Crop Phenotype Data Fusion Method, Device, Electronic Device and Storage Medium

Abstract

The present invention provides a method, device, electronic device and storage medium for fusing crop phenotypic data, which relates to the technical field of electrical digital data processing. The method includes: inputting target data corresponding to each first crop phenotypic data to be fused in three dimensions in each crop phenotypic data to be fused into a weight adjustment model, obtaining a weight value corresponding to each first crop phenotypic data output by the weight adjustment model, and performing data fusion on each crop phenotypic data to be fused based on the weight value corresponding to each first crop phenotypic data to be fused and the internal and external parameter data of the crop phenotypic sensor for collecting each crop phenotypic data. The target data includes environmental data when collecting the crop phenotypic data, relative position information between the crop phenotypic sensor and the crop, and target parameter values of the crop phenotypic data. By assigning dynamic weights to the crop phenotypic data to be fused, the present invention can improve the fusion effect, relevance, fusion efficiency and timeliness of data fusion.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a crop phenotype data fusion method, device, electronic equipment and storage medium. Background Art

[0002] Plant phenotype refers to the externally observable form and characteristics of individual plants, including the size, shape, structure, flowers, leaves, stems, roots, etc. Plant phenotype is the result of the interaction between the plant genome and the environment, and can reflect the plant's adaptation to the environment and its growth and development.

[0003] Using a crop phenotyping platform equipped with a variety of phenotyping sensors, crop phenotyping data of different dimensions can be obtained. Common phenotyping sensors mainly include lidar sensors, multispectral image sensors, visible light image sensors, infrared thermal imaging sensors, and depth image sensors.

[0004] Data fusion of crop phenotypic data of different dimensions is of great significance in improving data accuracy and completeness, reducing data redundancy and uncertainty, enhancing decision support capabilities, promoting agricultural informatization and intelligence, and promoting multidisciplinary cross-cutting and integration. However, when traditional crop phenotypic data fusion methods in related technologies are used to fuse crop phenotypic data of different dimensions and / or different periods, there are problems such as low fusion efficiency and poor fusion effect.

[0005] Therefore, how to improve the fusion efficiency and fusion effect of crop phenotypic data fusion is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0006] The present invention provides a crop phenotypic data fusion method, device, electronic device and storage medium, which are used to solve the defects of low fusion efficiency and poor fusion effect when fusing crop phenotypic data of different dimensions in the prior art, and to improve the fusion efficiency and fusion effect of crop phenotypic data fusion.

[0007] The present invention provides a crop phenotype data fusion method, comprising the following steps.

[0008] Acquire each crop phenotypic data to be fused, wherein each crop phenotypic data to be fused includes crop phenotypic data of a target crop collected by different types of crop phenotypic sensors and / or crop phenotypic data of the target crop at multiple growth stages;

[0009] Obtain the internal and external parameter data of each crop phenotype sensor when collecting each piece of the crop phenotype data to be fused, and the target data corresponding to the first crop phenotype data to be fused. The first crop phenotype data to be fused is the three-dimensional crop phenotype data to be fused. The target data includes the environmental data when collecting the crop phenotype data, the relative position information between the crop phenotype sensor collecting the crop phenotype data and the crop, and the target parameter values of the crop phenotype data. The target parameters include signal-to-noise ratio and / or feature entropy;

[0010] Input the target data corresponding to each of the first crop phenotype data to be fused into the weight adjustment model, and obtain the weight value corresponding to each of the first crop phenotype data to be fused output by the weight adjustment model. The weight adjustment model is obtained by training based on the target data corresponding to each sample crop phenotype data in the sample data group and the weight value corresponding to each sample crop phenotype data. The sample crop phenotype data includes the crop phenotype data of the sample crop collected by different types of crop phenotype sensors and the crop phenotype data of the sample crop at multiple growth stages;

[0011] Based on the weight value corresponding to each of the first crop phenotype data to be fused and the internal and external parameter data of the crop phenotype sensor that collects each piece of the crop phenotype data to be fused, perform data fusion on each piece of the crop phenotype data to be fused, and obtain the fusion data corresponding to each piece of the crop phenotype data to be fused.

[0012] According to a crop phenotype data fusion method provided by the present invention, the weight value corresponding to the sample crop phenotype data is obtained based on the following steps:

[0013] Based on the target parameter value of the sample crop phenotype data, obtain the first weight score corresponding to the sample crop phenotype data. Based on the relative position information between the sample crop phenotype sensor that collects the sample crop phenotype data and the sample crop, obtain the second weight score corresponding to the sample crop phenotype data. Based on the environmental data when collecting the sample crop phenotype data and the type of the sample crop phenotype sensor, obtain the third weight score corresponding to the sample crop phenotype data. Based on the growth stage of the sample crop and the type of the sample crop phenotype sensor when collecting the sample crop phenotype data, obtain the fourth weight score corresponding to the sample crop phenotype data;

[0014] Based on the first weight score, second weight score, third weight score, and fourth weight score corresponding to the sample crop phenotype data, calculate the weight value corresponding to the sample crop phenotype data.

[0015] A method for fusing crop phenotypic data provided by the present invention, which performs data fusion on each of the to-be-fused crop phenotypic data based on the weight value corresponding to each of the first to-be-fused crop phenotypic data and the internal and external parameter data of the crop phenotypic sensor that collects each of the to-be-fused crop phenotypic data, to obtain the fusion data corresponding to each of the to-be-fused crop phenotypic data, includes:

[0016] Perform denoising processing on each of the to-be-fused crop phenotypic data to obtain each of the to-be-fused crop phenotypic data after denoising processing;

[0017] Based on the internal and external parameter data of the crop phenotypic sensor that collects each of the to-be-fused crop phenotypic data, perform initial alignment on each of the to-be-fused crop phenotypic data after denoising processing to obtain each of the to-be-fused crop phenotypic data after initial alignment;

[0018] Based on the weight value corresponding to each of the first to-be-fused crop phenotypic data, perform data fusion on each of the first to-be-fused crop phenotypic data after initial alignment to obtain the first fusion data;

[0019] Perform data fusion on each of the second to-be-fused crop phenotypic data after initial alignment and the first fusion data to obtain the fusion data, where the second to-be-fused crop phenotypic data is the crop phenotypic data other than the first to-be-fused crop phenotypic data among each of the to-be-fused crop phenotypic data.

[0020] A method for fusing crop phenotypic data provided by the present invention, where obtaining the first weight score corresponding to the sample crop phenotypic data based on the target parameter value of the sample crop phenotypic data includes:

[0021] Determine the target parameter value of the sample crop phenotypic data as the independent variable, determine the first weight score corresponding to the sample crop phenotypic data as the dependent variable, and calculate the first weight score corresponding to the sample crop phenotypic data based on the target parameter value of the sample crop phenotypic data and a positive correlation function, where the positive correlation function is used to describe the positive correlation relationship between the independent variable and the dependent variable;

[0022] Obtaining the second weight score corresponding to the sample crop phenotypic data based on the relative position information between the sample crop phenotypic sensor that collects the sample crop phenotypic data and the sample crop includes:

[0023] Based on the relative position information between the sample crop phenotypic sensor and the sample crop, obtain the distance between the sample crop phenotypic sensor and the sample crop and the vertical distance between the sample crop and the target reference line, where the target reference line is the central axis of the field of view range of the sample crop phenotypic sensor;

[0024] Taking the distance as an independent variable and the first sub - weight score corresponding to the sample crop phenotype data as a dependent variable, based on the distance and a positive - correlation function, calculate the first sub - weight score corresponding to the sample crop phenotype data; taking the vertical distance as an independent variable and the second sub - weight score corresponding to the sample crop phenotype data as a dependent variable, based on the distance and a negative - correlation function, calculate the second sub - weight score corresponding to the sample crop phenotype data;

[0025] Based on the first sub - weight score and the second sub - weight score corresponding to the sample crop phenotype data, calculate the second weight score corresponding to the sample crop phenotype data;

[0026] The obtaining the third weight score corresponding to the sample crop phenotype data based on the environmental data at the time of collecting the sample crop phenotype data and the type of the sample crop phenotype sensor includes:

[0027] Obtain the first matching degree between the environmental data at the time of collecting the sample crop phenotype data and the type of the sample crop phenotype sensor;

[0028] Taking the first matching degree as an independent variable and the third weight score corresponding to the sample crop phenotype data as a dependent variable, based on the first matching degree and a positive - correlation function, calculate the third weight score corresponding to the sample crop phenotype data;

[0029] The obtaining the fourth weight score corresponding to the sample crop phenotype data based on the growth stage of the sample crop at the time of collecting the sample crop phenotype data and the type of the sample crop phenotype sensor includes:

[0030] Obtain the second matching degree between the growth stage of the sample crop at the time of collecting the sample crop phenotype data and the type of the sample crop phenotype sensor;

[0031] Taking the second matching degree as an independent variable and the fourth weight score corresponding to the sample crop phenotype data as a dependent variable, based on the second matching degree and a positive - correlation function, calculate the fourth weight score corresponding to the sample crop phenotype data.

