Intelligent analysis method for forestry seedling raising environment

Abstract

The invention belongs to the field of forestry seedling raising, relates to an environment regulation and control analysis technology, and aims to solve the problem that environmental parameters of seedlings of different varieties and different growth stages cannot be directly adjusted to an optimal range in the prior art, in particular to an intelligent forestry seedling raising environment analysis method. Marking a planting area of the forestry seedlings as an analysis area, and obtaining an environmental parameter i of the analysis area and a standard range i corresponding to the environmental parameter i; generating an environment cultivation and optimization test time period with fixed duration, setting a plurality of monitoring time points in the environment cultivation and optimization test time period, and adjusting the environment parameter i of the analysis area at the monitoring time points; according to the method, the growth monitoring values in the analysis area can be regularly acquired in the cultivation and optimization observation period, so that statistics is performed on the most suitable environmental parameter range of different varieties of forestry seedlings in each growth stage, and data support is provided for constructing an environment regulation and control model.

Description

Technical Field

[0001] The invention belongs to the field of forestry seedling cultivation, relates to an environmental regulation and analysis technology, and specifically is an intelligent analysis method for a forestry seedling cultivation environment. Background Art

[0002] Forestry science and technology has ushered in rapid development, especially in seedling and afforestation technology, where significant progress has been made. Through precise seedling cultivation, intelligent management and eco-friendly afforestation methods, not only the survival rate and growth quality of seedlings have been improved, but it has also played an important role in ecological environmental protection and sustainable development.

[0003] The intelligent analysis method of forestry seedling environment in the existing technology can only automatically adjust the forestry seedling environment parameters and alarm for environmental abnormalities. However, how to adjust the environmental parameters of seedlings of different varieties and different growth stages to the optimal range to ensure the full-cycle growth rate of the seedlings is a technical problem that needs to be urgently solved in this field.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a method for intelligent analysis of forestry seedling environment, which is used to solve the problem that the existing technology cannot directly adjust the environmental parameters of seedlings of different varieties and different growth stages to the optimal range;

[0006] The technical problem to be solved by the present invention is: how to provide an intelligent analysis method for forestry seedling environment that can directly adjust the environmental parameters of seedlings of different varieties and different growth stages to the optimal range.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for intelligent analysis of forestry seedling raising environment comprises the following steps:

[0009] Step 1: After the forestry seedlings are planted or transplanted, the planting area of ​​the forestry seedlings is marked as an analysis area, and the environmental parameter i of the analysis area and the standard range i corresponding to the environmental parameter i are obtained, where i=1, 2, ..., n, and n is a positive integer;

[0010] Step 2: Generate an environment optimization test period with a fixed duration, set a number of monitoring time points within the environment optimization test period, and adjust the environmental parameter i of the analysis area at the monitoring time points;

[0011] Step 3: Monitor and analyze the growth status of forestry seedlings in the analysis area at the monitoring time point: obtain the height data GD, leaf green data YL and leaf data YP of the analysis area and perform numerical calculations to obtain the growth coefficient SZ of the analysis area at the monitoring time point;

[0012] Step 4: At the end of the environmental optimization test period, perform environmental optimization analysis and obtain the optimization range i of environmental parameter i;

[0013] Step 5: Generate an optimization observation period of indefinite duration after the optimization test period. At the end of the optimization observation period, determine whether the number of executions of the optimization test period is not less than L3: if so, execute step 6; if not, execute step 2 again;

[0014] Step 6: Obtain the intelligent control parameters corresponding to the types of forestry seedlings; send the types of forestry seedlings and the intelligent control parameters to the database for storage.

[0015] Furthermore, in step 2, the specific process of adjusting the environmental parameter i of the analysis area at the monitoring time point includes: generating a standard value i corresponding to the environmental parameter i for the analysis area at the monitoring time point, the standard value i being a value randomly selected from the standard range i; and setting the value of the environmental parameter i of the analysis area according to the standard value i.

