Camellia oleifera young forest growth monitoring system and method based on deep learning

Abstract

The invention discloses a deep learning-based camellia oleifera young forest growth monitoring system and method, and relates to the technical field of agricultural monitoring, and the method comprises the steps: obtaining image data and ground environment sensing data, and generating a graph data structure which contains camellia oleifera young plant nodes, intercropping crop nodes and corresponding edge relationships and is bound with the camellia oleifera young plant nodes to a current height value according to the image data and the ground environment sensing data; extracting a corresponding ecological modeling function according to the edge relationship, calculating a dynamic edge weight by combining environment sensing data, updating the weight of each edge in the map data structure by using the dynamic edge weight, inputting the updated map structure data into a pre-trained map neural network model, outputting a theoretical height value of each camellia oleifera young plant after intercropping interference is removed at the current time point, and calculating the theoretical height value of each camellia oleifera young plant after intercropping interference is removed. Judging whether the corresponding camellia oleifera young plants grow abnormally or not; the method has the beneficial effects that the resource competition relationship between the intercropping crops and the camellia oleifera young plants can be quantified, the growth abnormity can be accurately identified, and the misjudgment rate can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural monitoring, and in particular to a camellia oleifera young forest growth monitoring system based on deep learning and a method thereof. BACKGROUND

[0002] Camellia oleifera is an important woody oil crop with high economic and ecological benefits. Due to the long growth cycle and slow fast-growing period of camellia oleifera, especially in the young forest stage, the quality of its cultivation and management directly determines the future oil production capacity and forest quality. Therefore, accurate monitoring of camellia oleifera young forest has important practical significance.

[0003] Existing forest growth monitoring technologies mainly rely on unmanned aerial vehicle visible light images, multispectral imaging or ground image acquisition, combined with deep learning algorithms for image segmentation, target recognition and growth trend evaluation. Such technologies can efficiently monitor forest canopy expansion, plant density changes and early symptoms of diseases and pests in a large area, and have the advantages of convenient operation, high information density and strong applicability. However, for camellia oleifera young forest, in order to fully utilize the land, an interplanting cultivation mode is usually implemented, that is, other interplanting crops are planted between camellia oleifera young plants. Under this planting mode, the growth speed of different camellia oleifera young plants is greatly different due to the influence of adjacent interplanting crop types. If only image is used for evaluation, it is difficult to distinguish abnormal individuals from camellia oleifera young plants with different growth speeds, which may lead to the situation that "normal" but actually abnormal young plants are neglected for maintenance, or "abnormal" but actually normal young plants are over-maintained.

[0004] Therefore, a camellia oleifera young forest growth monitoring system and method based on deep learning are proposed. SUMMARY

[0005] In view of the above existing technical conditions, the present application is proposed. The embodiments of the present application provide a camellia oleifera young forest growth monitoring system and method based on deep learning, which can quantify the resource competition relationship between interplanting crops and camellia oleifera young plants, accurately identify growth abnormalities and reduce the misjudgment rate.

[0006] According to an aspect of the present application, a deep learning-based camellia sapling growth monitoring method is provided, comprising: acquiring image data and ground environment sensing data covering a target camellia sapling area; generating a graph data structure corresponding to the target camellia sapling area and current height values of each camellia sapling according to the image data and the environment sensing data, the graph data structure comprising camellia sapling nodes, intercropping crop nodes and corresponding edge relationships, the camellia sapling nodes being bound to the current height values; extracting corresponding ecological modeling functions from a preset relationship-function mapping table according to the edge relationships; calculating dynamic edge weights reflecting resource competition relationships between each camellia sapling and adjacent intercropping crops according to the ecological modeling functions and the environment sensing data; updating the weights of each edge in the graph data structure according to the dynamic edge weights; inputting the updated graph structure data into a pre-trained graph neural network model to output theoretical height values of each camellia sapling in the target camellia sapling area after removing the influence of intercropping interference at a current time point; and determining that the corresponding camellia sapling is growing abnormally if the theoretical height values are less than a preset acceptable height value.

