Intelligent plant growth state evaluation and analysis system and method

Abstract

The invention provides an intelligent plant growth state evaluation and analysis system and method, and belongs to the technical field of state evaluation, and the system comprises a position determination module which is used for obtaining the vegetation information and geographic position of a target plant; the state determination module is used for acquiring a climate information data set during the planting period of the target plant, and determining first growth state data of the target plant according to the vegetation information and the climate information data set; the image acquisition module is used for acquiring a growth image of the target plant for image analysis to obtain second growth state data of the target plant; the state evaluation module is used for evaluating the growth state of the target plant according to the first growth state data and the second growth state data, and the release amount determination module is used for determining the pollen release amount of the target plant according to the growth state evaluation result of the target plant and the vegetation information of the target plant. By combining the vegetation information and the geographic position, the accuracy of growth state evaluation can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of growth status assessment, and in particular to an intelligent plant growth status assessment and analysis system and method. Background Art

[0002] Growth status assessment accurately monitors the dynamic relationship between plant physiological characteristics and environmental responses, providing a scientific basis for optimizing plant management. This process can promptly identify growth anomalies and predict potential risks, effectively guiding pest and disease control, nutritional regulation, and environmental adaptation, ensuring healthy plant growth and optimizing resource utilization efficiency.

[0003] The mainstream growth status assessment method in the existing technology mainly relies on the visual analysis of plant morphology, which collects two-dimensional or three-dimensional growth images of plants for contour recognition, color analysis and morphological parameter calculation.

[0004] These methods can only provide qualitative or semi-quantitative assessments of visible characteristics such as plant height, leaf spread, and canopy density, but are unable to penetrate the microscopic level of plant physiological activity and accurately estimate plant growth status. In particular, traditional image analysis techniques struggle to capture the relationship between flower development and pollen production, a key indicator. Summary of the Invention

[0005] The present invention provides an intelligent plant growth status evaluation and analysis system and method, which are used to solve the defect in the prior art that traditional image analysis is difficult to accurately estimate the growth status of plants.

[0006] The present invention provides an intelligent plant growth status assessment and analysis system, comprising: Position determination module: used to obtain vegetation information of the target plant and determine the geographical location of the target plant based on the vegetation information; A state determination module is configured to obtain a climate information dataset during the planting period of the target plant based on the vegetation information and the geographical location, and determine first growth state data of the target plant based on the vegetation information and the climate information dataset; Image acquisition module: used to collect growth images of target plants and perform image analysis on the growth images to obtain second growth status data of the target plants; A state evaluation module is configured to evaluate the growth state of the target plant based on the first growth state data and the second growth state data, and obtain an evaluation result of the growth state of the target plant; The release amount determination module is used to determine the pollen release amount of the target plant according to the growth status evaluation result of the target plant and the vegetation information of the target plant.

[0007] According to the present invention, an intelligent plant growth status evaluation and analysis system, a position determination module, includes: A data separation unit is used to perform data separation processing on vegetation information to obtain the geographical location of the target plant; The vegetation information includes the planting time of the target plant, the planting location of the target plant, the reproductive characteristics of the target plant, and the classification information of the target plant.

[0008] According to the present invention, an intelligent plant growth status evaluation and analysis system is provided, wherein the status determination module includes: An information determination unit, configured to determine a planting time of a target plant based on the vegetation information, and to determine planting period information of the target plant based on the planting time; The data set determining unit is used to determine the climate information data set of the target plant during the planting period based on the planting period information of the target plant and the geographical location of the target plant.

[0009] Preferably, the state determination module further includes: A data extraction unit is used to extract an initial first growth state data set of the target plant from a preset growth database of the target plant according to vegetation information of the target plant; The data adjustment unit is used to adjust the initial first growth status data set in chronological order based on the climate information data set to obtain the first growth status data of the target plant.

