A forestry nursery system and method

Abstract

The application provides a forestry seedling raising system and method, which comprises the following steps: acquiring solar radiation history data of a forestry seedling raising afforestation site, determining a target reference curve of photoperiodism throughout a seedling raising period based on the coupling of the radiation history data and photosynthetic efficiency of a target tree species; for each seedling raising stage of the seedling raising period, determining the spectral energy distribution of the seedling raising area of the target tree species in the seedling raising stage, determining spectral regulation instructions for dynamic light formula compensation of the seedling raising area according to the target reference curve and the spectral energy distribution; controlling the adjustable spectral light source array of the seedling raising area to output light based on the spectral regulation instructions, and determining a physiological response vector of the target tree species seedling to photoperiodism in the seedling raising stage; and performing parameter adaptive iterative optimization on the target reference curve of the next seedling raising stage through the physiological response vector until the end of the seedling raising period. The scheme of the application can realize an intelligent seedling raising method of dynamic light environment simulation and active guidance of seedling adaptation to a target site light environment.

Description

Technical Field

[0001] This application relates to the field of plant light environment regulation technology, and more specifically, to a forestry seedling system and method. Background Technology

[0002] Light environment regulation is one of the core technologies of modern forestry seedling cultivation. It is crucial for the morphological development, physiological health, and survival adaptation of seedlings after afforestation. Traditional seedling cultivation is mostly carried out in fixed greenhouses or sheds. The light management of these greenhouses is often seriously out of sync with the natural light and climate conditions of the afforestation site where the seedlings will actually grow. As a result, although the seedlings produced may perform well in the nursery, they are difficult to adapt to the complex and ever-changing light environment in the wild, leading to problems such as long seedling establishment period, low survival rate, and weak stress resistance.

[0003] Existing intelligent seedling illumination technologies mostly focus on monitoring and maintaining environmental parameters such as light intensity, temperature, and humidity within greenhouses within fixed threshold ranges through sensors, or supplementing light based on simple growth models. While these technologies achieve automated control, their regulation logic is essentially static and reactive, lacking long-term, dynamic planning based on the final growth environment of the seedlings. They also fail to create a closed loop between light regulation and real-time physiological feedback of the seedlings. This results in current technologies being unable to complete a photoacclimation process for afforestation sites throughout the entire seedling growth cycle, leading to insufficient scientific rigor in the cultivation process, reduced resource utilization efficiency, and a failure to fully unleash the seedlings' potential for adaptation to the wild. Therefore, how to achieve intelligent seedling cultivation methods that dynamically simulate the light environment and actively guide seedlings to adapt to the target site's light environment has become a challenge for the industry. Summary of the Invention

[0004] This application provides a forestry seedling cultivation system and method, which is an intelligent seedling cultivation method that can realize dynamic light environment simulation and actively guide seedlings to adapt to the target site light environment.

[0005] In a first aspect, this application provides a dynamic light acclimatization method for forestry seedling cultivation, comprising: Historical solar irradiance data of forestry seedling afforestation sites are obtained. Based on the historical irradiance data and the photosynthetic efficiency coupling model of the target tree species, different seedling stages of the target tree species are divided and corresponding spectral formulas are matched to obtain the target baseline curve of photoacclimation throughout the seedling cycle. For each seedling stage of the seedling cycle, the spectral energy distribution of the target tree species seedling area is determined, and the spectral control command for dynamic light formula compensation of the seedling area is determined based on the target reference curve and the spectral energy distribution. Based on the spectral modulation command, the adjustable spectral light source array in the seedling area is controlled to output light, and the physiological response vector of the target tree seedlings to photoacclimation is generated by analyzing the spectral response features of the canopy spectral image at the end of the seedling stage. The physiological response vector is used to perform adaptive iterative optimization of the target baseline curve for the next seedling stage until the end of the seedling cycle.

[0006] In some embodiments, based on the irradiation history data and the photosynthetic efficiency coupling model of the target tree species, different seedling stages of the target tree species are divided and corresponding spectral formulations are matched to obtain a target baseline curve for photoacclimation throughout the seedling period, specifically including: To obtain photomorphogenesis data of physiological parameters of seedlings of the target tree species at different growth stages; Based on the photomorphogenesis research data, a coupled model of photosynthetic efficiency, biomass accumulation and spectral response of the target tree species at different developmental stages was constructed. The historical irradiation data is input into the photosynthetic efficiency coupling model for spatiotemporal mapping and pattern matching to generate a target baseline curve for photoacclimation throughout the seedling growth cycle.

[0007] In some embodiments, determining the spectral energy distribution of the target tree species seedling area during the seedling stage specifically includes: Collect raw spectral data of the target tree species seedling area during the seedling stage; The original spectral data is radiometrically calibrated and frequency domain noise filtered to obtain a spectral response sequence with background interference removed. The spectral response sequence is spatially fused with the spatial information of the seedling area to obtain the spectral energy distribution of the target tree species seedling area during the seedling stage.

[0008] In some embodiments, determining the spectral control command for dynamic light formula compensation of the seedling area based on the target reference curve and the spectral energy distribution specifically includes: Perform band-by-band difference operations on the spectral energy distribution and the target spectral energy distribution of the current seedling stage in the target reference curve to generate a spectral deviation matrix. Based on the spectral deviation matrix, determine the photon flux density compensation value required to eliminate deviations in each band; The spectral control command for dynamic light formula compensation in the seedling area is determined based on all photon flux density compensation values.

[0009] In some embodiments, controlling the adjustable spectral light source array in the seedling area to output light based on the spectral modulation command specifically includes: The spectral modulation command is transmitted to the edge computing gateway deployed in the seedling area, and then the target light intensity parameters and dimming timing of each independent spectral channel in the adjustable spectral light source array are parsed. Based on the dimming timing and all target light intensity parameters, pulse width modulation signals are generated for each independent spectral channel; The light output is controlled by all pulse width modulation signals to control the light-emitting diode array in the tunable spectral light source array.

[0010] In some embodiments, generating a physiological response vector of the target tree seedlings to photoacclimation by analyzing the spectral response features of the canopy spectral image at the end of the seedling stage specifically includes: At the end of the seedling stage, reflectance spectral images of the canopy of the target tree species seedlings were collected to obtain canopy spectral images; The reflectance of sensitive spectral bands related to the physiological process of photoacclimation is extracted from the canopy spectral image, and then the light adaptation characteristic parameters of the target tree seedlings to photoacclimation are calculated. Based on the light adaptation characteristic parameters, the physiological response vector of the target tree seedlings to light acclimatization during the seedling stage is determined.