[0007] According to another aspect of the present application, a deep learning-based camellia sapling growth monitoring system is provided, comprising: a data acquisition module for acquiring image data and ground environment sensing data covering a target camellia sapling area; a graph generation module for generating a graph data structure corresponding to the target camellia sapling area and current height values of each camellia sapling according to the image data and the environment sensing data, the graph data structure comprising camellia sapling nodes, intercropping crop nodes and corresponding edge relationships, the camellia sapling nodes being bound to the current height values; a function extraction module for extracting corresponding ecological modeling functions from a preset relationship-function mapping table according to the edge relationships; an edge weight calculation module for calculating dynamic edge weights reflecting resource competition relationships between each camellia sapling and adjacent intercropping crops according to the ecological modeling functions and the environment sensing data; a graph update module for updating the weights of each edge in the graph data structure according to the dynamic edge weights; a growth prediction module for inputting the updated graph structure data into a pre-trained graph neural network model to output theoretical height values of each camellia sapling in the target camellia sapling area after removing the influence of intercropping interference; and a decision module for determining that the corresponding camellia sapling is growing abnormally if the theoretical height values are less than a preset acceptable height value.

[0008] According to another aspect of the present application, an electronic device is provided, comprising a memory for storing computer executable instructions and a processor for executing the computer executable instructions, the computer executable instructions being executed by the processor to implement the steps of the method described above.

[0009] According to another aspect of the present application, a computer storage medium is provided, having stored thereon computer-executable instructions that, when executed by a processor, implement the steps of the method as described above.

[0010] Compared with the prior art, the tea-oil camellia young forest growth monitoring system and method based on deep learning according to the embodiments of the present application can effectively quantify the hidden ecological influence of intercropping crops on tea-oil camellia young trees by constructing a graph data structure containing ecological competition relationships and dynamically updating edge weights, and combining a graph neural network model to predict the theoretical growth height without the interference of intercropping, and has the advantages of accurately identifying growth abnormalities and reducing the misjudgment rate by quantifying the resource competition relationship between intercropping crops and tea-oil camellia young trees. BRIEF DESCRIPTION OF DRAWINGS

[0011] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description of the preferred embodiments of the present application taken in conjunction with the accompanying drawings. The drawings provided herein are for illustrative purposes only and, therefore, are not to be construed as being to scale. The following figures are provided to further illustrate the embodiments of the present application and are not to be construed as limiting the scope of the present application.

[0012] Figure 1 Flow chart of the tea-oil camellia young forest growth monitoring method based on deep learning of the present application.

[0013] Figure 2 Block diagram of the tea-oil camellia young forest growth monitoring system based on deep learning of the present application.

[0014] Figure 3 Block diagram of an electronic device of the present application. DETAILED DESCRIPTION

[0015] Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are merely a part of the embodiments of the present application, but not the whole embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.

[0016] Exemplary method

[0017] Figure 1The illustration shows a deep learning-based method for monitoring the growth of young Camellia oleifera forests according to an embodiment of this application. The method includes: acquiring image data and ground environmental sensor data covering a target young Camellia oleifera forest area; generating a graph data structure corresponding to the target young Camellia oleifera forest area and the current height value of each young Camellia oleifera plant based on the image data and environmental sensor data. The graph data structure includes young Camellia oleifera plant nodes, intercropping crop nodes, and corresponding edge relationships, with each young Camellia oleifera plant node bound to its current height value; extracting the corresponding ecological modeling function from a preset relation-function mapping table based on the edge relationships; calculating dynamic edge weights based on the ecological modeling function and environmental sensor data to reflect the resource competition relationship between each young Camellia oleifera plant and adjacent intercropping crops; updating the weights of each edge in the graph data structure based on the dynamic edge weights; inputting the updated graph structure data into a pre-trained graph neural network model, outputting the theoretical height value of each young Camellia oleifera plant in the target young Camellia oleifera forest area at the current time point after removing the influence of intercropping interference; determining whether the theoretical height value is less than a preset acceptable height value, and if so, determining that the corresponding young Camellia oleifera plant is growing abnormally.

[0018] The image data covering the target camellia oleifera sapling area refers to visible light and multispectral images collected by drones or ground-based camera equipment. Specifically, target detection algorithms can be used to identify the spatial distribution of camellia oleifera saplings and intercropped crops. Ground environmental sensing data refers to real-time parameters obtained through soil moisture sensors, light intensity sensors, temperature and humidity sensors, lidar, etc. Specifically, IoT devices can be used to periodically collect data and transmit it to a cloud server.