[0010] Preferably, the data adjustment unit includes: The climate data sorting block is used to sort the climate data in the climate information dataset in chronological order to obtain a sorted climate information dataset; a time matching block, configured to perform time matching on the initial growth state data in the initial first growth state data set and the climate data in the sorted climate information data set, to obtain climate data matched with each initial growth state data; a growth status data adjustment block for performing a first adjustment on the initial growth status data at each moment based on the growth status data of the target plant at the previous moment to obtain intermediate growth status data, and performing a second adjustment on the intermediate growth status data at the current moment based on the climate data matched with the initial growth status data at the current moment to obtain the growth status data at the current moment; The growth status data determination block is used to sort all the growth status data in chronological order from front to back, and determine the last growth status data as the first growth status data.

[0011] Preferably, the image acquisition module includes: An image stitching unit is used to collect sub-growth images of the target plant at different positions and stitch the sub-growth images to obtain a growth image of the target plant; A feature extraction unit is used to extract features from the growth image to obtain the contour information of the target plant; The data screening unit is used to screen the target plant from the growth database based on the outline information of the target plant to obtain the second growth status data of the target plant.

[0012] Preferably, the status assessment module includes: a first evaluation unit, configured to evaluate the growth status of the target plant according to the first growth status data, and obtain a first growth status evaluation result of the target plant; a second evaluation unit, configured to evaluate the growth status of the target plant according to the second growth status data, and obtain a second growth status evaluation result of the target plant; The data fusion unit is used to perform data fusion on the first growth status evaluation result and the second growth status evaluation result to obtain a growth status evaluation result of the target plant.

[0013] Preferably, the data fusion unit includes: a first feature determination block for determining a plurality of first morphological feature data of the target plant based on the first growth state assessment result; a second feature determination block for determining a plurality of second morphological feature data of the target plant based on the second growth state assessment result; a feature fusion block, configured to perform feature fusion on each first morphological feature data and the corresponding second feature data to obtain a plurality of third morphological feature data of the target plant; a correction parameter determination block for determining a morphological data correction parameter of each third morphological data and virtual third morphological data based on all third morphological feature data; a weight determination block, for determining a weight of each third morphological data based on the classification information of the target plant; a data correction block, configured to perform morphological data correction on each third morphological data according to the morphological data correction parameters of all third morphological data, the virtual third morphological data, and the weight of the third morphological data, to obtain a data correction result for each third morphological data;

[0014] in, For the The data correction result of the third form data, For the The original value of the third form data, For the Correction parameters for the third form data, For the A virtual reference value of the third form data, For the exception of The total number of third form data other than third form data, For the The original value of the third form data, For the Correction parameters for the third form data, For the A virtual reference value of the third form data, For the The weight of the third form data, For the The third form data is The influence coefficient of the third form data; The state determination block is used to determine the growth state evaluation result of the target plant based on the data correction results of all the third morphological data.

[0015] The present invention also provides an intelligent plant growth status assessment and analysis method, comprising: Obtaining vegetation information of the target plant, and determining the geographical location of the target plant based on the vegetation information; Based on the vegetation information and the geographical location, a climate information dataset is obtained during the planting period of the target plant, and first growth state data of the target plant is determined according to the vegetation information and the climate information dataset; Collecting a growth image of the target plant and performing image analysis on the growth image to obtain second growth state data of the target plant; performing a growth status assessment on the target plant according to the first growth status data and the second growth status data to obtain a growth status assessment result of the target plant; The pollen release amount of the target plant is determined according to the growth status evaluation result of the target plant and the vegetation information of the target plant.

[0016] The present invention provides an intelligent plant growth status assessment and analysis system and method. By integrating vegetation information, geographic location, climate data, and multi-dimensional image analysis, this system constructs a multi-source data fusion assessment system for plant growth status. First growth status data is determined based on climate data combined with plant vegetation information. Second growth status data is obtained based on image analysis of plant growth images. A comprehensive assessment is performed combining the first and second growth status data to produce a growth status assessment result. This collaborative analysis of environmental factors and plant phenotypic characteristics significantly improves the objectivity and accuracy of growth status assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a schematic structural diagram of an intelligent plant growth status evaluation and analysis system provided by an embodiment of the present invention; Figure 2 This is a flow chart of an intelligent plant growth status assessment and analysis method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all 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.