[0011] In some embodiments, adaptive iterative optimization of the target baseline curve for the next seedling stage using the physiological response vector specifically includes: The effect evaluation index of light acclimatization at the current seedling stage is determined by comparing the physiological response vector with the physiological state vector of the healthy growth of the seedlings of the preset target tree species. Based on the effect evaluation index, the target spectral energy distribution of the next seedling stage in the target baseline curve is adjusted in a directional manner to generate an optimized baseline curve for light acclimatization in the next seedling stage. The optimized baseline curve is updated to the target baseline curve and applied to the actual light acclimatization control in the next seedling stage.

[0012] Secondly, this application provides a forestry seedling cultivation system, which includes a dynamic light acclimatization unit, the dynamic light acclimatization unit comprising: The baseline module is used to acquire historical solar irradiance data of forestry seedling afforestation sites, and based on the irradiance history data and the photosynthetic efficiency coupling model of the target tree species, it divides the different seedling stages of the target tree species and matches the corresponding spectral formula to obtain the target baseline curve of photoacclimation throughout the seedling cycle. The control module is used to determine the spectral energy distribution of the target tree species seedling area for each seedling stage of the seedling cycle, and to determine the spectral control command for dynamic light formula compensation of the seedling area based on the target reference curve and the spectral energy distribution. The control module is used to control the adjustable spectral light source array in the seedling area to output light based on the spectral control command, and to generate the physiological response vector of the target tree seedlings to photoacclimation by analyzing the spectral response features of the canopy spectral image at the end of the seedling stage. The optimization module is used to perform adaptive iterative optimization of the target baseline curve for the next seedling stage using the physiological response vector until the end of the seedling cycle.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described dynamic light acclimatization method for forestry seedling cultivation.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described dynamic photoacclimation method for forestry seedling cultivation.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The forestry seedling cultivation system and method provided in this application first acquire historical solar irradiance data of the forestry seedling afforestation site. Based on the historical irradiance data and the photosynthetic efficiency coupling model of the target tree species, different seedling cultivation stages of the target tree species are divided and corresponding spectral formulas are matched to obtain a target reference curve for photoacclimation throughout the seedling cultivation cycle. For each seedling cultivation stage of the seedling cultivation cycle, the spectral energy distribution of the seedling area of ​​the target tree species in the seedling cultivation stage is determined. According to the target reference curve and the spectral energy distribution, a spectral control command for dynamic photoacclimation compensation of the seedling area is determined. Based on the spectral control command, an adjustable spectral light source array in the seedling area is controlled to output light. By analyzing the spectral response features of the canopy spectral image at the end of the seedling stage, a physiological response vector of the target tree seedlings to photoacclimation in the seedling stage is generated. The target reference curve for the next seedling stage is adaptively iteratively optimized using the physiological response vector until the end of the seedling cultivation cycle.

[0016] Therefore, this application uses the physiological response vector to perform adaptive iterative optimization of the target baseline curve for the next seedling stage until the end of the seedling cycle. Firstly, determining the target baseline curve yields an ideal light environment regulation execution path throughout the seedling cycle of the target tree species. The determination of the target baseline curve addresses the fundamental problem in existing technologies of "disconnect between the seedling light environment and the actual conditions of the target afforestation site," providing a clear and quantifiable target guidance for the photoacclimation process. This transforms seedling management from maintaining fixed greenhouse parameters to actively simulating and adapting to the outdoor light climate, fundamentally improving the environmental predictability and adaptability potential of seedlings. Simultaneously, this target baseline curve serves as a basis for subsequent implementation... A unified benchmark for time-based regulation and iterative optimization ensures that light formula compensation, physiological feedback evaluation, and strategy optimization all revolve around the same scientific goal, forming a complete closed loop of "goal setting → dynamic execution → effect feedback → self-optimization." This significantly enhances the scientific rigor and consistency of the seedling cultivation process and the survival and adaptability of the final seedlings in afforestation. Furthermore, determining the spectral regulation command provides specific control commands to drive the adjustable spectral light source array to perform light quantity compensation. This determination addresses the problems of disconnect between light management and the afforestation environment, and the static and rigid regulation strategies in existing technologies. Through real-time perception and precise compensation of natural light fluctuations, it ensures that the spectral intensity received by the seedlings at every moment is consistent. Precisely targeting the ideal state of simulated target sites and promoting morphogenesis elevates light management from maintaining constant environmental parameters to actively shaping spectral characteristics through directional induction, providing a quantifiable and precise operational guideline for achieving true light environment acclimatization. Simultaneously, quantitative compensation based on spectral deviations significantly improves the efficiency of light energy resource utilization, avoiding energy waste caused by indiscriminate supplemental lighting. Finally, determining the physiological response vector yields a multidimensional mathematical vector characterizing the complex physiological state of seedlings under light acclimatization intervention during the seedling stage. The determination of the physiological response vector provides standardized feedback signals that directly reflect the intrinsic physiological state of seedlings, enabling light environment regulation to move beyond traditional static thresholds or simplistic methods. Overcoming the limitations of single-growth models, this approach enables targeted and adaptive iterative optimization of the spectral formulation for the next stage based on the actual physiological responses of seedlings to photoacclimation at each growth stage. Therefore, the physiological response vector not only solves the core problem of "disconnect between regulation and physiological feedback" in existing technologies but also drives the entire cultivation process from a preset environment to a dynamic, closed-loop paradigm that adapts to physiological conditions. This ensures that the physiological state of the final cultivated seedlings is highly matched to the light environment requirements of the target afforestation site, significantly improving the seedlings' adaptability in the wild and the survival rate of afforestation. In summary, based on the above scheme, an intelligent seedling cultivation method can be implemented to dynamically simulate the light environment and actively guide seedlings to adapt to the target site's light environment. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a dynamic light acclimatization method for forestry seedling cultivation according to some embodiments of this application; Figure 2 This is a flowchart illustrating the operation of determining a target reference curve according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of a physiological response vector according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a dynamic light domestication unit according to some embodiments of this application; Figure 5 This is an internal structural diagram of a computer device for implementing a dynamic light acclimatization method for forestry seedling cultivation, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a dynamic light acclimatization method for forestry seedling cultivation according to some embodiments of this application. The dynamic light acclimatization method for forestry seedling cultivation mainly includes the following steps: In step 101, the solar irradiance history data of the forestry seedling afforestation site is obtained. Based on the irradiance history data and the photosynthetic efficiency coupling model of the target tree species, different seedling stages of the target tree species are divided and the corresponding spectral formulas are matched to obtain the target reference curve for photoacclimation throughout the seedling cycle.