[0016] Furthermore, in step three, the process of obtaining the height data GD includes: obtaining the growth height values ​​of all forestry seedlings in the analysis area and summing and averaging them to obtain the height data GD; the process of obtaining the chlorophyll data YL includes: randomly selecting leaves on a number of forestry seedlings as monitoring objects, using a spectral method to obtain the chlorophyll content of the monitoring objects and summing and averaging the chlorophyll content of all monitoring objects to obtain the chlorophyll data YL; the process of obtaining the leaf data YP includes: taking images of the forestry seedlings in the analysis area and marking the captured images as analysis images, enlarging the analysis images into pixel grid images and performing grayscale transformation, and then using a binary method to perform contour segmentation on the complete leaves in the analysis images, calculating the average area values ​​of all complete leaves and marking them as leaf data YP.

[0017] Furthermore, in step four, the process of obtaining the optimized training range i of environmental parameter i includes: marking the difference between the growth coefficient SZ at the monitoring time point and the growth coefficient SZ at the previous monitoring time point as the growth rate value at the current monitoring time point; marking the standard value i at the previous monitoring time point as the evaluation value i at the current monitoring time point; marking the L1 monitoring time points with the largest growth rate values ​​as optimized training time points, and the maximum and minimum values ​​of the evaluation values ​​i of all the optimized training time points constitute the optimized training range i of environmental parameter i.

[0018] Furthermore, in step five, at the beginning of the training observation period, an advanced training value i is generated for the analysis area, and the advanced training value i is a value randomly selected from the advanced training range i; after the start of the advanced training observation period, the growth coefficient SZ of the analysis area is obtained every L2 days, and the difference between the growth coefficient SZ and the growth coefficient SZ obtained last time is marked as a growth monitoring value, and the growth monitoring value is compared with a preset growth monitoring threshold: if the growth monitoring value is greater than or equal to the growth monitoring threshold, the advanced training observation period continues to execute; if the growth monitoring value is less than the growth monitoring threshold, the advanced training observation period ends.

[0019] Furthermore, in step six, the process of obtaining the intelligent control parameters of the corresponding types of forestry seedlings includes: marking the growth coefficient SZ corresponding to the end of the optimization observation period as the stage marking value of the optimization observation period, and all the stage marking values ​​constitute the stage marking parameters of the corresponding types of forestry seedlings; marking the optimization value i set in the optimization observation period as the environmental priority parameter of the optimization observation period; and the stage marking parameters and the environmental priority parameters constitute the intelligent control parameters of the corresponding types of forestry seedlings.

[0020] Furthermore, the method further comprises the following steps:

[0021] Step P1: Generate an environmental control model based on all types of forestry seedlings in the database and corresponding intelligent control parameters;

[0022] Step P2: Use the environmental control model to control and analyze the cultivation environment of forestry seedlings: obtain the type of forestry seedlings and call the corresponding intelligent control parameters; set the environmental parameters i of the forestry seedlings according to the first group of cultivation values ​​i;

[0023] Step P3: Monitor the overall growth coefficient SZ of forestry seedlings in real time. When the growth coefficient SZ reaches the first stage marking value, set the growth environment parameter i of the forestry seedlings according to the second set of optimization values ​​i, and so on, until the overall growth coefficient SZ of forestry seedlings reaches the last stage marking value.

[0024] The present invention has the following beneficial effects:

[0025] 1. During the environmental optimization test period, the environmental parameters are set by generating standard values, and then the growth parameters of the forestry seedlings in the analysis area are obtained and the growth coefficients are calculated numerically. The growth rate of the forestry seedlings in the analysis area is evaluated by the growth coefficients;

[0026] 2. During the optimization observation period, the growth monitoring values ​​in the analysis area are regularly obtained, and then the standard parameters of the forestry seedling growth environment are cyclically obtained according to the number of executions of the environmental optimization test period, so that the most suitable environmental parameter ranges for different varieties of forestry seedlings at each growth stage can be statistically analyzed to provide data support for the construction of the environmental regulation model;

[0027] 3. Through the environmental control model, the most suitable set value range of environmental parameters can be allocated to forestry seedlings of different varieties and growth stages, so as to ensure the growth state and growth rate of forestry seedlings throughout the entire growth cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0030] Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Embodiment 1: Figure 1 As shown, a method for intelligent analysis of forestry seedling environment comprises the following steps:

[0033] Step 1: After the forestry seedlings are planted or transplanted, the planting area of ​​the forestry seedlings is marked as the analysis area, and the environmental parameter i of the analysis area and the standard range i corresponding to the environmental parameter i are obtained, i=1, 2, ..., n, and n is a positive integer; illustratively, the types of forestry seedlings include oak, pine, cypress, azalea, crape myrtle, and Chinese ash, etc.; the environmental parameters of forestry seedlings generally include parameters such as temperature, humidity, light intensity, and CO2 concentration. If the type of forestry seedling is oak and the environmental parameter is temperature, the standard range (15-25) can be obtained based on the suitable growth temperature of oak being 15-25 degrees Celsius;

[0034] Step 2: Generate an environmental optimization test period with a fixed duration, set a number of monitoring time points within the environmental optimization test period, generate a standard value i corresponding to the environmental parameter i for the analysis area at the monitoring time point, and the standard value i is a value randomly selected from the standard range i; set the environmental parameter i of the analysis area according to the standard value i; set the environmental parameter by generating the standard value within the environmental optimization test period, and then obtain various growth parameters of the forestry seedlings in the analysis area and perform numerical calculations to obtain the growth coefficient, and evaluate the growth rate of the forestry seedlings in the analysis area through the growth coefficient;

[0035] Step 3: Monitor and analyze the growth status of forestry seedlings in the analysis area at the monitoring time point: obtain the height data GD, chlorophyll data YL and leaf data YP of the analysis area; the process of obtaining the height data GD includes: obtaining the growth height values ​​of all forestry seedlings in the analysis area and summing and averaging to obtain the height data GD; the process of obtaining the chlorophyll data YL includes: randomly selecting leaves on a number of forestry seedlings as monitoring objects, using the spectral method to obtain the chlorophyll content of the monitoring objects and summing and averaging the chlorophyll content of all monitoring objects to obtain the chlorophyll data YL; the process of obtaining the leaf data YP includes: taking images of the forestry seedlings in the analysis area and marking the images taken as analysis images, enlarging the analysis images into pixel grid images and performing grayscale transformation, and then using the binary method to perform contour segmentation on the complete leaves in the analysis images, calculating the average area values ​​of all complete leaves and marking them as leaf data YP; the growth coefficient SZ of the analysis area at the monitoring time point is obtained by the formula SZ=k1*GD+k2*YL+k3*YP;

[0036] Step 4: Conduct environmental optimization analysis at the end of the environmental optimization test period: mark the difference between the growth coefficient SZ at the monitoring time point and the growth coefficient SZ at the previous monitoring time point as the growth rate value at the current monitoring time point; mark the standard value i at the previous monitoring time point as the evaluation value i at the current monitoring time point; mark the L1 monitoring time points with the largest growth rate values ​​as optimization time points, and the maximum and minimum values ​​of the evaluation values ​​i of all optimization time points constitute the optimization range i of the environmental parameter i; regularly obtain the growth monitoring values ​​in the analysis area during the optimization observation period, and then cyclically obtain the standard parameters of the forestry seedling growth environment according to the number of executions of the environmental optimization test period, so as to make statistics on the most suitable environmental parameter ranges for different varieties of forestry seedlings at each growth stage, and provide data support for building an environmental regulation model;

[0037] Step 5: Generate an optimization observation period of indefinite duration after the environment optimization test period, and generate an optimization value i for the analysis area at the beginning of the optimization observation period, where the optimization value i is a value randomly selected from the optimization range i; after the optimization observation period starts, obtain the growth coefficient SZ of the analysis area every L2 days, and mark the difference between the growth coefficient SZ and the growth coefficient SZ obtained last time as the growth monitoring value, and compare the growth monitoring value with the preset growth monitoring threshold: if the growth monitoring value is greater than or equal to the growth monitoring threshold, the optimization observation period continues to execute; if the growth monitoring value is less than the growth monitoring threshold, the optimization observation period ends; determine whether the number of executions of the environment optimization test period is not less than L3: if so, execute step 6; if not, execute step 2 again;

[0038] Step six: Mark the growth coefficient SZ corresponding to the end of the superior training observation period as the stage marking value of the superior training observation period, and all the stage marking values ​​constitute the stage marking parameters of the corresponding types of forestry seedlings; mark the superior training value i set during the superior training observation period as the environmental priority parameter of the superior training observation period; the stage marking parameters and the environmental priority parameters constitute the intelligent control parameters of the corresponding types of forestry seedlings; send the types of forestry seedlings and the intelligent control parameters to the database for storage.