[0032] According to a crop phenotype data fusion method provided by the present invention, the calculating the weight value corresponding to the sample crop phenotype data based on the first weight score, the second weight score, the third weight score, and the fourth weight score of the sample crop phenotype data includes:

[0033] Obtain the time - consistency evaluation value of the sample crop phenotype data;

[0034] Calculate the product of the first weight score, the second weight score, the third weight score, and the fourth weight score of the sample crop phenotype data as an intermediate result;

[0035] Calculate the quotient of the intermediate result and the time consistency evaluation value of the sample crop phenotype data as the weight value corresponding to the sample crop phenotype data.

[0036] According to a crop phenotype data fusion method provided by the present invention, the data fusion of each of the first crop phenotype data to be fused based on the weight value corresponding to each of the first crop phenotype data to be fused to obtain first fusion data includes:

[0037] Based on the weight value corresponding to each of the first crop phenotype data to be fused, use the iterative closest point algorithm to perform data fusion on each of the first crop phenotype data to be fused after initial alignment to obtain the first fusion data;

[0038] The fusion of each of the second crop phenotype data to be fused after initial alignment with the first fusion data to obtain the fusion data corresponding to each of the crop phenotype data to be fused, where the second crop phenotype data to be fused is the crop phenotype data of the target crop collected by different types of crop phenotype sensors and / or the crop phenotype data of the target crop at multiple growth stages among each of the crop phenotype data to be fused, includes:

[0039] Extract features from each of the second crop phenotype data to be fused after initial alignment to obtain the feature information corresponding to each of the second crop phenotype data to be fused after initial alignment;

[0040] Map the feature information corresponding to each of the second crop phenotype data to be fused after initial alignment into the first fusion data to obtain the fusion data.

[0041] The present invention also provides a crop phenotype data fusion device, including the following modules:

[0042] The first data acquisition module is used to acquire each crop phenotype data to be fused, and each of the crop phenotype data to be fused includes the crop phenotype data of the target crop collected by different types of crop phenotype sensors and / or the crop phenotype data of the target crop at multiple growth stages;

[0043] A second data acquisition module, configured to acquire internal and external reference data of each crop phenotype sensor when collecting each piece of the to-be-fused crop phenotype data, and target data corresponding to the first to-be-fused crop phenotype data, where the first to-be-fused crop phenotype data is three-dimensional to-be-fused crop phenotype data, and the target data includes environmental data when collecting the crop phenotype data, relative position information between the crop phenotype sensor collecting the crop phenotype data and the crop, and target parameter values of the crop phenotype data, and the target parameters include signal-to-noise ratio and / or feature entropy;

[0044] A dynamic weight determination module, configured to input the target data corresponding to each piece of the first to-be-fused crop phenotype data into a weight adjustment model, and obtain a weight value corresponding to each piece of the first to-be-fused crop phenotype data output by the weight adjustment model, where the weight adjustment model is obtained by training based on the target data corresponding to each sample crop phenotype data in a sample data group and the weight value corresponding to each piece of the sample crop phenotype data, and the sample crop phenotype data includes crop phenotype data of a sample crop collected by different types of crop phenotype sensors and crop phenotype data of the sample crop at multiple growth stages;

[0045] A multi-source data fusion module, configured to perform data fusion on each piece of the to-be-fused crop phenotype data based on the weight value corresponding to each piece of the first to-be-fused crop phenotype data and the internal and external reference data of the crop phenotype sensor collecting each piece of the to-be-fused crop phenotype data, and obtain fusion data corresponding to each piece of the to-be-fused crop phenotype data.

[0046] The present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the computer program, the crop phenotype data fusion method as described in any one of the above is implemented.

[0047] The present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the crop phenotype data fusion method as described in any one of the above is implemented.

[0048] The present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the crop phenotype data fusion method as described in any one of the above is implemented.

[0049] The crop phenotype data fusion method, device, electronic device and storage medium provided by the present invention input the target data corresponding to each first crop phenotype data to be fused into a weight adjustment model. After obtaining the weight value corresponding to each first crop phenotype data to be fused output by the weight adjustment model, based on the weight value corresponding to each first crop phenotype data to be fused and the internal and external parameter data of the crop phenotype sensor that collects each crop phenotype data to be fused, data fusion is performed on each crop phenotype data to be fused to obtain the fusion data corresponding to each crop phenotype data to be fused. It can comprehensively consider the data quality of the sample crop phenotype data, the relative position relationship between the sample crop phenotype sensor and the sample crop when collecting the sample crop phenotype data, the environmental factors when the sample crop phenotype sensor collects the sample crop phenotype data, and the influence of the growth stage of the sample crop on the sample crop phenotype data, and more accurately obtain the weight value corresponding to the sample crop phenotype data. Furthermore, through deep learning, the weight value corresponding to each crop phenotype data to be fused can be obtained more efficiently and accurately, which can improve the fusion efficiency and fusion effect of crop phenotype data, and can provide a more accurate data basis for high-precision three-dimensional modeling, crop feature extraction and other application scenarios of precision agriculture, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a schematic flowchart of the crop phenotype data fusion method provided by the present invention.

[0052] Figure 2 It is a schematic structural diagram of the crop phenotype data fusion device provided by the present invention.

[0053] Figure 3 It is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0055] In the description of the invention, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", and "linked" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] In the description of this application, the terms "first", "second", etc. are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of this application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are usually of the same category, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, in the description of this application, "and / or" means at least one of the connected objects, and the character " / ", generally represents an "or" relationship between the associated objects before and after.

[0057] It should be noted that when fusing crop phenotype data of different dimensions and different periods based on the traditional crop phenotype data fusion method in the related art, fixed weights are usually assigned to the crop phenotype data of different dimensions, and then the crop phenotype data of different dimensions are fused based on the weights of the crop phenotype data of each dimension.

[0058] However, when the above traditional phenotype data fusion method fuses crop performance data of different dimensions and / or different periods, it ignores the influence of factors such as crop type, crop growth stage, environmental change, and data source quality on data fusion, resulting in poor fusion effect of fusing crop phenotype data of different dimensions based on the traditional crop phenotype data fusion method in the related art, and unable to make full use of the advantages of multi-dimensional crop phenotype data.

[0059] The processing flow of the above traditional phenotype data fusion method for fusing crop phenotype data of different dimensions and / or different periods is long, resulting in low fusion efficiency of fusing crop phenotype data of different dimensions based on the above traditional crop phenotype data fusion method, making the above traditional crop phenotype data fusion method unable to meet the requirements of high-timeliness data analysis, and restricting the application of the above traditional crop phenotype data fusion method in scenarios that require quick response and real-time decision-making.

[0060] When the above-mentioned traditional phenotypic data fusion methods fuse crop phenotypic data of different dimensions and / or different periods, they lack intelligent adjustment strategies to explore the potential correlation and complementarity between crop phenotypic data of different dimensions and / or different periods, resulting in that the fused crop phenotypic data are difficult to achieve effective feature enhancement and precise crop characterization, which in turn affects the application effect of the above-mentioned traditional crop phenotypic data fusion methods in the fields of precision agriculture and environmental monitoring.

[0061] The above-mentioned traditional phenotypic data fusion methods are also difficult to cope with dynamic changes in crop growth environment, sensor status, etc., and have poor adaptability, which leads to the above-mentioned traditional phenotypic data fusion methods being flexibly adjusted according to actual conditions, limiting the application of the above-mentioned traditional crop phenotypic data fusion methods in complex agricultural environments and other dynamically changing scenarios.

[0062] In this regard, the present invention provides a crop phenotypic data fusion method, which analyzes the quality and characteristics of different data sources and dynamically adjusts their weights in data fusion, thereby improving the relevance, utilization efficiency and timeliness of data fusion, solving the problems of fixed weights, low timeliness and low data utilization in related technologies, and providing a more flexible, efficient and intelligent multi-source data fusion solution.

[0063] Combine the following Figure 1 The crop phenotypic data fusion method of the present invention is described.

[0064] Figure 1 FIG. 1 is a flow chart of the crop phenotype data fusion method provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101, obtaining each crop phenotypic data to be fused, wherein each crop phenotypic data to be fused includes crop phenotypic data of the target crop collected by different types of crop phenotypic sensors and / or crop phenotypic data of the target crop at multiple growth stages.

[0065] It should be noted that the executor of the embodiment of the present invention is a crop phenotype data fusion device. The crop phenotype data fusion device can be configured in electronic devices such as computers and servers.

[0066] Specifically, each crop phenotypic data to be fused is a fusion object of the crop phenotypic data fusion method provided by the present invention.

[0067] It should be noted that each crop phenotypic data to be fused includes crop phenotypic data of the target crop collected by different types of crop phenotypic sensors and / or crop phenotypic data of the target crop at multiple growth stages.

