Dynamic control method, system, device and storage medium for plant growth environment

Abstract

The present invention relates to the field of plant cultivation technology, and discloses a method, system, device, and storage medium for dynamically controlling a plant growth environment. The method obtains image data and environmental data of plant growth; inputs the image data into a growth recognition model to obtain the growth stage and growth state of the plant; according to the growth stage, a response surface method is used to obtain the optimal soil temperature and humidity of the plant growth environment; the environmental data is input into a temperature and humidity prediction model to obtain a predicted soil temperature and humidity value; the predicted soil temperature and humidity value is compared with the optimal soil temperature and humidity value, and based on the comparison result, the soil temperature and humidity of the plant growth environment is regulated. The present invention dynamically controls the temperature, humidity, light, and pest and disease control required for plant growth through refined environmental management, thereby achieving global management and precise control of the cultivation process, improving the intensiveness and intelligent level of the overall cultivation method, and achieving cost reduction and efficiency improvement in plant cultivation.

Description

Technical Field

[0001] The present invention relates to the field of plant cultivation technology, and in particular to a method, system, equipment and storage medium for dynamically regulating a plant growth environment. Background Art

[0002] The Lucky Red (Golden Red) is a highly ornamental plant. Its distinctive red leaves symbolize good fortune and prosperity, making it a popular choice among consumers and promising in the Chinese horticultural market. However, the Lucky Red has demanding growing conditions and is highly sensitive to even the slightest environmental changes. Slight fluctuations in light, soil, temperature, and humidity can significantly impact its growth. Therefore, adopting a scientific, data-driven cultivation and management model is key to improving the growth quality of the Lucky Red and promoting its industrial development.

[0003] Conventional methods for cultivating jilihong include greenhouse cultivation, large-scale greenhouse cultivation, and small-scale, dispersed cultivation. While these methods provide basic environmental protection for jilihong cultivation and reduce the impact of climate on its growth, they still have significant drawbacks. While greenhouse cultivation offers some environmental protection, it relies on manual control, lacks automation and precision, and struggles to cope with complex environmental changes. Due to the simplicity of greenhouse cultivation equipment, key environmental factors such as temperature and humidity cannot be monitored and adjusted in real time. Small-scale, dispersed cultivation is limited by technology and scale, making it difficult to maintain stable growing conditions. Furthermore, the current cultivation process lacks a high level of intensiveness and intelligence, making it difficult to identify and classify the various growth stages of jilihong and to identify and regulate key environmental factors for each stage. Due to the inability to accurately determine the temperature, humidity, light, and other factors required for each growth stage, improper control can lead to slow growth, dull leaf color, and decreased immunity, thus affecting jilihong's quality and market competitiveness. Furthermore, existing cultivation methods lack the ability to predict changes in environmental factors. For example, there's a lack of integrated regulation of both atmospheric and indoor soil temperature and humidity, as well as a lack of targeted supplemental lighting and shading measures. This prevents effective adjustment to varying light requirements, leading to issues of either too much or too little light. Furthermore, a lack of pest and disease identification methods makes it difficult to identify and guide appropriate pesticide application. These factors further exacerbate management challenges in Lucky Red cultivation, severely impacting its growth and economic benefits. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method, system, equipment and storage medium for dynamic regulation of plant growth environment, which achieves the effect of global management and precise regulation of plant growth environment by identifying key factors of plant growth environment and systematically analyzing them.

[0005] In a first aspect, the present invention provides a method for dynamically controlling a plant growth environment, the method comprising:

[0006] Acquiring image data and environmental data of plant growth, wherein the image data includes plant image data, and the environmental data includes soil temperature and humidity data and meteorological data;

[0007] Inputting the image data into a growth recognition model to obtain the growth stage and growth state of the plant, the growth recognition model comprising a plant recognition module, a stage recognition module and a state recognition module;

[0008] spraying the plants with drugs and / or supplementing light according to the growth stage and the growth state;

[0009] According to the growth stage, the response surface method is used to obtain the optimal soil temperature and humidity of the plant growth environment, and the environmental data is input into the temperature and humidity prediction model to obtain the predicted value of soil temperature and humidity;

[0010] The predicted soil temperature and humidity value is compared with the optimal soil temperature and humidity value, and the soil temperature and humidity of the plant growth environment are regulated based on the comparison result.