[0020] It should be noted that, in this application, historical irradiance data is a time-series record of the long-term solar radiation conditions of the target afforestation site. This data can serve as an objective environmental benchmark and spatiotemporal anchor for the entire photoacclimation program, transforming indoor seedling light environment control from aimless, experience-based operations into a quantifiable, reproducible, and dynamically simulated scientific process closely aligned with the climatic characteristics of the seedlings' future growing location. Specifically, obtaining historical solar irradiance data for forestry seedling afforestation sites can be achieved by calling the National Meteorological Data Center or authoritative commercial meteorological data services via an application programming interface (API). The supplier provides solar radiation monitoring data for the target afforestation area for at least five years, including the original time-series dataset containing total irradiance, direct irradiance, diffuse irradiance, and sunshine duration. The original time-series dataset is then subjected to quality screening based on spatiotemporal consistency tests to remove obvious outliers and continuous missing segments caused by sensor failures or transmission anomalies. Short-term missing data are then reasonably filled using linear interpolation in time series analysis or spatial interpolation methods based on data from nearby meteorological stations to obtain a continuous and complete dataset as historical solar irradiance data for forestry seedling afforestation areas.

[0021] In some embodiments, reference Figure 2The figure is a flowchart illustrating the operation of determining the target reference curve according to some embodiments of this application. In this application, the target tree species is divided into different seedling stages based on the irradiation history data and the photosynthetic efficiency coupling model of the target tree species, and the corresponding spectral formula is matched to obtain the target reference curve for photoacclimation throughout the seedling period. This can be achieved by the following steps: To obtain photomorphogenesis data of physiological parameters of seedlings of the target tree species at different growth stages; Based on the photomorphogenesis research data, a coupled model of photosynthetic efficiency, biomass accumulation and spectral response of the target tree species at different developmental stages was constructed. The historical irradiation data is input into the photosynthetic efficiency coupling model for spatiotemporal mapping and pattern matching to generate a target baseline curve for photoacclimation throughout the seedling growth cycle.

[0022] It should be noted that in this application, the physiological parameters of seedlings are quantifiable indicators that characterize the internal growth state and function of seedlings, such as photosynthetic rate and chlorophyll content. As the biological state benchmark of the entire photoacclimation program, they can upgrade the system's regulatory logic from simply responding to environmental variables to a precise intervention mode with the plant's actual physiological state as the feedback core. The photomorphogenesis research data are a special dataset that reveals how spectral signals regulate the series of morphological response laws of seedlings from cell differentiation to organ development and finally to the construction of the overall structure. As a knowledge bridge connecting spectral input and morphological output, it can enable the regulation of the light environment to go beyond the basic energy supply function and actively guide seedlings to develop towards the ideal morphological pattern of thick stems, well-developed roots, and enhanced resistance.

[0023] In practice, obtaining photomorphogenesis data of physiological parameters of seedlings of the target tree species at different growth stages can be achieved in the following way: First, basic physiological parameters of the target tree species at different growth stages, including photosynthetic rate, chlorophyll content, and morphological index range, can be collected by reviewing publicly published literature (e.g., relevant papers in core journals such as *Science of Forestry* and *Acta Ecologica Sinica*) and national standards (e.g., forest tree seedling standards). Then, in an artificial climate chamber or controlled environment incubator, different light intensities (e.g., 50, 100, 200 micromoles per square meter per second) and light qualities (…) can be set. For example, in the comparative experimental groups with red-blue light ratios of 3:1, 1:1, and 1:3, the seedlings of the target tree species were cultivated for a complete growth cycle. During different growth stages of the cultivation process, the photosynthetic rate of the seedlings was measured regularly using a portable photosynthesis meter, the morphological indicators of the seedlings were measured regularly using a leaf area scanner and a digital caliper, and the chlorophyll content of the seedlings was determined by spectrophotometry. Finally, a complete time-series dataset consisting of the basic physiological parameters of the target tree species at different growth stages collected from literature and experiments, as well as experimental control data, was used as the photomorphological data for the physiological parameters of the target tree species seedlings.

[0024] In specific implementation, the photosynthetic efficiency coupled with photosynthetic efficiency, biomass accumulation, and spectral response at different developmental stages of the target tree species can be constructed based on the aforementioned photomorphogenesis research data in the following manner: First, the photomorphogenesis research database can be preprocessed, i.e., data fusion technology (such as z-score normalization) can be used to normalize and align the measured values ​​from different sources and scales to obtain a standardized model training dataset; then, a multivariate nonlinear regression analysis method can be used with light intensity and spectral composition (such as the ratio of red to far-red light and the proportion of blue light) as core input variables, and key indicators such as net photosynthetic rate and relative biomass growth rate as output variables, to construct models targeting seedlings. Dedicated light response sub-models for different developmental stages of trees (e.g., budding, seedling, and rapid growth stages) were developed, and a time-series-based model coupling mechanism was introduced. The corresponding light response sub-models were dynamically selected and invoked through phenological period judgment functions (e.g., thresholds reached by effective accumulated temperature or morphological indicators). This integrated the models into a photosynthetic efficiency coupling model framework that can simulate the continuous response of seedlings to dynamic light environments throughout their entire growth cycle. Finally, the parameters of the photosynthetic efficiency coupling model framework were calibrated and validated using the model training dataset. Through iterative optimization, the model prediction error was stabilized within an acceptable range, ultimately yielding a photosynthetic efficiency coupling model that correlates photosynthetic efficiency, biomass accumulation, and spectral response at different developmental stages of the target tree species.

