Crop growth trend prediction method based on ambient light recognition and related equipment thereof

Abstract

The invention discloses a crop growth trend prediction method based on ambient light recognition and related equipment thereof, and belongs to the technical field of artificial intelligence. The method comprises the following steps: firstly, collecting growth parameters in a crop growth process and corresponding ambient light parameters; then, the linear correlation degree between the growth parameters and ambient light is calculated through statistical analysis, the linear correlation degree serves as a weighted weight, and weighting processing is carried out on the parameters; thirdly, a model training data set is constructed through the weighted data, and the model training data set is used for training and generating a model capable of predicting the crop growth risk trend; and when a growth trend prediction instruction is received, obtaining to-be-predicted crop growth data, inputting the to-be-predicted crop growth data into the risk trend prediction model, and outputting a crop growth risk trend prediction result. The invention also relates to the technical field of block chains, and the growth data is stored on a block chain network. According to the invention, through deep fusion of ambient light identification and crop growth data, accurate prediction of the crop growth risk trend is realized.

Description

Technical Field

[0001] The present application belongs to the field of artificial intelligence technology, and specifically relates to a method for predicting crop growth trends based on ambient light recognition and related equipment. Background Art

[0002] Smart agricultural insurance assessment based on the growth trend of agricultural products is the product of the deep integration of modern technology and agriculture. It collects farmland environment and crop growth data in real time through technologies such as the Internet of Things and sensors, and uses advanced methods such as big data and machine learning to analyze and predict crop growth trends and potential risks, providing farmers with accurate and personalized insurance services. This assessment method not only improves the scientificity and accuracy of risk assessment, but also realizes the automated processing and rapid claims settlement of insurance business, effectively reducing the uncertainty in agricultural production and the economic losses of farmers. With the continuous development of information technology, smart agricultural insurance will show a broader application prospect and safeguard the sustainable development of agricultural production.

[0003] At present, intelligent agricultural insurance assessment based on the growth trend of agricultural products will introduce ambient light recognition technology to achieve it. It usually uses high-precision sensors to monitor light intensity, duration and spectrum in real time, combined with big data analysis, to accurately predict crop growth trends. However, when introducing ambient light recognition technology, the difficulty lies in the variability and complexity of ambient light, that is, different crops have different requirements for light intensity, duration and spectral composition, and environmental factors such as weather changes, seasonal changes and farmland layout can significantly affect light conditions.

[0004] Therefore, accurately identifying and analyzing the specific impact of ambient light on the growth of agricultural products and establishing an accurate growth trend prediction model are technical challenges that urgently need to be overcome in smart agricultural insurance assessment. Summary of the invention

[0005] The purpose of the embodiments of the present application is to propose a crop growth trend prediction method based on ambient light recognition and related equipment, so as to solve the technical problem of how to accurately identify and analyze the specific impact of ambient light on the growth of agricultural products, and conduct a comprehensive analysis in combination with other environmental factors and crop characteristics to establish an accurate growth trend prediction model.

[0006] In order to solve the above technical problems, the embodiment of the present application provides a method for predicting crop growth trends based on ambient light recognition, which adopts the following technical solutions:

[0007] A method for predicting crop growth trends based on ambient light recognition, comprising:

[0008] Acquire pre-collected crop growth data, wherein the crop growth data includes crop growth parameters and ambient light parameters corresponding to the crop growth parameters;

[0009] Calculate the linear correlation between crop growth parameters and ambient light parameters;

[0010] The linear correlation degree is used as a weighted weight value, and the crop growth parameter and the ambient light parameter are weighted by using the weighted weight value to obtain a weighted growth parameter and a weighted ambient light parameter;

[0011] A model training data set is constructed based on weighted growth parameters and weighted ambient light parameters;

[0012] The preset initial prediction model is trained using the model training data set to obtain a risk trend prediction model for crop growth;

[0013] Receive a growth trend prediction instruction, obtain growth data of the crop to be predicted, input the growth data of the crop to be predicted into a risk trend prediction model, and obtain a growth risk trend prediction result.

[0014] Furthermore, the step of calculating the linear correlation between the crop growth parameter and the ambient light parameter specifically includes:

[0015] Obtain crop growth parameters at different growth stages;

[0016] Obtain the ambient light parameters corresponding to the crop growth parameters at different growth stages;

[0017] The Pearson correlation coefficient between the crop growth parameters at different growth stages and the ambient light parameters corresponding to the crop growth parameters at different growth stages was calculated to obtain the linear correlation between the crop growth parameters at different growth stages and the ambient light parameters.