[0068] It can be understood that the crop phenotype data to be fused may include the crop phenotype data of different growth stages of the target crop collected by the same crop phenotype sensor; the crop phenotype data to be fused may also include the crop phenotype data of the same growth stage of the target crop collected by different crop phenotype sensors.

[0069] It can be understood that the target crop in the embodiments of the present invention can be determined based on actual needs. There is no specific limitation on the target crop in the embodiments of the present invention.

[0070] It should be noted that different types of crop phenotype sensors in the embodiments of the present invention may include, but are not limited to, lidar sensors, multispectral image sensors, visible light image sensors, infrared thermal imaging sensors, and depth image sensors, etc.

[0071] It can be understood that the crop phenotype data collected by crop phenotype sensors such as lidar sensors and depth image sensors is three-dimensional, and the crop phenotype data collected by crop phenotype sensors such as multispectral image sensors, visible light image sensors, and infrared thermal imaging sensors is two-dimensional.

[0072] In the embodiments of the present invention, the crop phenotype data to be fused can be obtained in various ways. For example, in the embodiments of the present invention, the crop phenotype data to be fused can be obtained based on the input of the user; alternatively, the embodiments of the present invention can also receive the crop phenotype data to be fused sent by other electronic devices. There is no limitation on the specific way of obtaining the crop phenotype data to be fused in the embodiments of the present invention.

[0073] Step 102: Obtain the internal and external parameter data of each crop phenotype sensor when collecting each piece of crop phenotype data to be fused and the target data corresponding to the first piece of crop phenotype data to be fused. The first piece of crop phenotype data to be fused is three-dimensional crop phenotype data to be fused. The target data includes the environmental data when collecting the crop phenotype data, the relative position information between the crop phenotype sensor collecting the crop phenotype data and the crop, and the target parameter values of the crop phenotype data. The target parameters include signal-to-noise ratio and / or feature entropy.

[0074] It should be noted that when the crop phenotype sensor in the embodiments of the present invention collects crop phenotype data, the internal and external parameter data of the crop phenotype sensor when collecting data will be recorded. Therefore, in the embodiments of the present invention, the internal and external parameter data of each crop phenotype sensor when collecting each piece of crop phenotype data to be fused can be obtained by means of data query.

[0075] It should be noted that in the embodiments of the present invention, the three-dimensional crop phenotype data to be fused collected by crop phenotype sensors such as lidar sensors and depth image sensors in each crop phenotype data to be fused is determined as the first crop phenotype data to be fused; the two-dimensional crop phenotype data to be fused collected by crop phenotype sensors such as multispectral image sensors, visible light image sensors, and infrared thermal imaging sensors in each crop phenotype data to be fused is determined as the second crop phenotype data to be fused.

[0076] It should be noted that each crop phenotype data to be fused in the embodiments of the present invention only includes one or more first crop phenotype data to be fused, and / or one or more second crop phenotype data to be fused.

[0077] It should be noted that when the crop phenotype sensors in the embodiments of the present invention collect crop phenotype data, environmental perception sensors are used to collect the environmental data when the crop phenotype sensors collect crop phenotype data. Therefore, in the embodiments of the present invention, the environmental data when each crop phenotype sensor collects each crop phenotype data to be fused can be obtained through data query as the target data corresponding to each crop phenotype data to be fused.

[0078] After obtaining the internal and external parameter data of each crop phenotype sensor when collecting each crop phenotype data to be fused, the relative position information between each crop phenotype sensor and the target crop when collecting each crop phenotype data to be fused can be calculated based on the internal and external parameter data of each crop phenotype sensor when collecting each crop phenotype data to be fused as the target data corresponding to each crop phenotype data to be fused.

[0079] After obtaining each crop phenotype data to be fused, the signal-to-noise ratio and / or characteristic entropy of each crop phenotype data to be fused can be calculated through numerical calculation as the target data corresponding to each crop phenotype data to be fused.

[0080] Step 103: Input the target data corresponding to each first crop phenotype data to be fused into the weight adjustment model to obtain the weight value corresponding to each first crop phenotype data output by the weight adjustment model. The weight adjustment model is obtained by training based on the target data corresponding to each sample crop phenotype data and the weight value corresponding to each sample crop phenotype data in the sample data group. The sample crop phenotype data includes the crop phenotype data of the sample crop collected by different types of crop phenotype sensors and the crop phenotype data of the sample crop at multiple growth stages.

[0081] It should be noted that in the embodiments of the present invention, the sample data group can be obtained in various ways. For example, the sample data group can be obtained based on the input of the user; alternatively, the sample data sent by other electronic devices can also be received. In the embodiments of the present invention, the specific manner of obtaining the sample data group is not limited.

[0082] Optionally, in the embodiments of the present invention, the crop phenotype data groups with better fusion effects after data fusion can be selected from the crop phenotype data groups that have undergone data fusion, and then each crop phenotype data in the crop phenotype data groups with better fusion effects after the above data fusion can be determined as each sample crop phenotype data. Furthermore, the above each sample crop phenotype data and the weight value corresponding to the above each sample crop phenotype data can be determined as a sample data group.

[0083] After obtaining the sample data group, the target data corresponding to each sample crop phenotype data can be used as training samples, and the weight value corresponding to each sample crop phenotype data can be used as a sample label to train the weight adjustment model to obtain a trained weight adjustment model.

[0084] It can be understood that the number of sample data groups in the embodiments of the present invention is multiple.

[0085] It can be understood that the sample crop table data in the embodiments of the present invention is three-dimensional.

[0086] After obtaining the trained weight adjustment model, the target data corresponding to each first crop phenotype data to be fused is input into the above trained weight adjustment model, and the weight value corresponding to each first crop phenotype data output by the weight adjustment model can be obtained.

[0087] It should be noted that in order to further improve the adaptability of the weight adjustment model, a self-learning mechanism is introduced in the embodiments of the present invention. By retrospectively analyzing the historical acquisition data, the weight adjustment model can automatically optimize its weight adjustment strategy and fusion algorithm. This self-learning mechanism includes the following core modules: The historical data retrospective module analyzes the results of historical multi-source data, extracts the scene features with better fusion effects, and feeds them back to the weight adjustment model for model optimization.

[0088] In the incremental learning mechanism, after each new data acquisition is completed, the weight adjustment model will perform incremental learning based on the latest data and continuously adjust the fusion parameters so that the model can still efficiently process in the face of new sensor configurations or environmental changes.

[0089] Through this self-learning mechanism, the crop phenotype data fusion method provided by the present invention can continuously optimize and improve the data fusion effect in the face of complex scenarios, dynamically changing sensor configurations or environmental conditions.

[0090] As an optional embodiment, the weight value corresponding to the sample crop phenotypic data is obtained based on the following steps: based on the target parameter value of the sample crop phenotypic data, obtain the first weight score corresponding to the sample crop phenotypic data; based on the relative position information between the sample crop phenotypic sensor that collects the sample crop phenotypic data and the sample crop, obtain the second weight score corresponding to the sample crop phenotypic data; based on the environmental data when collecting the sample crop phenotypic data and the type of the sample crop phenotypic sensor, obtain the third weight score corresponding to the sample crop phenotypic data; based on the growth stage of the sample crop when collecting the sample crop phenotypic data and the type of the sample crop phenotypic sensor, obtain the fourth weight score corresponding to the sample crop phenotypic data.

[0091] Specifically, in the embodiments of the present invention, numerical calculation, mathematical statistics, deep learning and other methods can be used to obtain the first weight score corresponding to the sample crop phenotypic data based on the target parameter value of the sample crop phenotypic data, obtain the second weight score corresponding to the sample crop phenotypic data based on the relative position information between the sample crop phenotypic sensor that collects the sample crop phenotypic data and the sample crop, obtain the third weight score corresponding to the sample crop phenotypic data based on the environmental data when collecting the sample crop phenotypic data and the type of the sample crop phenotypic sensor, and obtain the fourth weight score corresponding to the sample crop phenotypic data based on the growth stage of the sample crop when collecting the sample crop phenotypic data and the type of the sample crop phenotypic sensor.

[0092] As an optional embodiment, obtaining the first weight score corresponding to the sample crop phenotypic data based on the target parameter value of the sample crop phenotypic data includes: determining the target parameter value of the sample crop phenotypic data as the independent variable, determining the first weight score corresponding to the sample crop phenotypic data as the dependent variable, and calculating the first weight score corresponding to the sample crop phenotypic data based on the target parameter value of the sample crop phenotypic data and a positive correlation function, where the positive correlation function is used to describe the positive correlation relationship between the independent variable and the dependent variable.

[0093] It should be noted that the positive correlation relationship means that if the independent variable changes from large to small, the dependent variable also changes from large to small; if the independent variable changes from small to large, the dependent variable also changes from small to large. The positive correlation function in the embodiments of the present invention may include, but is not limited to, a linear positive correlation function, a quadratic function, an exponential function, a logarithmic function, etc. The positive correlation function in the embodiments of the present invention can be determined based on prior knowledge and / or actual situations. In the embodiments of the present invention, the positive correlation function is not specifically limited.