[0025] In specific implementation, the historical irradiation data is input into the photosynthetic efficiency coupling model for spatiotemporal mapping and pattern matching to generate a target baseline curve for photoacclimation throughout the seedling cultivation cycle. This can be achieved in the following way: First, the historical irradiation data can be preprocessed using a time-series smoothing method (e.g., moving average method) and a missing value imputation algorithm (e.g., linear interpolation method) to generate a light climate data sequence with daily time units that fully includes the distribution of photosynthetically active radiation intensity and spectral energy in different bands (e.g., decomposed into blue, green, red, and far-red bands); Second, data segments corresponding to the monthly time intervals are extracted from the light climate data sequence according to the start and end dates set for this seedling cultivation task, and these data segments are rearranged and formatted according to the data structure and time resolution required by the photosynthetic efficiency coupling model; Then, the aligned light climate data is processed... Data fragments are sequentially input into the photosynthetic efficiency coupled prediction model as environmental driving variables. The model runs to simulate and predict the growth dynamics, photosynthetic product accumulation, and morphological development process of ideal seedlings under the natural light sequence on a daily basis, and outputs the simulated growth trajectory. Finally, based on the simulated growth trajectory output by the photosynthetic efficiency coupled prediction model, with the optimization principle of promoting the robust morphological formation of seedlings and synchronously adapting to the natural light cycle rhythm of the afforestation site, the model back-calculation and decision-making algorithms (such as rule-based condition judgment or optimization algorithms) determine the target value of artificial supplemental lighting or light adjustment, the spectral component ratio (such as specifying the red light to blue light ratio), and the duration when the natural light conditions do not meet the ideal growth requirements during the key phenological period. This set of light environment regulation instructions arranged in chronological order is used as the target baseline curve for photoacclimation throughout the seedling cultivation cycle.

[0026] It should be noted that in this application, the target baseline curve is the ideal light environment regulation execution path throughout the seedling cultivation cycle of the target tree species. It can provide a standardized, quantifiable and continuously optimizable management benchmark for the entire light acclimatization process, ensuring the consistency and goal orientation of the cultivation process.

[0027] In step 102, for each seedling stage of the seedling cycle, the spectral energy distribution of the target tree species seedling area at each seedling stage is determined, and the spectral control command for dynamic light formula compensation of the seedling area is determined based on the target reference curve and the spectral energy distribution.

[0028] In some embodiments, determining the spectral energy distribution of the target tree species seedling area during the seedling stage can be achieved using the following steps: Collect raw spectral data of the target tree species seedling area during the seedling stage; The original spectral data is radiometrically calibrated and frequency domain noise filtered to obtain a spectral response sequence with background interference removed. The spectral response sequence is spatially fused with the spatial information of the seedling area to obtain the spectral energy distribution of the target tree species seedling area during the seedling stage.

[0029] In practice, the collection of raw spectral data of the target tree species' seedling nursery area during the seedling stage can be achieved in the following way: Raw spectral data can be collected through a canopy-level multispectral sensing network deployed above the seedling bed. This canopy-level multispectral sensing network is a hybrid sensing array composed of a high-resolution imaging spectrometer and a point-type quantum sensor. The imaging spectrometer uses its optical imaging mechanism (e.g., pushbroom scanning) to acquire spectral reflectance images reflecting the spatial distribution of the seedlings, while the quantum sensor records the photosynthetically active radiation intensity of specified bands (e.g., blue and red light bands) using a high-frequency sampling method. During the collection process, heterogeneous sensors are coordinated using synchronous triggering and timestamp alignment technology. The sampling period of the sensor ensures that the data has a unified time reference. The seedling bed is scanned by an imaging spectrometer to obtain the continuous spectral reflectance curve of each pixel from the visible light to the near-infrared range. At the same time, the quantum sensor synchronously records the instantaneous light intensity data of key bands. Finally, the spatial spectral image data block obtained by the imaging spectrometer and the temporal light intensity data stream recorded by the quantum sensor are fused and encapsulated to form a data cube that integrates spatial spectral and temporal information as the raw spectral data. The raw spectral data is a mixed spectral reading that characterizes the light environment of the seedling area of ​​the target tree species. It can provide an unmodified and complete initial observation basis for the entire light environment analysis.

[0030] In specific implementation, the original spectral data is radiometrically calibrated and frequency-domain noise filtered to obtain a spectral response sequence with background interference removed. This can be achieved as follows: First, using calibration coefficients obtained from pre-measured standard reflectance reference plates, the original observation values ​​output by the imaging spectrometer in the original spectral data are corrected for sensor dark current and pixel response non-uniformity using linear or polynomial fitting methods, converting them into surface reflectance data with real physical meaning. Then, a fast Fourier transform is applied to convert the surface reflectance data from the time domain to the frequency domain, identifying and filtering out high-frequency noise components caused by instrument thermal noise or external electromagnetic interference, and reconstructing the filtered frequency domain signal into a time domain signal to obtain a smoothed mixed spectral curve. Next, spectral unmixing or difference analysis techniques are applied to identify and subtract pre-measured spectral characteristic signals of the soil or cultivation substrate from the mixed spectral curve, finally obtaining a spectral response sequence with background interference removed. The spectral response sequence is a more purely ordered spectral dataset reflecting the optical characteristics of the seedling canopy itself, which can significantly improve the accuracy and reliability of the spectral data used for subsequent analysis.

[0031] In specific implementation, the spectral response sequence is spatially fused with the spatial information of the seedling area to obtain the spectral energy distribution of the target tree species seedling area during the seedling stage. This can be achieved in the following way: First, the absolute coordinates and network topology provided by the positioning module integrated in the sensor node can be used to bind the canopy incident spectral data of discrete points in the spectral response sequence to geographic coordinates through coordinate transformation technology, so that each data is associated with a clear spatial location, and the registered spectral data is obtained. Then, according to the boundary of the seedling area and the distribution of seedlings, a unified spatial grid is defined, and the registered spectral data is assigned to the corresponding grid cells. For grid cells without direct sensor coverage, a spatial interpolation algorithm is used to perform spatial correlation analysis on the spectral data of known grid nodes, predict and fill the spectral data of unknown locations, and generate a spatial spectral distribution surface that continuously covers the seedling area. Finally, the spatial spectral distribution surface is combined with the time dimension of the seedling stage through a spatiotemporal data aggregation method, and the spectral energy distribution representing the overall light conditions is output as the spectral energy distribution of the target tree species seedling area during the seedling stage.