[0018] Further, the step of calculating the Pearson correlation coefficient between the crop growth parameters at different growth stages and the ambient light parameters corresponding to the crop growth parameters at different growth stages to obtain the linear correlation between the crop growth parameters at different growth stages and the ambient light parameters specifically includes:

[0019] Calculate the average growth parameter value according to the growth parameters of crops at different growth stages;

[0020] Calculate the average value of the ambient light parameters according to the ambient light parameters corresponding to the crop growth parameters at different growth stages;

[0021] Based on the average values ​​of growth parameters and ambient light parameters, the Pearson correlation coefficient formula was used to calculate the Pearson correlation coefficient of crops at different growth stages, and the linear correlation between crop growth parameters and ambient light parameters at different growth stages was obtained.

[0022] Furthermore, the Pearson correlation coefficient formula is expressed as follows:

[0023]

[0024] In the formula, r is the Pearson correlation coefficient, x i and i They represent the crop growth parameters and ambient light parameters corresponding to the i-th growth stage, represents the average value of growth parameters, represents the average value of the ambient light parameter, and n represents the number of growth stages.

[0025] Furthermore, the linear correlation is used as a weighted weight value, and the weighted weight value is used to perform weighted processing on the crop growth parameter and the ambient light parameter to obtain the weighted growth parameter and the weighted ambient light parameter, specifically including:

[0026] Determine the growth stage of crops according to crop growth parameters and ambient light parameters;

[0027] Obtain matching linear correlation according to the growth stage of the crop to obtain matching correlation;

[0028] Using the matching correlation degree to perform weighted processing on the crop growth parameters, a weighted growth parameter is obtained;

[0029] The ambient light parameters are weighted using the matching correlation degree to obtain weighted ambient light parameters.

[0030] Furthermore, the step of constructing a model training data set based on the weighted growth parameter and the weighted ambient light parameter specifically includes:

[0031] Classify the weighted growth parameters and weighted ambient light parameters according to the growth stages of crops, and construct data parameter subsets at different growth stages of crops;

[0032] Construct crop growth time series according to the growth stages of crops;

[0033] The data parameter subsets are sorted according to the crop growth time series to generate the model training data set.

[0034] Furthermore, the steps of using the model training data set to train the preset initial prediction model to obtain the risk trend prediction model of crop growth specifically include:

[0035] Perform feature extraction on the model training data set to obtain crop growth time-parameter sequence characteristics;

[0036] Use the initial prediction model to learn the crop growth time-parameter sequence characteristics and output the risk trend prediction results;

[0037] Compare the risk trend prediction results with the preset standard results and calculate the prediction error of the initial prediction model;

[0038] Use the back-propagation algorithm to propagate the prediction error in the initial prediction model;

[0039] The gradient descent algorithm is used to iteratively update the model parameters until the model is fitted, and a risk trend prediction model for crop growth is obtained.

[0040] In order to solve the above technical problems, the embodiment of the present application further provides a crop growth trend prediction device based on ambient light recognition, which adopts the following technical solution:

[0041] A crop growth trend prediction device based on ambient light recognition, comprising:

[0042] A data acquisition module, used to acquire pre-collected crop growth data, wherein the crop growth data includes crop growth parameters and ambient light parameters corresponding to the crop growth parameters;

[0043] A correlation calculation module is used to calculate the linear correlation between crop growth parameters and ambient light parameters;

[0044] A parameter weighting module is used to use the linear correlation as a weighted weight value, and use the weighted weight value to perform weighted processing on the crop growth parameter and the ambient light parameter to obtain a weighted growth parameter and a weighted ambient light parameter;

[0045] A data set construction module, used for constructing a model training data set based on weighted growth parameters and weighted ambient light parameters;

[0046] A model training module is used to train a preset initial prediction model using a model training data set to obtain a risk trend prediction model for crop growth;

[0047] The trend prediction module is used to receive a growth trend prediction instruction, obtain the growth data of the crop to be predicted, input the growth data of the crop to be predicted into the risk trend prediction model, and obtain the growth risk trend prediction result.

[0048] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0049] A computer device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the crop growth trend prediction method based on ambient light recognition as described in any one of the above are implemented.

[0050] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0051] A computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement the steps of any of the above-mentioned methods for predicting crop growth trends based on ambient light recognition.

[0052] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0053] The present application discloses a crop growth trend prediction method based on ambient light recognition and related equipment, which belongs to the field of artificial intelligence technology. First, the growth parameters of crops during their growth process and their corresponding ambient light parameters are collected. Subsequently, the linear correlation between the growth parameters and the ambient light is calculated using statistical analysis, and the parameters are weighted using this as the weighted weight. Next, a model training data set is constructed using the weighted data to train and generate a model that can predict the risk trend of crop growth. When a growth trend prediction instruction is received, the growth data of the crop to be predicted is obtained and input into the risk trend prediction model, and the prediction result of the growth risk trend of the crop is output. The present application realizes the accurate prediction of the risk trend of crop growth through the deep integration of ambient light recognition and crop growth data, and trains a model of crop growth risk trend, which provides strong data support for intelligent agricultural insurance assessment, promotes the personalized customization of agricultural insurance products, and enhances the risk resistance of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 An exemplary system architecture diagram is shown in which the present application can be applied;

[0056] Figure 2 A flowchart of an embodiment of a method for predicting crop growth trends based on ambient light recognition according to the present application is shown;

[0057] Figure 3 A schematic structural diagram of an embodiment of a device for predicting crop growth trends based on ambient light recognition according to the present application is shown;

[0058] Figure 4A schematic structural diagram of an embodiment of a computer device according to the present application is shown. DETAILED DESCRIPTION

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0060] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0061] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0062] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0063] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0064] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, the tablet computer 1012 or the mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer and a desktop computer, etc.