[0011] Furthermore, the step of inputting the image data into a growth recognition model to obtain the growth stage and growth state of the plant includes:

[0012] Inputting the image data into a plant recognition module constructed based on the YOLO model to identify plant parts and obtain plant part images;

[0013] Inputting the plant part image into a stage recognition module constructed based on a convolutional neural network model to obtain the growth stage of the plant, wherein the growth stage includes a budding stage, a growing stage, and a mature stage;

[0014] The plant part image is input into a state recognition module for feature extraction and feature comparison to obtain the growth state of the plant, which includes the pest state and the disease state.

[0015] Furthermore, the step of inputting the plant part image into a state recognition module for feature extraction and feature comparison to obtain the growth state of the plant includes:

[0016] Color feature extraction is performed on the plant part image using color space conversion and color segmentation technology to obtain color feature data;

[0017] Using a texture statistics method to extract texture features from the plant part image to obtain texture feature data;

[0018] Using edge detection technology and contour extraction technology to extract shape features from the plant part image to obtain shape feature data;

[0019] The color feature data, texture feature data and shape feature data are compared with the feature data in the pest and disease database, and the growth status of the plant is obtained based on the comparison results.

[0020] Furthermore, the step of obtaining the optimal soil temperature and humidity of the plant growth environment by using the response surface method according to the growth stage includes:

[0021] A response surface model was established with soil temperature and soil moisture as independent variables and the rate of change of plant leaf color area as the dependent variable.

[0022] Solving the response surface model to obtain the soil temperature and humidity combination value that makes the change rate of plant leaf color area reach the optimal value;

[0023] Construct an environmental parameter database based on the soil temperature and humidity combination values ​​corresponding to different growth stages;

[0024] According to the growth stage, the corresponding optimal value of soil temperature and humidity is selected from the environmental parameter database.

[0025] Furthermore, the step of extracting texture features from the plant part image using a texture statistics method to obtain texture feature data includes:

[0026] Converting the plant part image into a grayscale image, calculating a grayscale co-occurrence matrix of the grayscale image, and extracting first texture feature data from the grayscale co-occurrence matrix;

[0027] Defining a neighborhood area for each pixel in the grayscale image, and calculating a local binary pattern of the pixel in the neighborhood area to obtain a histogram of the local binary pattern, and calculating second texture feature data based on the histogram;

[0028] The first texture feature data and the second texture feature data are fused to obtain texture feature data.

[0029] Furthermore, the step of extracting shape features from the plant part image using edge detection technology and contour extraction technology to obtain shape feature data includes:

[0030] extracting edge information of the plant part image using edge detection technology;

[0031] According to each edge point in the edge information, a neighborhood search strategy is adopted to perform a neighborhood search, and each adjacent edge point found is connected using a connection strategy to obtain edge connection information;

[0032] Contour extraction is performed based on the edge connection information, and the extracted contour is analyzed to obtain shape feature data.

[0033] Furthermore, the step of obtaining the optimal soil temperature and humidity of the plant growth environment by using the response surface method according to the growth stage includes:

[0034] A response surface model was established with soil temperature and soil moisture as independent variables and the rate of change of plant leaf color area as the dependent variable.

[0035] Solving the response surface model to obtain the soil temperature and humidity combination value that makes the change rate of plant leaf color area reach the optimal value;

[0036] Construct an environmental parameter database based on the soil temperature and humidity combination values ​​corresponding to different growth stages;

[0037] According to the growth stage, the corresponding optimal value of soil temperature and humidity is selected from the environmental parameter database.

[0038] Furthermore, the temperature and humidity prediction model is constructed based on the Transformer-XL model and trained using the Grey Wolf Optimization Algorithm.