[0020] Figure 1 This is a schematic diagram of the system structure of an intelligent plant growth status assessment and analysis system provided by an embodiment of the present invention.

[0021] like Figure 1 As shown, an embodiment of the present invention provides an intelligent plant growth status assessment and analysis system, which mainly includes: Position determination module: used to obtain vegetation information of the target plant and determine the geographical location of the target plant based on the vegetation information; A state determination module is configured to obtain a climate information dataset during the planting period of the target plant based on the vegetation information and the geographical location, and determine first growth state data of the target plant based on the vegetation information and the climate information dataset; Image acquisition module: used to collect growth images of target plants and perform image analysis on the growth images to obtain second growth status data of the target plants; A state evaluation module is configured to evaluate the growth state of the target plant based on the first growth state data and the second growth state data, and obtain an evaluation result of the growth state of the target plant; The release amount determination module is used to determine the pollen release amount of the target plant according to the growth status evaluation result of the target plant and the vegetation information of the target plant.

[0022] In this embodiment, the target plant refers to a specific plant individual or species to be analyzed, including juniper, pine, ginkgo, and other plants.

[0023] In this embodiment, vegetation information refers to attribute data of target plants, including basic information such as planting time, planting location, physiological characteristics, and classification. Physiological characteristics include: variety characteristics, growth cycle, and other characteristics.

[0024] In this embodiment, the geographical location refers to the specific geographical coordinate information of the plant determined by the planting position in the vegetation information.

[0025] In this embodiment, the climate information dataset is a set of climate parameters during the planting period obtained based on geographical location and time period, including environmental data such as temperature, humidity, light, and precipitation.

[0026] In this embodiment, the first growth status data is a theoretical plant growth index calculated by combining vegetation information and climate data. The second growth status data is a quantitative growth index obtained by extracting features from the growth image. Feature extraction includes extraction of features such as morphological structure, color change, and signs of disease.

[0027] In this embodiment, the growth image is a multi-angle, current image data of the plant captured by photography or sensors, and is used for visual analysis.

[0028] In this embodiment, the growth status assessment result refers to the fusion result of the plant theoretical growth index (first growth status data) and the plant quantitative growth index (second growth status data), which is used to determine the actual growth status of the target plant.

[0029] In this embodiment, image analysis refers to the process of digitally processing multi-directional growth images of target plants captured using computer vision technology to analyze their morphological characteristics. Specifically, it includes steps such as stitching complete plant images, extracting visual features such as contours, leaf spread, and canopy density, and combining color analysis to identify leaf color changes or diseased areas. Image analysis can convert image information into quantifiable growth indicators, such as plant height growth rate, leaf area index, or disease coverage, providing real-time phenotypic data support for comprehensive evaluation.

[0030] In this embodiment, the pollen release amount refers to the total amount of pollen released by a plant during a specific growth stage, and is closely related to reproductive capacity and environmental influences.

[0031] By integrating vegetation information, geographic location, climate data, and multi-dimensional image analysis, a multi-source data fusion assessment system for plant growth status was constructed. First growth status data was determined based on climate data combined with plant vegetation information. Second growth status data was obtained based on image analysis of plant growth images. A comprehensive assessment was performed combining the first and second growth status data to obtain a growth status assessment result. The collaborative analysis of environmental factors and plant phenotypic characteristics significantly improved the objectivity and accuracy of growth status assessments, thereby scientifically inferring pollen release levels and improving the accuracy of pollen release measurements.