[0019] Among them, graph data structure refers to expressing the topological relationship between camellia seedlings and intercropped crops in the form of graph theory.

[0020] Among them, the graph neural network model refers to a deep learning model that learns features based on graph structure data. Specifically, it can use graph attention network or graph convolutional network to predict the theoretical growth state after removing interfering interference by aggregating node features and edge weight information.

[0021] The theoretical height refers to the expected growth height of Camellia oleifera seedlings after excluding the competition effect of intercropping crops. Specifically, it can be obtained through regression analysis of historical growth data and competing factors using a graph neural network model. By eliminating the influence of intercropping crops on all Camellia oleifera seedlings under the intercropping pattern, individuals exhibiting abnormal growth can be accurately identified.

[0022] The core innovation of this application lies in constructing a dynamic graph structure that integrates multi-source data. It quantifies the competitive effect of intercropping on young Camellia oleifera plants through an ecological modeling function, and predicts theoretical growth values ​​after interference removal based on a graph neural network, thereby accurately identifying growth anomalies caused by resource competition. This method overcomes the limitation of traditional image monitoring techniques in distinguishing between natural growth differences and intercropping interference, achieving precise diagnosis of the growth status of young Camellia oleifera forests.

[0023] This application effectively addresses the problem of traditional Camellia oleifera seedling monitoring methods failing to accurately identify abnormally growing individuals under intercropping conditions. By constructing a dynamic graph structure and combining it with a graph neural network model, this method can precisely quantify the complex ecological competition between Camellia oleifera seedlings and intercropped crops, thereby accurately assessing the actual growth status of each seedling. This method not only distinguishes between natural growth differences and growth abnormalities caused by intercropping disturbances but also adapts to variations in different intercropping combinations and planting densities, significantly improving the accuracy and applicability of Camellia oleifera seedling growth monitoring. By promptly identifying truly abnormal plants requiring intervention, this method helps optimize the allocation of maintenance resources, improve seedling survival rates and forest quality, thus laying the foundation for increasing Camellia oleifera yield and ecological benefits.

[0024] In some of the schemes described above in this application, the ecological modeling function includes at least one of the following functions: a water competition function to represent the soil water absorption relationship between Camellia oleifera seedlings and intercropped crops; an allelopathic inhibition diffusion function to characterize the effect of allelopathic substances released by intercropped crops on Camellia oleifera seedlings; and a light shading exponent function to quantify the light attenuation intensity caused by intercropped crops shading Camellia oleifera seedlings.

[0025] Specifically, the water competition function is:

[0026] ;

[0027] in, This represents the water competition coefficient between young camellia oleifera plant i and intercropped crop j. This represents the root system's water absorption capacity per unit time for crop j. This represents the root coverage radius of the intercropped crop j. This represents the horizontal distance between camellia oleifera seedling i and crop j. The preset minimum value is set to avoid division by zero errors.

[0028] Among them, root water absorption capacity The root coverage radius was determined through joint calibration using soil moisture sensors and a root distribution model. The crop parameter is matched and obtained from a pre-set crop parameter library based on the type of intercropped crop. Horizontal distance The minimum value is obtained by calculating the Euclidean distance from the plant location coordinates in the image data. Set to 1e-6 to avoid numerical errors where the denominator is zero. Water competition coefficient and and Positively correlated with The square is inversely proportional, reflecting the combined effect of intercropping crop water absorption capacity, root range, and spatial distance on water competition.

[0029] Specifically, after generating the graph data structure, the competition intensity between each pair of camellia seedlings and adjacent intercropped crops is calculated using a water competition function. The water competition coefficient increases significantly when the intercropped crop has a stronger root system for water absorption, a larger coverage radius, and is closer to the camellia seedlings. This coefficient is used as the dynamic edge weight input to the graph neural network, enabling the model to quantify the water deprivation effect of intercropped crops on camellia seedlings. For example, when corn is used as an intercrop, its well-developed fibrous root system leads to… A higher value results in a larger water competition coefficient at the same distance, thus more accurately correcting the theoretical height prediction during graph neural network inference and avoiding the masking of real growth anomalies due to water competition.