[0094] It can be understood that the signal-to-noise ratio and the feature entropy are important indicators for measuring data quality. The higher the signal-to-noise ratio of the data, the higher the data quality; the higher the feature entropy of the data, the more information the data contains.

[0095] Therefore, in the embodiments of the present invention, the target parameter value of the sample crop phenotype data is determined as the independent variable, and the first weight score corresponding to the sample crop phenotype data is determined as the dependent variable. Furthermore, based on the target parameter value of the sample crop phenotype data and the positive correlation function, the first weight score corresponding to the sample crop phenotype data can be calculated, and the first weight score corresponding to the sample crop phenotype data having a positive correlation with the target parameter value of the sample crop phenotype data can be obtained.

[0096] Based on the relative position information between the sample crop phenotype sensor for collecting the sample crop phenotype data and the sample crop, the second weight score corresponding to the sample crop phenotype data is obtained, including: based on the relative position information between the sample crop phenotype sensor and the sample crop, the distance between the sample crop phenotype sensor and the sample crop and the vertical distance between the sample crop and the target reference line are obtained, and the target reference line is the central axis of the field of view range of the sample crop phenotype sensor.

[0097] The distance is determined as the independent variable, and the first sub-weight score corresponding to the sample crop phenotype data is determined as the dependent variable. Based on the distance and the positive correlation function, the first sub-weight score corresponding to the sample crop phenotype data is calculated. The vertical distance is determined as the independent variable, and the second sub-weight score corresponding to the sample crop phenotype data is determined as the dependent variable. Based on the distance and the negative correlation function, the second sub-weight score corresponding to the sample crop phenotype data is calculated.

[0098] Based on the first sub-weight score and the second sub-weight score corresponding to the sample crop phenotype data, the second weight score corresponding to the sample crop phenotype data is calculated.

[0099] It can be understood that when using a crop phenotype sensor to collect crop phenotype data, the crop phenotype sensor is usually set at different positions. For example, a visible light image sensor can be set on a drone to obtain a wide-area view, while a lidar sensor is usually set on the ground to obtain a point cloud with rich details. Therefore, the relative position relationship between the crop phenotype sensor and the crop needs to be considered during the data fusion process.

[0100] For three-dimensional sample crop phenotype data, the closer the distance between the sample crop phenotype sensor and the sample crop, the closer the sample crop is to the central position of the field of view range of the sample crop phenotype sensor, and the more crop features are included in the obtained sample crop phenotype data.

[0101] Therefore, in the embodiments of the present invention, the distance between the sample crop phenotype sensor and the sample crop is determined as the independent variable, and the first sub-weight score corresponding to the sample crop phenotype data is determined as the dependent variable. Furthermore, based on the distance between the sample crop phenotype sensor and the sample crop and the positive correlation function, the first sub-weight score corresponding to the sample crop phenotype data can be calculated, and the first sub-weight score corresponding to the sample crop phenotype data having a positive correlation with the distance between the sample crop phenotype sensor and the sample crop is obtained.

[0102] It should be noted that in the embodiments of the present invention, the vertical distance between the sample crop and the central axis of the field of view of the sample crop phenotype sensor is used to describe the position of the sample crop in the field of view of the sample crop phenotype sensor. The smaller the vertical distance between the sample crop and the central axis of the field of view of the sample crop phenotype sensor, the closer the crop is to the central position of the field of view of the crop phenotype sensor.

[0103] Correspondingly, in the embodiments of the present invention, the vertical distance between the sample crop and the central axis of the field of view of the sample crop phenotype sensor is determined as the independent variable, and the second sub-weight score corresponding to the sample crop phenotype data is determined as the dependent variable. Furthermore, based on the vertical distance between the sample crop and the central axis of the field of view of the sample crop phenotype sensor and the negative correlation function, the second sub-weight score corresponding to the sample crop phenotype data can be calculated, and the second sub-weight score corresponding to the sample crop phenotype data having a negative correlation with the vertical distance between the sample crop and the central axis of the field of view of the sample crop phenotype sensor is obtained.

[0104] It should be noted that the negative correlation relationship means that if the independent variable changes from large to small, the dependent variable changes from small to large, and if the independent variable changes from small to large, the dependent variable changes from large to small. The negative correlation function in the embodiments of the present invention may include, but is not limited to, a linear negative correlation function, a quadratic function, an exponential function, and a logarithmic function, etc. The positive correlation function in the embodiments of the present invention can be determined based on prior knowledge and / or actual situations. The positive correlation function in the embodiments of the present invention is not specifically limited.

[0105] After obtaining the first sub-weight score and the second sub-weight score corresponding to the sample crop phenotype data, the average value of the first sub-weight score and the second sub-weight score corresponding to the sample crop phenotype data can be calculated by means of numerical calculation as the second weight score corresponding to the sample crop phenotype data.

[0106] Obtaining the third weight score corresponding to the sample crop phenotype data based on the environmental data at the time of collecting the sample crop phenotype data and the type of the sample crop phenotype sensor includes: obtaining the first matching degree between the environmental data at the time of collecting the sample crop phenotype data and the type of the sample crop phenotype sensor.

[0107] Taking the first matching degree as the independent variable and the third weight score corresponding to the sample crop phenotype data as the dependent variable, based on the first matching degree and the positive correlation function, the third weight score corresponding to the sample crop phenotype data is calculated.

[0108] It can be understood that the confidence levels of the crop phenotype data collected by different types of crop phenotype sensors in different environments are not the same. For example, in the case of a relatively high environmental temperature, the confidence level of the crop phenotype data collected by the infrared thermal imaging sensor is relatively high; in the case of a relatively high light intensity, the confidence level of the crop phenotype data collected by the visible light image sensor is relatively high; in the case of a relatively low light intensity, the confidence level of the crop phenotype data collected by the visible light image sensor is relatively low, and the confidence levels of the crop phenotype data collected by the multispectral image sensor and the lidar sensor are relatively high; in the case of a relatively high wind speed, the confidence levels of the crop phenotype data collected by various crop phenotype sensors are relatively low.

[0109] Therefore, in the embodiments of the present invention, by taking the first matching degree between the environmental data when collecting the sample crop phenotype data and the type of the sample crop phenotype sensor as the independent variable and the third weight score corresponding to the sample crop phenotype data as the dependent variable, the third weight score corresponding to the sample crop phenotype data can be calculated based on the above first matching degree and the positive correlation function, and the third weight score corresponding to the sample crop phenotype data having a positive correlation with the above first matching degree can be obtained.

[0110] It should be noted that based on the type of sensitive environmental data corresponding to the sample crop phenotype sensor, the value of the environmental data of the type of sensitive environmental data in the environmental data when collecting the sample crop phenotype data is determined, and further, based on the value of the environmental data of the type of sensitive environmental data in the environmental data when collecting the sample crop phenotype data, the first matching degree between the environmental data when collecting the sample crop phenotype data and the type of the sample crop phenotype sensor is determined. Among them, the type of sensitive environmental data corresponding to the sample crop phenotype sensor can be predefined based on prior knowledge and / or actual situations.

[0111] For example, based on prior knowledge and / or it can be determined that the type of sensitive environmental data corresponding to the infrared thermal imaging sensor is environmental temperature. Therefore, when the sample crop phenotype data is collected by the infrared thermal imaging sensor, the first original matching degree between the environmental data at the time of collecting the sample crop phenotype data and the type of the sample crop phenotype sensor can be determined based on the temperature range where the environmental temperature is located when the infrared thermal imaging sensor collects the sample crop phenotype data. The second original matching degree between the environmental data at the time of collecting the sample crop phenotype data and the type of the sample crop phenotype sensor can be determined based on the wind speed when the infrared thermal imaging sensor collects the sample crop phenotype data. Furthermore, the average value of the first original matching degree and the second original matching degree can be determined as the first matching degree between the infrared thermal imaging sensor and the environmental temperature when the infrared thermal imaging sensor collects the sample crop phenotype data. Among them, the corresponding relationship between different temperature ranges and different matching degrees can be determined based on prior knowledge and / or actual situations.

[0112] For another example, based on prior knowledge and / or it can be determined that the types of sensitive environmental data corresponding to the visible light image sensor, the spectral image sensor, and the lidar sensor are light intensity. Therefore, when the sample crop phenotype data is collected by the visible light image sensor, the spectral image sensor, or the lidar sensor, the first original matching degree between the environmental data at the time of collecting the sample crop phenotype data and the type of the sample crop phenotype sensor can be determined based on the light intensity range where the light intensity is located when collecting the sample crop phenotype data. The second original matching degree between the environmental data at the time of collecting the sample crop phenotype data and the type of the sample crop phenotype sensor can be determined based on the wind speed when collecting the sample crop phenotype data. Furthermore, the average value of the first original matching degree and the second original matching degree can be determined as the first matching degree between the sample crop phenotype sensor and the environmental temperature when the sample crop phenotype sensor collects the sample crop phenotype data. Among them, the corresponding relationship between different light intensity ranges and different matching degrees can be determined based on prior knowledge and / or actual situations.