[0065] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0066] It should be noted that the crop growth trend prediction method based on ambient light recognition provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the crop growth trend prediction device based on ambient light recognition is generally set in the server / terminal device.

[0067] It should be understood that Figure 1 The numbers of terminal devices, networks and servers in the embodiment are merely illustrative. The above system may have any number of terminal devices, networks and servers according to implementation requirements.

[0068] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for predicting crop growth trends based on ambient light recognition according to the present application. The method for predicting crop growth trends based on ambient light recognition comprises the following steps:

[0069] S201, obtaining pre-collected crop growth data, wherein the crop growth data includes crop growth parameters and ambient light parameters corresponding to the crop growth parameters.

[0070] Specifically, the system needs to obtain the pre-collected crop growth data, which includes crop growth parameters at different growth stages (such as seedling stage, growth stage, maturity stage, etc.), as well as the ambient light parameters corresponding to these growth stages. Crop growth parameters refer to various indicators that directly reflect the growth status of crops, such as height, leaf area, biomass, etc.; ambient light parameters refer to the lighting conditions that affect crop growth, such as light intensity, light duration, light quality, etc.

[0071] S202, calculating the linear correlation between the crop growth parameter and the ambient light parameter.

[0072] Specifically, the purpose of calculating the linear correlation is to quantify the strength and direction of the linear relationship between crop growth parameters (such as plant height, yield, etc.) and ambient light parameters (such as light intensity, light duration, etc.). This quantification helps to understand how light conditions affect crop growth and whether this effect is significant.

[0073] Linear correlation (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) is used as an analysis tool. These statistical indicators can effectively measure the strength and direction of the linear relationship between two variables. The higher the linear correlation, the stronger the linear relationship between the two variables; and the positive or negative correlation reflects whether the relationship is positive or negative.

[0074] S203, using the linear correlation as a weighted weight value, and using the weighted weight value to perform weighted processing on the crop growth parameter and the ambient light parameter to obtain a weighted growth parameter and a weighted ambient light parameter.

[0075] Specifically, the linear correlation is used as the weighted value. The higher the linear correlation, the stronger the linear relationship between the two parameters. Therefore, a higher weight should be given in the weighted processing. This weighted processing method helps to more accurately reflect the impact of light conditions on crop growth. The purpose of weighted processing is to adjust the weights of each parameter so that the importance and impact of different parameters can be more reasonably considered in subsequent analysis or models.

[0076] By multiplying the original value of each crop growth parameter or ambient light parameter by its corresponding weighted weight, a weighted parameter value, namely a weighted growth parameter and a weighted ambient light parameter, is obtained.

[0077] S204: construct a model training data set based on the weighted growth parameters and the weighted ambient light parameters.

[0078] Specifically, according to the division of crop growth stages, a time series of crop growth is formed, which records the changes in the growth status of crops at different growth stages. Then, based on this crop growth time series combined with weighted growth parameters and weighted ambient light parameters, a model training data set is constructed, so that the data in the model training data set is arranged according to the crop growth time series, so that the model can more clearly learn the growth parameter and ambient light parameter characteristics at these different growth stages.

[0079] S205, using the model training data set to train the preset initial prediction model to obtain a risk trend prediction model for crop growth.

[0080] Specifically, the model training dataset is constructed based on the crop growth time series and contains key information such as weighted growth parameters and weighted ambient light parameters. These data have been preprocessed and weighted to reflect the importance and impact of different parameters on crop growth.

[0081] Since the data are organized in time series, they contain the time dependence and dynamic changes in the crop growth process. This time series characteristic is crucial for capturing crop growth trends and predicting risks.

[0082] Before training, select a prediction model suitable for processing time series data. The prediction model can adopt a time series analysis model (such as ARIMA, seasonal decomposition, etc.). These models can handle the time dependency in the sequence data. Then use the model training data set to perform iterative model training, and finally obtain a model that can accurately predict the risk trend of crop growth.

[0083] S206, receiving a growth trend prediction instruction, obtaining growth data of the crop to be predicted, inputting the growth data of the crop to be predicted into a risk trend prediction model, and obtaining a growth risk trend prediction result.