[0030] Specifically, the allelopathic inhibition diffusion function is:

[0031] ;

[0032] in, This indicates the allelopathic inhibition intensity of intercropped crop j on young camellia oleifera plantlets i. This represents the concentration coefficient of allelochemicals released by intercropped crop j per unit time. This represents the horizontal distance between camellia oleifera seedling i and crop j. This represents the diffusion attenuation coefficient of allelochemicals released by intercropped crop j in the soil.

[0033] in, Obtained through laboratory measurements or fitting of historical data, reflecting the potential ability of different intercropped crops to release allelochemicals. Calculations based on plant location coordinates from image data ensure spatial distance accuracy. λ is dynamically adjusted according to soil type, moisture, and microbial activity parameters; for example, it is set to 0.15 in clay and 0.25 in sandy soil to match the actual diffusion rate of allelochemicals in different media. Exponential function. Used to simulate the decay law of allelochemical concentration with distance, when As the concentration increases, the inhibitory effect decreases nonlinearly.

[0034] Specifically, when calculating the allelopathic inhibition intensity between camellia oleifera seedling i and intercropped crop j, the allelopathic substance concentration coefficient of intercropped crop j is first obtained. Combining the horizontal distance between the two And the preset diffusion attenuation coefficient λ, substituted into the exponential function to calculate... For example, when the intercropping crop is soybeans, The value is 2.3, if If the distance is 1.5 meters and λ is 0.2, then... =2.3·exp(-0.2·1.5)=2.3·0.7408≈1.704. This value is used as the dynamic edge weight input to the graph neural network to correct the theoretical height prediction of camellia oleifera seedlings.

[0035] Specifically, the light shading exponential function is:

[0036] ;

[0037] in, This indicates the intensity of the light shading effect of intercropping crop j on young camellia oleifera plants i. This represents the canopy density index of intercropped crop j. This represents the leaf area index per unit of intercropped crop j. This represents the horizontal distance between camellia oleifera seedling i and crop j. This represents the current height difference between the intercropped crop j and the young camellia oleifera plant i. This represents the sensitivity coefficient of vertical height difference to the degree of shading. This represents the light shading weight factor. A very small constant set to avoid division by zero errors.

[0038] Among them, the canopy density index The leaf area index was obtained by analyzing the foliage cover density of intercropped crop j using multispectral image analysis. Horizontal distance is calculated based on leaf morphology characteristics and the number of leaves per unit area. Calculate the Euclidean distance and height difference using image-based coordinate positioning. The vertical height difference between the intercropped crop j and the young camellia oleifera plant i was measured using lidar point cloud data or stereo vision. Sensitivity coefficient. The light shading weighting factor was determined by fitting historical data to the degree of influence of different height differences on light attenuation. Empirical adjustments were made to the light competition characteristics of young Camellia oleifera plants based on the types of intercropped crops, and a minimum constant was obtained. Set it to 1e-6 to avoid the denominator being zero.

[0039] Specifically, the canopy density index With unit leaf area index The product of these values ​​reflects the overall shading capacity of the intercropped crop j. This product, along with the reciprocal of the square of the horizontal distance, constitutes the basic shading intensity term, demonstrating that the shading effect decreases inversely with increasing distance. Height difference The shading intensity is adjusted by an exponential function. When the intercropped crop j is higher than the camellia oleifera seedling i, The value is positive, and the exponential term decreases as the height difference increases, indicating that increasing the height difference can reduce the shading effect; when the camellia oleifera seedling i is higher than the intercropped crop j, When the value is negative, the exponential term increases to enhance the masking effect. Sensitivity coefficient. Controlling the degree of nonlinear influence of height difference on shielding strength, for example when When the value is 0.1, for every 1-meter increase in height difference, the shading intensity decreases by approximately 9.5%. (Light shading weighting factor) Set according to the type of intercropping crop, for example, set for tall crops. =0.8, set for low-growing herbaceous crops =0.3. By inputting the dynamically calculated light shading coefficient into a graph neural network, the impact of intercropping shading on the growth of Camellia oleifera seedlings can be accurately separated, thereby accurately identifying growth abnormalities caused by resource competition.