[0113] Obtaining the fourth weight score corresponding to the sample crop phenotype data based on the growth stage of the sample crop and the type of the sample crop phenotype sensor when collecting the sample crop phenotype data includes: obtaining the second matching degree between the growth stage of the sample crop and the type of the sample crop phenotype sensor when collecting the sample crop phenotype data.

[0114] Taking the second matching degree as the independent variable and the fourth weight score corresponding to the sample crop phenotype data as the dependent variable, and calculating the fourth weight score corresponding to the sample crop phenotype data based on the second matching degree and the positive correlation function.

[0115] It should be noted that at different growth stages of crops, the contribution degrees of crop phenotype data collected by different types of crop phenotype sensors to the analysis of crop growth status are not the same. For example, at the early growth stage of crops, the color information of leaves is more important for the analysis of the normal state of crops. Therefore, the contribution degree of crop phenotype data collected by visible light image sensors to the analysis of crop growth status is relatively large, while the contribution degrees of crop phenotype data collected by depth image sensors and lidar sensors to the analysis of crop growth status are relatively small. At the middle and late growth stages of crops, the plant height and volume of crops change significantly, and the contribution degrees of crop phenotype data collected by depth image sensors and lidar sensors to the analysis of crop growth status are greater.

[0116] Therefore, in the embodiments of the present invention, by taking the second matching degree between the growth stage of the sample crop when collecting the sample crop phenotype data and the type of the sample crop phenotype sensor as the independent variable, and taking the fourth weight score corresponding to the sample crop phenotype data as the dependent variable, the fourth weight score corresponding to the sample crop phenotype data can be calculated based on the above second matching degree and the positive correlation function, and the fourth weight score corresponding to the sample crop phenotype data having a positive correlation with the above second matching degree can be obtained.

[0117] It should be noted that the above second matching degree can be determined based on the sensitive growth stage corresponding to the type of the sample crop phenotype sensor. Among them, the sensitive growth stage corresponding to the type of the sample crop phenotype sensor can be determined based on prior knowledge and / or actual situations.

[0118] For example, when the sample crop phenotype data is collected by a visible light image sensor, if the growth stage of the sample crop when collecting the sample crop phenotype data is the early growth stage, the second matching degree between the growth stage of the sample crop when collecting the sample crop phenotype data and the type of the sample crop phenotype sensor can be determined to be 1; if the growth stage of the sample crop when collecting the sample crop phenotype data is not the early growth stage, the second matching degree between the growth stage of the sample crop when collecting the sample crop phenotype data and the type of the sample crop phenotype sensor can be determined to be 0.

[0119] For example, when the sample crop phenotype data is collected by a depth image sensor, if the growth stage of the sample crop when collecting the sample crop phenotype data is the middle growth stage, the second matching degree between the growth stage of the sample crop when collecting the sample crop phenotype data and the type of the sample crop phenotype sensor can be determined to be 1; if the growth stage of the sample crop when collecting the sample crop phenotype data is the early growth stage, the second matching degree between the growth stage of the sample crop when collecting the sample crop phenotype data and the type of the sample crop phenotype sensor can be determined to be 0.

[0120] Based on the first weight score, the second weight score, the third weight score, and the fourth weight score corresponding to the sample crop phenotypic data, the weight value corresponding to the sample crop phenotypic data is calculated.

[0121] Specifically, after obtaining the first weight score, the second weight score, the third weight score, and the fourth weight score corresponding to the sample crop phenotypic data, the weight value corresponding to the sample crop phenotypic data can be calculated by means of numerical calculation.

[0122] As an optional embodiment, calculating the weight value corresponding to the sample crop phenotypic data based on the first weight score, the second weight score, the third weight score, and the fourth weight score corresponding to the sample crop phenotypic data includes: obtaining the time consistency evaluation value of the sample crop phenotypic data.

[0123] Specifically, since the acquisition times of the phenotypic data of each fusion object for data fusion are not exactly the same, a problem of time asynchronization may occur during data fusion. Therefore, in the model training stage of the embodiments of the present invention, the time consistency evaluation value of the sample crop phenotypic data is introduced to calculate the weight value corresponding to the sample crop phenotypic data.

[0124] It should be noted that the time consistency evaluation value of the sample crop phenotypic data in the embodiments of the present invention can be used as a metric standard to measure whether the sample crop phenotypic data remains consistent between different time points.

[0125] The time consistency evaluation value of the sample crop phenotypic data can be calculated through the following steps: calculating the time difference between the time when the sample crop phenotypic data is collected and the reference time . Wherein, the reference time can be predefined based on the actual situation.

[0126] Based on the above time difference , the time consistency evaluation value of the sample crop phenotypic data is calculated through the following formula:

[0127]

[0128] Wherein, represents the time consistency evaluation value of the th sample crop phenotypic data; represents the time difference between the time when the th sample crop phenotypic data is collected and the reference time; represents the time decay factor, which determines the influence degree of the time difference on Ti, and the value of

[0129] Calculate the product of the first weight score, the second weight score, the third weight score, and the fourth weight score corresponding to the sample crop phenotypic data as an intermediate result.

[0130] Calculate the quotient of the intermediate result and the time consistency evaluation value of the sample crop phenotypic data as the weight value corresponding to the sample crop phenotypic data.

[0131] Specifically, the weight value corresponding to the sample crop phenotypic data can be calculated by the following formula:

[0132]

[0133] Where represents the weight value corresponding to the th sample crop phenotypic data; represents the first weight score corresponding to the th sample crop phenotypic data; represents the second weight score corresponding to the th sample crop phenotypic data; represents the third weight score corresponding to the th sample crop phenotypic data; represents the fourth weight score corresponding to the th sample crop phenotypic data.

[0134] In the embodiments of the present invention, based on the target parameter value of the sample crop phenotypic data, the first weight score corresponding to the sample crop phenotypic data is obtained. Based on the relative position information between the sample crop phenotypic sensor that collects the sample crop phenotypic data and the sample crop, the second weight score corresponding to the sample crop phenotypic data is obtained. Based on the environmental data and the type of the sample crop phenotypic sensor when collecting the sample crop phenotypic data, the third weight score corresponding to the sample crop phenotypic data is obtained. Based on the growth stage of the sample crop and the type of the sample crop phenotypic sensor when collecting the sample crop phenotypic data, the fourth weight score corresponding to the sample crop phenotypic data is obtained. It can comprehensively consider the influence of the data quality of the sample crop phenotypic data, the relative position relationship between the sample crop phenotypic sensor and the sample crop when collecting the sample crop phenotypic data, the environmental factors when the sample crop phenotypic sensor collects the sample crop phenotypic data, and the growth stage of the sample crop on the sample crop phenotypic data, and more accurately obtain the weight value corresponding to the sample crop phenotypic data, which can provide a more accurate data basis for the weight adjustment model.

[0135] Step 104: Based on the weight value corresponding to each first crop phenotypic data to be fused and the internal and external parameter data of the crop phenotypic sensor that collects each crop phenotypic data to be fused, perform data fusion on each crop phenotypic data to be fused to obtain the fused data corresponding to each crop phenotypic data to be fused.

[0136] Specifically, after obtaining the weight value corresponding to each first crop phenotype data to be fused, based on the weight value corresponding to each first crop phenotype data to be fused and the internal and external parameter data of the crop phenotype sensor that collects each crop phenotype data to be fused, through numerical calculation, mathematical statistics, deep learning technology and other methods, data fusion is performed on each crop phenotype data to be fused to obtain the fusion data corresponding to each crop phenotype data to be fused.

[0137] As an optional embodiment, based on the weight value corresponding to each first crop phenotype data to be fused and the internal and external parameter data of the crop phenotype sensor that collects each crop phenotype data to be fused, data fusion is performed on each crop phenotype data to be fused to obtain the fusion data corresponding to each crop phenotype data to be fused, including: performing denoising processing on each crop phenotype data to be fused to obtain each crop phenotype data after denoising processing.

[0138] Specifically, in the embodiments of the present invention, wavelet denoising, Kalman filtering and other methods can be used to perform denoising processing on each crop phenotype data to be fused to obtain each crop phenotype data after denoising processing.

[0139] Based on the internal and external parameter data of the crop phenotype sensor that collects each crop phenotype data to be fused, initial alignment is performed on the denoised crop phenotype data to be fused to obtain the denoised crop phenotype data after initial alignment.

[0140] Specifically, after obtaining each crop phenotype data after denoising processing, based on the internal and external parameter data of the crop phenotype sensor that collects each crop phenotype data to be fused, the synchronous multi-view geometric correction method is used to perform initial alignment on the denoised crop phenotype data to be fused, so as to align the denoised crop phenotype data to be fused in the time and space dimensions, and obtain the denoised crop phenotype data after initial alignment.

[0141] The specific alignment method includes: using the Levenberg-Marquardt optimization algorithm and the external parameter self-calibration algorithm to align the denoised crop phenotype data to be fused in the time and space positions.