[0084] Specifically, after completing the training of the risk trend prediction model for crop growth, when the system receives a growth trend prediction instruction, it obtains the growth data of the crops to be predicted, including crop growth parameters and ambient light parameters corresponding to the crop growth parameters, and inputs these data into the previously trained risk trend prediction model. The risk trend prediction model has learned the key patterns and relationships in the crop growth process and can predict crop growth risk trends based on the input data.

[0085] In the above embodiments, the present application realizes accurate prediction of crop growth risk trends through deep integration of ambient light recognition and crop growth data, and trains a model of crop growth risk trends, provides strong data support for intelligent agricultural insurance assessment, promotes personalized customization of agricultural insurance products, and enhances the risk resistance of agricultural production.

[0086] Furthermore, the step of calculating the linear correlation between the crop growth parameter and the ambient light parameter specifically includes:

[0087] Obtain crop growth parameters at different growth stages;

[0088] Obtain the ambient light parameters corresponding to the crop growth parameters at different growth stages;

[0089] The Pearson correlation coefficient between the crop growth parameters at different growth stages and the ambient light parameters corresponding to the crop growth parameters at different growth stages was calculated to obtain the linear correlation between the crop growth parameters at different growth stages and the ambient light parameters.

[0090] The Pearson correlation coefficient is an indicator that measures the degree of linear correlation between two variables, and its value ranges from -1 to 1. Among them, 1 represents a perfect positive correlation, -1 represents a perfect negative correlation, and 0 represents no linear correlation. In this step, the system will calculate the Pearson correlation coefficient between the crop growth parameters and the corresponding ambient light parameters at different growth stages as the linear correlation.

[0091] In this embodiment, first, it is necessary to collect the growth parameters of crops at different growth stages. These parameters may include but are not limited to plant height, leaf area, biomass, yield, etc., which can reflect the growth status and development process of crops. At the same time, it is necessary to obtain the ambient light parameters corresponding to these growth stages. Ambient light parameters generally refer to light intensity, light duration, spectral distribution, etc.

[0092] After obtaining the crop growth parameters and ambient light parameters, the growth parameters and ambient light parameters need to be matched according to the growth stage. That is, ensure that the growth parameters of each growth stage are associated with their corresponding ambient light parameters. For each growth stage, the Pearson correlation coefficient between the crop growth parameters and the ambient light parameters in that stage is calculated and used as the linear correlation in that stage.

[0093] Through the above embodiment, by collecting crop growth parameters and ambient light parameters, performing data matching and correlation analysis (using the Pearson correlation coefficient), the linear correlation between the two at different growth stages is effectively calculated.

[0094] Further, the step of calculating the Pearson correlation coefficient between the crop growth parameters at different growth stages and the ambient light parameters corresponding to the crop growth parameters at different growth stages to obtain the linear correlation between the crop growth parameters at different growth stages and the ambient light parameters specifically includes:

[0095] Calculate the average growth parameter value according to the crop growth parameters at different growth stages;

[0096] Calculate the average value of the ambient light parameters according to the ambient light parameters corresponding to the crop growth parameters at different growth stages;

[0097] Based on the average values ​​of growth parameters and ambient light parameters, the Pearson correlation coefficient formula was used to calculate the Pearson correlation coefficient of crops at different growth stages, and the linear correlation between crop growth parameters and ambient light parameters at different growth stages was obtained.

[0098] In this embodiment, first, for each growth stage, the average values ​​of the crop growth parameters and the ambient light parameters are calculated respectively. That is, the average value of the growth parameters is calculated according to the crop growth parameters at different growth stages, and the average value of the ambient light parameters is calculated according to the ambient light parameters corresponding to the crop growth parameters at different growth stages. Finally, based on the average values ​​of the growth parameters and the average values ​​of the ambient light parameters, the Pearson correlation coefficient formula is used to calculate the Pearson correlation coefficient of the crops at different growth stages, and the linear correlation between the crop growth parameters and the ambient light parameters at different growth stages is obtained.

[0099] Through the above embodiment, the average values ​​of the crop growth parameters and the ambient light parameters in each growth stage are calculated, and the Pearson correlation coefficient is calculated in combination with the Pearson correlation coefficient formula, and is used as the linear correlation degree in this stage.

[0100] Furthermore, the Pearson correlation coefficient formula is expressed as follows:

[0101]

[0102] In the formula, r is the Pearson correlation coefficient, x i and i They represent the crop growth parameters and ambient light parameters corresponding to the i-th growth stage, represents the average value of growth parameters, represents the average value of the ambient light parameter, and n represents the number of growth stages.

[0103] In this embodiment, the value range of r is -1 to 1, r=1 indicates a complete positive correlation, that is, when the ambient light parameter increases, the crop growth parameter also increases; r=-1 indicates a complete negative correlation, that is, when the ambient light parameter increases, the crop growth parameter decreases; r=0 indicates no linear correlation; the closer the absolute value of r is to 1, the stronger the linear relationship between the two variables is.