[0040] Specifically, in the water competition function, root water absorption capacity and coverage radius determine the water absorption efficiency of intercropped crops, while the horizontal distance squared term reflects the influence of spatial distribution on water competition. In the allelopathic inhibition diffusion function, the allelopathic substance concentration coefficient and exponential decay term jointly determine the variation of inhibition intensity with distance. In the light shading exponential function, canopy density and leaf area index characterize the shading capacity of intercropped crops, while horizontal distance and height difference jointly correct for shading intensity. The dynamic edge weights output by these three functions are input into a graph neural network. Through the superposition calculation of multi-source competition relationships, the theoretical height value after removing intercropping interference is accurately predicted. When the theoretical height value is lower than a preset threshold, intercropping interference factors can be excluded, and abnormal growth of camellia oleifera seedlings can be directly determined.

[0041] Through the above technical solution, this application can quantitatively describe the resource competition relationship between Camellia oleifera seedlings and intercropped crops, thereby more accurately assessing the impact of intercropping on the growth of Camellia oleifera seedlings. By considering multiple ecological factors such as water competition, allelopathic inhibition, and light shading, this scheme can comprehensively reflect the growth status of Camellia oleifera seedlings under intercropping conditions. This ecological modeling-based method can improve the accuracy and reliability of monitoring the growth of Camellia oleifera seedlings, helping to promptly identify and resolve abnormal growth problems, thereby improving the cultivation and management quality of Camellia oleifera seedlings.

[0042] In some of the solutions described above in this application, the interaction between camellia seedlings and intercropped crops is reflected through the edge relationships in the graph data structure. However, in real-world scenarios, when the spatial distance between camellia seedlings and intercropped crops is too large, there may not be a substantial resource competition relationship between them. If the corresponding edge relationships are retained, it will lead to invalid computations introduced by the graph neural network model when processing the graph data, thereby affecting the prediction accuracy of the theoretical height value.

[0043] This application further proposes that before extracting the corresponding ecological modeling function, the following steps are also included: calculating the spatial distance between each camellia oleifera sapling and the adjacent intercropping crop based on the position of each camellia oleifera sapling and the intercropping crop in the image data in the target camellia oleifera sapling forest area; determining whether the spatial distance is greater than a preset distance threshold, and if so, deleting the edge relationship between the corresponding camellia oleifera sapling node and the intercropping crop node in the map data structure.

[0044] Specifically, after acquiring image data, the pixel coordinates of the camellia oleifera seedlings and intercropped crops are identified using a target detection algorithm. These coordinates are then converted to actual spatial coordinates, and the horizontal distance between them is calculated. A preset distance threshold is dynamically adjusted based on the type of intercropped crop. For example, for intercropped crops with well-developed root systems, the threshold is set to 1.5 times the root coverage radius; for intercropped crops with dense canopies, the threshold is set to 2 times the canopy projection radius. When the calculated spatial distance exceeds the threshold, it is determined that there is no resource competition relationship between the two crops, and the corresponding edge relationship is automatically removed. This process effectively eliminates redundant connections in the graph data, reduces the processing of invalid relationships when the graph neural network model calculates dynamic edge weights, improves model computational efficiency, and avoids prediction bias caused by retaining invalid edges.

[0045] Through the above technical solutions, this application can effectively reduce computational complexity and improve system operating efficiency. By setting a reasonable distance threshold, distant intercropping crops with little impact on the growth of Camellia oleifera seedlings can be filtered out, allowing ecological modeling to focus more on key influencing factors. Simultaneously, this edge relationship filtering method based on spatial distance also improves the accuracy of the graph data structure, providing a more reliable data foundation for subsequent ecological modeling and growth prediction.

[0046] In some of the solutions described above in this application, since different camellia varieties have different growth rate characteristics in the young forest stage, if the preset acceptable height value is a fixed value, it may be impossible to accurately match the expected growth curve of a specific variety, thus creating a risk of misjudgment.