[0142] Based on the weight value corresponding to each first crop phenotype data to be fused, data fusion is performed on the denoised first crop phenotype data to obtain the first fusion data.

[0143] Specifically, after obtaining the denoised crop phenotype data to be fused, based on the weight value corresponding to each first crop phenotype data to be fused, through numerical calculation, data fusion is performed on the denoised crop phenotype data to be fused to obtain the first fusion data.

[0144] As an optional embodiment, based on the weight value corresponding to each first crop phenotypic data to be fused, data fusion is performed on each first crop phenotypic data after initial alignment to obtain first fusion data, including: based on the weight value corresponding to each first crop phenotypic data to be fused, the iterative closest point algorithm is used to perform data fusion on each first crop phenotypic data after initial alignment to obtain first fusion data.

[0145] It should be noted that the Iterative Closest Point (ICP) algorithm is an iterative calculation method, mainly used for the precise stitching of depth images in computer vision and point cloud matching (rigid registration). The ICP algorithm realizes precise stitching or registration by continuously iteratively minimizing the distance between corresponding points of the source data and the target data.

[0146] In the embodiment of the present invention, when performing data fusion on each first crop phenotypic data after initial alignment by using the iterative closest point algorithm based on the weight value corresponding to each first crop phenotypic data to be fused, according to the weight value corresponding to each first crop phenotypic data to be fused, calculate the minimum error between the th first crop phenotypic data to be fused and the first crop phenotypic data closest to the th first crop phenotypic data, and the specific calculation formula is as follows: Specifically,

[0147]

[0148] where represents the total number of each first crop phenotypic data to be fused; represents the weight value corresponding to the th first crop phenotypic data to be fused; represents the th first crop phenotypic data to be fused; represents the first crop phenotypic data closest to the th first crop phenotypic data to be fused.

[0149] In each iterative calculation, adjust the pose (rotation matrix and displacement vector) of the first crop phenotypic data to be fused to minimize the weighted error function and ensure the precise alignment of each first crop phenotypic data.

[0150] Through the above algorithm, the data fusion accuracy can be maximized among each first crop phenotypic data, ensuring that the first fusion data obtained by data fusion has higher spatial consistency and feature expression ability.

[0151] In the embodiments of the present invention, not only are the phenotypic data of each crop to be fused spatially aligned through least squares optimization, but also, based on the weight value corresponding to each first phenotypic data of the crop to be fused, the iterative closest point algorithm is used to perform data fusion on each first phenotypic data of the crop to be fused after initial alignment to obtain the first fusion data, which can improve the fusion accuracy of the first phenotypic data of the crop to be fused, thereby further improving the data fusion accuracy and data fusion effect of the data fusion method provided by the present invention.

[0152] Perform data fusion on each second phenotypic data of the crop to be fused after initial alignment and the first fusion data to obtain the fusion data, where the second phenotypic data of the crop to be fused is the phenotypic data of the crop other than the first phenotypic data of the crop to be fused among the phenotypic data of each crop to be fused.

[0153] Specifically, after obtaining the first fusion data, each second phenotypic data of the crop to be fused after initial alignment and the first fusion data can be subjected to data fusion through deep learning techniques, numerical calculations, mathematical statistics, etc. to obtain the fusion data of the phenotypic data of each crop to be fused.

[0154] As an optional embodiment, perform fusion on each second phenotypic data of the crop to be fused after initial alignment and the first fusion data to obtain the fusion data corresponding to the phenotypic data of each crop to be fused, where the second phenotypic data of the crop to be fused is the phenotypic data of the crop other than the first phenotypic data of the crop to be fused among the phenotypic data of each crop to be fused, including: extracting features from each second phenotypic data of the crop to be fused after initial alignment to obtain the feature information corresponding to each second phenotypic data of the crop to be fused after initial alignment.

[0155] Specifically, in the embodiments of the present invention, multi-scale convolutional kernels of a convolutional neural network (CNN) can be used to extract feature information of different scales in each second phenotypic data of the crop to be fused. Through feature extraction of different scales, effective fusion of high-resolution and low-resolution data can be ensured, and the feature expression of the fusion data can be enhanced.

[0156] Map the feature information corresponding to each second phenotypic data of the crop to be fused after initial alignment into the first fusion data to obtain the fusion data.

[0157] Specifically, in the embodiments of the present invention, a deep learning network based on spatio-temporal consistency is adopted to map the feature information corresponding to each second phenotypic data of the crop to be fused after initial alignment into the first fusion data, thereby realizing the deep fusion of the feature information corresponding to each second phenotypic data of the crop to be fused and the first fusion data.

[0158] The deep learning network based on spatio-temporal consistency includes a spatio-temporal consistency calibration module and a multi-level feature enhancement module.

[0159] The spatio-temporal consistency calibration module processes each second crop phenotype data to be fused using a Recurrent Neural Network (RNN) to ensure the consistency of each second crop phenotype data in the time dimension.

[0160] The data mapping module maps the feature information corresponding to each second crop phenotype data after initial alignment into the first fused data based on the spatial position correspondence between the first fused data and the feature information corresponding to each second crop phenotype data, obtaining the original fused data.

[0161] The multi-level feature enhancement module performs feature enhancement processing on the original fused data obtained after fusion through a deep convolutional network, extracts more detailed crop features, and further improves the weights of key features through an Attention Mechanism, thereby obtaining the fused data.

[0162] It should be noted that after obtaining the fused data of each crop phenotype data to be fused, the above fused data can be stored in a standardized adaptive data structure. The structure of any data point in the above fused data can include: XYZRGBS1S2S3S4T1T2WsEm1Em2TimeType1….

[0163] Among them, XYZ represents the three-dimensional spatial position of the data point. RGB represents the color information obtained by a visible light image sensor. S1 - S4 represent the multi-spectral reflectance of different bands. T1 represents the thermal imaging temperature data, and T2 represents the ambient temperature. Ws represents the ambient wind speed. Em1 - Em2 identify the sensor exposure time. Time is the acquisition timestamp, and Type1 represents semantic annotation (such as plant type).

[0164] In an embodiment of the present invention, after inputting the target data corresponding to each first crop phenotype data to be fused into a weight adjustment model and obtaining the weight value corresponding to each first crop phenotype data to be fused output by the weight adjustment model, data fusion is performed on each crop phenotype data to be fused based on the weight value corresponding to each first crop phenotype data to be fused and the internal and external parameter data of the crop phenotype sensor that collects each crop phenotype data to be fused, and the fused data corresponding to each crop phenotype data to be fused is obtained. It can comprehensively consider the data quality of the sample crop phenotype data, the relative position relationship between the sample crop phenotype sensor and the sample crop when collecting the sample crop phenotype data, the environmental factors when the sample crop phenotype sensor collects the sample crop phenotype data, and the influence of the growth stage of the sample crop on the sample crop phenotype data, and more accurately obtain the weight value corresponding to the sample crop phenotype data. Furthermore, through deep learning, the weight value corresponding to each crop phenotype data to be fused can be obtained more efficiently and accurately, the fusion efficiency and fusion effect of crop phenotype data fusion can be improved, and a more accurate data basis can be provided for high-precision three-dimensional modeling, crop feature extraction, and other application scenarios of precision agriculture, having broad application prospects.

[0165] The crop phenotype data fusion method provided by the present invention, through an adaptive weight adjustment method, the contribution degrees of different data sources change in real time according to the actual situation, improving the flexibility and accuracy of data fusion. Combining machine learning algorithms and self-learning mechanisms, it can quickly adjust the fusion strategy, greatly reducing the data processing time and meeting the application requirements of high-timeliness crop phenotypes. Through the fusion of multi-dimensional data, especially the synchronous processing of spatio-temporal data, the overall relevance and analysis accuracy of the data are significantly improved.

[0166] Figure 2 It is a schematic structural diagram of the crop phenotype data fusion device provided by the present invention. The following combines Figure 2 Describe the crop phenotype data fusion device provided by the present invention. The crop phenotype data fusion device described below can be mutually corresponding and referred to with the crop phenotype data fusion method provided by the present invention described above. As Figure 2 shown, the device includes: a first data acquisition module 201, a second data acquisition module 202, a dynamic weight determination module 203, and a multi-source data fusion module 204.

[0167] The first data acquisition module 201 is used to acquire each crop phenotype data to be fused, and each crop phenotype data to be fused includes the crop phenotype data of the target crop collected by different types of crop phenotype sensors and / or the crop phenotype data of the target crop at multiple growth stages.

[0168] The second data acquisition module 202 is configured to acquire the internal and external parameter data of each crop phenotype sensor when collecting each crop phenotype data to be fused, and the target data corresponding to the first crop phenotype data to be fused. The first crop phenotype data to be fused is three-dimensional crop phenotype data to be fused. The target data includes the environmental data when collecting the crop phenotype data, the relative position information between the crop phenotype sensor collecting the crop phenotype data and the crop, and the target parameter values of the crop phenotype data. The target parameters include signal-to-noise ratio and / or feature entropy.