[0032] An embodiment of the present invention provides an intelligent plant growth status assessment system, including a position determination module, comprising: A data separation unit is used to perform data separation processing on vegetation information to obtain the geographical location of the target plant; The vegetation information includes the planting time of the target plant, the planting location of the target plant, the reproductive characteristics of the target plant, and the classification information of the target plant.

[0033] In this embodiment, data separation involves parsing the structured data within the vegetation information to extract independent geographic location parameters, which are used to clarify the spatial distribution of target plants. This process separates the planting locations from the composite vegetation data and converts them into recognizable geographic coordinates, providing a spatial reference for subsequent environmental data analysis.

[0034] By modularizing the planting time, geographical location, reproductive characteristics and classification information, the clear separation and precise calling of data dimensions are achieved, and the geographical location can be obtained, providing a basis for subsequent environmental adaptation analysis.

[0035] The embodiment of the present invention provides an intelligent plant growth status assessment system, including a status determination module, comprising: An information determination unit, configured to determine a planting time of a target plant based on the vegetation information, and to determine planting period information of the target plant based on the planting time; The data set determining unit is used to determine the climate information data set of the target plant during the planting period based on the planting period information of the target plant and the geographical location of the target plant.

[0036] In this embodiment, the planting time refers to the specific time point when the target plant is planted, which is usually recorded in the form of a date or a timestamp.

[0037] In this embodiment, the planting period information is plant growth cycle information determined according to the planting time, covering the time range from the start of planting to the current state, and may include stage definitions such as the germination period, the growth period, and the maturity period.

[0038] In this embodiment, the climate information dataset refers to a set of environmental parameters during plant growth obtained based on geographic location and planting period information, including meteorological data such as temperature, humidity, light intensity, and precipitation.

[0039] Vegetation information is used to extract the planting time of target plants and classify their growth stages accordingly. The geographical location of the plants is then combined with their location to locate the area. Finally, a climate information dataset reflecting environmental conditions is generated based on historical or real-time climate records for that area during the planting period. This process links plant attributes, spatiotemporal dimensions, and meteorological parameters, providing environmental data support for subsequent growth status analysis.

[0040] The embodiment of the present invention provides an intelligent plant growth status assessment system, wherein the status determination module further includes: A data extraction unit is used to extract an initial first growth state data set of the target plant from a preset growth database of the target plant according to vegetation information of the target plant; The data adjustment unit is used to adjust the initial first growth status data set in chronological order based on the climate information data set to obtain the first growth status data of the target plant.

[0041] In this embodiment, the preset growth database of target plants is a pre-established standardized database that stores theoretical growth parameters and historical model data of different plants under ideal environments.

[0042] In this embodiment, the initial first growth status data set is a set of theoretical growth indicators extracted from a preset growth database of target plants according to vegetation information matching, reflecting the expected growth trajectory of the plant under standard conditions.

[0043] In this embodiment, the time sequence refers to sorting the data from shortest to longest planting time to ensure the temporal consistency of the growth status and the climatic conditions.

[0044] In this embodiment, data adjustment is a process of dynamically revising theoretical growth parameters by matching climate data with the initial data set in time to adapt them to actual environmental changes, thereby ultimately generating first growth state data.

[0045] In this embodiment, the first growth status data refers to a quantitative indicator of plant growth status after integrating climate influence.

[0046] By dynamically integrating theoretical growth models with real-time environmental parameters, the accuracy and adaptability of plant growth status assessments have been significantly improved. By invoking a standardized growth database based on vegetation information, baseline data can be quickly obtained, ensuring the scientific and efficient nature of the assessments. Dynamic calibration of theoretical data along a timeline, combined with climate datasets, effectively captures the phased impacts of environmental fluctuations on plants, enhancing the real-time nature of the data and its relevance to reality.

