:

obtaining a plurality of third preset culture parameters; the plurality of third preset culture parameters are culture parameters determined based on the first preset culture parameters and/or the second preset culture parameters;

determining direct influence values of the plurality of third preset culture parameters on the target culture varieties;

determining indirect influence values of the plurality of third preset culture parameters on the target culture varieties; the indirect influence value is determined based on the direct influence value of the plurality of first preset culture parameters on a third associated culture variety of the target culture variety; the similarity between the cultivation environment of the third associated cultivation variety and the cultivation environment of the target cultivation variety is greater than a first preset similarity, and/or the similarity between the artificial cultivation condition of the third associated variety and the artificial cultivation condition of the target cultivation variety is greater than a second preset similarity;

determining an integrated influence value of the plurality of third preset culture parameters based on the direct influence value on the target culture variety and the indirect influence value on the target culture variety;

And determining the third culture parameters from the third preset culture parameters based on the integration influence values.

7. The AI algorithm construction method for smart farming according to claim 1, wherein the determining a third farming parameter and a third AI algorithm corresponding to the third farming parameter comprises:

acquiring a plurality of third preset AI algorithms; the plurality of third preset AI algorithms respectively correspond to different cultivars;

determining a third AI algorithm from the plurality of third preset AI algorithms; the third AI algorithm is the highest in the number of the corresponding cultivation varieties among the third preset AI algorithms, the cultivation environment of the corresponding cultivation varieties accords with preset cultivation environment conditions, and/or the artificial cultivation conditions of the corresponding cultivation varieties accord with preset artificial cultivation conditions.

8. The AI algorithm construction method for smart farming according to claim 1, wherein the constructing a target AI algorithm based on the first AI algorithm, the second AI algorithm, the third AI algorithm, a target farming variety, and a target parameter relationship comprises:

determining connection relations among the first AI algorithm, the second AI algorithm and the third AI algorithm based on the target parameter relation; the connection relationship is used for indicating the relationship between the input and the output of each AI algorithm;

Based on the connection relation, connecting the first AI algorithm, the second AI algorithm and the third AI algorithm to construct an initial target AI algorithm;

and determining the target AI algorithm based on a preset AI algorithm corresponding to the target culture variety and the initial target AI algorithm.

9. The AI algorithm construction method for smart farming according to claim 8, wherein the determining the target AI algorithm based on the preset AI algorithm corresponding to the target farming variety and the initial target AI algorithm comprises:

determining whether at least one of the first culture parameter, the second culture parameter and the third culture parameter is included in input parameters and output parameters corresponding to the preset AI algorithm;

if yes, judging whether the preset AI algorithm has an association relation with at least one AI algorithm of the first AI algorithm, the second AI algorithm and the third AI algorithm;

if yes, determining the target AI algorithm based on the initial target AI algorithm;

if not, adjusting the initial target AI algorithm based on the preset AI algorithm, and determining the target AI algorithm based on the adjusted AI algorithm.

10. The AI algorithm construction method for smart farming according to claim 1, further comprising:

obtaining sample culture data;

determining a first sample culture parameter based on the sample culture data and the target AI algorithm;

determining a second sample culture parameter based on the sample culture data and a preset smart culture model; the model algorithm corresponding to the preset intelligent culture model is different from the target AI algorithm;

and optimizing the target AI algorithm and the preset intelligent culture model based on the first sample culture parameter, the second sample culture parameter and the real culture parameter corresponding to the sample culture parameter.