[0032] It should be noted that, in this application, spectral energy distribution is a comprehensive quantitative description of the light radiation energy of different bands in the seedling space area in the spatial and temporal dimensions, which can provide an accurate, unified and spatially representative quantitative environmental input for light formula compensation decision-making.

[0033] In some embodiments, determining the spectral control command for dynamic light formula compensation in the seedling area based on the target reference curve and the spectral energy distribution can be achieved through the following steps: Perform band-by-band difference operations on the spectral energy distribution and the target spectral energy distribution of the current seedling stage in the target reference curve to generate a spectral deviation matrix. Based on the spectral deviation matrix, determine the photon flux density compensation value required to eliminate deviations in each band; The spectral control command for dynamic light formula compensation in the seedling area is determined based on all photon flux density compensation values.

[0034] In specific implementation, the spectral deviation matrix is ​​generated by performing band-by-band difference operations on the spectral energy distribution and the target spectral energy distribution of the current seedling stage in the target reference curve. This can be achieved in the following way: First, retrieve the target spectral energy distribution that precisely corresponds to the current seedling stage and date from the target reference curve; then, in the local computing device (e.g., an edge computing gateway), perform numerical subtraction between the spectral energy distribution and the target spectral energy distribution at each wavelength point to calculate the absolute deviation value for each wavelength; finally, organize and arrange the deviation values ​​of all wavelength points in wavelength order to form a two-dimensional data table, i.e., the spectral deviation matrix, where rows can correspond to different sensor locations, columns correspond to wavelengths, and the values ​​in the table are the specific deviation amounts. The spectral deviation matrix is ​​a data structure that quantifies the differences between the current light environment and the target light environment in each spectral band. It can serve as a direct basis for judging whether the light environment meets the standards and calculating the compensation amount, achieving accurate and visual positioning of the light environment deviation and providing accurate input basis for subsequent compensation.

[0035] In specific implementation, the photon flux density compensation value required to eliminate the deviation of each band based on the spectral deviation matrix can be achieved in the following way: First, a preset target tree species light response weight coefficient table is called, which records the relative importance level of different spectral bands (e.g., blue light band, red light band) on the growth of the target tree species; then, a weighted compensation algorithm (e.g., a deviation allocation algorithm based on linear weighting) is used to calculate the spectral deviation amount that needs to be corrected for each band by combining the deviation value of each band in the spectral deviation matrix and the corresponding weight in the light response weight coefficient table, and all spectral deviation amounts are converted into physical light quantity values ​​in micromoles per square meter per second required to drive artificial light sources through a photon quantum conversion model. That is, the specific compensation value for each band is used as the photon flux density compensation value required to eliminate the band deviation; wherein, the photon flux density compensation value is the number of photons that need to be supplemented or reduced for each band to eliminate the spectral deviation, which can convert the theoretical deviation into a quantitatively executable physical operation quantity, ensuring the efficiency and energy saving of the supplemental lighting measures.

[0036] In practice, the spectral control command for dynamic light formula compensation in the seedling area, determined based on all photon flux density compensation values, can be implemented in the following way: First, a pre-stored spectral database of tunable spectral light source arrays can be accessed. This database pre-calibrates the absolute spectral power distribution and corresponding photon flux output capability of each independent emission channel in the tunable spectral light source array under different driving currents. Then, based on all photon flux density compensation values, one or more physical emission channels are matched for each spectral band requiring compensation. Mathematical optimization methods (such as linear programming algorithms or least squares fitting) are used to calculate a set of signals that can most accurately and energy-efficiently synthesize the target light formula. The process involves several steps: first, determining the channel drive current combination scheme for the tunable spectral light source array; then, converting this scheme into low-level control parameters that can directly drive the hardware, such as the specific values ​​of the pulse width modulation duty cycle or analog voltage settings for each light-emitting channel; finally, encapsulating and encoding all low-level control parameters according to the communication protocol recognized by the seedling area light source array controller to form a final transmittable instruction data packet as a spectral control instruction; wherein, the spectral control instruction is a specific control command that drives the tunable spectral light source array to perform light quantity compensation, which can bridge the last mile from algorithm decision-making to device execution, ensuring that the control intention is accurately and timely translated into changes in the physical light environment.

[0037] In step 103, the adjustable spectral light source array in the seedling area is controlled to output light based on the spectral control command, and the physiological response vector of the target tree seedlings to photoacclimation is generated by analyzing the spectral response features of the canopy spectral image at the end of the seedling stage.

[0038] In some embodiments, controlling the adjustable spectral light source array in the seedling area to output light based on the spectral modulation command can be achieved by the following steps: The spectral modulation command is transmitted to the edge computing gateway deployed in the seedling area, and then the target light intensity parameters and dimming timing of each independent spectral channel in the adjustable spectral light source array are parsed. Based on the dimming timing and all target light intensity parameters, pulse width modulation signals are generated for each independent spectral channel; The light output is controlled by all pulse width modulation signals to control the light-emitting diode array in the tunable spectral light source array.

[0039] In specific implementation, the spectral modulation command is transmitted to the edge computing gateway deployed in the seedling area, and then the target light intensity parameters and dimming timing of each independent spectral channel in the tunable spectral light source array are parsed. This can be achieved in the following way: First, the spectral modulation command can be securely and reliably transmitted to the edge computing gateway deployed locally in the seedling area through an IoT communication link based on a message queue telemetry transmission protocol. The command parsing service embedded in the edge computing gateway decodes the data frame structure of the spectral modulation command and separates the parameters for each independent spectral channel in the tunable spectral light source array (such as the 450 nm blue light channel, the 660 nm red light channel, the 7nm red light channel, the 6 ... The system identifies target light intensity parameters and corresponding dimming timing plans for a 30nm far-infrared light channel. It performs format verification and logical conflict checks on each separated target light intensity parameter and dimming timing plan to ensure the executability of the instructions. The target light intensity parameters are preset data that quantifies the required illumination intensity values ​​for each spectral channel in the tunable spectral light source array, providing a clear and quantifiable intensity benchmark for the precise execution of the light formulation. The dimming timing plan is a time control instruction that specifies the on / off rhythm, duration, gradual transition, and shutdown rhythm of each spectral channel in the tunable spectral light source array. It enables dynamic simulation and programmed control of the light environment, ensuring the biorhythm of the photoacclimation process.