[0039] In a second aspect, the present invention provides a dynamic control system for a plant growth environment, the system comprising:

[0040] A data acquisition module is used to acquire image data and environmental data of plant growth, wherein the image data includes plant image data, and the environmental data includes soil temperature and humidity data and meteorological data;

[0041] a growth recognition module, configured to input the image data into a growth recognition model to obtain the growth stage and growth state of the plant, wherein the growth recognition model includes a plant recognition module, a stage recognition module, and a state recognition module;

[0042] A first control module is used to spray the plant with medicine and / or supplement light according to the growth stage and the growth state;

[0043] A temperature and humidity prediction module is used to obtain the optimal soil temperature and humidity of the plant growth environment according to the growth stage using the response surface method, and input the environmental data into the temperature and humidity prediction model to obtain the predicted value of soil temperature and humidity;

[0044] The second control module is used to compare the predicted soil temperature and humidity value with the optimal soil temperature and humidity value, and to control the soil temperature and humidity of the plant growth environment according to the comparison result.

[0045] In a third aspect, an embodiment of the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0046] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0047] The present invention provides a method, system, computer device, and storage medium for dynamically controlling the plant growth environment. By establishing a dynamic adjustment mechanism to identify and distinguish between different plant growth stages and states, the present invention can optimize cultivation strategies. By strengthening the long-term dynamic prediction capabilities of key factors such as soil temperature and humidity, the present invention can improve the ability to respond to complex coupled changes, thereby meeting the control needs of plant cultivation in a multivariate environment. Through refined environmental management, the present invention dynamically controls the temperature, humidity, light, and pest and disease control required for plant growth, enabling global management and precise control of the cultivation process, improving the intensiveness and intelligence of the overall cultivation method, and achieving cost reduction and efficiency improvement in plant cultivation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 1 is a flow chart of a method for dynamically controlling a plant growth environment according to an embodiment of the present invention;

[0049] Figure 2 is another flow chart of the method for dynamically controlling the plant growth environment in an embodiment of the present invention;

[0050] Figure 3 Schematic diagram of the structure of the dynamic control system of the plant growth environment in an embodiment of the present invention;

[0051] Figure 4 1 is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0053] See also Figure 1 The first embodiment of the present invention provides a method for dynamically controlling a plant growth environment, which includes steps S10 to S50:

[0054] Step S10, acquiring image data and environmental data of plant growth, wherein the image data includes plant image data, and the environmental data includes soil temperature and humidity data and meteorological data;

[0055] Step S20, inputting the image data into a growth recognition model to obtain the growth stage and growth state of the plant, wherein the growth recognition model includes a plant recognition module, a stage recognition module, and a state recognition module;

[0056] Step S30, spraying the plant with medicine and / or supplementing light according to the growth stage and the growth state;

[0057] Step S40, according to the growth stage, using the response surface method to obtain the optimal soil temperature and humidity of the plant growth environment, inputting the environmental data into the temperature and humidity prediction model to obtain the predicted value of soil temperature and humidity;

[0058] Step S50 , comparing the predicted soil temperature and humidity value with the optimal soil temperature and humidity value, and regulating the soil temperature and humidity of the plant growth environment according to the comparison result.

[0059] The method for dynamically controlling the plant growth environment provided by the present invention can be used for cultivating various types of plants. The specific control steps of the present invention are described in detail below using Jilihong as an example.

[0060] First, the data used in the present invention are collected and preprocessed. The data used include image data of plants and parameter data of the growth environment. Specifically, image data of the growth process of plants are collected by camera equipment, soil temperature data and soil moisture data are collected by sensors, and meteorological data are obtained through weather websites, etc. For the received image data and environmental data, data preprocessing is performed, including specifically eliminating abnormal data, interpolating missing values, and unifying all data into the same time frequency. The purpose of data preprocessing is to ensure the accuracy and consistency of model input data and provide reliable input for the prediction of subsequent models. In order to further optimize the efficiency and performance of the model, principal component analysis (PCA) is introduced after data preprocessing to perform feature screening and extract the most important features in the data. The specific steps of preprocessing can refer to the conventional steps and will not be repeated here.

[0061] See also Figure 2 The processed image data and environmental data are input into different models for analysis. The image data is input into a pre-established growth recognition model to identify the growth stage and growth status of the plant, while the environmental data is input into a pre-established temperature and humidity prediction model to predict the soil temperature and humidity. Specifically, the steps of identifying the growth stage and growth status of the plant using the growth recognition model include:

[0062] Inputting the image data into a plant recognition module constructed based on the YOLO model to identify plant parts and obtain plant part images;

[0063] Inputting the plant part image into a stage recognition module constructed based on a convolutional neural network model to obtain the growth stage of the plant, wherein the growth stage includes a budding stage, a growing stage, and a mature stage;

[0064] The plant part image is input into a state recognition module for feature extraction and feature comparison to obtain the growth state of the plant, which includes the pest state and the disease state.