[0047] An embodiment of the present invention provides an intelligent plant growth status assessment system, a data adjustment unit, including: The climate data sorting block is used to sort the climate data in the climate information dataset in chronological order to obtain a sorted climate information dataset; a time matching block, configured to perform time matching on the initial growth state data in the initial first growth state data set and the climate data in the sorted climate information data set, to obtain climate data matched with each initial growth state data; a growth status data adjustment block for performing a first adjustment on the initial growth status data at each moment based on the growth status data of the target plant at the previous moment to obtain intermediate growth status data, and performing a second adjustment on the intermediate growth status data at the current moment based on the climate data matched with the initial growth status data at the current moment to obtain the growth status data at the current moment; The growth status data determination block is used to sort all the growth status data in chronological order from front to back, and determine the last growth status data as the first growth status data.

[0048] In this embodiment, the sorted climate information data set refers to a climate data sequence rearranged in the order of planting time.

[0049] In this embodiment, the initial growth status data refers to the expected growth index of the plant at a certain time point in the theoretical data set.

[0050] In this embodiment, time matching is a process of associating the time nodes of theoretical growth data with the climate data of the corresponding time period.

[0051] In this embodiment, the intermediate growth state data is a preliminary correction result of the current theoretical value, ie, the initial growth state data, based on the actual growth data at the previous moment. The first adjustment is the process of adjusting the initial growth state to the intermediate growth state.

[0052] In this embodiment, the second adjustment is a process of secondary correction of the intermediate data based on the currently matched climate data, such as the impact of extreme weather.

[0053] In this embodiment, the growth status data refers to the quantitative index of the plant growth status at the current moment obtained after two adjustments.

[0054] In this embodiment, the first growth status data is the plant growth status data finally output, which is determined by the correction result at the end of the time series.

[0055] By arranging climate data in chronological order and matching it with theoretical growth data, the system dynamically refines the model in two steps: first, adjusting the current theoretical value based on the continuity of historical growth conditions, and then performing a secondary optimization based on real-time climate conditions. Finally, all revisions are integrated along a timeline, with the final data used as the basis for a comprehensive assessment of the current growth status. This approach dynamically couples environmental factors with plant growth models, improving the temporal consistency and environmental adaptability of status assessments.

[0056] The embodiment of the present invention provides an intelligent plant growth status assessment system, an image acquisition module, including: An image stitching unit is used to collect sub-growth images of the target plant at different positions and stitch the sub-growth images to obtain a growth image of the target plant; A feature extraction unit is used to extract features from the growth image to obtain the contour information of the target plant; The data screening unit is used to screen the target plant from the growth database based on the outline information of the target plant to obtain the second growth status data of the target plant.

[0057] In this embodiment, the sub-growth images in different orientations refer to local images of the target plant taken from multiple angles (such as the front, side, and top view), which are used to cover the overall morphology of the plant from multiple perspectives.

[0058] In this embodiment, image stitching is a process of registering and fusing the multi-angle sub-images through a computer vision algorithm to generate a two-dimensional or three-dimensional panoramic view of the complete plant.

[0059] In this embodiment, the growth image is a complete plant image formed after splicing processing, including the overall morphology and detailed features of the plant.

[0060] In this embodiment, feature extraction refers to the process of parsing the geometric parameters of the plant's external contour from the growth image using image processing techniques such as edge detection and semantic segmentation. The geometric parameters include plant height, crown width, and leaf distribution.

[0061] In this embodiment, the contour information is a digital feature that quantitatively describes the edge of the plant morphology, including contour length, curvature change, and key part dimensions.

[0062] In this embodiment, the growth database is a preset standardized database that stores typical morphological parameters of different plants at different growth stages and their corresponding growth status indicators.

[0063] In this embodiment, screening is a process of matching the extracted contour information with historical data of similar plants in a database to screen out growth state reference data that is closest to the current morphological characteristics.

[0064] In this embodiment, the second growth status data is a quantitative index of plant growth obtained through image analysis and database matching, and is used to reflect the actual phenotypic characteristics of the plant, including biomass and developmental stage.