[0039] Embodiment 2: Figure 2 As shown, a method for intelligent analysis of forestry seedling environment also includes the following steps:

[0040] Step P1: Generate an environmental control model based on all the types of forestry seedlings in the database and the corresponding intelligent control parameters; through the environmental control model, the most suitable setting value range of environmental parameters can be allocated to forestry seedlings of different varieties and growth stages, so as to ensure the growth state and growth rate of forestry seedlings throughout the growth cycle;

[0041] Step P2: Use the environmental control model to control and analyze the cultivation environment of forestry seedlings: obtain the type of forestry seedlings and call the corresponding intelligent control parameters; set the environmental parameters i of the forestry seedlings according to the first group of cultivation values ​​i;

[0042] Step P3: Monitor the overall growth coefficient SZ of forestry seedlings in real time. When the growth coefficient SZ reaches the first stage marking value, set the growth environment parameter i of the forestry seedlings according to the second set of optimization values ​​i, and so on, until the overall growth coefficient SZ of forestry seedlings reaches the last stage marking value.

[0043] The invention discloses an intelligent analysis method for forestry seedling raising environment. When working, after the forestry seedlings are planted or transplanted, the planting area of ​​the forestry seedlings is marked as an analysis area, the environmental parameter i of the analysis area and the standard range i corresponding to the environmental parameter i are obtained, an environmental optimization test period with a fixed duration is generated, a plurality of monitoring time points are set in the environmental optimization test period, the environmental parameter i in the analysis area is set at the monitoring time point, and the growth coefficient SZ of the forestry seedlings in the analysis area is obtained; then, the optimization range i of the environmental parameter i is extracted, an optimization observation period with a non-fixed duration is generated after the environmental optimization test period, an optimization value i is generated for the analysis area at the beginning of the optimization observation period, and the growth monitoring value of the analysis area is regularly obtained, and the execution process is controlled according to the growth monitoring value and the execution times of the environmental optimization test period; finally, an environmental regulation model is constructed, and the growth environment parameters of forestry seedlings of different varieties and different growth states are adaptively regulated according to the environmental regulation model.

[0044] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0045] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to actual conditions; such as: formula SZ = k1*GD+k2*YL+k3*YP; technicians in this field collect multiple groups of sample data and set corresponding growth coefficients for each group of sample data; substitute the set growth coefficients and the collected sample data into the formula, any three formulas constitute a three-variable linear equation group, screen the calculated coefficients and take the average, and obtain the values ​​of k1, k2 and k3 as 3.25, 2.81 and 2.06 respectively;

[0046] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding growth coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the growth coefficient is proportional to the value of the height data.

[0047] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0048] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for intelligent analysis of forestry seedling raising environment, characterized in that: The following steps are involved: Step 1: After the forestry seedlings are planted or transplanted, the planting area of ​​the forestry seedlings is marked as an analysis area, and the environmental parameter i of the analysis area and the standard range i corresponding to the environmental parameter i are obtained, where i=1, 2, ..., n, and n is a positive integer; Step 2: Generate an environment optimization test period with a fixed duration, set a number of monitoring time points within the environment optimization test period, and adjust the environmental parameter i of the analysis area at the monitoring time points; Step 3: Monitor and analyze the growth status of forestry seedlings in the analysis area at the monitoring time point: obtain the height data GD, leaf green data YL and leaf data YP of the analysis area and perform numerical calculations to obtain the growth coefficient SZ of the analysis area at the monitoring time point; Step 4: At the end of the environmental optimization test period, perform environmental optimization analysis and obtain the optimization range i of environmental parameter i; Step 5: Generate an optimization observation period of indefinite duration after the optimization test period. At the end of the optimization observation period, determine whether the number of executions of the optimization test period is not less than L3: if so, execute step 6; if not, execute step 2 again; Step 6: Obtain the intelligent control parameters corresponding to the types of forestry seedlings; send the types of forestry seedlings and the intelligent control parameters to the database for storage.