[0065] In this embodiment, the growth recognition model consists of three modules, namely a plant recognition module, a stage recognition module and a state recognition module. Among them, the plant recognition module is constructed using the YOLO model, which is used to identify various parts of the plant, including roots, stems, leaves and other parts. The stage recognition module is constructed using a convolutional neural network model, which is used to perform in-depth analysis of the images of various parts identified by the plant recognition module to identify the specific stage of the growth cycle of the plant. The growth stages here include the germination stage, the growth stage and the maturity stage. It should be noted that as long as the neural network model can achieve the above functions, it can be used to construct the growth recognition model of this embodiment, and there are no excessive restrictions here.

[0066] The state recognition module in this embodiment uses color, texture, and shape analysis technology to determine the current growth state of the plant by extracting and analyzing features from the images of each part identified by the plant recognition module. The specific steps include:

[0067] Color feature extraction is performed on the plant part image using color space conversion and color segmentation technology to obtain color feature data;

[0068] Using a texture statistics method to extract texture features from the plant part image to obtain texture feature data;

[0069] Using edge detection technology and contour extraction technology to extract shape features from the plant part image to obtain shape feature data;

[0070] The color feature data, texture feature data and shape feature data are compared with the feature data in the pest and disease database, and the growth status of the plant is obtained based on the comparison results.

[0071] In this embodiment, the features of the plant part image are extracted and analyzed from three aspects: color, texture, and shape. For color features, color space conversion and color segmentation technology are used for feature extraction. First, the plant part image is converted from the RGB color space to other color spaces that are more suitable for color feature extraction, such as the HSI color space. The purpose of the conversion is to separate color information and brightness information. In the converted color space, color components or combinations that are useful for color feature extraction of the plant image are selected. In the HSI color space, the H component and S component used to describe the color are extracted. Then, the color segmentation threshold is set according to the extracted color components, and the color segmentation algorithm is used to segment the image into regions of different colors. Finally, color features are extracted from the segmented color regions.

[0072] For texture features, texture statistics method is used for feature extraction. The specific steps include:

[0073] Converting the plant part image into a grayscale image, calculating a grayscale co-occurrence matrix of the grayscale image, and extracting first texture feature data from the grayscale co-occurrence matrix;

[0074] Defining a neighborhood area for each pixel in the grayscale image, and calculating a local binary pattern of the pixel in the neighborhood area to obtain a histogram of the local binary pattern, and calculating second texture feature data based on the histogram;

[0075] The first texture feature data and the second texture feature data are fused to obtain texture feature data.

[0076] In this embodiment, the plant part image is first converted from a color image to a grayscale image, and then the grayscale co-occurrence matrix is ​​calculated by counting the frequency of occurrence of grayscale value pairs between each pixel and its neighboring pixels. In order to eliminate the differences in image size and grayscale level, the grayscale co-occurrence matrix is ​​normalized, and then the first texture features, such as energy, contrast, correlation, entropy, etc., are extracted from the normalized grayscale co-occurrence matrix. The first texture features reflect information such as the texture roughness, directionality, and grayscale distribution of different regions in the image. For each pixel on the grayscale image, its neighborhood area is defined, and the local binary pattern is calculated by comparing the grayscale values ​​of the pixel with its neighboring pixels. For each pixel in the image, the histogram of its local binary pattern is counted, and based on the histogram data, the second texture features, such as the histogram mean, variance, energy, contrast, etc., are calculated. The second texture features reflect the distribution and complexity of the local texture in the image. Finally, the first texture feature and the second texture feature are fused by splicing or weighted summation as the final texture feature data. The texture feature data obtained in this way contains more texture features of the plant image, providing more texture details for subsequent analysis, thereby improving the accuracy of the recognition results.

[0077] For shape features, this embodiment uses edge detection technology and contour extraction technology to extract features. The specific steps include:

[0078] extracting edge information of the plant part image using edge detection technology;

[0079] According to each edge point in the edge information, a neighborhood search strategy is adopted to perform a neighborhood search, and each adjacent edge point found is connected using a connection strategy to obtain edge connection information;

[0080] Contour extraction is performed based on the edge connection information, and the extracted contour is analyzed to obtain shape feature data.