[0065] By capturing local plant images from multiple angles, the complete growth image is generated through stitching, and the contour features are extracted. This contour information is then compared and screened with a preset database, ultimately outputting quantitative data on plant growth status based on visual analysis, providing strong support for subsequent data fusion.

[0066] The embodiment of the present invention provides an intelligent plant growth status assessment system, the status assessment module including: a first evaluation unit, configured to evaluate the growth status of the target plant according to the first growth status data, and obtain a first growth status evaluation result of the target plant; a second evaluation unit, configured to evaluate the growth status of the target plant according to the second growth status data, and obtain a second growth status evaluation result of the target plant; The data fusion unit is used to perform data fusion on the first growth status evaluation result and the second growth status evaluation result to obtain a growth status evaluation result of the target plant.

[0067] In this embodiment, the first growth status evaluation result is obtained by quantitatively evaluating the climate adaptability and growth trend through a theoretical model.

[0068] In this embodiment, the second growth status evaluation result is an evaluation result obtained by objectively evaluating the actual growth status of the plant through visual analysis.

[0069] In this embodiment, data fusion is the process of collaboratively analyzing theoretical predictions and phenotypic observations. By eliminating single-source data biases (such as climate model errors or image noise), a final evaluation result that comprehensively reflects the plant's physiological state and environmental response can be generated.

[0070] Through a phased assessment and data fusion mechanism, accurate, multi-dimensional assessment of plant growth status is achieved. Its core advantage lies in combining theoretical model assessment based on environmental factors with phenotypic assessment based on image analysis. This approach fully considers the dynamic impact of climatic conditions on growth while objectively reflecting the actual morphological changes of plants. By integrating these two types of data, the limitations of a single data source are effectively overcome.

[0071] The embodiment of the present invention provides an intelligent plant growth status assessment system, a data fusion unit, including: a first feature determination block for determining a plurality of first morphological feature data of the target plant based on the first growth state assessment result; a second feature determination block for determining a plurality of second morphological feature data of the target plant based on the second growth state assessment result; a feature fusion block, configured to perform feature fusion on each first morphological feature data and the corresponding second feature data to obtain a plurality of third morphological feature data of the target plant; a correction parameter determination block for determining a morphological data correction parameter of each third morphological data and virtual third morphological data based on all third morphological feature data; a weight determination block, for determining a weight of each third morphological data based on the classification information of the target plant; a data correction block, configured to perform morphological data correction on each third morphological data according to the morphological data correction parameters of all third morphological data, the virtual third morphological data, and the weight of the third morphological data, to obtain a data correction result for each third morphological data;

[0072] in, For the The data correction result of the third form data, For the The original value of the third form data, For the Correction parameters for the third form data, For the A virtual reference value of the third form data, For the exception of The total number of third form data other than third form data, For the The original value of the third form data, For the Correction parameters for the third form data, For the A virtual reference value of the third form data, For the The weight of the third form data, For the The third form data is The influence coefficient of the third form data; The state determination block is used to determine the growth state evaluation result of the target plant based on the data correction results of all the third morphological data.

[0073] In this embodiment, the first morphological characteristic data is a quantitative index of plant morphology extracted from the first growth state assessment result, reflecting theoretical growth characteristics based on climate and vegetation information, such as theoretical predicted values of plant height and leaf area.

[0074] In this embodiment, the second morphological characteristic data refers to the quantitative index of plant morphology extracted from the second growth state assessment result, which is based on the actual phenotypic characteristics of the image analysis, such as the measured values of actual plant height and canopy density.

[0075] In this embodiment, the third morphological feature data is a comprehensive feature generated by fusing the first and second morphological feature data, and includes collaborative information of theoretical predictions and actual observations.

[0076] In this embodiment, the morphological data correction parameters are used to calibrate the coefficients of the third morphological feature data to eliminate the deviation of the single-source data.

[0077] In this embodiment, the virtual third form data refers to preset reference form feature data, which can be used as a reference value in the correction process.