[0047] This application further proposes a dynamic adjustment of the preset acceptable height value, specifically including: obtaining the Camellia oleifera variety identifier of the target Camellia oleifera sapling area; and extracting the corresponding acceptable height value from the preset variety-time-growth threshold mapping table based on the Camellia oleifera variety identifier as the preset acceptable height value.

[0048] Among these features, camellia oleifera varieties are automatically identified through the morphological characteristics of young plant leaves in image data or through the geographic coding information of the planting area. A variety-time-growth threshold mapping table stores the theoretical height range of different varieties at different growth stages. This table, constructed based on historical observation data, includes the correlation between variety genotypes and phenotypic growth parameters. A dynamic adjustment mechanism is implemented by real-time matching of the current monitoring time point with the corresponding growth stage threshold for the variety.

[0049] Specifically, during the initialization phase, a leaf image classification model is used to identify camellia varieties and generate corresponding variety identification codes. These codes serve as index keys to retrieve acceptable height values ​​matching the current monitoring date from a mapping table. For example, when monitoring variety A in early July, the system automatically extracts the minimum height threshold of 42 cm defined for that variety within the standard July growth cycle. An anomaly alarm is triggered when the theoretical height value output by the graph neural network falls below this threshold. This approach, by introducing dynamic thresholds at the variety level, eliminates misjudgments caused by differences in genetic characteristics, ensuring that the growth anomaly detection standards are strictly aligned with the biological characteristics of the variety.

[0050] Through the above technical solution, this application can dynamically adjust the growth assessment standards according to the growth characteristics of different Camellia oleifera varieties, avoiding misjudgments that may occur when using a uniform standard. At the same time, it considers the impact of time and environmental factors on Camellia oleifera growth, making anomaly detection more accurate. This flexible assessment mechanism helps to promptly identify Camellia oleifera seedlings with abnormal growth, providing a basis for subsequent precise maintenance, thereby improving the overall growth quality of Camellia oleifera seedling forests.

[0051] Exemplary System

[0052] Figure 2 The figure illustrates a deep learning-based monitoring system for the growth of young Camellia oleifera forests according to an embodiment of this application. The system includes: a data acquisition module for acquiring image data and ground environment sensor data covering a target young Camellia oleifera forest area; a map generation module for generating a map data structure corresponding to the target young Camellia oleifera forest area and the current height value of each young Camellia oleifera plant based on the image data and environmental sensor data. The map data structure includes young Camellia oleifera plant nodes, intercropping crop nodes, and corresponding edge relationships, with each young Camellia oleifera plant node bound to its current height value; and a function extraction module for extracting corresponding ecological functions from a preset relation-function mapping table based on the edge relationships. The system comprises the following modules: a modeling function; a boundary weight calculation module, which calculates dynamic boundary weights based on the ecological modeling function and environmental sensor data to reflect the resource competition relationship between each Camellia oleifera sapling and adjacent intercropped crops; a graph update module, which updates the weights of each edge in the graph data structure based on the dynamic boundary weights; a growth prediction module, which inputs the updated graph structure data into a pre-trained graph neural network model and outputs the theoretical height values ​​of each Camellia oleifera sapling in the target Camellia oleifera sapling area after removing the influence of intercropping interference; and a decision module, which determines whether the theoretical height value is less than the preset acceptable height value, and if so, determines that the corresponding Camellia oleifera sapling is growing abnormally.

[0053] In one example, the ecological modeling functions in the function extraction module include at least one of the following functions: a water competition function to represent the soil water absorption relationship between Camellia oleifera seedlings and intercropped crops; an allelopathic inhibition diffusion function to characterize the effect of allelopathic substances released by intercropped crops on Camellia oleifera seedlings; and a light shading exponent function to quantify the light attenuation intensity caused by intercropped crops shading Camellia oleifera seedlings.

[0054] In one example, the water contention function extracted by the function extraction module is as follows:

[0055] ;

[0056] in, This represents the water competition coefficient between young camellia oleifera plant i and intercropped crop j. This represents the root system's water absorption capacity per unit time for crop j. This represents the root coverage radius of the intercropped crop j. This represents the horizontal distance between camellia oleifera seedling i and crop j. The preset minimum value is set to avoid division by zero errors.