[0081] In this embodiment, the image is first processed through edge detection to extract edge information from the image. The edge information is represented in the form of a binary image, where edge points are set to white (or high brightness) and non-edge points are set to black (or low brightness). For each edge point, a neighborhood search strategy is used to find adjacent edge points, and adjacent edge points are connected through connection strategies such as line segment connection and curve fitting to form continuous edge segments. Based on the edge connection, contour information is identified and extracted. Contour extraction includes external contour extraction and internal contour extraction. For example, the edge line of the object's periphery is selected as the contour line, and the boundary tracking algorithm is used to extract the external contour. For wormholes or damage on the blade, internal contour extraction is required. The internal contour can be extracted using a region filling algorithm. For the extracted contour information, shape feature data is obtained by analyzing the shape, length, curvature, etc. of the contour.

[0082] After obtaining color, texture, and shape feature data through the above steps, the plant's current growth state is determined by calculating the similarity between the extracted feature data and standard feature data in the pest and disease database. This growth state includes health, disease, and pest status. For different growth states, automated mechanical control can be used to automatically spray pesticides or supplement light.

[0083] For the growth stage of the plant identified by the growth recognition model, in this embodiment, a standard environmental parameter database is established by using the response surface method, and the optimal value of the corresponding environmental parameter is selected according to the identified growth stage. The specific steps include:

[0084] A response surface model was established with soil temperature and soil moisture as independent variables and the rate of change of plant leaf color area as the dependent variable.

[0085] Solving the response surface model to obtain the soil temperature and humidity combination value that makes the change rate of plant leaf color area reach the optimal value;

[0086] Construct an environmental parameter database based on the soil temperature and humidity combination values ​​corresponding to different growth stages;

[0087] According to the growth stage, the corresponding optimal value of soil temperature and humidity is selected from the environmental parameter database.

[0088] In this embodiment, the response surface method is used to determine the optimal values ​​of soil temperature and humidity at different growth stages. Taking Jilihong as an example, the value of Jilihong is reflected in the red proportion of the leaves. Therefore, for the response surface model of Jilihong, its independent variables are soil temperature value and soil humidity value, and the dependent variable is the change rate of the color area of ​​the plant leaves. The response surface model is established by analyzing the relationship between temperature and humidity and the change rate of the red area. The solution is performed through various methods such as designed experiments or dynamic simulations, so that the soil temperature and humidity combination values ​​that make the change rate of the color area of ​​the plant leaves reach the optimal value at different growth stages can be obtained, and an environmental parameter database is established based on the optimal values ​​of these environmental parameters obtained by the solution, and the corresponding optimal soil temperature and humidity values ​​are extracted from the database through the identified growth stage, wherein the optimal soil temperature and humidity values ​​include the optimal soil temperature value and the optimal soil humidity value.

[0089] During the above-mentioned data collection process, environmental data for plant growth is also collected, including soil temperature, soil moisture, and meteorological data. In this embodiment, this environmental data is analyzed by a temperature and humidity prediction model to predict the temperature and humidity values ​​of the soil within a certain period of time. The temperature and humidity prediction model is preferably constructed using the Transformer-XL model. To further improve the prediction accuracy, the Gray Wolf Optimization (GWO) algorithm is used to optimize the training process of the Transformer-XL model to reduce the training cost of the model. GWO iteratively updates the model's hyperparameters to ensure that the model is updated in a timely manner, thereby ensuring the accuracy and stability of soil temperature and humidity predictions. The specific model construction and training process can refer to the steps of the conventional method and will not be repeated here.

[0090] Through the above steps, we can obtain the optimal soil temperature and humidity values ​​and the predicted soil temperature and humidity values ​​corresponding to the current growth stage of the plant. By comparing these two types of data, we can determine whether the temperature and humidity of the soil environment in the future stage meet the optimal values ​​for plant growth. If the optimal values ​​are not reached, the soil environment is dynamically adjusted according to the difference between the two, so as to achieve the optimal environmental parameters for plant growth.