[0078] In this embodiment, the weight refers to a proportional coefficient assigned to the importance of the third morphological characteristic data according to different plant classifications, reflecting the priority of the influence of plant types on the characteristics.

[0079] In this embodiment, the data correction result is the final morphological feature data after parameter calibration, weight allocation and benchmark reference adjustment.

[0080] In this embodiment, morphological data correction is a process of dynamically calibrating plant morphological characteristics by fusing the differences between the theoretical model (first morphological feature data) and actual observations (second morphological feature data), combining correction parameters, virtual reference values and classification weights.

[0081] In this embodiment, the growth status assessment results can comprehensively reflect the physiological health, developmental stage and environmental adaptability of the plant, provide a quantitative basis for precise management, and ensure that the assessment results have both theoretical rationality and actual phenotypic authenticity.

[0082] Plant morphological data is extracted from theoretical models and image analysis, and then fused to generate comprehensive third-party morphological data. Based on all fused features, the system calculates correction parameters and virtual reference values. Weights are assigned to different features based on plant classification, and each feature is calibrated and optimized to generate a corrected morphological data set. By integrating the advantages of theoretical predictions and actual observations, single-source errors are eliminated, and a comprehensive and objective growth status assessment is ultimately derived based on the corrected results.

[0083] like Figure 2 As shown, an embodiment of the present invention provides an intelligent plant growth status assessment and analysis method, which mainly includes: Step 1: Obtain vegetation information of the target plant and determine the geographical location of the target plant based on the vegetation information; Step 2: Based on the vegetation information and the geographical location, a climate information dataset during the planting period of the target plant is obtained, and first growth state data of the target plant is determined based on the vegetation information and the climate information dataset; Step 3: collecting a growth image of the target plant and performing image analysis on the growth image to obtain second growth state data of the target plant; Step 4: Evaluate the growth status of the target plant based on the first growth status data and the second growth status data to obtain a growth status evaluation result of the target plant; Step 5: Determine the pollen release amount of the target plant based on the growth status evaluation result of the target plant and the vegetation information of the target plant.

[0084] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0085] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent plant growth status assessment and analysis system, characterized in that: include: Position determination module: used to obtain vegetation information of a target plant and determine the geographical location of the target plant based on the vegetation information; a state determination module configured to obtain a climate information dataset during the planting period of the target plant based on the vegetation information and the geographical location, and determine first growth state data of the target plant according to the vegetation information and the climate information dataset; Image acquisition module: used for acquiring a growth image of the target plant and performing image analysis on the growth image to obtain second growth status data of the target plant; A state evaluation module is configured to evaluate the growth state of the target plant according to the first growth state data and the second growth state data, and obtain an evaluation result of the growth state of the target plant; The release amount determination module is used to determine the pollen release amount of the target plant according to the growth status evaluation result of the target plant and the vegetation information of the target plant.

2. The intelligent plant growth status evaluation and analysis system according to claim 1, characterized in that: Position determination module, including: a data separation unit, configured to perform data separation processing on the vegetation information to obtain the geographical location of the target plant; The vegetation information includes the planting time of the target plant, the planting location of the target plant, the reproductive characteristics of the target plant, and the classification information of the target plant.

3. The intelligent plant growth status evaluation and analysis system according to claim 1, characterized in that: A status determination module includes: an information determining unit, configured to determine a planting time of the target plant according to the vegetation information, and determine planting period information of the target plant according to the planting time; The data set determining unit is configured to determine a climate information data set of the target plant during the planting period based on the planting period information of the target plant and the geographical location of the target plant.

4. The intelligent plant growth status evaluation and analysis system according to claim 1, characterized in that: The state determination module further includes: a data extraction unit, configured to extract an initial first growth state data set of the target plant from a preset growth database of the target plant according to the vegetation information of the target plant; The data adjustment unit is configured to adjust the initial first growth status data set in chronological order based on the climate information data set to obtain the first growth status data of the target plant.