[0040] In specific implementation, the generation of pulse width modulation (PWM) signals for each independent spectral channel based on the dimming timing and all target light intensity parameters can be achieved in the following way: For each independent spectral channel, its target light intensity parameter can be converted into a corresponding PWM target duty cycle value through a preset mapping relationship calculation method (e.g., lookup table method or linear proportional calculation formula); then, the microcontroller calls its internal hardware timer and PWM signal generator to generate a PWM digital waveform with a specified frequency and precise duty cycle in real time, strictly according to the dimming time plan and the calculated PWM target duty cycle value, as the PWM signal for the independent spectral channel. Through the above steps, the PWM signals for each independent spectral channel can be obtained; finally, the generated PWM signals for all independent spectral channels are synchronously output to the subsequent multi-channel constant current drive circuit through a general-purpose input / output interface or dedicated bus; wherein, the PWM signal is a carrier signal that continuously controls the average power of the light-emitting unit by adjusting the electrical pulse duty cycle, which can realize stepless, linear and high-resolution brightness adjustment of the light source, ensuring the smoothness and accuracy of the light intensity output.

[0041] In practice, controlling the light-emitting diode (LED) array in the tunable spectral light source array to output light through all pulse width modulation (PWM) signals can be achieved in the following way: After receiving the PWM signals, the multi-channel constant current drive circuit uses signal isolation and conversion technology to linearly convert the duty cycle information of each PWM signal into a constant driving current value for the corresponding spectral channel in the LED array, driving the LED chips (such as blue LED chips based on gallium nitride and white and red LED chips coated with phosphor) to light up. The luminous intensity is proportional to the current value, thereby synthesizing the required composite light for light output.

[0042] In some embodiments, reference Figure 3 The figure is an exemplary flowchart illustrating the determination of physiological response vectors according to some embodiments of this application. In this application, the physiological response vector of the target tree seedling to photoacclimation during the seedling stage is generated by analyzing the spectral response features of the canopy spectral image at the end of the seedling stage. This can be achieved by the following steps: In step 1031, at the end of the seedling stage, the reflectance spectrum image of the canopy of the target tree seedling is acquired to obtain a canopy spectral image; In step 1032, the reflectance of sensitive spectral segments related to the physiological process of photoacclimation is extracted from the canopy spectral image, and then the light adaptation characteristic parameters of the target tree seedlings to photoacclimation are calculated. In step 1033, the physiological response vector of the target tree seedlings to photoacclimation during the seedling stage is determined based on the light adaptation characteristic parameters.

[0043] In specific implementation, the canopy reflectance spectrum image of the target tree seedling is acquired at the end of the seedling stage. This can be achieved by: at the end of the physiological stabilization period during the seedling stage (e.g., budding stage, seedling stage, early hardening stage), using an imaging hyperspectral acquisition device suspended above the seedbed to scan the canopy of the target seedling from top to bottom. Through its internal optical beam splitting component and area array detector, the device simultaneously captures the continuous reflectance spectrum curve of each spatial pixel within the observation field of view in the visible to near-infrared wavelength range. The acquired continuous reflectance spectrum curve is then compared with its corresponding two-dimensional spatial coordinates. The hyperspectral image data is constructed by line registration and stitching, which includes horizontal spatial information and vertical spectral information. Then, the hyperspectral image data is processed by radiometric calibration based on a standard reflectance reference plate to convert the physical reflectance, and the hyperspectral image data is processed by geometric correction based on ground control points to eliminate image distortion, thereby obtaining a canopy spectral image that characterizes the spatial heterogeneity and spectral details of the canopy. The canopy spectral image refers to a two-dimensional data set that characterizes the spatial structure and spectral reflectance information of the seedling canopy, which can provide non-invasive, high-dimensional original observation data of seedling phenotype and physiological state for subsequent analysis.

[0044] In practice, extracting the reflectance of sensitive spectral bands related to the photoacclimation physiological process from the canopy spectral image, and then calculating the photoacclimation characteristic parameters of the target tree seedlings, can be achieved in the following way: First, based on the principles of plant photophysiology, a set of characteristic spectral bands directly related to photoacclimation and abiotic stress response can be predefined, mainly including photochemical reflectance sensitive bands reflecting the energy dissipation state of the photosystem (such as the bands near 531 nm and 570 nm), carotenoid absorption characteristic bands related to photoprotective pigments, and near-infrared plateau and red edge region bands indicating biomass and chlorophyll content; then, using spectral data analysis technology, the reflectance values ​​of each characteristic spectral band are read from each effective spatial pixel of the canopy spectral image, and all reflectance values ​​are substituted into the established vegetation index. The mathematical models are combined for calculation. For example, the reflectance of the 531 nm and 570 nm bands is substituted into the formula for calculating the photochemical reflectance index, and the reflectance of the red-edge and near-infrared bands is substituted into the formula for calculating the normalized difference red-edge index, to obtain the photochemical reflectance index, the carotenoid reflectance index, and the normalized difference red-edge index. Finally, the vector composed of the calculated photochemical reflectance index, carotenoid reflectance index, and normalized difference red-edge index is used as the light adaptation characteristic parameter of the target tree seedling to photoacclimation. Among them, the light adaptation characteristic parameter is a series of core indicators that characterize the adaptive changes of key physiological functions of seedlings under dynamic light environment, including the photochemical reflectance index, the carotenoid reflectance index, and the normalized difference red-edge index. It can transform complex spectral information into core features that can be objectively measured and compared, indicating the process of light adaptation.

[0045] In specific implementation, the physiological response vector of the target tree seedlings to photoacclimation during the seedling stage can be determined based on the light adaptation characteristic parameters in the following manner: First, the light adaptation characteristic parameters are preprocessed by data standardization using a standardization method (e.g., using the minimum-maximum standardization method) to eliminate the differences in the dimensions and numerical ranges of each parameter, thus obtaining standard adaptation characteristics; Second, according to the importance of each characteristic parameter in evaluating the physiological effect of photoacclimation, appropriate weight coefficients are assigned to the standard adaptation characteristics using a weight allocation method (e.g., the analytic hierarchy process or expert scoring method), and the weighted standard adaptation characteristics are arranged and combined according to a predefined parameter order to construct an ordered multidimensional vector as the physiological response vector of the target tree seedlings to photoacclimation during the seedling stage.