[0091] This embodiment provides a method for dynamically controlling a plant growth environment. The present invention comprehensively acquires and screens all environmental factors that affect plant growth, and conducts a systematic principal component analysis to effectively identify key factors and their interrelationships, thereby ensuring the scientific nature and effectiveness of the overall control. A dynamic adjustment mechanism is established to identify and distinguish different growth stages and growth states of plants to optimize cultivation strategies. The ability to respond to complex coupled changes is enhanced by strengthening the long-term dynamic prediction capability of key factors such as soil temperature and humidity, thereby meeting the control needs of plant cultivation in a multivariate environment. Through refined environmental management, the present invention dynamically controls the temperature, humidity, light, and pest and disease control required for plant growth, thereby achieving global management and precise control of the cultivation process, improving the intensiveness and intelligence level of the overall cultivation method, and achieving cost reduction and efficiency improvement in plant cultivation.

[0092] See also Figure 3 Based on the same inventive concept, a second embodiment of the present invention provides a dynamic control system for a plant growth environment, comprising:

[0093] A data acquisition module 10 is used to acquire image data and environmental data of plant growth, wherein the image data includes plant image data, and the environmental data includes soil temperature and humidity data and meteorological data;

[0094] A growth recognition module 20 is used to input the image data into a growth recognition model to obtain the growth stage and growth state of the plant. The growth recognition model includes a plant recognition module, a stage recognition module, and a state recognition module;

[0095] A first control module 30 is used to spray the plant with medicine and / or supplement light according to the growth stage and the growth state;

[0096] The temperature and humidity prediction module 40 is used to obtain the optimal soil temperature and humidity of the plant growth environment according to the growth stage using the response surface method, and input the environmental data into the temperature and humidity prediction model to obtain the predicted value of soil temperature and humidity;

[0097] The second control module 50 is used to compare the predicted soil temperature and humidity value with the optimal soil temperature and humidity value, and to control the soil temperature and humidity of the plant growth environment according to the comparison result.

[0098] The technical features and technical effects of the dynamic control system for a plant growth environment proposed in an embodiment of the present invention are the same as those of the method proposed in an embodiment of the present invention and are not further described here. Each module in the above-mentioned dynamic control system for a plant growth environment can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in the form of software in a memory in the computer device so that the processor can call and execute the corresponding operations of each of the above modules.

[0099] In addition, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0100] See also Figure 4 , an internal structure diagram of a computer device in one embodiment, the computer device can specifically be a terminal or a server. The computer device includes a processor, a memory, a network interface, a display and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for dynamically controlling the plant growth environment is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0101] It can be understood by those skilled in the art that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0102] In addition, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0103] In summary, the embodiments of the present invention propose a method, system, device and storage medium for dynamically controlling a plant growth environment. The method comprises acquiring image data and environmental data of plant growth, wherein the image data includes plant image data, and the environmental data includes soil temperature and humidity data and meteorological data; inputting the image data into a growth recognition model to obtain the growth stage and growth state of the plant, wherein the growth recognition model includes a plant recognition module, a stage recognition module and a state recognition module; spraying the plant with medicine and / or supplementing light according to the growth stage and the growth state; according to the growth stage, using the response surface method to obtain the optimal soil temperature and humidity of the plant growth environment, inputting the environmental data into a temperature and humidity prediction model to obtain a predicted soil temperature and humidity value; comparing the predicted soil temperature and humidity value with the optimal soil temperature and humidity value, and regulating the soil temperature and humidity of the plant growth environment according to the comparison result. The present invention dynamically controls the temperature, humidity, light and pest control required for plant growth through refined environmental management, thereby achieving global management and precise control of the cultivation process, improving the intensiveness and intelligent level of the overall cultivation method, and achieving cost reduction and efficiency improvement of plant cultivation.