5. The intelligent plant growth status evaluation and analysis system according to claim 4, characterized in that: A data adjustment unit, comprising: A climate data sorting block is used to sort the climate data in the climate information dataset in chronological order to obtain a sorted climate information dataset; a time matching block, configured to perform time matching on the initial growth state data in the initial first growth state data set and the climate data in the sorted climate information data set, to obtain climate data matched with each initial growth state data; a growth status data adjustment block for performing a first adjustment on the initial growth status data at each moment based on the growth status data of the target plant at the previous moment to obtain intermediate growth status data, and performing a second adjustment on the intermediate growth status data at the current moment based on the climate data matched with the initial growth status data at the current moment to obtain the growth status data at the current moment; The growth status data determination block is used to sort all the growth status data in chronological order from front to back, and determine the last growth status data as the first growth status data.

6. The intelligent plant growth status evaluation and analysis system according to claim 1, characterized in that: Image acquisition module, including: an image stitching unit, configured to collect sub-growth images of the target plant at different positions, and stitch the sub-growth images to obtain a growth image of the target plant; a feature extraction unit, configured to extract features from the growth image to obtain contour information of the target plant; The data screening unit is used to screen the target plant from the growth database based on the outline information of the target plant to obtain the second growth status data of the target plant.

7. The intelligent plant growth status evaluation and analysis system according to claim 1, characterized in that: Condition assessment module, including: a first evaluation unit, configured to evaluate the growth status of the target plant according to the first growth status data, and obtain a first growth status evaluation result of the target plant; a second evaluation unit, configured to evaluate the growth status of the target plant according to the second growth status data, and obtain a second growth status evaluation result of the target plant; A data fusion unit is used to perform data fusion on the first growth status evaluation result and the second growth status evaluation result to obtain the growth status evaluation result of the target plant.

8. The intelligent plant growth status evaluation and analysis system according to claim 7, characterized in that: Data fusion unit, including: a first feature determination block, configured to determine a plurality of first morphological feature data of the target plant based on the first growth state assessment result; a second feature determination block, configured to determine a plurality of second morphological feature data of the target plant based on the second growth state assessment result; a feature fusion block, configured to perform feature fusion on each first morphological feature data and the corresponding second feature data to obtain a plurality of third morphological feature data of the target plant; a correction parameter determination block for determining a morphological data correction parameter of each third morphological data and virtual third morphological data based on all third morphological feature data; a weight determination block, configured to determine a weight of each third morphological data based on the classification information of the target plant; a data correction block, configured to perform morphological data correction on each third morphological data according to the morphological data correction parameters of all third morphological data, the virtual third morphological data, and the weight of the third morphological data, to obtain a data correction result for each third morphological data; in, For the The data correction result of the third form data, For the The original value of the third form data, For the Correction parameters for the third form data, For the A virtual reference value of the third form data, For the exception of The total number of third form data other than third form data, For the The original value of the third form data, For the Correction parameters for the third form data, For the A virtual reference value of the third form data, For the The weight of the third form data, For the The third form data is The influence coefficient of the third form data; The state determination block is used to determine the growth state evaluation result of the target plant based on the data correction results of all the third morphological data.

9. An intelligent plant growth status assessment and analysis method, characterized in that: include: Acquiring vegetation information of a target plant, and determining a geographical location of the target plant based on the vegetation information; Based on the vegetation information and the geographical location, obtaining a climate information dataset during the planting period of the target plant, and determining first growth status data of the target plant according to the vegetation information and the climate information dataset; collecting a growth image of the target plant and performing image analysis on the growth image to obtain second growth status data of the target plant; performing a growth status assessment on the target plant according to the first growth status data and the second growth status data to obtain a growth status assessment result of the target plant; The pollen release amount of the target plant is determined according to the growth status evaluation result of the target plant and the vegetation information of the target plant.

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