[0104] Repeating the above calculation process for each crop growth stage to obtain the Pearson correlation coefficient between the crop growth parameters and the ambient light parameters at that stage helps to identify which growth stages are more sensitive to ambient light conditions and how these conditions affect the growth of crops.

[0105] In the above embodiment, by calculating the Pearson correlation coefficient and analyzing its changes in different growth stages, the complex relationship between crop growth and environmental light conditions can be deeply understood.

[0106] Furthermore, the linear correlation is used as a weighted weight value, and the weighted weight value is used to perform weighted processing on the crop growth parameter and the ambient light parameter to obtain the weighted growth parameter and the weighted ambient light parameter, specifically including:

[0107] Determine the growth stage of crops according to crop growth parameters and ambient light parameters;

[0108] Obtain matching linear correlation according to the growth stage of the crop to obtain matching correlation;

[0109] Using the matching correlation degree to perform weighted processing on the crop growth parameters, a weighted growth parameter is obtained;

[0110] The ambient light parameters are weighted using the matching correlation degree to obtain weighted ambient light parameters.

[0111] After calculating the Pearson correlation coefficients, these coefficients can be directly used as weighted weight values ​​for the respective crop growth parameters and ambient light parameters. These weight values ​​reflect the importance of each parameter on crop growth, so that different degrees of importance can be given in the subsequent weighted processing. It should be noted that in order to ensure the non-negativity and ease of processing of the weights, the correlation coefficients can be converted into absolute values ​​or properly normalized.

[0112] In this embodiment, during the growth of crops, their growth parameters (such as plant height, leaf area, etc.) will change with the change of ambient light parameters (such as light intensity, light duration, etc.), and the system determines the growth stage of the crops based on the crop growth parameters and the ambient light parameters. Then, the matching linear correlation is obtained according to the growth stage of the crops to obtain the matching correlation. The matching correlation (Pearson correlation coefficient) between the growth parameters and the ambient light parameters is different in different growth stages. The crop growth parameters are weighted using the matching correlation to obtain weighted growth parameters. Similarly, the ambient light parameters are weighted using the matching correlation to obtain weighted ambient light parameters.

[0113] Through the above embodiment, by weighting the growth parameters and the ambient light parameters, the impact of different factors on the growth of crops can be more accurately evaluated. This weighting operation enables the subsequent model to learn the characteristic differences between different growth stages.

[0114] Furthermore, the step of constructing a model training data set based on the weighted growth parameter and the weighted ambient light parameter specifically includes:

[0115] Classify the weighted growth parameters and weighted ambient light parameters according to the growth stages of crops, and construct data parameter subsets at different growth stages of crops;

[0116] Construct crop growth time series according to the growth stages of crops;

[0117] The data parameter subsets are sorted according to the crop growth time series to generate the model training data set.

[0118] In this embodiment, weighted growth parameters and weighted ambient light parameters are classified according to the growth stage of crops, and data parameter subsets at different growth stages are constructed. The purpose is to divide the data according to the growth stage so that subsequent models can perform data analysis and learning based on the characteristics of each stage.

[0119] Crop growth time series are constructed according to the growth stages of crops. The construction of time series is usually based on timestamps and corresponding growth parameters or environmental parameters. In this technical solution, the construction of time series also needs to consider the division of growth stages to ensure that the time series can accurately reflect the characteristics of each growth stage.

[0120] The data parameter subsets are sorted according to the crop growth time series to ensure that the data in the dataset are arranged according to the natural order of crop growth, thereby maintaining the consistency and coherence of the data.

[0121] Through the above-mentioned embodiments, through the steps of classification, time series construction and data sorting, a more accurate and targeted model training data set can be generated, thereby improving the accuracy and reliability of the model.

[0122] Furthermore, the steps of using the model training data set to train the preset initial prediction model to obtain the risk trend prediction model of crop growth specifically include:

[0123] Perform feature extraction on the model training data set to obtain crop growth time-parameter sequence characteristics;

[0124] Use the initial prediction model to learn the crop growth time-parameter sequence characteristics and output the risk trend prediction results;

[0125] Compare the risk trend prediction results with the preset standard results and calculate the prediction error of the initial prediction model;

[0126] Use the back-propagation algorithm to propagate the prediction error in the initial prediction model;

[0127] The gradient descent algorithm is used to iteratively update the model parameters until the model is fitted, and a risk trend prediction model for crop growth is obtained.

[0128] In this embodiment, feature extraction is performed on the model training data set to obtain crop growth time-parameter sequence features. These crop growth time-parameter sequence features represent key information such as growth parameters and ambient light parameters of crops at different growth stages, which are crucial for predicting growth risk trends of crops.

[0129] The initial prediction model built based on time series analysis is used to learn the time-parameter sequence characteristics of crop growth and output the risk trend prediction results. At the same time, the risk trend prediction results are compared with the preset standard results, and the prediction error of the initial prediction model is calculated. Then, the prediction error is transferred in the initial prediction model using a back propagation algorithm. Through the back propagation algorithm, the prediction error can be transferred to each network layer of the model layer by layer, and the gradient descent algorithm is used to iteratively update the model parameters until the model is fitted. By continuously iteratively updating the model parameters, the prediction error can be gradually reduced and the prediction accuracy of the model can be improved.