[0104] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0105] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. A method for dynamically controlling a plant growth environment, characterized in that: include: Acquiring image data and environmental data of plant growth, wherein the image data includes plant image data, and the environmental data includes soil temperature and humidity data and meteorological data; Inputting the image data into a growth recognition model to obtain the growth stage and growth state of the plant, the growth recognition model comprising a plant recognition module, a stage recognition module and a state recognition module; spraying the plants with drugs and / or supplementing light according to the growth stage and the growth state; According to the growth stage, the response surface method is used to obtain the optimal soil temperature and humidity of the plant growth environment, and the environmental data is input into the temperature and humidity prediction model to obtain the predicted value of soil temperature and humidity; Comparing the predicted soil temperature and humidity value with the optimal soil temperature and humidity value, and regulating the soil temperature and humidity of the plant growth environment based on the comparison result; The step of inputting the image data into a growth recognition model to obtain the growth stage and growth status of the plant comprises: Inputting the image data into a plant recognition module constructed based on the YOLO model to identify plant parts and obtain plant part images; Inputting the plant part image into a stage recognition module constructed based on a convolutional neural network model to obtain the growth stage of the plant, wherein the growth stage includes a budding stage, a growing stage, and a mature stage; Inputting the plant part image into a state recognition module for feature extraction and feature comparison to obtain the growth state of the plant, wherein the growth state includes the pest state and the disease state; The step of inputting the plant part image into a state recognition module for feature extraction and feature comparison to obtain the growth state of the plant comprises: Color feature extraction is performed on the plant part image using color space conversion and color segmentation technology to obtain color feature data; Using a texture statistics method to extract texture features from the plant part image to obtain texture feature data; Using edge detection technology and contour extraction technology to extract shape features from the plant part image to obtain shape feature data; Comparing the color feature data, texture feature data, and shape feature data with the feature data in the pest and disease database, and obtaining the growth status of the plant based on the comparison results; The step of obtaining the optimal soil temperature and humidity of the plant growth environment using the response surface method according to the growth stage includes: A response surface model was established with soil temperature and soil moisture as independent variables and the rate of change of plant leaf color area as the dependent variable. Solving the response surface model to obtain the soil temperature and humidity combination value that makes the change rate of plant leaf color area reach the optimal value; Construct an environmental parameter database based on the soil temperature and humidity combination values ​​corresponding to different growth stages; According to the growth stage, the corresponding optimal value of soil temperature and humidity is selected from the environmental parameter database.

2. The method for dynamically controlling the plant growth environment according to claim 1, wherein: The step of extracting texture features from the plant part image using a texture statistics method to obtain texture feature data comprises: Converting the plant part image into a grayscale image, calculating a grayscale co-occurrence matrix of the grayscale image, and extracting first texture feature data from the grayscale co-occurrence matrix; Defining a neighborhood area for each pixel in the grayscale image, and calculating a local binary pattern of the pixel in the neighborhood area to obtain a histogram of the local binary pattern, and calculating second texture feature data based on the histogram; The first texture feature data and the second texture feature data are fused to obtain texture feature data.

3. The method for dynamically controlling the plant growth environment according to claim 1, wherein: The step of extracting shape features from the plant part image using edge detection technology and contour extraction technology to obtain shape feature data comprises: extracting edge information of the plant part image using edge detection technology; According to each edge point in the edge information, a neighborhood search strategy is adopted to perform a neighborhood search, and each adjacent edge point found is connected using a connection strategy to obtain edge connection information; Contour extraction is performed based on the edge connection information, and the extracted contour is analyzed to obtain shape feature data.

4. The method for dynamically controlling the plant growth environment according to claim 1, wherein: The temperature and humidity prediction model is built based on the Transformer-XL model and trained using the Grey Wolf Optimization Algorithm.

5. A dynamic control system for plant growth environment, characterized in that: The system is applied to the method according to any one of claims 1 to 4, comprising: A data acquisition module is used to acquire image data and environmental data of plant growth, wherein the image data includes plant image data, and the environmental data includes soil temperature and humidity data and meteorological data; a growth recognition module, configured to input the image data into a growth recognition model to obtain the growth stage and growth state of the plant, wherein the growth recognition model includes a plant recognition module, a stage recognition module, and a state recognition module; A first control module is used to spray the plant with medicine and / or supplement light according to the growth stage and the growth state; A temperature and humidity prediction module is used to obtain the optimal soil temperature and humidity of the plant growth environment according to the growth stage using the response surface method, and input the environmental data into the temperature and humidity prediction model to obtain the predicted value of soil temperature and humidity; The second control module is used to compare the predicted soil temperature and humidity value with the optimal soil temperature and humidity value, and to control the soil temperature and humidity of the plant growth environment according to the comparison result.

6. A computer 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 steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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