[0046] It should be noted that in this application, the physiological response vector is a multidimensional mathematical vector that represents the complex physiological state of seedlings under photoacclimation intervention during the seedling stage. It can provide a comprehensive and standardized mathematical representation of the overall physiological state of seedlings during the photoacclimation stage, and provide a clear and calculable decision basis for the system to perform adaptive iterative optimization.

[0047] In step 104, the target baseline curve for the next seedling stage is adaptively and iteratively optimized using the physiological response vector until the seedling cycle ends.

[0048] In some embodiments, adaptive iterative optimization of the target baseline curve for the next seedling stage using the physiological response vector can be achieved through the following steps: The effect evaluation index of light acclimatization at the current seedling stage is determined by comparing the physiological response vector with the physiological state vector of the healthy growth of the seedlings of the preset target tree species. Based on the effect evaluation index, the target spectral energy distribution of the next seedling stage in the target baseline curve is adjusted in a directional manner to generate an optimized baseline curve for light acclimatization in the next seedling stage. The optimized baseline curve is updated to the target baseline curve and applied to the actual light acclimatization control in the next seedling stage.

[0049] In specific implementation, the effect evaluation index of light acclimatization at the current seedling stage can be determined by comparing the physiological response vector with the physiological state vector of the healthy growth of the target tree seedlings in the preset method. This can be achieved in the following way: First, based on historical high-quality seedling cultivation data and expert knowledge, a multidimensional reference vector representing the ideal physiological state can be constructed using data dimensionality reduction methods (e.g., principal component analysis). The expected target values ​​of standardized key physiological indicators (e.g., photochemical reflectance index, carotenoid reflectance index, and normalized difference red-edge index) corresponding to each dimension are then used. Then, spatial metric algorithms (e.g., Euclidean distance formula or cosine similarity calculation) are applied to... The physiological response vector and the multidimensional reference vector are calculated to obtain the difference value representing the difference between the two in the multidimensional space. Finally, a normalization function (e.g., min-max scaling) is used to map the difference value to a unified scaling interval (e.g., 0 to 1), transforming it into an intuitive, quantifiable, and comparable single value. This single value is then used as the effect assessment index of light acclimatization at the current seedling stage. The effect assessment index is a single indicator that comprehensively quantifies the complex physiological state of seedlings under light acclimatization intervention at the seedling stage. It can objectively measure the actual impact of light acclimatization management on the physiological state of seedlings and provide a decision-making basis for optimizing subsequent control strategies.

[0050] In specific implementation, the optimized baseline curve for the next seedling stage is generated by directionally adjusting the target spectral energy distribution in the target baseline curve based on the effect evaluation index. This can be achieved in the following way: First, an expected threshold for the effect evaluation index can be set (e.g., the target value of the vegetation health index is set to 0.85). The calculated effect evaluation index is then compared with this expected threshold. If the effect evaluation index is greater than or equal to the expected threshold, it is determined whether the light acclimatization effect has reached the expected level. If the effect evaluation index is less than the expected threshold, parameter optimization for the baseline curve in the subsequent stage is initiated. Once the optimization decision is triggered, an automated parameter optimization algorithm (e.g., gradient descent or particle swarm optimization) can be used, with the effect evaluation index as feedback input, and the next seedling stage's optimized baseline curve as the target baseline curve. The spectral formula parameters pre-set for the corresponding curve segment in the seedling stage are used as core optimization variables. Iterative search is performed within the predefined reasonable value boundaries of each parameter to solve a mathematical optimization problem with the goal of maximizing the effect evaluation index predicted for the next stage. Through simulation calculation, the parameter combination that can achieve the optimal value of the objective function is screened out. Then, the obtained parameter combination is substituted into the spectral distribution calculation model to regenerate a spectral formula containing adjusted light intensity, spectrum, and photoperiod as the optimized baseline curve for photoacclimation in the next seedling stage. The optimized baseline curve is a new light environment execution scheme formed after closed-loop correction of the original future plan based on stage physiological feedback. It can realize the key transformation of management strategy from static preset to dynamic adaptation, ensuring that the photoacclimation process can continuously respond to the actual growth state of the seedlings and tend towards the optimal cultivation goal.

[0051] In specific implementation, updating the optimized baseline curve to the target baseline curve and applying it to the actual light acclimatization control in the next seedling stage can be achieved in the following way: the curve segment corresponding to the next seedling stage in the original target baseline curve can be replaced with a newly generated optimized baseline curve using data overwriting technology, thereby completing the real-time update of the target baseline curve. When the seedling process enters the next stage, the system automatically reads the latest light environment parameters corresponding to the current stage from the updated target baseline curve, including spectral formula, intensity and photoperiod, and compiles them into a specific set of equipment control instructions, thereby transforming the optimization decision based on physiological feedback into actual cultivation management actions, realizing the closed-loop application and adaptive evolution of the light acclimatization strategy.

[0052] Furthermore, in another aspect of this application, in some embodiments, this application provides a forestry seedling cultivation system, which includes a dynamic light acclimatization unit, with reference to... Figure 4 The figure is a schematic diagram of the structure of a dynamic light domestication unit according to some embodiments of this application. The dynamic light domestication unit 400 includes: a base-fixing module 401, a regulation module 402, and an optimization module 403, which are described below: Baseline module 401, in this application, is mainly used to obtain the solar irradiance history data of forestry seedling afforestation land, divide the different seedling stages of the target tree species based on the irradiance history data and the photosynthetic efficiency coupling model of the target tree species, and match the corresponding spectral formula to obtain the target baseline curve of photoacclimation throughout the seedling cycle. The control module 402 in this application is mainly used to determine the spectral energy distribution of the seedling area of ​​the target tree species for each seedling stage of the seedling cycle, and to determine the spectral control command for dynamic light formula compensation of the seedling area based on the target reference curve and the spectral energy distribution. It should be noted that the control module 402 in this application is also used to control the adjustable spectral light source array of the seedling area to output light based on the spectral control command, and to generate the physiological response vector of the target tree seedling to photoacclimation by analyzing the spectral response features of the canopy spectral image at the end of the seedling stage. The optimization module 403 in this application is mainly used to perform adaptive iterative optimization of the target baseline curve for the next seedling stage through the physiological response vector until the end of the seedling cycle.