[0169] The dynamic weight determination module 203 is configured to input the target data corresponding to each first crop phenotype data to be fused into the weight adjustment model, and obtain the weight value corresponding to each first crop phenotype data output by the weight adjustment model. The weight adjustment model is obtained by training based on the target data corresponding to each sample crop phenotype data and the weight value corresponding to each sample crop phenotype data in the sample data group. The sample crop phenotype data includes the crop phenotype data of the sample crop collected by different types of crop phenotype sensors and the crop phenotype data of the sample crop at multiple growth stages.

[0170] The multi-source data fusion module 204 is configured to perform data fusion on each crop phenotype data to be fused based on the weight value corresponding to each first crop phenotype data to be fused and the internal and external parameter data of the crop phenotype sensor collecting each crop phenotype data to be fused, so as to obtain the fusion data corresponding to each crop phenotype data to be fused.

[0171] Specifically, the first data acquisition module 201, the second data acquisition module 202, the dynamic weight determination module 203 and the multi-source data fusion module 204 are electrically connected.

[0172] In the crop phenotypic data fusion device in the embodiments of the present invention, after inputting the target data corresponding to each first crop phenotypic data to be fused into a weight adjustment model and obtaining the weight value corresponding to each first crop phenotypic data output by the weight adjustment model, based on the weight value corresponding to each first crop phenotypic data to be fused and the internal and external parameter data of the crop phenotypic sensor that collects each crop phenotypic data to be fused, data fusion is performed on each crop phenotypic data to be fused to obtain the fusion data corresponding to each crop phenotypic data to be fused. It can comprehensively consider the data quality of the sample crop phenotypic data, the relative position relationship between the sample crop phenotypic sensor and the sample crop when collecting the sample crop phenotypic data, the environmental factors when the sample crop phenotypic sensor collects the sample crop phenotypic data, and the influence of the growth stage of the sample crop on the sample crop phenotypic data, and more accurately obtain the weight value corresponding to the sample crop phenotypic data. Furthermore, through deep learning, the weight value corresponding to each crop phenotypic data to be fused can be obtained more efficiently and accurately, the fusion efficiency and fusion effect of crop phenotypic data fusion can be improved, and a more accurate data basis can be provided for high-precision three-dimensional modeling, crop feature extraction, and other application scenarios of precision agriculture, having broad application prospects.

[0173] Figure 3 Illustrates a schematic physical structure diagram of an electronic device, such as Figure 3As shown in the figure, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340. Among them, the processor 310, the communications interface 320, and the memory 330 complete communication with each other through the communication bus 340. The processor 310 may call the logical instructions in the memory 330 to execute the crop phenotype data fusion method, which includes: obtaining each crop phenotype data to be fused, and each crop phenotype data to be fused includes the crop phenotype data of the target crop collected by different types of crop phenotype sensors and / or the crop phenotype data of the target crop at multiple growth stages; obtaining the internal and external parameter data of each crop phenotype sensor when collecting each crop phenotype data to be fused and the target data corresponding to the first crop phenotype data to be fused, where the first crop phenotype data to be fused is three-dimensional crop phenotype data to be fused, and the target data includes the environmental data when collecting the crop phenotype data, the relative position information between the crop phenotype sensor collecting the crop phenotype data and the crop, and the target parameter values of the crop phenotype data, and the target parameters include signal-to-noise ratio and / or feature entropy; inputting the target data corresponding to each first crop phenotype data to be fused into the weight adjustment model to obtain the weight value corresponding to each first crop phenotype data output by the weight adjustment model, and the weight adjustment model is obtained after being trained based on the target data corresponding to each sample crop phenotype data in the sample data group and the weight value corresponding to each sample crop phenotype data, and the sample crop phenotype data includes the crop phenotype data of the sample crop collected by different types of crop phenotype sensors and the crop phenotype data of the sample crop at multiple growth stages; based on the weight value corresponding to each first crop phenotype data to be fused and the internal and external parameter data of the crop phenotype sensor collecting each crop phenotype data to be fused, performing data fusion on each crop phenotype data to be fused to obtain the fusion data corresponding to each crop phenotype data to be fused.

[0174] In addition, when the logical instructions in the above-mentioned memory 330 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0175] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the crop phenotype data fusion method provided by the above-mentioned various methods. The method includes: obtaining each crop phenotype data to be fused, and each crop phenotype data to be fused includes crop phenotype data of a target crop collected by different types of crop phenotype sensors and / or crop phenotype data of the target crop at multiple growth stages; obtaining the internal and external reference data of each crop phenotype sensor when collecting each crop phenotype data to be fused and the target data corresponding to the first crop phenotype data to be fused. The first crop phenotype data to be fused is three-dimensional crop phenotype data to be fused, and the target data includes environmental data when collecting the crop phenotype data, the relative position information between the crop phenotype sensor collecting the crop phenotype data and the crop, and the target parameter values of the crop phenotype data. The target parameters include signal-to-noise ratio and / or characteristic entropy; inputting the target data corresponding to each first crop phenotype data to be fused into a weight adjustment model, and obtaining the weight value corresponding to each first crop phenotype data output by the weight adjustment model. The weight adjustment model is obtained after being trained based on the target data corresponding to each sample crop phenotype data in the sample data group and the weight value corresponding to each sample crop phenotype data. The sample crop phenotype data includes crop phenotype data of a sample crop collected by different types of crop phenotype sensors and crop phenotype data of the sample crop at multiple growth stages; based on the weight value corresponding to each first crop phenotype data to be fused and the internal and external reference data of the crop phenotype sensor collecting each crop phenotype data to be fused, performing data fusion on each crop phenotype data to be fused to obtain the fusion data corresponding to each crop phenotype data to be fused.

[0176] In another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the crop phenotype data fusion method provided by the above-mentioned various methods. The method includes: obtaining each crop phenotype data to be fused, where each crop phenotype data to be fused includes the crop phenotype data of the target crop collected by different types of crop phenotype sensors and / or the crop phenotype data of the target crop at multiple growth stages; obtaining the internal and external parameter data of each crop phenotype sensor when collecting each crop phenotype data to be fused and the target data corresponding to the first crop phenotype data to be fused, where the first crop phenotype data to be fused is three-dimensional crop phenotype data to be fused, and the target data includes the environmental data when collecting the crop phenotype data, the relative position information between the crop phenotype sensor collecting the crop phenotype data and the crop, and the target parameter values of the crop phenotype data, and the target parameters include signal-to-noise ratio and / or feature entropy; inputting the target data corresponding to each first crop phenotype data to be fused into a weight adjustment model, and obtaining the weight value corresponding to each first crop phenotype data output by the weight adjustment model. The weight adjustment model is obtained by training based on the target data corresponding to each sample crop phenotype data in the sample data group and the weight value corresponding to each sample crop phenotype data. The sample crop phenotype data includes the crop phenotype data of the sample crop collected by different types of crop phenotype sensors and the crop phenotype data of the sample crop at multiple growth stages; based on the weight value corresponding to each first crop phenotype data to be fused and the internal and external parameter data of the crop phenotype sensor collecting each crop phenotype data to be fused, performing data fusion on each crop phenotype data to be fused to obtain the fusion data corresponding to each crop phenotype data to be fused.

[0177] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0178] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A crop phenotypic data fusion method, characterized in that: include: Acquire each crop phenotypic data to be fused, wherein each crop phenotypic data to be fused includes crop phenotypic data of a target crop collected by different types of crop phenotypic sensors and / or crop phenotypic data of the target crop at multiple growth stages; Acquire internal and external parameter data of each crop phenotypic sensor when collecting each crop phenotypic data to be fused and target data corresponding to the first crop phenotypic data to be fused, wherein the first crop phenotypic data to be fused is three-dimensional crop phenotypic data to be fused, and the target data includes environmental data when collecting crop phenotypic data, relative position information between the crop phenotypic sensor collecting crop phenotypic data and the crop, and target parameter value of the crop phenotypic data, and the target parameter includes signal-to-noise ratio and / or characteristic entropy; Inputting target data corresponding to each of the first crop phenotypic data to be fused into a weight adjustment model, and obtaining a weight value corresponding to each of the first crop phenotypic data to be fused output by the weight adjustment model, wherein the weight adjustment model is obtained after training based on the target data corresponding to each sample crop phenotypic data in the sample data group and the weight value corresponding to each of the sample crop phenotypic data, and the sample crop phenotypic data includes crop phenotypic data of sample crops collected by different types of crop phenotypic sensors and crop phenotypic data of the sample crops at multiple growth stages; Based on the weight value corresponding to each of the first crop phenotypic data to be fused and the internal and external reference data of the crop phenotypic sensor that collects each of the crop phenotypic data to be fused, data fusion is performed on each of the crop phenotypic data to be fused to obtain fused data corresponding to each of the crop phenotypic data to be fused; The weight values ​​corresponding to the sample crop phenotypic data are obtained based on the following steps: Based on the target parameter value of the sample crop phenotypic data, a first weight score corresponding to the sample crop phenotypic data is obtained; based on the relative position information between the sample crop phenotypic sensor that collects the sample crop phenotypic data and the sample crop, a second weight score corresponding to the sample crop phenotypic data is obtained; based on the environmental data when the sample crop phenotypic data is collected and the type of the sample crop phenotypic sensor, a third weight score corresponding to the sample crop phenotypic data is obtained; based on the growth stage of the sample crop when the sample crop phenotypic data is collected and the type of the sample crop phenotypic sensor, a fourth weight score corresponding to the sample crop phenotypic data is obtained; Based on the first weight score, the second weight score, the third weight score and the fourth weight score corresponding to the sample crop phenotypic data, the weight value corresponding to the sample crop phenotypic data is calculated.