[0057] In one example, the allelopathic suppression diffusion function extracted by the function extraction module is as follows:

[0058] ;

[0059] in, This indicates the allelopathic inhibition intensity of intercropped crop j on young camellia oleifera plantlets i. This represents the concentration coefficient of allelochemicals released by intercropped crop j per unit time. This represents the horizontal distance between camellia oleifera seedling i and crop j. This represents the diffusion attenuation coefficient of allelochemicals released by intercropped crop j in the soil.

[0060] In one example, the illumination occlusion exponent function extracted by the function extraction module is as follows:

[0061] ;

[0062] in, This indicates the intensity of the light shading effect of intercropping crop j on young camellia oleifera plants i. This represents the canopy density index of intercropped crop j. This represents the leaf area index per unit of intercropped crop j. This represents the horizontal distance between camellia oleifera seedling i and crop j. This represents the current height difference between the intercropped crop j and the young camellia oleifera plant i. This represents the sensitivity coefficient of vertical height difference to the degree of shading. This represents the light shading weight factor. A very small constant set to avoid division by zero errors.

[0063] In one example, before extracting the corresponding ecological modeling function, the function extraction module also includes: calculating the spatial distance between each camellia oleifera sapling and the intercropped crop in the image data based on the position of each camellia oleifera sapling and the intercropped crop in the target camellia oleifera sapling forest area; determining whether the spatial distance is greater than a preset distance threshold; if so, deleting the edge relationship between the corresponding camellia oleifera sapling node and the intercropped crop node in the graph data structure.

[0064] In one example, the preset acceptable height value in the decision module is dynamically adjusted, specifically including: obtaining the Camellia oleifera variety identifier of the target Camellia oleifera sapling area; and extracting the corresponding acceptable height value from the preset variety-time-growth threshold mapping table based on the Camellia oleifera variety identifier as the preset acceptable height value.

[0065] Exemplary electronic devices

[0066] Figure 3 An electronic device according to an embodiment of this application is illustrated. The electronic device may be the mobile device itself, or a standalone device independent of it, which may communicate with the mobile device to receive collected input signals from it and send selected target driving behaviors to it.

[0067] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0068] like Figure 3 As shown, the electronic device includes one or more processors and memory.

[0069] A processor can be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and can control other components in an electronic device to perform desired functions.

[0070] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and a processor may execute the program instructions to implement the driving behavior decision-making methods of the various embodiments of this application described above, and / or other desired functions.

[0071] In one example, the electronic device may also include input devices and output devices, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0072] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device may include any other suitable components depending on the specific application.

[0073] Exemplary computer-readable media

[0074] Embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the driving behavior decision-making methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0075] Computer-readable storage media may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0076] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0077] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0078] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0079] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0080] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A deep learning-based method for monitoring the growth of young Camellia oleifera forests, characterized in that, include: Acquire image data and ground environment sensor data covering the target Camellia oleifera sapling forest area; Based on the image data and the environmental sensing data, a map data structure corresponding to the target Camellia oleifera sapling area and the current height value of each Camellia oleifera sapling are generated. The map data structure includes Camellia oleifera sapling nodes, intercropping crop nodes and corresponding edge relationships. The Camellia oleifera sapling nodes are bound to the current height value. Based on the edge relationships, the corresponding ecological modeling functions are extracted from the preset relation-function mapping table; The dynamic edge weights, which reflect the resource competition relationship between each camellia oleifera seedling and adjacent intercropped crops, are calculated based on the ecological modeling function and the environmental sensing data. The weights of each edge in the graph data structure are updated according to the dynamic edge weights; The updated graph structure data is input into a pre-trained graph neural network model, which outputs the theoretical height of each Camellia oleifera seedling in the target Camellia oleifera seedling area at the current time point after removing the influence of intercropping interference. Determine whether the theoretical height value is less than the preset acceptable height value. If so, determine that the corresponding camellia oleifera seedling is growing abnormally.

2. The method for monitoring the growth of young Camellia oleifera forests based on deep learning according to claim 1, characterized in that, The ecological modeling function includes at least one of the following functions: A water competition function used to represent the relationship between soil moisture absorption between young camellia oleifera plants and intercropped crops; Allelopathic inhibition diffusion function used to characterize the effect of allelochemicals released by intercropped crops on young Camellia oleifera plants; and The light shading exponential function is used to quantify the light attenuation caused by intercropping crops shading young camellia oleifera plants.