[0130] Through the above embodiments, a crop growth risk trend prediction model with high prediction accuracy can be constructed through the steps of feature extraction, model learning, prediction result evaluation, error propagation and model parameter updating.

[0131] In the above embodiment, the present application discloses a method for predicting crop growth trends based on ambient light recognition, which belongs to the field of artificial intelligence technology. First, the growth parameters of crops during their growth and their corresponding ambient light parameters are collected. Subsequently, the linear correlation between the growth parameters and the ambient light is calculated using statistical analysis, and the parameters are weighted using this as the weighted weight. Next, a model training data set is constructed using the weighted data to train and generate a model that can predict the risk trend of crop growth. When a growth trend prediction instruction is received, the growth data of the crop to be predicted is obtained and input into the risk trend prediction model, and the prediction result of the growth risk trend of the crop is output. The present application achieves accurate prediction of the risk trend of crop growth through the deep integration of ambient light recognition and crop growth data, and trains a model of crop growth risk trend, which provides strong data support for intelligent agricultural insurance assessment, promotes the personalized customization of agricultural insurance products, and enhances the risk resistance of agricultural production.

[0132] In this embodiment, the method for predicting crop growth trend based on ambient light recognition is executed on an electronic device (eg Figure 1The server shown in the figure can receive instructions or obtain data through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G connection, Wi-Fi connection, Bluetooth connection, Wi MAX connection, Zigbee connection, UWB (Ultra Wi-Fi Band) connection, and other wireless connection methods currently known or to be developed in the future.

[0133] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned crop growth data information, the above-mentioned crop growth data information can also be stored in a node of a blockchain.

[0134] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0135] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0136] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0138] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0139] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a device for predicting crop growth trends based on ambient light recognition. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0140] like Figure 3 As shown, the crop growth trend prediction device 300 based on ambient light recognition described in this embodiment includes:

[0141] The data acquisition module 301 is used to acquire the crop growth data collected in advance, wherein the crop growth data includes the crop growth parameters and the ambient light parameters corresponding to the crop growth parameters;

[0142] A correlation calculation module 302 is used to calculate the linear correlation between the crop growth parameter and the ambient light parameter;

[0143] The parameter weighting module 303 is used to use the linear correlation as a weighted weight value, and use the weighted weight value to perform weighted processing on the crop growth parameter and the ambient light parameter to obtain a weighted growth parameter and a weighted ambient light parameter;

[0144] A data set construction module 304 is used to construct a model training data set based on weighted growth parameters and weighted ambient light parameters;

[0145] A model training module 305 is used to train a preset initial prediction model using a model training data set to obtain a risk trend prediction model for crop growth;

[0146] The trend prediction module 306 is used to receive a growth trend prediction instruction, obtain growth data of the crop to be predicted, input the growth data of the crop to be predicted into the risk trend prediction model, and obtain a growth risk trend prediction result.

[0147] Furthermore, the correlation calculation module 302 is specifically used for:

[0148] Acquire crop growth parameters of crops at different growth stages; acquire ambient light parameters corresponding to crop growth parameters at different growth stages; calculate the Pearson correlation coefficient between crop growth parameters at different growth stages and ambient light parameters corresponding to crop growth parameters at different growth stages, and obtain the linear correlation between crop growth parameters and ambient light parameters at different growth stages.

[0149] Furthermore, the association degree calculation module 302 is further used for:

[0150] The average value of growth parameters is calculated according to the growth parameters of crops at different growth stages; the average value of ambient light parameters is calculated according to the ambient light parameters corresponding to the growth parameters of crops at different growth stages; based on the average value of growth parameters and the average value of ambient light parameters, the Pearson correlation coefficient of crops at different growth stages is calculated using the Pearson correlation coefficient formula to obtain the linear correlation between the growth parameters of crops and the ambient light parameters at different growth stages.

[0151] Furthermore, the Pearson correlation coefficient formula is expressed as follows:

[0152]

[0153] In the formula, r is the Pearson correlation coefficient, x i and i They represent the crop growth parameters and ambient light parameters corresponding to the i-th growth stage, represents the average value of growth parameters, represents the average value of the ambient light parameter, and n represents the number of growth stages.

[0154] Furthermore, the parameter weighting module 303 is specifically used for:

[0155] The growth stage of crops is determined according to the crop growth parameters and the ambient light parameters; the matching linear correlation is obtained according to the growth stage of crops to obtain the matching correlation; the crop growth parameters are weighted using the matching correlation to obtain the weighted growth parameters; the ambient light parameters are weighted using the matching correlation to obtain the weighted ambient light parameters.