2. The method for intelligent analysis of forestry seedling raising environment according to claim 1, characterized in that: In step 2, the specific process of adjusting the environmental parameter i of the analysis area at the monitoring time point includes: generating a standard value i corresponding to the environmental parameter i for the analysis area at the monitoring time point, the standard value i is a value randomly selected from the standard range i; and setting the value of the environmental parameter i of the analysis area according to the standard value i.

3. The method for intelligent analysis of forestry seedling raising environment according to claim 2, characterized in that: In step three, the process of obtaining the height data GD includes: obtaining the growth height values ​​of all forestry seedlings in the analysis area and summing and averaging them to obtain the height data GD; the process of obtaining the chlorophyll data YL includes: randomly selecting leaves on a number of forestry seedlings as monitoring objects, using the spectral method to obtain the chlorophyll content of the monitoring objects and summing and averaging the chlorophyll content of all monitoring objects to obtain the chlorophyll data YL; the process of obtaining the leaf data YP includes: taking images of the forestry seedlings in the analysis area and marking the captured images as analysis images, enlarging the analysis images into pixel grid images and performing grayscale transformation, and then using the binary method to perform contour segmentation on the complete leaves in the analysis images, calculating the average area values ​​of all complete leaves and marking them as leaf data YP.

4. The method for intelligent analysis of forestry seedling raising environment according to claim 3 is characterized in that: In step four, the process of obtaining the optimized training range i of environmental parameter i includes: marking the difference between the growth coefficient SZ at the monitoring time point and the growth coefficient SZ at the previous monitoring time point as the growth rate value at the current monitoring time point; marking the standard value i at the previous monitoring time point as the evaluation value i at the current monitoring time point; marking the L1 monitoring time points with the largest growth rate values ​​as optimized training time points, and the maximum and minimum values ​​of the evaluation values ​​i of all the optimized time points constitute the optimized training range i of environmental parameter i.

5. The method for intelligent analysis of forestry seedling raising environment according to claim 4, characterized in that: In step five, at the beginning of the training observation period, an advanced training value i is generated for the analysis area, and the advanced training value i is a value randomly selected from the advanced training range i; after the start of the advanced training observation period, the growth coefficient SZ of the analysis area is obtained every L2 days, and the difference between the growth coefficient SZ and the growth coefficient SZ obtained last time is marked as the growth monitoring value, and the growth monitoring value is compared with the preset growth monitoring threshold: if the growth monitoring value is greater than or equal to the growth monitoring threshold, the advanced training observation period continues to execute; if the growth monitoring value is less than the growth monitoring threshold, the advanced training observation period ends.

6. The method for intelligent analysis of forestry seedling raising environment according to claim 5, characterized in that: In step six, the process of obtaining the intelligent control parameters of the corresponding types of forestry seedlings includes: marking the growth coefficient SZ corresponding to the end of the optimization observation period as the stage marking value of the optimization observation period, and all the stage marking values ​​constitute the stage marking parameters of the corresponding types of forestry seedlings; marking the optimization value i set in the optimization observation period as the environmental priority parameter of the optimization observation period; and the stage marking parameters and the environmental priority parameters constitute the intelligent control parameters of the corresponding types of forestry seedlings.

7. The method for intelligent analysis of forestry seedling raising environment according to claim 6, characterized in that: The following steps are also included: Step P1: Generate an environmental control model based on all types of forestry seedlings in the database and corresponding intelligent control parameters; Step P2: Use the environmental control model to control and analyze the cultivation environment of forestry seedlings: obtain the type of forestry seedlings and call the corresponding intelligent control parameters; set the environmental parameters i of the forestry seedlings according to the first group of cultivation values ​​i; Step P3: Monitor the overall growth coefficient SZ of forestry seedlings in real time. When the growth coefficient SZ reaches the first stage marking value, set the growth environment parameter i of the forestry seedlings according to the second set of optimization values ​​i, and so on, until the overall growth coefficient SZ of forestry seedlings reaches the last stage marking value.

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