3. The method for monitoring the growth of young Camellia oleifera forests based on deep learning according to claim 2, characterized in that, The water competition function is specifically as follows: ； in, This represents the water competition coefficient between young camellia oleifera plant i and intercropped crop j. This represents the root system's water absorption capacity per unit time for crop j. This represents the root coverage radius of the intercropped crop j. This represents the horizontal distance between camellia oleifera seedling i and crop j. The preset minimum value is set to avoid division by zero errors.

4. The method for monitoring the growth of young Camellia oleifera forests based on deep learning according to claim 2, characterized in that, The allelopathic suppression diffusion function is specifically: ； in, This indicates the allelopathic inhibition intensity of intercropped crop j on young camellia oleifera plantlets i. This represents the concentration coefficient of allelochemicals released by intercropped crop j per unit time. This represents the horizontal distance between camellia oleifera seedling i and crop j. This represents the diffusion attenuation coefficient of allelochemicals released by intercropped crop j in the soil.

5. The method for monitoring the growth of young Camellia oleifera forests based on deep learning according to claim 2, characterized in that, The light shading exponential function is specifically as follows: ； in, This indicates the intensity of the light shading effect of intercropping crop j on young camellia oleifera plants i. This represents the canopy density index of intercropped crop j. This represents the leaf area index per unit of intercropped crop j. This represents the horizontal distance between camellia oleifera seedling i and crop j. This represents the current height difference between the intercropped crop j and the young camellia oleifera plant i. This represents the sensitivity coefficient of vertical height difference to the degree of shading. This represents the light shading weight factor. A very small constant set to avoid division by zero errors.

6. The method for monitoring the growth of young Camellia oleifera forests based on deep learning according to claim 1, characterized in that, Before extracting the corresponding ecological modeling function, the following also applies: Based on the positions of each Camellia oleifera sapling and intercropped crop in the target Camellia oleifera sapling forest area in the image data, calculate the spatial distance between each Camellia oleifera sapling and the adjacent intercropped crop. Determine whether the spatial distance is greater than a preset distance threshold. If so, delete the edge relationship between the corresponding camellia oleifera seedling node and the intercropping node in the graph data structure.

7. The method for monitoring the growth of young Camellia oleifera forests based on deep learning according to claim 1, characterized in that, The preset acceptable height value is dynamically adjusted, specifically including: Obtain identification of Camellia oleifera varieties in the target Camellia oleifera sapling area; Based on the camellia oleifera variety identifier, the corresponding acceptable height value is extracted from the preset variety-time-growth threshold mapping table as the preset acceptable height value.

8. A deep learning-based growth monitoring system for young Camellia oleifera forests, characterized in that, include: The data acquisition module acquires image data and ground environment sensor data covering the target camellia oleifera sapling forest area; The map generation module generates a map data structure corresponding to the target camellia oleifera sapling area and the current height value of each camellia oleifera sapling based on the image data and the environmental sensing data. The map data structure includes camellia oleifera sapling nodes, intercropping crop nodes and corresponding edge relationships. The camellia oleifera sapling nodes are bound to the current height value. The function extraction module extracts the corresponding ecological modeling function from a preset relation-function mapping table based on the edge relationship; The edge weight calculation module calculates dynamic edge weight values ​​based on the ecological modeling function and the environmental sensing data to reflect the resource competition relationship between each camellia oleifera seedling and adjacent intercropped crops. The graph update module updates the weights of each edge in the graph data structure according to the dynamic edge weights; The growth prediction module inputs the updated graph structure data into a pre-trained graph neural network model and outputs the theoretical height values ​​of each Camellia oleifera sapling in the target Camellia oleifera sapling area after removing the influence of intercropping interference. The decision module determines whether the theoretical height value is less than the preset acceptable height value. If so, it determines that the corresponding camellia oleifera seedling is growing abnormally.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method as described in any one of claims 1 to 7.

10. A computer storage medium storing computer-executable instructions thereon, characterized in that: When the computer-executable instructions are executed by a processor, they implement the steps of the method as described in any one of claims 1 to 7.

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