[0156] Furthermore, the data set construction module 304 is specifically used for:

[0157] The weighted growth parameters and weighted ambient light parameters are classified according to the growth stage of crops, and data parameter subsets at different growth stages of crops are constructed; the crop growth time series is constructed according to the growth stage of crops; the data parameter subsets are sorted according to the crop growth time series to generate a model training data set.

[0158] Furthermore, the trend prediction module 306 is specifically used for:

[0159] Perform feature extraction on the model training data set to obtain crop growth time-parameter sequence characteristics;

[0160] Use the initial prediction model to learn the crop growth time-parameter sequence characteristics and output the risk trend prediction results;

[0161] Compare the risk trend prediction results with the preset standard results and calculate the prediction error of the initial prediction model;

[0162] Use the back-propagation algorithm to propagate the prediction error in the initial prediction model;

[0163] The gradient descent algorithm is used to iteratively update the model parameters until the model is fitted, and a risk trend prediction model for crop growth is obtained.

[0164] In the above embodiment, the present application discloses a crop growth trend prediction device based on ambient light recognition, which belongs to the field of artificial intelligence technology. First, by collecting the growth parameters of crops during their growth and their corresponding ambient light parameters. Subsequently, the linear correlation between the growth parameters and the ambient light is calculated using statistical analysis, and the parameters are weighted using this as the weighted weight. Next, a model training data set is constructed using the weighted data to train and generate a model that can predict the risk trend of crop growth. When a growth trend prediction instruction is received, the growth data of the crop to be predicted is obtained and input into the risk trend prediction model, and the prediction result of the growth risk trend of the crop is output. The present application realizes the accurate prediction of the risk trend of crop growth through the deep integration of ambient light recognition and crop growth data, and trains a model of crop growth risk trend, which provides strong data support for the evaluation of intelligent agricultural insurance, promotes the personalized customization of agricultural insurance products, and enhances the risk resistance of agricultural production.

[0165] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0166] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 4 having a memory 41, a processor 42, and a network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital processor (DSP), an embedded device, etc.

[0167] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0168] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of a crop growth trend prediction method based on ambient light recognition, etc. In addition, the memory 41 can also be used to temporarily store various data that have been output or are to be output.

[0169] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions or process data stored in the memory 41, such as computer-readable instructions for running the crop growth trend prediction method based on ambient light recognition.

[0170] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0171] In the above embodiment, the present application discloses a computer device, which belongs to the field of artificial intelligence technology. First, by collecting the growth parameters of crops during their growth and their corresponding ambient light parameters. Subsequently, the linear correlation between the growth parameters and the ambient light is calculated using statistical analysis, and the parameters are weighted using this as the weighted weight. Next, a model training data set is constructed using the weighted data to train and generate a model that can predict the risk trend of crop growth. When a growth trend prediction instruction is received, the growth data of the crop to be predicted is obtained and input into the risk trend prediction model, and the prediction result of the growth risk trend of the crop is output. The present application realizes the accurate prediction of the risk trend of crop growth through the deep integration of ambient light recognition and crop growth data, and trains a model of crop growth risk trend, which provides strong data support for intelligent agricultural insurance assessment, promotes the personalized customization of agricultural insurance products, and enhances the risk resistance of agricultural production.

[0172] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned crop growth trend prediction method based on ambient light recognition.

[0173] In the above embodiment, the present application discloses a computer-readable storage medium, which belongs to the field of artificial intelligence technology. First, by collecting the growth parameters of crops during their growth and their corresponding ambient light parameters. Subsequently, the linear correlation between the growth parameters and the ambient light is calculated using statistical analysis, and the parameters are weighted using this as the weighted weight. Next, a model training data set is constructed using the weighted data to train and generate a model that can predict the risk trend of crop growth. When a growth trend prediction instruction is received, the growth data of the crop to be predicted is obtained and input into the risk trend prediction model, and the prediction result of the growth risk trend of the crop is output. The present application realizes the accurate prediction of the risk trend of crop growth through the deep integration of ambient light recognition and crop growth data, and trains a model of crop growth risk trend, which provides strong data support for intelligent agricultural insurance assessment, promotes the personalized customization of agricultural insurance products, and enhances the risk resistance of agricultural production.

[0174] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0175] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0176] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for predicting crop growth trends based on ambient light recognition, characterized in that: include: Acquire pre-collected crop growth data, wherein the crop growth data includes crop growth parameters and ambient light parameters corresponding to the crop growth parameters; Calculating the linear correlation between the crop growth parameter and the ambient light parameter; Taking the linear correlation degree as a weighted weight value, and using the weighted weight value to perform weighted processing on the crop growth parameter and the ambient light parameter to obtain a weighted growth parameter and a weighted ambient light parameter; Constructing a model training data set based on the weighted growth parameter and the weighted ambient light parameter; Using the model training data set to train a preset initial prediction model to obtain a risk trend prediction model for crop growth; A growth trend prediction instruction is received, growth data of the crop to be predicted is acquired, and the growth data of the crop to be predicted is input into the risk trend prediction model to obtain a growth risk trend prediction result.