[0053] Each module in the aforementioned forestry seedling system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0054] In another embodiment, this application provides a computer device, which may be a server, and its internal structure diagram may be as follows. Figure 5 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data on dynamic photoacclimation methods for forestry seedling cultivation. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program can implement a dynamic photoacclimation method for forestry seedling cultivation.

[0055] Those skilled in the art will understand that Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0056] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described embodiment of the dynamic light acclimatization method for forestry seedling cultivation.

[0057] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described embodiment of the dynamic photoacclimation method for forestry seedling cultivation.

[0058] In one embodiment, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the above-described embodiment of the dynamic light acclimatization method for forestry seedling cultivation.

[0059] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0060] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A dynamic light acclimatization method for forestry seedling cultivation, characterized in that, Includes the following steps: Historical solar irradiance data of forestry seedling afforestation sites are obtained. Based on the historical irradiance data and the photosynthetic efficiency coupling model of the target tree species, different seedling stages of the target tree species are divided and corresponding spectral formulas are matched to obtain the target baseline curve of photoacclimation throughout the seedling cycle. For each seedling stage of the seedling cycle, the spectral energy distribution of the target tree species seedling area is determined, and the spectral control command for dynamic light formula compensation of the seedling area is determined based on the target reference curve and the spectral energy distribution. Based on the spectral modulation command, the adjustable spectral light source array in the seedling area is controlled to output light, and the physiological response vector of the target tree seedlings to photoacclimation is generated by analyzing the spectral response features of the canopy spectral image at the end of the seedling stage. The physiological response vector is used to perform adaptive iterative optimization of the target baseline curve for the next seedling stage until the end of the seedling cycle.

2. The method as described in claim 1, characterized in that, Based on the coupled model of historical irradiation data and photosynthetic efficiency of the target tree species, different seedling stages of the target tree species are divided and corresponding spectral formulations are matched to obtain the target baseline curve for photoacclimation throughout the seedling period, specifically including: To obtain photomorphogenesis data of physiological parameters of seedlings of the target tree species at different growth stages; Based on the photomorphogenesis research data, a coupled model of photosynthetic efficiency, biomass accumulation and spectral response of the target tree species at different developmental stages was constructed. The historical irradiation data is input into the photosynthetic efficiency coupling model for spatiotemporal mapping and pattern matching to generate a target baseline curve for photoacclimation throughout the seedling growth cycle.

3. The method as described in claim 1, characterized in that, Determining the spectral energy distribution of the target tree species seedling area during the seedling stage specifically includes: Collect raw spectral data of the target tree species seedling area during the seedling stage; The original spectral data is radiometrically calibrated and frequency domain noise filtered to obtain a spectral response sequence with background interference removed. The spectral response sequence is spatially fused with the spatial information of the seedling area to obtain the spectral energy distribution of the target tree species seedling area during the seedling stage.

4. The method as described in claim 1, characterized in that, The spectral control instructions for dynamic light formula compensation in the seedling area, determined based on the target reference curve and the spectral energy distribution, specifically include: Perform band-by-band difference operations on the spectral energy distribution and the target spectral energy distribution of the current seedling stage in the target reference curve to generate a spectral deviation matrix. Based on the spectral deviation matrix, determine the photon flux density compensation value required to eliminate deviations in each band; The spectral control command for dynamic light formula compensation in the seedling area is determined based on all photon flux density compensation values.

5. The method as described in claim 1, characterized in that, Controlling the adjustable spectral light source array in the seedling area to output light based on the aforementioned spectral modulation command specifically includes: The spectral modulation command is transmitted to the edge computing gateway deployed in the seedling area, and then the target light intensity parameters and dimming timing of each independent spectral channel in the adjustable spectral light source array are parsed. Based on the dimming timing and all target light intensity parameters, pulse width modulation signals are generated for each independent spectral channel; The light output is controlled by all pulse width modulation signals to control the light-emitting diode array in the tunable spectral light source array.

6. The method as described in claim 1, characterized in that, By analyzing the spectral response features of the canopy spectral images at the end of the seedling stage, the physiological response vector of the target tree seedlings to photoacclimation during the seedling stage is generated, specifically including: At the end of the seedling stage, reflectance spectral images of the canopy of the target tree species seedlings were collected to obtain canopy spectral images; The reflectance of sensitive spectral bands related to the physiological process of photoacclimation is extracted from the canopy spectral image, and then the light adaptation characteristic parameters of the target tree seedlings to photoacclimation are calculated. Based on the light adaptation characteristic parameters, the physiological response vector of the target tree seedlings to light acclimatization during the seedling stage is determined.

7. The method as described in claim 1, characterized in that, The specific steps of adaptive iterative optimization of the target baseline curve for the next seedling stage using the physiological response vector include: The effect evaluation index of light acclimatization at the current seedling stage is determined by comparing the physiological response vector with the physiological state vector of the healthy growth of the seedlings of the preset target tree species. Based on the effect evaluation index, the target spectral energy distribution of the next seedling stage in the target baseline curve is adjusted in a directional manner to generate an optimized baseline curve for light acclimatization in the next seedling stage. The optimized baseline curve is updated to the target baseline curve and applied to the actual light acclimatization control in the next seedling stage.

8. A forestry seedling cultivation system, the system comprising a dynamic light acclimatization unit, characterized in that, The dynamic light domestication unit includes: The baseline module is used to acquire historical solar irradiance data of forestry seedling afforestation sites, and based on the historical irradiance data and the photosynthetic efficiency coupling model of the target tree species, it divides the different seedling stages of the target tree species and matches the corresponding spectral formula to obtain the target baseline curve of photoacclimation throughout the seedling cycle. The control module is used to determine the spectral energy distribution of the target tree species seedling area for each seedling stage of the seedling cycle, and to determine the spectral control command for dynamic light formula compensation of the seedling area based on the target reference curve and the spectral energy distribution. The control module is used to control the adjustable spectral light source array in the seedling area to output light based on the spectral control command, and to generate the physiological response vector of the target tree seedlings to photoacclimation by analyzing the spectral response features of the canopy spectral image at the end of the seedling stage. The optimization module is used to perform adaptive iterative optimization of the target baseline curve for the next seedling stage using the physiological response vector until the end of the seedling cycle.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the dynamic light acclimatization method for forestry seedling cultivation as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic light acclimatization method for forestry seedling cultivation as described in any one of claims 1 to 7.

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