2. The crop phenotypic data fusion method according to claim 1, characterized in that: The method of fusing each of the crop phenotypic data to be fused based on the weight value corresponding to each of the first crop phenotypic data to be fused and the internal and external parameter data of the crop phenotypic sensor that collects each of the crop phenotypic data to be fused to obtain fused data corresponding to each of the crop phenotypic data to be fused includes: Performing denoising processing on each of the crop phenotypic data to be fused, and obtaining each of the crop phenotypic data to be fused after denoising processing; Based on the internal and external reference data of the crop phenotypic sensor that collects each of the crop phenotypic data to be fused, initially aligning the denoised crop phenotypic data to be fused to obtain the crop phenotypic data to be fused after the initial alignment; Based on the weight value corresponding to each of the first crop phenotypic data to be fused, data fusion is performed on each of the first crop phenotypic data to be fused after initial alignment to obtain first fused data; Each second crop phenotypic data to be fused after initial alignment is fused with the first fused data to obtain the fused data, wherein the second crop phenotypic data to be fused is the crop phenotypic data in each crop phenotypic data to be fused except the first crop phenotypic data to be fused.

3. The crop phenotypic data fusion method according to claim 1, characterized in that: The step of obtaining a first weight score corresponding to the sample crop phenotypic data based on the target parameter value of the sample crop phenotypic data includes: Determine the target parameter value of the sample crop phenotypic data as an independent variable, determine the first weight score corresponding to the sample crop phenotypic data as a dependent variable, and calculate the first weight score corresponding to the sample crop phenotypic data based on the target parameter value of the sample crop phenotypic data and a positive correlation function, wherein the positive correlation function is used to describe the positive correlation between the independent variable and the dependent variable; The step of obtaining a second weight score corresponding to the sample crop phenotypic data based on the relative position information between the sample crop phenotypic sensor that collects the sample crop phenotypic data and the sample crop comprises: Based on the relative position information between the sample crop phenotype sensor and the sample crop, the distance between the sample crop phenotype sensor and the sample crop and the vertical distance between the sample crop and a target reference line are acquired, where the target reference line is the central axis of the field of view of the sample crop phenotype sensor; Determine the distance as an independent variable, determine the first sub-weight score corresponding to the sample crop phenotypic data as a dependent variable, calculate the first sub-weight score corresponding to the sample crop phenotypic data based on the distance and a positive correlation function, determine the vertical distance as an independent variable, determine the second sub-weight score corresponding to the sample crop phenotypic data as a dependent variable, and calculate the second sub-weight score corresponding to the sample crop phenotypic data based on the distance and a negative correlation function; Based on the first sub-weight score and the second sub-weight score corresponding to the sample crop phenotypic data, a second weight score corresponding to the sample crop phenotypic data is calculated; The obtaining, based on the environmental data when the sample crop phenotypic data is collected and the type of the sample crop phenotypic sensor, a third weight score corresponding to the sample crop phenotypic data comprises: Acquire a first matching degree between the environmental data when the sample crop phenotypic data is collected and the type of the sample crop phenotypic sensor; Determine the first matching degree as an independent variable, determine the third weight score corresponding to the sample crop phenotypic data as a dependent variable, and calculate the third weight score corresponding to the sample crop phenotypic data based on the first matching degree and a positive correlation function; The acquiring, based on the growth stage of the sample crop and the type of the sample crop phenotypic sensor when the sample crop phenotypic data is collected, a fourth weight score corresponding to the sample crop phenotypic data, comprises: Acquiring a second matching degree between the growth stage of the sample crop and the type of the sample crop phenotypic sensor when collecting the sample crop phenotypic data; The second matching degree is determined as an independent variable, and the fourth weight score corresponding to the sample crop phenotypic data is determined as a dependent variable. Based on the second matching degree and a positive correlation function, the fourth weight score corresponding to the sample crop phenotypic data is calculated.

4. The crop phenotypic data fusion method according to claim 3, characterized in that: The first weight score, the second weight score, the third weight score and the fourth weight score based on the sample crop phenotypic data are used to calculate the weight value corresponding to the sample crop phenotypic data, including: Obtaining a temporal consistency evaluation value of the sample crop phenotypic data; Calculating the product of the first weight score, the second weight score, the third weight score and the fourth weight score of the sample crop phenotypic data as an intermediate result; The quotient of the intermediate result and the time consistency evaluation value of the sample crop phenotypic data is calculated as the weight value corresponding to the sample crop phenotypic data.

5. The crop phenotypic data fusion method according to claim 2, characterized in that: The step of fusing the first crop phenotypic data to be fused after initial alignment based on the weight value corresponding to each of the first crop phenotypic data to be fused to obtain first fused data includes: Based on the weight value corresponding to each of the first crop phenotypic data to be fused, an iterative closest point algorithm is used to perform data fusion on the first crop phenotypic data to be fused after initial alignment to obtain the first fused data.

6. The crop phenotypic data fusion method according to claim 5, characterized in that: The step of fusing each second crop phenotypic data to be fused after the initial alignment with the first fused data to obtain fused data corresponding to each crop phenotypic data to be fused, wherein the second crop phenotypic data to be fused is the crop phenotypic data in each crop phenotypic data to be fused except the first crop phenotypic data to be fused, including: Performing feature extraction on each of the second crop phenotypic data to be fused after the initial alignment, and acquiring feature information corresponding to each of the second crop phenotypic data to be fused after the initial alignment; The characteristic information corresponding to each of the second crop phenotypic data to be fused after initial alignment is mapped into the first fused data to obtain the fused data.

7. A crop phenotypic data fusion device, characterized in that: include: A first data acquisition module is used to acquire phenotypic data of each crop to be fused, wherein each of the phenotypic data of the crop to be fused includes crop phenotypic data of the target crop collected by different types of crop phenotypic sensors and / or crop phenotypic data of the target crop at multiple growth stages; A second data acquisition module is used to acquire internal and external parameter data of each crop phenotypic sensor when collecting each crop phenotypic data to be fused and target data corresponding to the first crop phenotypic data to be fused, wherein the first crop phenotypic data to be fused is three-dimensional crop phenotypic data to be fused, and the target data includes environmental data when collecting crop phenotypic data, relative position information between the crop phenotypic sensor collecting crop phenotypic data and the crop, and target parameter values ​​of the crop phenotypic data, and the target parameters include signal-to-noise ratio and / or characteristic entropy; a dynamic weight determination module, for inputting target data corresponding to each of the first crop phenotypic data to be fused into a weight adjustment model, and obtaining a weight value corresponding to each of the first crop phenotypic data to be fused output by the weight adjustment model, wherein the weight adjustment model is obtained after training based on the target data corresponding to each sample crop phenotypic data in the sample data group and the weight value corresponding to each of the sample crop phenotypic data, and the sample crop phenotypic data include crop phenotypic data of sample crops collected by different types of crop phenotypic sensors and crop phenotypic data of the sample crops at multiple growth stages; A multi-source data fusion module, configured to fuse each of the crop phenotypic data to be fused based on a weight value corresponding to each of the first crop phenotypic data to be fused and internal and external reference data of a crop phenotypic sensor that collects each of the crop phenotypic data to be fused, so as to obtain fused data corresponding to each of the crop phenotypic data to be fused; The weight values ​​corresponding to the sample crop phenotypic data are obtained based on the following steps: Based on the target parameter value of the sample crop phenotypic data, a first weight score corresponding to the sample crop phenotypic data is obtained; based on the relative position information between the sample crop phenotypic sensor that collects the sample crop phenotypic data and the sample crop, a second weight score corresponding to the sample crop phenotypic data is obtained; based on the environmental data when the sample crop phenotypic data is collected and the type of the sample crop phenotypic sensor, a third weight score corresponding to the sample crop phenotypic data is obtained; based on the growth stage of the sample crop when the sample crop phenotypic data is collected and the type of the sample crop phenotypic sensor, a fourth weight score corresponding to the sample crop phenotypic data is obtained; Based on the first weight score, the second weight score, the third weight score and the fourth weight score corresponding to the sample crop phenotypic data, the weight value corresponding to the sample crop phenotypic data is calculated.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the crop phenotypic data fusion method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the crop phenotypic data fusion method according to any one of claims 1 to 6 is implemented.

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