2. The crop growth trend prediction method based on ambient light recognition according to claim 1, characterized in that: The step of calculating the linear correlation between the crop growth parameter and the ambient light parameter specifically includes: Obtain crop growth parameters at different growth stages; Obtain the ambient light parameters corresponding to the crop growth parameters at different growth stages; The Pearson correlation coefficient between the crop growth parameters at different growth stages and the ambient light parameters corresponding to the crop growth parameters at different growth stages is calculated to obtain the linear correlation between the crop growth parameters at different growth stages and the ambient light parameters.

3. The method for predicting crop growth trend based on ambient light recognition according to claim 2, characterized in that: The step of calculating the Pearson correlation coefficient between the crop growth parameters at different growth stages and the ambient light parameters corresponding to the crop growth parameters at different growth stages to obtain the linear correlation between the crop growth parameters at different growth stages and the ambient light parameters specifically includes: Calculating the average value of the growth parameters according to the crop growth parameters at different growth stages; Calculating an average value of the ambient light parameters according to the ambient light parameters corresponding to the crop growth parameters at different growth stages; Based on the average value of the growth parameter and the average value of the ambient light parameter, the Pearson correlation coefficient of the crops at different growth stages is calculated using the Pearson correlation coefficient formula to obtain the linear correlation between the crop growth parameter and the ambient light parameter at different growth stages.

4. The method for predicting crop growth trend based on ambient light recognition according to claim 3, characterized in that: The Pearson correlation coefficient formula is expressed as follows: In the formula, r is the Pearson correlation coefficient, x i and i They represent the crop growth parameters and ambient light parameters corresponding to the i-th growth stage, represents the average value of growth parameters, represents the average value of the ambient light parameter, and n represents the number of growth stages.

5. The method for predicting crop growth trend based on ambient light recognition according to claim 2, characterized in that: The step of taking the linear correlation as a weighted weight value, and using the weighted weight value to perform weighted processing on the crop growth parameter and the ambient light parameter to obtain the weighted growth parameter and the weighted ambient light parameter specifically includes: determining the growth stage of the crop according to the crop growth parameter and the ambient light parameter; Obtain matching linear correlation according to the growth stage of the crop to obtain matching correlation; Using the matching association degree to perform weighted processing on the crop growth parameter to obtain the weighted growth parameter; The ambient light parameter is weighted using the matching association degree to obtain the weighted ambient light parameter.

6. The method for predicting crop growth trend based on ambient light recognition according to claim 5, characterized in that: The step of constructing a model training data set based on the weighted growth parameter and the weighted ambient light parameter specifically includes: Classifying the weighted growth parameters and the weighted ambient light parameters according to the growth stages of the crops, and constructing data parameter subsets at different growth stages of the crops; constructing a crop growth time series according to the growth stage of the crop; The data parameter subsets are sorted according to the crop growth time series to generate the model training data set.

7. The method for predicting crop growth trend based on ambient light recognition according to claim 6, characterized in that: The step of using the model training data set to train the preset initial prediction model to obtain the risk trend prediction model for crop growth specifically includes: Performing feature extraction on the model training data set to obtain crop growth time-parameter sequence features; Using the initial prediction model to learn the crop growth time-parameter sequence characteristics and output risk trend prediction results; Comparing the risk trend prediction result with a preset standard result, and calculating the prediction error of the initial prediction model; Using a back propagation algorithm to propagate the prediction error in the initial prediction model; The model parameters are iteratively updated using a gradient descent algorithm until the model is fitted, thereby obtaining the risk trend prediction model for crop growth.

8. A device for predicting crop growth trends based on ambient light recognition, characterized in that: include: A data acquisition module, used to acquire pre-collected crop growth data, wherein the crop growth data includes crop growth parameters and ambient light parameters corresponding to the crop growth parameters; A correlation calculation module, used for calculating the linear correlation between the crop growth parameter and the ambient light parameter; A parameter weighting module, configured to use the linear correlation as a weighted weight value, and use the weighted weight value to perform weighted processing on the crop growth parameter and the ambient light parameter to obtain a weighted growth parameter and a weighted ambient light parameter; A data set construction module, used to construct a model training data set based on the weighted growth parameter and the weighted ambient light parameter; A model training module, used to train a preset initial prediction model using the model training data set to obtain a risk trend prediction model for crop growth; The trend prediction module is used to receive a growth trend prediction instruction, obtain growth data of crops to be predicted, input the growth data of crops to be predicted into the risk trend prediction model, and obtain a growth risk trend prediction result.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the crop growth trend prediction method based on ambient light recognition as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the crop growth trend prediction method based on ambient light recognition as described in any one of claims 1 to 7 are implemented.

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